method to simulate a fractional flow of wetting fluids and non-wetting fluids through a porous mediu

专利摘要:
METHOD FOR SIMULATING MULTIPLE PHASE FLOW / MULTIPLE FRACTIONAL COMPONENTS THROUGH POROUS MEDIA. A method to calculate or estimate flow of multiple phases / multiple fractional components through a porous medium employing a 3D digital representation of a porous medium and a computational fluid dynamics method to calculate vectors of flow rates, pressures, saturations, internal speed and other flow parameters are described. The method uses a single method of introducing non-wetting fluids and wetting fluids into the pores on the inlet face of the 3D digital representation of a porous medium and a new process control application to achieve near-steady state flow at low inlet concentrations. of non-wetting fluid. In addition, the method of the present invention reduces the time required to simulate to complete dynamic fluid calculations. The values resulting from the flow of non-wetting fluid, wetting fluid, saturation, and other parameters are used to generate plots of drainage curves and relative permeability absorption. Computerized systems and programs for carrying out the method are also provided.
公开号:BR112014000758B1
申请号:R112014000758-6
申请日:2012-07-02
公开日:2021-01-19
发明作者:Giuseppe DE PRISCO；Jonas Toelke；Yaoming MU
申请人:Ingrain, Inc.；
IPC主号:

专利说明:

Fundamentals of the Invention
[001] The present invention relates to a method for estimating fluid flow of multiple phases / multiple components through porous media and estimating relative permeabilities at various levels of saturation. Relative permeability data estimated with the present method can be used, for example, in many areas such as oil field simulation, estimating oil or gas production rates, estimating recoverable reserves, designing hydrocarbon recovery strategies, such as fracture or "fracking", life sciences, papermaking, food industry, agriculture and other areas related to geology and geophysics. The present invention also relates to a computerized system and components thereof for carrying out such a method.
[002] Relative permeability is used to quantify the flow of multiple phases, such as the flow of oil, in the presence of water and water in the presence of oil. In an example with two of these fluids, the relative permeabilities km and krw, by definition, are given by equations [9] and [10]:
where the subscripts "n" and "w" refer to non-wetting fluid and wetting fluid, respectively. The flows Qn and Qw are measured by the fixed saturation Sw. Relative permeability is usually plotted versus Sw.
[003] Relative permeability depends on more factors than kAbsolute, including the wetting capacity of the fluid and mineral system, interfacial surface tension, and viscosity contrast between the fluid phases, fluid speeds, the saturation level of the fluid. liquid in the pores, the structure and connectivity of the pores in the porous solid and the geometry of the pore space. Another important factor that influences the relative permeability is the time history of the flows that passed through the porous medium. These parameters can vary in space and time and the resulting fluid state and composition changes during fluid production.
[004] In a porous medium, capillary attraction is determined by the adhesion between a liquid present in the body and the body itself and by the cohesive force of the liquid for itself. A liquid that wets a solid surface has a greater adherence to the particular solid than a non-wetting fluid. A fluid can wet a solid and not another solid. In multi-phase fluid flow, wetting capacity is a relative property. For example, if the adhesion force of a first fluid to a porous medium is greater than the adhesion force of a second fluid to a porous medium, then the first fluid is said to be wetting and the second fluid is referred to as being non-humectant.
[005] Saturation, Sx is the volume fraction of the total pore volume in a porous medium that is occupied by the material "X". The saturation level is a value between 0 and 1. A saturation level of 1 indicates that the entire pore space available in a given porous medium is filled by the fluid under consideration. Relative permeabilities are a function of fluid saturation. As the saturation of a given phase increases, its relative permeability increases. Saturation history also has a great effect on relative permeability. The permeability - relative saturation relationship has a hysteresis effect between the drainage process (decreasing wetting phase) and imbibition process (increasing non-wetting phase). It is believed that most of the underground porous rock formations were initially filled with water and hydrocarbons entered these porous formations displacing part of the water. This history must be reproduced or evaluated before any estimate of relative permeability is attempted in order to establish realistic starting conditions. Plots of soaking and draining relative permeability versus saturation are shown in Figure 1.
[006] When a porous medium contains two or more immiscible fluids, the local volume of material in any specific pore may differ from the total or average saturation level for the entire porous rock sample. For example, a fluid can adhere strongly to surfaces within a given pore while another material may have no effective contact with the solid material. The geometry of local pore space within a given porous medium can vary considerably and these variations in geometry can affect local saturation levels.
[007] In practice, the relative permeability can be estimated through physical laboratory tests or through numerical simulations.
[008] One of the previous physical laboratory methods for measuring relative permeability is described in United States patent US 2,345,935 (Hassler). The method involves sealing all but two opposing surfaces in a sample of porous rock. A fluid or fluids under pressure are introduced into an open surface and forced to flow through the sample at a specified flow rate. Fluid pressures are generated by pumps or other similar means. The pressures and flow rates are entered to calculate the relative permeability. A disadvantage of the Hassler technique is the need to determine the internal wetting fluid pressures within the porous medium. This problem is described by W. Rose, "Some problems in the application of Hassler's Relative Permeability Method", 32 J. Petroleum Technology, 1161-63 (July 1980). US patent 4,506,542 (Rose) describes an apparatus and method that does not require the measurement of internal pressures to estimate relative permeability.
[009] Hassler's method is a steady-state method that can be used to calculate relative permeability versus saturation for a range of saturations from 0 to 1. For two-phase systems of immiscible fluids, the rock sample can be first purged with a fluid for a sufficient time such that the rock sample saturation of the selected fluid is 1. Then, the other fluid or combinations of the two fluids are forced through the sample for a time sufficient to reach the steady state of the two flows Qw and Qn. At this time, the flow and pressure readings can be used to calculate kn, kw for a given value of Sw and plotted. The proportion of wetting and non-wetting fluids at the sample inlet can be changed. This new combination of wetting and non-wetting fluids is forced through the sample for a time sufficient to reach the steady state of the two flows Qw and Qn. Another pair of relative permeabilities, kn, kw corresponding to another value of Sw, are calculated and another point is plotted. By repeating this procedure for different combinations of wetting and non-wetting fluids, a graph of relative permeability versus saturation can be plotted as shown in Figure 2.
[0010] Other physical steady-state methods for calculating relative permeability include the Penn State method (Snell, RW, Measurements of gas-phase saturation in a porous medium, J. Inst Pet, 45 (428), 80, 1959; Hafford's method (Naar, J. et al, Relative permeability of three-phase imbibition, So. Pet. Eng. J., 12, 254, 1961; Dynamic single-sample method (Saraf, DN et al, three-phase relative permeability measurement using a nuclear magnetic resonance technique to estimate fluid saturation, Soc. Pet. Eng. J., 9, 235, 1967); Stationary fluid method (Saraf, DN et al, Measurement of relative three-phase permeability using an magnetic resonance technique nuclear to estimate fluid saturation, Soc. Pet. Eng. J., 9, 235, 1967) and the diffuse feeding method (Saraf, DN et al, three-phase relative permeability measurement using a nuclear magnetic resonance technique to estimate fluid saturation, Soc. Pet. Eng. J., 9, 235, 1967).
[0011] Another method, the non-steady state method, also begins with the rock sample initially saturated with the wetting fluid. Then, the non-wetting fluid is forced through the sample, the fraction of non-wetting fluid recovered and the pressure drop across the sample is recorded and used to calculate the various combinations of kn, kw in corresponding Sw values.
[0012] Laboratory methods may suffer from a number of deficiencies, which may include one or more of the following:
[0013] 1. The sample to be tested is in the laboratory under surface conditions while the IN SITU sample can be at temperatures above 100 ° C and 10 to 70 MPa (100-700 bar). When samples are brought to the surface, many properties of the rock change. The creation of artificial conditions to reproduce rock bottom conditions is difficult, inaccurate and / or inaccurate.
[0014] 2. The pressures necessary to achieve the desired flow rates can be extremely high causing leakage problems and / or equipment malfunction.
[0015] 3. A large volume of fluid must be processed for the sample to come close to steady state.
[0016] 4. Tests can take a long time up to weeks or months or more than a year to complete.
[0017] 5. Very tight formations such as shales can be difficult or impossible to measure.
[0018] 6. Initial conditions such as saturation, wetting capacity, and fluid distributions are difficult to establish.
[0019] 7. Establishing wetting capacity in the laboratory is difficult because the cores are generally cleaned prior to testing and the initial wetting capacity cannot be accurately restored.
[0020] 8. In the laboratory, it is difficult and expensive to conduct tests with reservoir fluids under reservoir conditions. Mixing gas and oil at reservoir temperatures and pressures is difficult and can be dangerous.
[0021] Numerical simulations to calculate relative permeability usually use numerical methods such as pore network modeling or direct simulation of the flow of multiple phases / multiple components in a porous medium.
[0022] Such a general method for calculating relative permeability is described in United States Patent US 6,516,080 (Nur). This method, as with most numerical methods, is based on the production of a digital representation of a porous medium, designated as a "sample", for which the relative permeability must be estimated. The digital representation is typically produced by a CT X-ray scanner and then refined to compensate for the scanner's resolution limitations. This representation, together with fluid properties, rock properties, initial saturation, wetting capacity, interfacial tension and viscosity are used as input to the Lattice-Boltzmann algorithm. The Lattice-Boltzmann method is a tool for flow simulation, especially in media with complex pore geometry. See, for example, Ladd, Numerical Simulations of Particular Suspensions through a discretized Boltzmann equation, Part 1: Theoretical Foundation, J. Fluid Mechanics, v 271, 1994, pp 285-309; Gunstensen et al, "Lattice Boltzmann model of immiscible fluids, Phys Rev. A., v 43, No. 8, April 15, 1991, pp 4320-4327; Olsen et al, Flow of two fluids in Sedimentary Rock: Simulation , Transport and Complexity, J. Mecânica dos Fluidos, Vol. 341, 1997, pp 343-370; and Gustensen et al, Lattice-Boltzmann Studies of Biphasic Immiscible Fluid through Porous Media ", J. of Geophysical Research, V 98, No. B 4, April 10, 1993, pp 6431-6441). The Lattice-Boltzmann method simulates fluid movement as collisions of imaginary particles, which are much larger than molecules in real fluids, but in which such particles show almost the same behavior on a macroscopic scale. The algorithm used in the Lattice-Boltzmann method repeats collisions of these imaginary particles until the steady state is reached, and provides a local mass flow distribution.
[0023] The accuracy of the numerical methods for calculating the relative permeability, just like the Nur method depends in part on the precision of the sample. The sample is composed of discrete elements called voxels. Voxels are volumetric pixels. The digital representation of a three-dimensional object can be subdivided into voxels. Ideally, each voxel is precisely classified as a solid or pore. The choice between solid or pore may not always be clear due to differences in scanning resolution and the minimum grain size in the porous medium. If a voxel is classified as a solid, the nature or composition of the solid must also be known or determined in order to model numerically and make estimates of its physical properties.
[0024] In addition, the accuracy of the numerical methods to calculate the relative permeability also depends on the numerical methods applied. The robustness of these methods may depend on how the boundary conditions are handled in the algorithm. There may be boundary conditions of entry and exit, boundary conditions at the top, bottom, left or right of the sample and boundary conditions within the porous medium. The latter include effects on wetting capacity especially when relatively small fractional flows of one fluid or others are present. Boundary conditions are a very complex problem in numerical methods. Selection of boundary conditions can significantly affect the time required for the calculation, the accuracy of the results and the stability of the simulation. This can be especially true for the simulation of multiple phases or multiple immiscible components. Difficulties can arise from the fact that the pressure and distribution of the phases and speeds of the digital simulation are unknown and these conditions must be established in such a way that they imitate the physical conditions. There is no standardized and unique way to define the appropriate boundary conditions and many authors propose their own solution. The boundary conditions chosen can be of paramount importance, since they can significantly affect the numerical precision of the simulation and also its stability.
[0025] Numerical methods can have advantages over laboratory methods, such as in one or more of the following ways.
[0026] 1. Because numerical simulations are virtual, they do not require physical presence as downhole fluids in downhole conditions. In the case of relative permeability in oil and gas formations, hydrocarbons at high temperatures and pressures, often above the critical point, are difficult to control and dangerous to handle.
[0027] 2. Because numerical simulations can speed up the time scale used, numerical simulations can be completed in a matter of hours or days instead of weeks, months, or more. Because of this, other variations in fluid composition and flow can be processed using numerical methods that are practical in laboratory tests.
[0028] 3. Numerical simulations have the advantage that the properties of any component can be calculated accurately in any location and at any time.
[0029] Numerical methods may also suffer from some drawbacks, including one or more of the following options:
[0030] 1. The initial and boundary conditions are difficult or impossible to assess which results in inability in some cases to accurately calculate relative permeabilities or instability in computation. This is especially true when the fractional flow of one or more components is small.
[0031] 2. The distribution of wetting capacity in space and time within a porous medium is difficult to assess.
[0032] The present researchers recognized that there is a need for new methods and systems to simulate multi-phase, multi-component fractionated fluid flow through porous media to provide, for example, improved evaluations and estimates of the productivity potential of a field oil or other underground reservoir, and / or that can provide improvements in modeled multi-phase, multi-component fluid flow estimates through other types of porous media. SUMMARY OF THE INVENTION
[0033] An important feature of the present invention is a method for calculating or estimating multi-phase, multi-component fractionated fluid flow through porous media.
[0034] Another feature of the present invention is a method for calculating or estimating the relative permeability for multi-phase, multi-component fractionated fluid flow through porous media.
[0035] Another feature of the present invention is a method for defining boundary conditions for numerical methods, for example, for dynamic computational fluid simulations (CFD), which more accurately represent real-world conditions and improve the speed of calculation and stability.
[0036] Another feature of the present invention is a method of adjusting the inlet pressure for CFD calculations to achieve the target fractional flow through a porous medium.
[0037] Another feature of the present invention is a method for calculating or estimating the multi-phase, multi-component fluid flow through a porous medium under conditions where the fraction of non-wetting fluid or wetting fluid is low.
[0038] An additional feature of the present invention is a method for calculating or estimating relative permeability versus saturation for imbibition and drainage.
[0039] Another feature of the present invention is a method for calculating or estimating relative permeability versus saturation curves including data points where the saturation level is low.
[0040] Another feature of the present invention is a method of using the relative permeabilities calculated or estimated to evaluate an underground oil reservoir or other type of porous media.
[0041] To achieve these and other advantages and in accordance with the purposes of the present invention, as embodied and widely described herein, the present invention relates, in part, to a process for simulating a fractional flow of wetting fluids or non-fluids humectants through a porous medium comprising the steps of (a) creating a digital three-dimensional representation of a porous medium ("Sample") containing a total volume of fluids comprising wetting fluids or non-wetting fluids, (b) defining a first fraction of the total fluid volume comprising the wetting fluids and a second fraction of the total fluid volume comprising the non-wetting fluids (c) setting a value for a flow rate of the total fluid volume flowing through the sample, (d) evaluating the properties of wetting fluids and non-wetting fluids, (e) define initial conditions for the saturation of wetting fluids (Sw), saturation of non-wetting fluids (Sn), pressure inlet of wetting fluids (Pw) and inlet pressure of non-wetting fluids (Pn), (f) defining conditions on the sample inlet face in which non-wetting fluids and wetting fluids enter the sample pores in separate and distinct areas, and (g) calculating internal pressures, saturation, and velocity vectors for the sample, (h) calculating the flow rates of the non-wetting fluids (Qn) across the sample, the flow rate of the wetting fluids (Qw) across the sample. , and the pressure at the Sample output, (i) repeat steps a) to h) for a predefined number of time increments, t, and (j) periodically adjust the input pressures Pn and Pw using a reporting control algorithm where quasi-steady state values for Qn and Qw are achieved.
[0042] The present invention also relates to a computing system or determining or estimating multi-phase, multi-component fractional flow through a porous medium comprising (a) a digitizer capable of producing a three-dimensional digital image of a porous medium, ( b) a computer comprising at least one processor operable for the execution of a computer program capable of classifying elements in the three-dimensional digital image as solids (grains) and pores (void), (c) a computer comprising at least one processor operable for the running a computer program capable of performing the indicated calculations, and (d) at least one device for viewing, printing or storing the calculation results.
[0043] The present invention also relates to a computer program product in a computer-readable medium which, when run on a controller of a computerized device, provides a method for performing one or more or all of the indicated calculations.
[0044] The present invention also relates to the use of the indicated method and / or system to calculate or estimate the multi-phase / multi-component fluid flow fractionated through porous media from an underground reservoir, such as an underground oil reservoir, and to calculate or estimate relative permeabilities at various levels of saturation, and use of calculated or estimated relative permeabilities to provide improved assessments and estimates of underground reservoir productivity. The methods and systems of the present invention can also be used to provide numerically modeled assessments of fractionated multi-phase / multi-component fluid flow by means of another type of porous media.
[0045] It should be understood that both the previous general description and the following detailed description are only exemplary and explanatory and are intended to provide a further explanation of the present invention as claimed.
[0046] The attached drawings which are incorporated and constitute a part of this application, illustrate some of the characteristics of the present invention and together with the description serve to explain the principles of the present invention. Brief Description of Drawings
[0047] Figure 1 shows a representative plot of the hysteresis effect on the relative permeability under imbibition and drainage.
[0048] Figure 2 is a plot representing the relative permeability for wetting and non-wetting fluids at saturation levels ranging from 0 to 1.
[0049] Figure 3 is a schematic representation of physical laboratory methods, both in steady state and unstable state, which can be used to calculate or estimate the flow of multiple phases, of multiple components through a porous medium.
[0050] Figure 4 is a flowchart that shows how the initial conditions for the simulation are defined according to an example of the present application.
[0051] Figures 5a-5f show several views of the sample entry face with the pore area divided into wetting and non-wetting subareas, according to an example of the present application.
[0052] Figures 6a and 6b show the detail of the sample entrance face with the pore area divided into sub-areas for wetting and non-wetting fluids, according to an example of the present application.
[0053] Figure 7 is a process flow chart of a numerical simulation method to calculate or estimate the transport properties of fluids including relative permeabilities and saturations of a porous medium, according to an example of the present application.
[0054] Figures 8a and 8b are diagrams that outline the process control scheme for the flow of wetting and non-wetting fluid, according to an example of the present application.
[0055] Figure 9 shows a system that integrates three-dimensional (3D) digitizer image analysis of a porous medium with a computational fluid dynamic method (CFD) applied to a 3D digital representation of the porous medium, according to an example of this application.
[0056] Figure 10 shows a representative photograph of a carbonate sample that includes a 4 mm diameter pillar marked by a rectangle that was taken from the sample and photographed on a CT scanner, according to an example of the present application.
[0057] Figure 11 shows a representative scanned image with the CT scanner of the selected area of the carbonate sample indicated in Figure 10, according to an example of the present application.
[0058] Figure 12 shows a representative plot of the permeability ratio and estimated water saturation values using a method according to an example of the present application. Detailed Description of the Invention
[0059] The present invention relates, in part, to a method for calculating fractional multi-phase / multi-component flow through a porous medium that employs a three-dimensional (3D) digital representation of a porous medium integrated with a Dynamic method of Computational Fluid (CFD) to calculate flow rates, pressures, saturations, internal velocity vectors, and / or other flow parameters that can provide improved determinations, for example, faster and / or more accurate determinations of fluid transport properties of the porous medium, how to calculate or estimate relative permeability versus saturation for imbibition and / or drainage. These determinations can be made without the need for expensive and time-consuming laboratory experiments on physical samples of the porous medium. The method can employ a unique method of simulating the introduction of non-wetting and wetting fluids into the pores at the entrance face of the 3D digital representation of a porous medium and a process control application to achieve the almost steady state flow at low concentrations of non-wetting fluid inlet. In addition, the method of the present invention reduces the time required to complete dynamic fluid calculations. The values resulting from the flow of non-wetting fluid, wetting fluid, saturation and other parameters can be used to generate plots of drainage curves and relative permeability soaking. The ability to make these types of determinations about the fluid transport characteristics of porous media can improve the accuracy of cost and technical decision making regarding production in porous media. Computerized systems and computer programs to perform the method are also provided.
[0060] The method of the present invention can be used to calculate the flow of multi-phase fluids, immiscible in porous media, as for example shown schematically in Figure 3. For the purposes of this invention, the term "multiple phases" refers to to various phases of a compound element, such as liquid and steam, and to various compounds in a mixture, such as oil and water. Fluids are classified as wetting fluids and non-wetting fluids. Wetting fluids are fluids that tend to cover or adhere to the inner pore surface of the porous medium. Wetting capacity is the tendency for a fluid to spread over, or adhere to, a solid surface in the presence of other immiscible fluids. Wetting capacity is defined by the angle of contact of the fluid with the solid phase. An example of the present invention describes a system comprising a wetting fluid and a non-wetting fluid. However, the methods described herein can be applied to systems that comprise multiple wetting fluids and / or non-wetting fluids. The porous medium to which the methods described herein can be applied is not necessarily limited. The porous medium can include, for example, rocks, soils, zeolites, biological tissues such as bones, wood, cork and similar materials; cements, ceramics, compacted solid particles such as sand, clay, stone, ceramics, inorganic compounds, organic compounds, metals and similar materials, synthetic materials, such as polymers, and other similar materials.
[0061] The following descriptions, references to numbers in parentheses (1) to (13) refer to the numbered boxes correspondingly shown in Figure 4, references to the number in parentheses (14) refer to the numbered boxes correspondingly shown in Figures 4 and 7, and references to the numbers in parentheses (15) to (34) refer to the numbered boxes correspondingly shown in Figure 7. With reference to Figure 4, a physical sample from a porous medium can be scanned (5) with a device capable of producing a three-dimensional (3D) digital representation of the porous structure of the sample. The source of the sample, as in the example of a rock formation sample, is not particularly limited. For rock formation samples, for example, the sample can be sidewall cores, integral cores, drill cuttings, quarry outcrop samples, or other sample sources that can provide suitable samples for analysis using methods according to the present invention. Devices such as a CT scanner can be used for this purpose where the sample is exposed to x-rays of a certain frequency. The frequency determines the resolution of the scan. Examples of suitable CT equipment for making images usable with the methods according to the present invention include, for example, 3D tomographic x-ray transmission microscopes, such as MicroXCT-200 and Ultra XRM-L200 CT, which are made by Xradia, Inc. (Concord, California, USA). For very refined porous media, such as shales, scans can be performed using a scanning electron microscope, SEM. The software provided with the scanning machine tomographically reconstructs the 3D volume into an ordered set of voxels.
[0062] The segmentation process (6) classifies individual voxels as solid or pores. The three-dimensional digital representation can be created from the Sample (porous medium), for example, which comprises several planes, ordered of voxels in which each of the voxels can represent a pore (pore voxel) or solid (grain voxel). There may be more than one class of materials in the porous medium. The segmentation process is necessary due to the resolution of the scanner compared to the size of the grains and pores of the porous medium. A number of methods for segmenting 3D grayscale representation can be used for this purpose. Such a method, for example, is described by Nur in U.S. Patent 6,516,080, which is incorporated herein by reference in its entirety. Another segmentation and gray scale process that can be adapted for use in the present methods is the US Patent Application Publication US 2010/0128932 A1, which is incorporated herein by reference in its entirety. Any method capable of producing a 3D digital representation of a porous medium can be sufficient for the present invention. After image segmentation (6), each of the pores on the entrance face can be divided into equal and isolated central annular areas (regions) (7), and the sample is initially flooded with the wetting fluid (8), as is described in greater detail in discussions of other related Figures provided here.
[0063] Initial definitions (14) are shown in Figure 4. In the present invention, the CFD method used can be the Lattice-Boltzmann method or other methods. Various indexes can be used in the method to control the actions taken in the simulation. The y parameter is an index for the number of wetting / non-wetting fluid combinations to be performed in the simulation. Initially, the y-index is set to 1 so that it points to the first composition to be simulated (1). The parameter t is an index for the number of time steps in the simulation, t is initially defined as 1 (2). The q parameter is an index to the number of time steps in which the report control action will take place, and q is initially set to 1 (3). The parameter tmax (4) is a value for the maximum number of time steps to be completed for each combination of wetting and non-wetting fluids to be performed through the simulation. Fluid properties are necessary for the calculation of fluid flows and for the calculation of relative permeability (10). The properties of wetting fluids and non-wetting fluids that can be used in calculations may comprise, for example, viscosity, contact angle, interfacial tension and other physical or chemical properties. The wetting fluid viscosity values, μw, non-wetting fluid viscosity, μn, interfacial tension, o, and contact angle are defined. As also shown in Figure 4, an initial value of Pw, the pressure exerted on the wetting fluid, and Pn, the pressure exerted on the non-wetting fluid, are entered (9). The values for the fraction of non-wetting fluid Fn and wetting fluid Fw that enter the sample inlet face are also initially defined (11). The user enters a desired total flow rate, QT, typically measured in meters per second or feet per day or any other desired units, to be forced through the sample (13), and the initial values Qwt and Qnt are determined (12) , which are calculated flow rates of wetting and non-wetting fluids, respectively.
[0064] As shown in Figure 3, a sample (60) is subjected to a wall boundary condition, as represented by the thick black lines (61) in such a way that a multi-phase fluid can be forced through the sample by a pressure gradient (62). The sample may comprise an entrance face and an exit face where the entrance and exit faces are parallel to each other and three or more surfaces orthogonal to the entrance face and the exit face where the three or more orthogonal surfaces are impermeable to wetting fluids and non-wetting fluids. Due to the wetting fluid and non-wetting fluid entering the sample they can be at different pressures, a strange back flow condition can occur at the inlet where some fluid can come out of the sample. In the event that a backflow condition occurs, a damping zone or region (63) at the sample inlet can be used to eliminate foreign backflow. The buffer zone or region can change calculations to at least 1 or 2 or 3 or more layers of voxels that make up the sample from the entry face. For example, the inlet face may comprise a buffer zone in parallel with the inlet face comprising a voxel plane, two voxel planes, 3 voxel planes or more. The sample is used in the computational fluid dynamic calculation to estimate the wetting and non-wetting fluid flow rates through the sample and to calculate the relative permeability of the porous medium for specific saturations within the sample.
[0065] With reference to Figure 7, once initial configurations (17) have been established, as described in relation to the configurations (14) shown in Figure 4, the simulation can begin by calculating the flow rates of wetting fluids and not humectants, pressures, saturation levels, velocity vectors and other properties for all points located in the sample using computational fluid dynamics (CFD) calculations (16). CFD calculations are repeated for a discrete number of time intervals, t (15). At specific time intervals, such as t = M (q) (21), wetting and non-wetting fluid flow values are stored (22) and a weighted average flow for wetting and non-wetting fluids are calculated and stored (18) . The weighted average fluid flows are compared with the desired or defined point values of wetting fluid flow and non-wetting fluid flow to produce an error (19) which is then used to calculate new wetting pressure values and pressure of non-wetting fluid on the sample inlet face (20). Until tmax is reached (24), an index is advanced to point to the next time that report control calculations should be made (23), and the new pressures for wetting fluid and non-wetting fluid at the input are introduced in CFD calculations ( 25). For example, a check is made to see if the simulation has reached almost steady state, for example, by checking to see if the maximum predefined number of time increments, tmax, has been reached (24), or by other methods indicated here. If tmax was reached, calculated values of weighted average flows of wetting and non-wetting fluids, as well as other properties, such as fluid saturation can be stored at this point (32) and used to calculate other properties of interest, such as permeability relative (33). This simulation can optionally continue for a number of additional compositions of wetting fluids and non-wetting fluids to be forced through the sample (30), (31). New values for the fraction of wetting fluid, Fw, and non-wetting fluid, Fn, that enter the sample inlet face, can be selected (29) and new fluid flow set points for wetting and non-wetting fluid ( 28). When the fraction of wetting fluid and non-wetting fluid changes, the fractional inlet pore area used for wetting and non-wetting fluid can be changed (27). The fraction of the inlet pore area for wetting fluid and non-wetting fluid is a function of the fraction of non-wetting fluid entering the sample inlet face. In general, the fractionated area for non-wetting fluid should be reduced when the fraction of non-wetting fluid entering the sample inlet face is less than about 10% by volume, or less than about 20% by volume, or less than 30% by volume. In the case where various combinations of wetting fluids and non-wetting fluids are forced through the sample, relative permeability versus saturation curves can be plotted (34).
[0066] As an example, the present invention simulates the flow of two phases through porous media. Figures 5a-5f show six images, respectively, of the sample entry face. In Figure 5a, the pores on the inlet face are filled with the non-wetting fluid shown in dark shading 52. If a non-wetting fluid or two-phase fluid is then forced through the sample, the distribution of the wetting and non-wetting fluid on the face input is critical for the CFD simulation producing a representative result and also critical for achieving near-steady state flow through the sample. One of the necessary input conditions for the CFD simulation is the pore area on the inlet face that is allocated for wetting fluid flow and the pore area on the inlet face that is allocated for non-wetting fluid flow and the distribution of areas for wetting fluid flow and non-wetting fluid flow. In the present invention, the area for wetting fluid at the entrance is first distributed on the inner face of the pores as a single layer of voxels in the digital sample as shown in the white areas 53 in Figures 5b-5f. The percentage of wetting fluid area can be increased by adding more voxels one way per layer. Figures 5a to 5f show an increasing area for the wetting fluid up to about 50%, which is represented in Figure 5f.
[0067] The inlet face (70) of the sample is shown in Figures 6a and 6b. Non-wetting fluids and wetting fluids can enter the sample through pores on the sample inlet face. The image on the left is the sample as shown in Figure 5a and the image on the right is the same as shown in Figure 5F. The image in Figure 6a on the left shows a porous rock in which pores filled with non-wetting fluid are shown in darker shading. Grains or solids (76) are shown in the intermediate shading. The sample face has pores (71) through which a two-phase fluid enters the sample. Each pore area is divided into sub-areas. An area (73) represents the annular area, Aw, through which the wetting fluid flows. A second area, An, (72) represents the central area through which the non-wetting fluid flows. Because the image is digital, the creation of the area, Aw, is done by selecting voxels adjacent to the grains and designating them as an area for wetting fluid. This is an accurate representation of the real world as the wetting fluid has an attraction for solid surfaces in the rock. Voxels are stratified starting with the voxels closest to the grain boundaries first. The remaining voxels (72) are designated as An. This is also an accurate representation of the real world, because the non-wetting fluid is immiscible with the wetting fluid and the surface tension and capillary pressure will force the non-wetting fluid into the interior space of the pore and away from the solid surface. Observe the area of the labeled sample face (75). This is an example of the 3D character of that separation. The sample entry face is a digital slice of the porous medium. Immediately behind the entrance face is another plane. In place (75), the back plane of the inlet face is a rock or solid in place (75) so that the area in the location effectively becomes an area for the inlet flow of wetting fluid. This area (75) is defined in the same way, by stratification of voxels designated as Aw in the grain boundary, but in this case, the grain boundary is in the direction perpendicular to the sample entry face. The two areas An and Aw are separated by a contour (74) in such a way that the two fluids are decoupled. The two areas (72) and (73) are initially defined approximately equal to each other. In this case, the central portion of the fractional pore area on the entrance face, An, and the fractional area of the annular portion of the pores on the entrance face, Aw, are approximately equal. So, in this initial case, An is about 0.5 and Aw is about 0.5. The proportion of these two areas can be adjusted to compensate for the condition when the flow rate of non-wetting fluid is less than about 50% by volume of the total flow through the sample, or less than about 10% by volume of the sample. total flow through the sample, or less than about 1% by volume of the total flow through the sample. If a single phase is injected into the sample, area An is defined as 1 in the case of a non-wetting fluid and area Aw is defined as 1 in the case of a wetting fluid. The inventors found that establishing and controlling the An to AW ratio results in the convergence of the CFD calculations used to calculate the fractional flow at different levels of saturation.
[0068] The sample is initially flooded with either the wetting fluid or non-wetting fluid to completely saturate the sample. As an option, the sample is initially flooded with the wetting fluid (8) (with reference to Figure 4). This provides an initial internal boundary condition for the simulation.
[0069] As indicated, the user introduces a desired total flow rate, QT, typically measured in meters per second or feet per day or any other desired units, to be forced through the sample (13). The total flow rate entry can be based on the need of the technician doing the simulation, typically a reservoir engineer or geologist. The flow rate entry can be a total existing flow rate from a well or a desired flow rate as well, for example.
[0070] Each of the two fluids, humectant and non-humectant, are subjected to different pressures. As indicated, an initial value of Pw, the pressure exerted on the wetting fluid, and Pn, the pressure exerted on the non-wetting fluid are entered (9). Darcy's law can be used, for example, to make an initial pressure estimate with the following equation [11]:
where Pi = initial pressure value of the desired phase, wetting or non-wetting μ = viscosity of the desired phase L = length of the sample in the direction of flow Q = desired flow of the desired phase Kabs = absolute permeability of the Sample A = area of the inlet face Sample C = constant
[0071] The simulation converges more quickly when the initial values of Pw and Pn are lower than the final value. Therefore, the values of the constant, C, can be about 20, or about 30, or about 40, or about 50, or higher values.
[0072] In the present invention, the non-wetting fluid can be forced through the pores on the sample inlet face to displace the wetting fluid (8). Alternatively, if the sample was initially saturated with the non-wetting fluid, then the wetting fluid can be initially forced through the pores on the face of the sample. Because there is optionally no wetting fluid being forced through the sample, the central fractional pore area, An, can be defined as 1. When these conditions are used, there is only a single phase flow at the sample inlet. However, because the sample was initially flooded with the wetting fluid, there is a two-phase flow inside the sample and at the sample outlet after a transient period of time. As an option, the initial sample saturation is a total wetting fluid saturation, Sw = 1.0, and a total non-wetting fluid saturation, Sn = 1.0. As another option, the initial sample saturation can be the final saturation conditions of a previous simulation.
[0073] CFD calculations can be performed in discrete increments as shown in Figures 5 (a) - (f). Each increment is mapped to the increment time t, where t can be, for example, seconds, milliseconds or other units of time. For each time increment, main parameters of the flow through the sample (16) are calculated. The main parameters may include, for example, the integrated wetting fluid flow rate over the sample volume, V, at a given time t,
, the integrated flow rate of the non-wetting fluid over the sample volume, V, at a given time t,
and the internal pressures, velocity vectors for each phase and the saturation of each voxel in the sample at a given time t. The time step indicator, t, is incremented by 1 each time the main parameters are calculated (15). Fractional flow rates can be determined based on calculations that comprise the use of the above equations as used for the determination of Qwt and Qnt, where the sample volume (V) can be for the entire volume of the original sample, or alternatively for a fraction of the original total volume. The volume fraction can be chosen, for example, close to the entrance to minimize the control time delay.
[0074] Periodically, the number of elapsed time steps is checked to see if the report control calculations should be made (21) (with reference to Figure 7). Report control calculations are made periodically based on a list of predefined total elapsed increments (21), where M (q) is the sequence of time steps q in which control action is taken. Corrections are made for the inlet pressures, Pw and Pn, so that the target flow rates QwT and QnT are achieved (20). Corrections are made, for example, with a reporting control algorithm. Corrections can be periodic adjustments of the inlet pressures set to occur, for example, once every 10 time increments, or once every 100 time increments, or once every 500 time increments, or once a every 1000 time increments, or once every 10,000 time increments, or more. A unique feature of the present invention is the use of a report control algorithm in a dynamic computational fluid algorithm for the two-phase fluid flow through porous means to establish the fractionated fluid flow. The total number of corrections can vary, for example, from about 100 to 500, or from about 10 to 1000, or any other interval, as needed to achieve the almost steady state flow. As an option, the number of time increments of subsequent periodic adjustments of the inlet pressure may be different. For example, periodic adjustment of inlet pressures may occur more frequently in the first half of the total simulation time than in the second half of the simulation. More corrections can be made at the beginning of the simulation than later in the simulation because the errors in QwT and QnT tend to be greater in the initial part of the simulation. The number of report corrections in the first half of the simulation time can be, for example, about 10, or about 15, or about 20 times or more the number of corrections made in the second half of the simulation time. The number of corrections can vary continuously over the course of the simulation, for example, with more corrections made at the beginning of the simulation compared to later in the simulation.
[0075] Qwt and Qnt are the calculated flow rates of wetting and non-wetting fluids, respectively, for the time interval t. For each time increment that the report control corrections are made, the values of Qwt and Qnt are stored (22) and a movement time or weighted average time of the stored values of wetting fluid flow at time t, QW, and the non-wetting fluid at time t, QN, is calculated (18). The weighted average, as used for QW, QN, or QT, can be, but is not limited to, an arithmetic weighted average, a geometric weighted average, or a harmonic weighted average. The moving average, as used for QW, QN, or QT, can be, but is not limited to, a simple rolling average or an exponential adjustment.
[0076] As shown in Figure 7, new values for Pn and Pw are calculated (20) using a reporting control algorithm. As an option, the report control algorithm can include a separate report control algorithm to set the inlet pressure for the wetting fluid and a separate report control algorithm to set the inlet pressure for the non-wetting fluid, where the inlet pressure for the wetting fluid and the inlet pressure for the non-wetting fluid are set independently. The report control algorithm may comprise, for example, a report control algorithm to adjust the inlet pressure for both wetting and non-wetting fluids, where the inlet pressure for the wetting fluid and the inlet pressure for the fluid non-humectant are the same. The present invention can use a negative reporting control algorithm in which errors, Ew and En (19), are calculated by subtracting the real value, QW and QN, from the target value, QwT and QnT. The present invention can use two proportional-integral-derivative control (PID) algorithms, one to control the flow of the wetting fluid fraction and the other to control the flow of the non-wetting fluid fraction. In the case of PID control, the integral and derivative of the errors Ew and En are calculated at each step of time t in a form to define the output of the PID controller, πw and πn. The output of the PID control is used to define the pressure variation from its initial value (9), so that, for each phase (humectant and non-humectant in the case of a double control) the new pressure is Pw = Pi + Pi * πw, and Pn = Pi + Pi * πn, where Pi * π is the pressure variation each time the controller is activated. The initial pressure value establishes the scale of both the pressure and its variation. For example, the PID control circuit can include an input error Ew and emit a new input pressure, Pw, where Pw = Pi + Pi * πw, Pi = the initial pressure defined at the beginning of the simulation, πw = f (Ew ), how
= proportional control constant, Kl = integral control constant, and KD = derivative control constant. When the PID control circuit comprises an input error En and emits a new input pressure, Pn, Pn = Pi + Pi * πn, Pi = the initial pressure defined at the beginning of the simulation, and πn = f (En), as

[0077] Where Kp, Ki and KD represent the same constants indicated. Other control algorithms, such as adaptive control, hierarchical control, intelligent control, optimal control, robust control, neural network control, fuzzy logic control, or stochastic control, can be used as alternatives.
[0078] In Figure 8a, the pressure of the wetting fluid is digitally raised by a digital representation of a device to increase the pressure (50) and the pressure of the non-wetting fluid is digitally lifted by a digital representation of a device for increasing the pressure (50). 51). The two fluids are thus forced through the sample (52).
[0079] In Figure 8a, the pressure of the wetting fluid, Pw, and the pressure of the non-wetting fluid, Pn, are different in most cases. Due to this pressure difference, the simulation can calculate the reflux for the wetting or non-wetting fluid. Reflux is a condition in which the fluid flows out of the sample due to the difference in applied pressures of wetting fluids and non-wetting fluids. Reflux is a strange effect of pressure differences at the inlet and does not occur in a physical test. Therefore, to compensate for this factor, the present researchers have provided a unique technique in which a buffer zone at the inlet (53) is created in which fluids cannot flow back. The buffer zone consists of a series of voxel planes at the entrance where the number of voxel plans can be, for example, 1 or 2 or 3 or more. To ensure that there will be no return in the buffer zone, the interfacial tension between the wetting and non-wetting fluid is set to zero and the viscosities of the wetting and non-wetting fluids are greatly increased. For example, the interfacial tension between the wetting and non-wetting fluid can be set to zero for all calculations within the buffer zone. Viscosities can be increased by a factor of about 10 times, or about 20 times, or about 30 times, or about 40 times, or about 50 times or more. Other techniques for treating reflux can also be developed and used.
[0080] The integrated or calculated flow of the wetting fluid over the sample volume, Qwt, and the integrated or calculated flow of the non-wetting fluid over the sample volume, Qnt, are measured and the average (weighted) flow of wetting fluid, QW, and the average (weighted) non-wetting fluid, QN, are calculated. The error between QW and the target flow of the wetting fluid is calculated, Ew = Qwt - QW and the error between QN and the target flow rate of the non-wetting fluid is calculated, En = QnT - QN. The errors, Ew and En, are inserted in two separate control algorithms to adjust the inlet pressures Pw and PN. The use of two separate control algorithms, one for wetting fluid and one for non-wetting fluid, results in simulations that better reflect the actual distributions of wetting and non-wetting fluids in a real-world sample.
[0081] An alternative control scheme is shown in Figure 8b, where a single controller is used. In this case, the pressure of wetting and non-wetting fluid at the inlet is always the same. The pressure of the wetting fluid is digitally raised by a digital representation of a device to increase the pressure (55) and the pressure of the non-wetting fluid is digitally lifted by a digital representation of a device for increasing the pressure (56). The two fluids are thus forced through the sample (57).
[0082] In Figure 8b, the pressure of the wetting fluid, Pw, and the pressure of the non-wetting fluid, Pn, are the same. There is no reflux when there is no pressure difference between the wetting fluid and the non-wetting fluid. However, to ensure that there is no calculated intermixture of wetting and non-wetting fluids at the inlet, a buffer zone at the inlet (58) can be created similar to the case where two separate controllers are used. As indicated, the buffer zone can be made up, for example, of a number of voxel planes at the entrance, where the number of voxel plans can be 1, or 2, or 3 or more. To ensure that there will be no intermixing in the buffer zone, the interfacial tension between the wetting and non-wetting fluid can be set to zero. There is no need to change viscosities, in the case of a controller, because there is no pressure difference to trigger reflux.
[0083] The integrated flow of the combined wetting and non-wetting fluids over the sample volume, Qct, is measured and the weighted average flow rate of the combined wetting and non-wetting fluids, QC, is calculated. The error between QC and the total target flow through the Sample, QT, is calculated, Ec = QT - QC. The error, Ec, is introduced in a single separate control algorithm to regulate the inlet pressure. For example, the PID control circuit can include an input error Ec and emit a new input pressure, Pc, where Pc = Pi + Pi * πc, Pi = the initial pressure defined at the beginning of the simulation, and πc = f ( Ec) as
where Kp, Ki, and KD represent the same constants indicated. Using a single controller does not produce simulation results as representatives of the real world like using two separate controllers. However, simulations using a controller are less complex, run faster and can produce results that are sufficiently representative in many cases. Single controller simulations can also be used for initial approximations of multi-phase / multi-component fractional flows through porous media. A PID control circuit, for example, can be used to determine at least one or more, or all, of Ew, En, and Ec.
[0084] In the case of a PID control algorithm, there can be three controller configurations: proportional gain (Kp), integral gain (Ki) and derivative gain (Kd). PID control circuits are tuned by adjusting select values for Kp, Ki and Kd to achieve the desired control response. The values of Kp, Ki and Kd can be selected by any of the known tuning methods, such as manual tuning, Ziegler-Nichols, Cohen-Coon, and other methods.
[0085] As indicated, the simulation is performed for a sufficient number of time increments, tmax (24), to reach an almost steady state. The number of time increments (tmax) used can be a pre-selected value or an unselected value that is triggered by a prescribed statistical threshold being met by certain computational results. The number of time increments can be defined experimentally or by quantitative statistical methods. Quasi-steady state means that the calculated values of Qn, Qw, Pn, Pw and / or saturation do not vary more than a predetermined value within a fixed number of time steps. For example, in the quasi-steady state, the variation in parameter values between consecutive time increments t or another selected number of increments t, such as, by QN, QW, Pw, Pn, saturation, or other parameters, does not vary by more than a pre-specified value or limit value. As an option, tmax (24) can be a predefined number. As an option, the predetermined number of time increments, t, can be set large enough to reach an almost stationary state. The present invention has found, for example, that the fixation of tmax to a sufficiently large number can reach almost steady state. The magnitude of tmax required in this regard may depend on the characteristics of the porous medium and the properties of the fluids that flow through the porous medium. The number of time increments can, for example, be adjusted to a value of 10000, or a higher value, or 100000 or a higher value, or 1000000 or a higher value, or other values. In general, smaller pore structures and higher viscosity rates between fluids may require higher tmax values. As another option, the number of time increments for tmax can be a value that depends on certain results calculated by finding some numerical variance threshold (Vt). The pre-specified value or threshold of variance (Vt) can be set to any desired value. For example, the threshold value of variance (Vc) can be a percentage difference from two or more consecutive calculated values for the selected parameters. When the threshold of variance (Vc) is calculated to be found, t becomes tmax (24) in this iteration, and the process proceeds to step 23. Optionally, a threshold value (VT), which can be used to determine if quasi-steady state conditions were achieved in the iteration according to the present method, it can be a value of about ± 10%, or about ± 7%, or about ± 5%, or about ± 3%, or of about ± 1%, or about ± 0.5%, or other values. For example, if a variation limit of ± 5% is selected and applied to all parameters of interest, for example, Qn, Qw, Pw, Pn, Sw, Sn and so on, in the simulation method, and each parameter had a first normalized value of 100 at t1 and a second normalized value in the range of 95-105 at t2, then the threshold of ± 5% to find quasi-steady state conditions would be found, and the method proceeds to the step 32 shown in Figure 7. In the other option, the simulator can be designed to verify that the selected threshold is found in more than one consecutive iteration before proceeding to step 32 shown in Figure 7. Multi-phase fluid, multiple components fractionated through porous media by nature tends to vary over time and usually does not reach an absolute or true steady state. However, determinations of properties in the almost steady state in the present methods were considered useful and advantageous for efficiently and precisely estimating the fluid transport properties useful for the evaluation of porous media. As indicated, the achievement of the quasi-steady state can be determined in the present methods, for example, through observation, experience of the person performing the simulation, or quantitative methods that examine variance, rolling averages, or other assessments of QN, QW , Pw, Pn, saturation or other parameters.
[0086] As shown in Figure 7, if tmax has not been reached (24), then the index for control action is increased by 1, q = q + 1 (23) and the new values of Pn and Pw calculated in the algorithm of report controls are introduced for CFD calculations (25). Steps (15), (16) and (17) are repeated until another control action is programmed (21).
[0087] When tmax is reached (24), final values for QN, QW and Sw are stored (32). Relative permeability can be calculated at this point from QN, QW, Sw and the fluid and rock properties data. The relative permeability at this point may be the relative permeability of imbibition in the saturation of irreducible wetting fluid, Swirr. This is a result of the way the simulation is run, starting with the Sample flooded with the wetting fluid and then replaced with the non-wetting fluid. Due to surface tension and wetting capacity, all wetting fluid generally cannot be eliminated and the remainder of wetting fluid is the irreducible wetting fluid for the sample and fluids in the simulation. As an option, a method for calculating the relative permeability of wetting fluids and non-wetting fluids flowing through a porous medium may comprise (a) defining a series of pairs of non-wetting fluids and wetting fluids, each pair being forced through the sample, as described herein, (b) defining initial sample saturation, (c) forcing each pair of non-wetting fluids and wetting fluids through the sample, as described herein, (d) recording calculated values of QN, QW and fluid saturation humectant, Sw for each pair of wetting and non-wetting fluids, (e) calculating the relative permeability values of the wetting fluid, kw; calculate values of the relative permeability of the non-wetting fluid, kn, and calculate the values of water saturation, Sw, and (f) generate a plot of values of kw and kn versus sw. The initial sample saturation can be, for example, saturation of total wetting fluid, Sw = 1.0, saturation of total non-wetting fluid, Sn = 1.0, or any other saturation. As another option, the initial sample saturation can be the final saturation conditions of a previous simulation.
[0088] Following the initial time steps in which the non-wetting fluid is forced through the sample, various combinations of wetting and non-wetting fluids are forced through the sample as described above and shown in blocks (15), (16), (21), (22), (18), (19), (20), (24), (23) and (25) in Figure 7. The index for the report control action, q, is reset to 1 (26). To perform the simulation with the new combination of wetting and non-wetting fluids, new values for the wetting fluid fraction, Fn, and the non-wetting fluid fraction, Fn, can be selected from the list of predefined fluid combinations (29 ). The number of value pairs for Fn and Fw can be, for example, 10, or 20, or more. Any combination of Fn and Fw can be used where their sum is equal to 1 (that is, Fn + Fw = 1). The sum of Fn and Fw represents 100% of the fluid (non-wetting fluid and wetting fluid) that enters the sample inlet face. The value pairs, Fw and Fn, stored in the list, B (y), as an option, can be: a) [0.8, 0.2], [0.6, 0.4], [0, 4, 0.6], [0.2, 0.8], [0, 1], [0.2, 0.8], [0.4, 0.6], [0.6, 0, 4], [0.8, 0.2], [1, 0], or b) b) [0.9, 0.1], [0.85, 0.15], [0.8, 0 , 2], [0.75, 0.25], [0.7, 0.3], [0.6, 0.4], [0.5, 0.5], [0.4, 0 , 6], [0.3, 0.7], [0.25, 0.75], [0.2, 0.8], [0.85, 0.15], [0.1, 0 , 9], [0, 1]; [0.1, 0.9], [0.15, 0.85], [0.2, 0.8], [0.25, 0.75], [0.3, 0.7], [0.4, 0.6], [0.5, 0.5], [0.6, 0.4], [0.7, 0.3], [0.75, 0.25], [0.8, 0.2], [0.85, 0.15], [0.9, 0.1], [1, 0], or c) other combinations.
[0089] As an option, the ratio (R) of the viscosities from the low viscosity phase to the high viscosity phase of the wetting fluids and non-wetting fluids can be used to scale the value pairs, Fn and Fw, and the scaled values resulting from them, Fn 'and Fw', can be stored in the list, B (y), replacing the values of Fn and Fw. The viscosity of wetting fluid (μw) can be high or low. The viscosity of non-wetting fluid (μn) can also be high or low. The ratio (R) can be μw / μn or μn / μw, depending on which viscosity is low, and which is higher. That is, the lower of the wetting fluid viscosity (μw) and the non-wetting fluid viscosity (μn) can be used as the numerator in proportion and the other viscosity is used as the value of the denominator. For example, where the wetting fluid viscosity is high and the non-wetting fluid viscosity is low, then the ratio (R) would be non-wetting fluid viscosity / wetting fluid viscosity (μn / μw. Where the wetting fluid viscosity is low and non-wetting fluid viscosity is higher, so the ratio (R) would be wetting fluid viscosity / non-wetting fluid viscosity (μw / μn). For example, for pairs of (Fn ', Fw'), Fn 'can be calculated as the value of (Fn x R) and Fw 'is calculated as the value of (1 - (Fn x R)), where R is the ratio of viscosities of the low viscosity phase / high viscosity phase indicated. Any combination of Fn 'and Fw' has a sum that is equal to 1. The sum of Fn 'and Fw' represents 100% of the fluid (non-wetting fluid and wetting fluid) that enters the sample inlet face. value pairs, Fn 'and Fw', calculated and stored in the list, B (y), for example, can be: d) [R, 0], [(0.9 * R), (1-0.9 * R)], [(0.8 * R ), (1-0.8 * R)].
[0090] The pairs of values for Fw and Fn indicated above cover simulation of imbibition and drainage curves as shown in Figures 5 (a) - (f). As an option, the non-wetting fluid and wetting fluid pairs can comprise an ordered series of values where Fn decreases in steps to zero and then increases to 1.0. The present invention is unique in its ability to digitally simulate and calculate the imbibition and drainage curves with a high degree of accuracy in part due to the inlet boundary conditions by establishing separate areas for wetting and non-wetting fluids at the inlet and inlet approach. unique process control that converges robust and practical calculations from the computation time point of view.
[0091] The fractional inlet pore areas for non-wetting fluid, An, and wetting fluid, Aw, can be adjusted for each combination of wetting and non-wetting fluid. Fractional areas that are approximately equal to 0.5 and Aw approximately equal to 0.5 are acceptable for many combinations of Fn and Fw. However, for low Fn values, the fractional area An may need to be reduced and the corresponding fractional area Aw increased. An + Aw add up to 1.0. An may need to be reduced when fractional flow becomes too low in relation to the available area to completely fill the available central pore area. This can result in instability and capture and release of large bubbles of non-wetting fluid in the simulation that do not meet the required flow rate. As an option, the pores on the inlet face may comprise separate and distinct areas formed by allocating pore voxels immediately adjacent to a grain voxel for the flow of wetting fluids (Aw) and the remaining pore voxels are assigned to the flow of the pores. non-wetting fluids (An). Aw can be increased, for example, by additionally assigning pore voxels adjacent to Aw for the flow of wetting fluids (Aw) and the remaining pore voxels are assigned for the flow of non-wetting fluids (AN). As an option, Aw and An can be provided, where the (sum of voxels in An) / ((sum of voxels in An) + (sum of voxels in Aw)) is about 0.5 or less. As an option, the fractional pore area on the inlet face allocated for the injection of non-wetting fluid, An, is decreased when Fn is less than about 0.2, or when Fn is less than about 0.1, wherein An is reduced to about 0.4 or less, or about 0.3 or less, or about 0.2 or less, or about 0.1 or less, or other values. Adjusting the fractional areas An and Aw is a unique feature of the present invention, which makes it possible for the simulations to reach almost steady state and produce usable results.
[0092] Target flow rates for new fractional flows can be calculated (28) as follows: e) QwT = QT * Fw f) QnT = QT * Fn.
[0093] After all combinations of fractionated wetting and non-wetting fluid streams have been processed and flow rates, pressures and saturations have been calculated, relative permeability of soaking and draining can be calculated for the water saturations corresponding to each pair fractional flows (33). Permeabilities and relative saturations can be plotted (34) as shown in Figure 1.
[0094] With reference to Figure 9, a system 100 is shown that can be adapted to execute the present methods. As shown in this example, a three-dimensional (3D) image of the porous media samples obtained from source 101 is generated by digitizer 102. The digitizer may include, for example, computed tomography (CT) digitizer, a scanning electron microscope (SEM), a concentrated ion beam scanning electron microscope (FIB-SEM), or similar device capable of producing a three-dimensional digital image of a porous medium. The 3D image output 103 from the digitizer can be transferred to a computer 104 with program instructions for performing 3D image analysis, and the indicated CFD data and simulation analysis, to generate output / sample modeling results that can be transmitted for one or more devices 105, such as a screen, a printer, a data storage medium, or combinations thereof. Computer programs used for 3D image analysis and CFD calculations and simulation modeling can be stored, as a program product, on at least one storage medium usable by a 104B computer (for example, a hard drive, flash memory, a compact disk, a tape / magnetic disk, or other media) associated with at least one 104A processor (for example, a CPU) that is adapted to run the programs or can be stored on a computer-usable storage medium external (not shown), which is accessible to the computer processor. Computer 104 may include at least one memory unit 104C for storing programs, input data and output data, and other program results, or combinations thereof. For output visualization, device 105 can be, for example, a display screen, CRT, or other visual means of display (not shown). Computer 104 can include one or more system computers, which can be implemented as a single personal computer or as a computer network. However, those skilled in the art will appreciate that implementations of the various techniques described herein can be practiced in a variety of computer system configurations, including Hypertext Transfer Protocol (HTTP) servers, handheld devices, multiprocessor systems, consumer electronics. programmable or microprocessor-based, networked PCs, minicomputers, mainframe computers, and the like. System units 100 including digitizer 102, computer 104, and output display and / or external data storage 105, can be connected to each other for communication (e.g., data transmission, etc.), via any wire , radio frequency communications, telecommunications, internet connection, or other means of communication.
[0095] The present invention includes the following aspects / modalities / resources in any order and / or in any combination:
[0096] 1. The present invention relates to a process for simulating a fractional flow of wetting fluids and non-wetting fluids through a porous medium comprising the steps of: g) creating a digital three-dimensional representation of a porous medium (Sample) containing a total volume of fluids comprising wetting fluids and non-wetting fluids, h) defining a first fraction of the total fluid volume comprising wetting fluids and a second fraction of the total fluid volume comprising non-wetting fluids, i) defining a value for a flow rate of the total volume of fluid flowing through the sample, j) evaluate properties of wetting fluids and non-wetting fluids, e) defining initial conditions for saturation of wetting fluids (Sw), saturation of non-wetting fluids (SN ), inlet pressure of wetting fluids (PP) and inlet pressure of non-wetting fluids (Pn), f) define conditions for the inlet face of the sample in which fluid non-wetting and wetting fluids enter the sample pores in separate and distinct areas, and k) calculating pressures, saturation and internal velocity vectors for the sample, l) calculating flow rates of non-wetting fluids (Qn) through the sample, rates flow of wetting fluids (Qw) through the sample, and pressure at the sample outlet, m) repeat steps a) to h) for a predefined number of time increments, t, and n) periodically adjust the inlet pressures Pn and Pw using a reporting control algorithm in which the quasi-steady state values for Qn and Qw are achieved.
[0097] 2. The method of any modality / feature / aspect, previous or next, in which the porous medium is rock, soil, zeolite, biological tissue, wood, cork, cement, ceramics, sand, clay, inorganic compound, organic compound , or metal.
[0098] 3. The method of any previous or next modality / feature / aspect, in which the sample comprises multiple ordered voxel planes, where each voxel represents a pore (pore voxel) or solid (grain voxel) .
[0099] 4. The method of any previous or next modality / feature / aspect, in which the properties of the wetting fluids comprise viscosity, contact angle, interfacial tension, other physical or chemical properties, or any combinations thereof.
[00100] 5. The method of any previous or next modality / feature / aspect, in which the properties of non-wetting fluids include viscosity, contact angle, interfacial tension, other physical or chemical properties, or any combinations thereof.
[00101] 6. The method of any previous or next modality / feature / aspect, in which the sample comprises (a) an entrance face and an exit face, in which the entrance face and the exit face are parallel to each other another, and (b), three or more orthogonal surfaces for the inlet and outlet faces, where the three or more orthogonal surfaces are impermeable to the flow of wetting fluids and non-wetting fluids.
[00102] 7. The method of any previous or next modality / feature / aspect, in which the entrance face also comprises a buffer zone parallel to the entrance face comprising at least one voxel plane.
[00103] 8. The method of any previous or next modality / feature / aspect, in which an interfacial tension between the wetting fluid and non-wetting fluid is set to zero for all calculations within the buffer zone.
[00104] 9. The method of any previous or next modality / feature / aspect, in which the interfacial tension between the wetting fluid and the non-wetting fluid is adjusted to zero and the viscosities of the wetting fluid and the non-wetting fluid are increased by a factor of at least about 10 times for all calculations within the buffer zone.
[00105] 10. The method of any previous or next modality / feature / aspect, in which non-wetting fluids and wetting fluids enter the sample through pores on the sample inlet face.
[00106] 11. The method of any previous or next modality / feature / aspect, in which the pores of the entrance face comprise separate and distinct areas formed by allocating pore voxels immediately adjacent to a grain voxel for the flow of wetting fluids (Aw) and remaining pore voxels are allocated to the flow of non-wetting fluids (An).
[00107] 12. The method of any previous or next modality / feature / aspect, in which Aw is increased by assigning more pore voxels adjacent to Aw for the flow of wetting fluids (Aw) and the remaining pore voxels are assigned to the flow of non-wetting fluids (An).
[00108] 13. The method of any previous or next modality / feature / aspect, where (sum of voxels in An) / ((sum of voxels in An) + (sum of voxels in Aw) is about 0 , 5 or less.
[00109] 14. The method of any previous or next modality / feature / aspect, in which the calculation comprises computational fluid dynamics.
[00110] 15. The method of any previous or next modality / feature / aspect, in which computational fluid dynamics comprises the Lattice-Boltzmann method.
[00111] 16. The method of any previous or next modality / feature / aspect, in which the time increment, t, can be seconds, milliseconds, or another unit of time.
[00112] 17. The method of any previous or next modality / feature / aspect, in which the number of time increments is 10,000 or more.
[00113] 18. The method of any previous or next modality / feature / aspect, in which the report control algorithm comprises a separate report control algorithm to define the inlet pressure for the wetting fluid and a control algorithm for separate report to define the inlet pressure for the non-wetting fluid, where the inlet pressure for the wetting fluid and the inlet pressure for the non-wetting fluid are set independently.
[00114] 19. The method of any previous or next modality / feature / aspect, in which the report control algorithm comprises a report control algorithm to adjust the inlet pressure for both the wetting fluid and the non-wetting fluid, wherein the inlet pressure for the wetting fluid and the inlet pressure for the non-wetting fluid are the same.
[00115] 20. The method of any previous or next modality / feature / aspect, in which the report control algorithm comprises a proportional-integral-derivative control circuit, an adaptive control, a hierarchical control, an intelligent control, an optimal control, robust control, neural network control, fuzzy logic control, or stochastic control.
[00116] 21. The method of any previous or next modality / feature / aspect, in which the report control algorithm is a negative report control algorithm.
[00117] 22. The method of any previous or next modality / feature / aspect, in which the report control algorithm includes a proportional-integral-derivative control circuit (PID).
[00118] 23. The method of any previous or next modality / feature / aspect, in which the PID control circuit comprises an input error Ew and emits a new input pressure, Pw, where

[00119] 24. The method of any previous or next modality / feature / aspect, in which QW comprises a weighted arithmetic average, a geometric weighted average, a harmonic weighted average, a simple rolling average, an exponentially weighted movement average or another method of weighting a series of numbers.
[00120] 25. The method of any previous or next modality / feature / aspect, in which the PID control circuit comprises an input error En and emits a new input pressure, Pn, where

[00121] 26. The method of any previous or next modality / feature / aspect, in which QN comprises a weighted arithmetic average, a geometric weighted average, a harmonic weighted average, a simple rolling average, an exponentially weighted movement average or another method of weighting a series of numbers.
[00122] 27. The method of any previous or next modality / feature / aspect, in which the PID control circuit comprises an input error Ec and emits a new input pressure, Pc, where
, Ec = QT - QC, and QT = the total target flow rate through the sample, QC = an average of Qwt + Qnt values, QnT = the calculated flow rate of non-wetting fluid and time interval, t, and Qwt = the calculated flow rate of wetting fluid and time interval, t.
[00123] 28. The method of any previous or next modality / feature / aspect, where QT comprises a weighted arithmetic average, a geometric weighted average, a harmonic weighted average, a simple rolling average, an exponentially weighted movement average or other method of weighting a series of numbers.
[00124] 29. The method of any previous or next modality / feature / aspect, in which periodically adjusting inlet pressures occurs about once every 10 time increments or higher.
[00125] 30. The method of any previous or next modality / feature / aspect, in which the number of time increments of subsequent periodic adjustments of the inlet pressure are different.
[00126] 31. The method of any previous or next modality / feature / aspect, in which periodically adjusting inlet pressures occurs more frequently in the first half of the total simulation time than in the second half of the simulation.
[00127] 32. The method of any previous or next modality / feature / aspect, in which periodically adjust the inlet pressures in the first half of the total simulation time occurs at least 10 times more than in the second half.
[00128] 33. The method of any previous or next modality / feature / aspect, in which almost stationary state is where the calculated values of Qn, Qw, Pn, Pw and / or saturation vary no more than a predetermined value.
[00129] 34. The method of any previous or next modality / feature / aspect, in which the predetermined number of time increments, t, is defined large enough to reach the almost stationary state.
[00130] 35. A method for calculating the relative permeability of wetting and non-wetting fluids flowing through a porous medium comprising a) defining a series of pairs of non-wetting fluids and wetting fluids, each pair being forced through the sample, b ) create an initial sample saturation, c) force each pair of non-wetting fluids and wetting fluids through the sample, d) record calculated values of QN, QW and wetting fluid saturation, Sw for each pair of wetting and non-wetting fluids, e) calculate values of relative permeability of the wetting fluid, kw; calculate values of the relative permeability of the non-wetting fluid, kn, and calculate the values of water saturation, Sw, and f) generate a plot of values of kw and kn versus Sw.
[00131] 36. The method of any previous or next modality / feature / aspect, in which the porous medium is rock, soil, zeolite, biological tissue, wood, cork, cement, ceramics, sand, clay, stone, inorganic compound, organic compound, or metal.
[00132] 37. The method of any previous or next modality / feature / aspect, in which a pair of non-wetting fluid and wetting fluid comprises a fractionated composition of the non-wetting fluid and a fractional value of the wetting fluid (Fn, Fw).
[00133] 38. The method of any previous or next modality / feature / aspect, in which the pair of non-wetting fluid and wetting fluid comprises a plurality of pairs of (Fn, Fw), in which any combination of Fn and Fw has a sum that is equal to 1.
[00134] 39. The method of any previous or next modality / feature / aspect, in which the pair of non-wetting fluid and wetting fluid comprises a plurality of pairs of (Fn ', Fw'), in which Fn 'is calculated as the value of (R x Fn) and Fw 'is calculated as the value of (1 - (Fn x R)), where R is the ratio between the viscosities of the low viscosity phase to the high viscosity phase of the wetting fluids and non-wetting fluids, and any combination of Fn 'and Fw' has a sum that is equal to 1.
[00135] 40. The method of any previous or next modality / feature / aspect, in which the pairs of non-wetting fluid and wetting fluid comprise an ordered series of values in which Fn decreases in steps to zero and then increases to 1.0.
[00136] 41. The method of any previous or next modality / feature / aspect, in which the initial sample saturation is a saturation of total wetting fluid, Sw = 1.0, and a saturation of total non-wetting fluid, Sn, = 1.0.
[00137] 42. The method of any previous or next modality / feature / aspect, in which the initial sample saturation is the final saturation conditions of a previous simulation.
[00138] 43. The method of any previous or next modality / feature / aspect, in which the fractional pore area on the inlet face allocated for injection of non-wetting fluid, An, is decreased when Fn is less than about 0, two.
[00139] 44. The method of any previous or next modality / feature / aspect, where An is reduced to about 0.4 or less.
[00140] 45. A system calculating the flow of multiple phases, multiple components fractionated through a porous medium comprising: a) a digitizer capable of producing a three-dimensional digital image of a porous medium, b) a computer comprising at least one operable processor for the execution of a computer program capable of classifying elements in the three-dimensional digital image as solids (grains) and pores (void), c) a computer (the same or different from b)) comprising at least one processor operable for the execution of a computer program capable of performing the calculations, in which the said calculations comprise (i) creating a three-dimensional digital representation of a porous medium (sample) containing a total volume of fluids comprising wetting fluids or non-wetting fluids, (ii ) defining a first fraction of the total fluid volume that comprises the wetting fluids and defining a second fraction of the total fluid volume that comprises addresses non-wetting fluids, (iii) defining a value for a flow rate of the total volume of fluids flowing through the sample, (iv) evaluating properties of wetting fluids and non-wetting fluids, (v) defining initial conditions for saturation of wetting fluids (sw), saturation of non-wetting fluids (Sn), inlet pressure of wetting fluids (Pw) and the inlet pressure of non-wetting fluids (Pn), (vi) defining conditions for the inlet face of the sample in that non-wetting fluids and wetting fluids enter the sample pores in separate and distinct areas, and (vii) calculating internal pressures, saturation, and velocity vectors for a sample of the porous medium, (viii) calculating flow rates of non-wetting fluids (Qn) through the sample, wetting flow rates (Qw) through the sample, and pressure at the sample outlet, (ix) repeat steps (i) to (viii) for a predefined number of time increments, t , and (x) periodically adjust the input pressures Pn and Pw using a reporting control algorithm in which quasi-steady state values for Qn and Qw are achieved, and d) at least one device for displaying, printing or storing the results of the calculations.
[00141] 46. The system of any modality / feature / previous or next aspect, in which the digitizer comprises a computerized tomography (CT) digitizer, a scanning electron microscope (SEM), an ion beam scanning electron microscope concentrate (FIB-SEM), or similar device capable of producing a three-dimensional digital image of a porous medium.
[00142] 47. The system of any previous or next modality / feature / aspect, in which the device comprises a memory device for recovering the results of said calculations in a recoverable manner.
[00143] 48. A computer program product in a computer-readable medium that, when executed in a controller of a computerized device, provides a method to perform the calculations of any previous or next modality / feature / aspect. This computer program may be on a non-transitory storage medium and / or the computer readable storage medium may exclude signals.
[00144] The present invention can include any combination of these various characteristics or modalities above and / or below, as set out in sentences and / or paragraphs. Any combination of features described herein is considered part of the present invention and is not intended to be limiting with respect to combinable features.
[00145] The present invention will be further clarified by the following examples, which are intended to be exemplary only in nature. EXAMPLES EXAMPLE 1
[00146] A sample of a carbonate rock was selected for analysis using a representative method of the present invention. The sample plug was weighed (125.299 g), physically measured for its diameter and length, and photographed. The plug was marked for orientation and placed in the oven to dry and weighed again (124.447 g).
[00147] The plug was photographed on a MicroXCT-200 manufactured by Xradia in a resolution of 0.5x, at about 40 microns (μm) per voxel. The plug was digitized with the CereTom Dual Energy X-Ray CT Digitizer manufactured by Neurologica to determine density and atomic number. A subsampling location was selected that showed a typical atomic number. Areas with a high atomic number were avoided.
[00148] A laser was used to remove a 4 mm diameter pillar from the carbonate sample (see the square region shown in Figure 10). The square region in Figure 10 is the area of the carbonate sample selected for further analysis. The selected subsample was photographed on the MicroXCT-200 scanner with a 40x resolution, about 500 nanometers per voxel. The scanned image resulting from the selected subsample is shown in Figure 11.
[00149] The image was reconstructed and cut to a 500 x 500 x 500 voxel cube for segmentation. The image was segmented in a manner described here and absolute permeability, formation factor and elasticity were estimated using methods available in the indicated literature. The permeability in the z direction was 22 mD and the porosity was 0.21.
[00150] The segmented image was cut to a 200 x 200 x 260 cube voxel, maintaining approximately the same absolute permeability and porosity in the flow direction. In this way, the segmented image used to estimate the relative permeability using a method of the present invention has the dimension of 200 x 200 grid points in the X and Y direction and 260 grid points in the direction parallel to the applied pressure gradient. The two fluids used in this example are brine and oil with the following properties: Brine with dynamic viscosity at 21 ° C and normal pressure 1664 cp, and Oil with dynamic viscosity at 21 ° C and normal pressure 7.71 cp.
[00151] The following initial conditions have been defined for the simulation:
[00152] steady-state flow rate (Darcy) is 77.724m / day (255 feet / day), interfacial tension between the two fluids is 0.35 mN / cm (35 dyn / cm), the contact angle is 45 degrees, so that water is considered the wetting fluid, and each time interval in the simulation corresponds to 0.1 microseconds.
[00153] At t = 0, the segmented image is filled with 100% oil and water is injected at different flow rates within the segmented image. At t = 0, the inlet area is 100% assigned to the wetting phase, water in this case, so that a flood of primary water is carried out (100% water injection).
[00154] After the initial flood, the inlet plane was divided into two areas (area close to the solid for the wetting fluid injection and the central pore area for the non-wetting fluid) with 67% of the area allocated for the wetting fluid . Of this area of wetting fluid, 67% was maintained for fractionated oil flows of 30%, 50%, 70%, and 80% by volume. For higher fractionated oil flows (90%, 93%, 96%), the wetting fluid area at the inlet has been increased to 82% of the total pore inlet area. 1.5 million time steps were used for the initial flood step. 1 million steps of time were used for subsequent water injections.
[00155] The first correction with the control report circuit was made after 50 steps of time. The second correction with the control report circuit was carried out after 5000 steps of time. The control reporting circuit was performed every 1000 steps of time from 5001 to 150000 steps of time and, in every 10,000 steps of time from 150001 to or 1.5 million steps of time (for the first flood) or to 1 million steps of time for the following fractional flow injections. Every time the fractional flow was changed at the input, the frequency of the report controller action was increased for every 1000 time steps until 150000 subsequent time steps were completed.
[00156] The constant C at the initial pressure was adjusted to 25, having an initial pressure of 1.8 KPa. The proportional, integral and derivative constant of the PID controller were set to 10000, 5000 (1 / time steps) and 1000 (time_ steps) respectively. The width of the exponential average time window was such that 33% of the new value and 66% of the previous values were added. The surface tension in the region of the damping input has been set to zero. The plug length has been adjusted to 15 grid units.
[00157] The relative permeability and water saturation values estimated for these criteria using the method of the present invention are shown in Figure 12.
[00158] Candidates specifically incorporate all the content of all references cited in this disclosure. In addition, when an amount, concentration, or other value or parameter is given as a range, preferred range, or a list of upper preferred values and lower preferred values, this should be understood as the specific disclosure of all ranges formed from of any pair of any upper limit of preferred range or value and any lower limit of preferred range or value, regardless of whether ranges are disclosed separately. When a range of numerical values is described here, unless otherwise stated, the range is intended to include their end points, and all the integers and fractions within the range. It is not intended that the scope of the invention be limited to the specific values recited in the definition of an interval.
[00159] Other embodiments of the present invention will be apparent to those skilled in the art from consideration of the present disclosure and practice of the present invention disclosed herein. It is intended that the present disclosure and examples are considered as exemplary only, with the true scope and spirit of the invention being indicated by the following claims and their equivalents.

权利要求:
Claims (13)
[0001]
1. Method to simulate a fractional flow of wetting fluids and non-wetting fluids through a porous medium, comprising the step of: a) creating a three-dimensional digital representation of a porous medium that is a sample containing a total volume of fluids comprising wetting fluids and non-wetting fluids, characterized by the fact that it also comprises the steps of: b) defining a first fraction of the total volume of fluids comprising wetting fluids and a second fraction of the total volume of fluids comprising non-wetting fluids, c) defining a value for a flow rate of the total volume of fluids flowing through the sample, d) evaluate properties of wetting fluids and non-wetting fluids, e) defining initial conditions for saturation of wetting fluids (Sw), saturation of non-wetting fluids ( Sn), inlet pressure of wetting fluids (Pw) and inlet pressure of non-wetting fluids (Pn), f) configure conditions on the face of en sample selection in which non-wetting fluids and wetting fluids enter the sample pores in separate and distinct areas, g) calculating pressure, saturation and velocity vectors internal to the sample, h) calculating flow rates of non-wetting fluids (Qn) using sample, wetting flow rates (Qw) through the sample, and pressure at the sample outlet, i) repeat steps a) through h) for a predefined number of time increments, t, and j) periodically adjust the pressures input Pn and Pw using a feedback control algorithm in which quasi-steady state values for Qn and Qw are achieved.
[0002]
2. Method according to claim 1, characterized by the fact that the sample comprises multiple ordered voxel planes, in which each voxel represents a pore (pore voxel) or solid (grain voxel), and in which the properties of wetting fluids comprise viscosity, contact angle, interfacial tension, other physical or chemical properties, or any combination thereof, and where the properties of non-wetting fluids include viscosity, contact angle, interfacial tension, other physical or chemical properties , or any combinations thereof.
[0003]
3. Method according to claim 1, characterized in that the sample comprises (a) an entrance face and an exit face in which the entrance face and the exit face are parallel to each other, and (b ) three or more surfaces orthogonal to the inlet and outlet face, where the three or more orthogonal surfaces are impermeable to the flow of wetting fluids and non-wetting fluids, and optionally where the inlet face further comprises an area of damping parallel to the inlet face comprising at least a voxel plane, and in which an interfacial tension between the wetting fluid and the non-wetting fluid is set to zero for all calculations within the buffer zone, and the wetting fluid viscosities and of the non-wetting fluid are increased by a factor of at least 10 times for all calculations within the buffer zone.
[0004]
Method according to claim 1, characterized in that the non-wetting fluids and the wetting fluids enter the sample through pores on the sample inlet face, and in which the pores of the inlet face comprise separate areas and formed by allocating pore voxels immediately adjacent to a grain voxel for the flow of wetting fluids (Aw) and the remaining pore voxels are allocated to the flow of non-wetting fluids (An), and where Aw is increased by further allocating pore voxels adjacent to Aw for the flow of wetting fluids (Aw) and the remaining pore voxels are allocated for the flow of non-wetting fluids (An), and where (sum of voxels in An) / ((sum of voxels in An) + (sum of voxels in Aw)) is 0.5 or less.
[0005]
5. Method according to claim 1, characterized by the fact that the feedback control algorithm comprises a separate feedback control algorithm to configure the inlet pressure for the wetting fluid and a separate feedback control algorithm to configure the inlet pressure for the non-wetting fluid, where the inlet pressure for the wetting fluid and the inlet pressure for the non-wetting fluid are set independently.
[0006]
6. Method according to claim 1, characterized by the fact that the feedback control algorithm comprises a feedback control algorithm to adjust the inlet pressure for both the wetting and non-wetting fluid, in which the inlet pressure for the wetting fluid and the inlet pressure for the non-wetting fluid are the same.
[0007]
7. Method according to claim 1, characterized by the fact that the feedback control algorithm comprises a proportional-integral-derivative control circuit, an adaptive control, a hierarchical control, an intelligent control, an optimal control, a control robust, a neural network control, a fuzzy logic control, a negative feedback control algorithm, or a stochastic control.
[0008]
8. Method according to claim 7, characterized by the fact that the PID control circuit comprises an input error Ew and emits a new input pressure, Pw, where Pw = Pi + Pi * πw Pi = configured initial pressure at the beginning of the simulation
[0009]
9. Method according to claim 1, characterized by the fact that the periodic adjustment of the inlet pressures occurs once every 10 time increments or greater, in which the number of time increments of subsequent periodic adjustments of the inlet pressure is different, and / or in which the periodic adjustment of the inlet pressures occurs more frequently in the first half of the total simulation time than in the second half of the simulation, in which the periodic adjustment of the inlet pressures in the first half of the total time of the simulation simulation occurs at least 10 times more than in the second half.
[0010]
10. Method according to claim 1, characterized by the fact that the almost steady state is where the calculated values of Qn, Qw, Pn, Pw and / or saturation vary no more than a predetermined value, and / or where the predetermined number of time increments, t, is set large enough to reach an almost stationary state.
[0011]
11. System (100) for calculating a multi-component, multi-phase fractionated flow through a porous medium, comprising: a) a reader (102) capable of producing a three-dimensional digital image of a porous medium, b) a computer ( 104) comprising at least one processor (104A) operable to execute computer-readable instructions capable of classifying elements in the three-dimensional digital image as solid (grain) and pores (empty), c) a computer (the same or different from b)) comprising at least one processor operable to execute computer-readable instructions capable of performing calculations, wherein said calculations comprise (i) creating a three-dimensional digital representation of a porous medium (sample) containing a total volume of fluids comprising wetting fluids and non-wetting fluids , characterized by the fact that the calculations comprise the steps of: (ii) defining a first fraction of the total volume of fluids that comprises the fluids s wetting and defining a second fraction of the total fluid volume comprising non-wetting fluids, (iii) setting a value for a flow rate of the total fluid volume flowing through the sample, (iv) evaluating properties of the wetting fluids and the non-wetting fluids, (v) define initial conditions for saturation of wetting fluids (Sw), saturation of non-wetting fluids (Sn), inlet pressure of wetting fluids (Pw) and inlet pressure of non-wetting fluids (Pn), ( vi) setting conditions on the sample inlet face in which non-wetting fluids and wetting fluids enter the sample pores in separate and distinct areas, and (vii) calculating pressure, saturation and internal velocity vectors for a sample of the porous medium, ( viii) calculate flow rates of non-wetting fluids (Qn) through the sample, flow rates of wetting fluids (Qw) through the sample, and pressure at the sample outlet, (ix) repeat steps (i) to (viii) by a naked predefined number of time increments, t, and (x) periodically adjust the input pressures Pn and Pw using a feedback control algorithm in which quasi-steady state values for Qn and Qw are achieved, and d) at least one device ( 105) to display, print or store calculation results.
[0012]
System according to claim 11, characterized in that the device comprises a memory device (104C) for recovering the results of the calculations in a recoverable manner.
[0013]
13. System according to claim 11, characterized by the fact that the reader comprises a computed tomography (CT) reader, a scanning electron microscope (SEM), a concentrated ion beam scanning electron microscope (FIB-SEM ), or similar device capable of producing a three-dimensional digital image of a porous medium.

类似技术:

---

公开号 | 公开日 | 专利标题

---

BR112014000758B1|2021-01-19|method to simulate a fractional flow of wetting fluids and non-wetting fluids through a porous medium, and system to calculate a multi-component, multi-phase fractional flow through a porous medium

---

US9140117B2|2015-09-22|Method for evaluating relative permeability for fractional multi-phase, multi-component fluid flow through porous media

---

Mason et al.2013|Developments in spontaneous imbibition and possibilities for future work

---

US10830713B2|2020-11-10|System and methods for computing physical properties of materials using imaging data

---

Prodanović et al.2007|3D image-based characterization of fluid displacement in a Berea core

---

US9747393B2|2017-08-29|Methods and systems for upscaling mechanical properties of geomaterials

---

BR112012006055B1|2020-09-15|METHODS FOR USE IN THE PROVISION OF SAND PRODUCTION OF A GEOMECHANICAL RESERVOIR SYSTEM, FOR SAND PRODUCTION OF A GEOMECHANICAL RESERVOIR SYSTEM, AND TO OPERATE A GEOMECHANICAL RESERVOIR SYSTEM TO CONTROL THE SAND PRODUCTION SYSTEM OF THE GEOMECHANICAL SYSTEM OF THE SAND SYSTEM OF THE SYSTEMATIC SAND SYSTEM.

---

Kalam2012|Digital rock physics for fast and accurate special core analysis in carbonates

---

Golfier et al.2015|Investigation of the effective permeability of vuggy or fractured porous media from a Darcy-Brinkman approach

---

Papafotiou et al.2008|From the pore scale to the lab scale: 3-D lab experiment and numerical simulation of drainage in heterogeneous porous media

---

CN108603402A|2018-09-28|The variation of capillary pressure and relative permeability in porous media caused by mineral deposits and dissolving is modeled and predicted

---

Van Stappen et al.2018|In situ triaxial testing to determine fracture permeability and aperture distribution for CO2 sequestration in Svalbard, Norway

---

Sheng et al.2011|Numerical prediction of relative permeability from microCT images: Comparison of steady-state versus displacement methods

---

Bashtani et al.2016|Single-phase and two-phase flow properties of mesaverde tight sandstone formation; random-network modeling approach

---

Jerauld et al.2017|Validation of a workflow for digitally measuring relative permeability

---

Korost et al.2012|Computation of reservoir properties based on 3D-structure of porous media

---

Goda et al.2012|Wettability quantification–prediction of wettability for australian formations

---

Bashtani2016|Random Network Modeling of Tight Formations

---

Dakhelpour-Ghoveifel et al.2020|Prediction of gas-oil capillary pressure of carbonate rock using pore network modeling

---

Kułynycz et al.2017|The application of X-Ray Computed Microtomography for estimation of petrophysical parameters of reservoir rocks

---

Kalam et al.2013|Validation of Digital Rock Physics Based Water-Oil Capillary Pressure and Saturation Exponents in Super Giant Carbonate Reservoirs

---

Chhatre et al.2018|A blind study of four digital rock physics vendor laboratories on porosity, absolute permeability, and primary drainage capillary pressure data on tight outcrops

---

Hussain2012|Testing Predictive Value of Image-Based Predictions for Two-Phase Drainage Relative Permeability

---

Zou2018|Computation of Relative Permeability from in-situ Imaged Fluid Distributions at the Pore Scale under Controlled Wettability

---

Engeskaug2018|A Numerical and Analytical Investigation of the Relation Between Transport Properties and Grain Size Distribution

同族专利:

---

公开号 | 公开日

---

BR112014000758A2|2019-10-01|

---

CO6930314A2|2014-04-28|

---

EP2732135B1|2016-08-24|

---

CA2840942A1|2013-01-17|

---

US20130018641A1|2013-01-17|

---

EP2732135A2|2014-05-21|

---

US9183326B2|2015-11-10|

---

MX350511B|2017-09-04|

---

RU2593853C2|2016-08-10|

---

MX2014000429A|2014-09-01|

---

CA2840942C|2017-05-02|

---

WO2013009512A3|2013-11-07|

---

AU2012283030A1|2014-01-16|

---

RU2014104788A|2015-08-20|

---

CN103906893A|2014-07-02|

---

AU2012283030B2|2016-01-28|

---

WO2013009512A2|2013-01-17|

引用文献:

---

公开号 | 申请日 | 公开日 | 申请人 | 专利标题

---

---

US2345935A|1942-04-06|1944-04-04|Shell Dev|Method and apparatus for permeability measurements|

---

US4506542A|1983-04-22|1985-03-26|Chandler Engineering Company|Apparatus and procedure for relative permeability measurements|

---

SU1183868A1|1983-06-09|1985-10-07|Всесоюзный нефтегазовый научно-исследовательский институт|Apparatus for measuring water saturation rate of porous materials|

---

SU1695176A1|1989-06-07|1991-11-30|Московский Геологоразведочный Институт Им.Серго Орджоникидзе|Method of determining relative phase permeabilities of a porous medium|

---

US5497321A|1994-01-11|1996-03-05|Schlumberger Technology Corporation|Well logging method for determining fractional flow characteristics of earth formations|

---

FR2772483B1|1997-12-15|2000-01-14|Inst Francais Du Petrole|METHOD FOR MODELING FLUID DISPLACEMENTS IN A POROUS MEDIUM|

---

US6516080B1|2000-04-05|2003-02-04|The Board Of Trustees Of The Leland Stanford Junior University|Numerical method of estimating physical properties of three-dimensional porous media|

---

EP1260920A1|2001-05-23|2002-11-27|Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.|Method and apparatus for computing an interface of a fluid in a space|

---

FR2836228B1|2002-02-21|2005-08-19|Inst Francais Du Petrole|METHOD AND DEVICE FOR EVALUATING PHYSICAL PARAMETERS OF AN UNDERGROUND DEPOSIT USING ROCK DEBRIS TAKEN FROM IT|

---

US7073392B2|2002-07-19|2006-07-11|Celerity, Inc.|Methods and apparatus for pressure compensation in a mass flow controller|

---

FR2864244B1|2003-12-19|2006-08-04|Inst Francais Du Petrole|METHOD AND DEVICE FOR MEASURING PHYSICAL CHARACTERISTICS OF A POROUS SOLID SAMPLE|

---

US7716028B2|2006-05-24|2010-05-11|Schlumberger Technology Corporation|Method for modeling a reservoir using a 3D wettability map generated from a wettability logging tool|

---

EP2289046A4|2008-05-23|2016-03-30|Fei Co|Image data processing|

---

US8155377B2|2008-11-24|2012-04-10|Ingrain, Inc.|Method for determining rock physics relationships using computer tomographic images thereof|

---

US8081802B2|2008-11-29|2011-12-20|Ingrain, Inc.|Method for determining permeability of rock formation using computer tomograpic images thereof|

---

CN101556703B|2009-05-16|2011-01-26|中国石油大学|Method for establishing network model based on serial section image|

---

CN102804185B|2009-06-30|2016-07-06|普拉德研究及开发股份有限公司|Calculate the numerical method of the heat of three-dimensional porous solid, quality, chemistry and electronic transport|

---

CN201575965U|2009-12-25|2010-09-08|北京首瑞测控技术有限公司|Concrete permeability tester|

---

CN102087195B|2010-12-03|2012-07-04|宁波大学|Full-automatic microfiltration membrane aperture distribution tester as well as automatic measuring method and application thereof|US9140117B2|2012-07-13|2015-09-22|Ingrain, Inc.|Method for evaluating relative permeability for fractional multi-phase, multi-component fluid flow through porous media|

---

RU2015104162A|2012-08-10|2016-09-27|Ингрейн, Инк.|METHOD FOR INCREASING THE ACCURACY OF VALUES OF ROCK PROPERTIES OBTAINED FROM DIGITAL IMAGES|

---

CN103176055A|2013-02-07|2013-06-26|吉林大学|Production method of stratified rock sample for measurement of dielectric property in direction perpendicular to texture direction|

---

WO2014186545A1|2013-05-16|2014-11-20|Exa Corporation|Mass exchange model for relative permeability simulation|

---

CN103344540B|2013-06-28|2016-02-03|武汉理工大学|A kind of method of testing of continuous sign Behavior of Hardened Cement Paste packing|

---

CN103806905B|2013-07-31|2016-08-24|中国石油大学|Preparation has the device and method of the carbonate reservoir physical model of double porosity|

---

EP3033484A4|2013-08-13|2017-03-08|Services Petroliers Schlumberger|Digital core sensitivity analysis|

---

RU2016105167A|2013-09-16|2017-08-22|Лэндмарк Графикс Корпорейшн|PSEVDOFAZOVOE SIMULATION OF MINING METHOD FOR SIGNAL PROCESSING FOR EVALUATION quasi-multiphase flows with extraction by a model-driven sequence of analog step function relative permeability modeling of flow into the reservoir to RANKING SETS PETROPHYSICAL realization|

---

US20160369601A1|2013-12-30|2016-12-22|Sergey Sergeevich Safonov|Method for estimating petrophysical properties of a hydrocarbon reservoir|

---

US10705250B2|2014-01-27|2020-07-07|Schlumberger Technology Corporation|Generation of isotherm datasets for reservoir volumetric estimation|

---

EP3120157B1|2014-03-18|2020-07-15|Dassault Systemes Simulia Corp.|Fluid blob tracking for evaluation of multiphase flow simulations|

---

CN104535472B|2014-12-22|2017-04-12|中国石油大学|Method and device for detecting dynamic permeability of coal petrography|

---

CN105043935A|2015-05-18|2015-11-11|华南理工大学|Device and method of measuring infiltration performance of porous metal material on the basis of digital image processing|

---

CN105156021B|2015-07-13|2017-07-14|中国海洋石油总公司|Borer system and boring method based on self adaptation down-hole equipment tool-face dynamic control|

---

CN105156091B|2015-07-13|2018-03-27|中国海洋石油总公司|Borer system and boring method based on adaptive down-hole equipment tool-face dynamic control|

---

WO2017035433A1|2015-08-26|2017-03-02|Board Of Regents, The University Of Texas System|Systems and methods for measuring relative permeability from unsteady state saturation profiles|

---

CN105158039B|2015-09-08|2018-05-08|河海大学|One kind is used for Flow In Jointed Rock Masses test simulation crack production method|

---

CN105203439B|2015-09-11|2018-01-05|珠海格力电器股份有限公司|Purification of air control method and device|

---

GB2569702A|2016-05-09|2019-06-26|1Qb Inf Tech Inc|Method and system for improving a policy for a stochastic control problem|

---

WO2018022956A1|2016-07-28|2018-02-01|Board Of Regents, The University Of Texas System|Systems and methods for determining unsteady-state two-phase relative permeability|

---

CN106353233B|2016-08-05|2019-01-11|山东大学|A kind of coarse fracture seepage simulation model of rock mass and production method|

---

CN108459034A|2016-11-18|2018-08-28|中国石油化工股份有限公司|A kind of sandstone acid rock reaction effect visualization quantitative evaluation method|

---

CN106780724A|2016-12-21|2017-05-31|中国石油天然气股份有限公司|Obtain the method and apparatus of leakage channel model|

---

EP3574291A4|2017-01-26|2020-01-08|Dassault Systemes Simulia Corp.|Multi-phase flow visualizations based on fluid occupation time|

---

CN108986167B|2017-06-05|2022-01-11|梭特科技股份有限公司|Correction method of crystal setting equipment and crystal setting equipment using same|

---

JP6869621B2|2017-08-30|2021-05-12|住友重機械工業株式会社|Simulation method and simulation equipment|

---

US11081212B2|2017-10-06|2021-08-03|DigiM Solution LLC|System and method for computing drug controlled release performance using images|

---

US10830713B2|2017-11-20|2020-11-10|DigiM Solution LLC|System and methods for computing physical properties of materials using imaging data|

---

US10426424B2|2017-11-21|2019-10-01|General Electric Company|System and method for generating and performing imaging protocol simulations|

---

US11009497B2|2018-06-22|2021-05-18|Bp Corporation North America Inc.|Systems and methods for estimating mechanical properties of rocks using grain contact models|

---

US11119025B2|2018-12-18|2021-09-14|Shell Oil Company|Method for characterizing the porosity of rock|

---

RU2692369C1|2018-12-26|2019-06-24|Публичное акционерное общество "Газпром нефть"|Method of selecting deposit development system|

---

CN109520894B|2018-12-29|2020-04-24|西南石油大学|Shale organic matter forced self-absorption amount prediction method|

---

CN109556996B|2019-01-04|2021-06-25|中国石油大学|Method for measuring oil-water two-phase interference pressure gradient|

---

US11079581B2|2019-11-25|2021-08-03|Saudi Arabian Oil Company|Resolution preserving methodology to generate continuous log scale reservoir permeability profile from petrographic thin section images|

---

WO2021194499A1|2020-03-26|2021-09-30|Hewlett-Packard Development Company, L.P.|Fluid dynamics modeling to determine a pore property of a screen device|

---

CN111879674B|2020-07-15|2022-02-01|西南石油大学|Testing device and method for determining reasonable well closing time based on shale imbibition permeability|

---

CN112179815A|2020-09-21|2021-01-05|西南石油大学|Single-phase unsteady state seepage model building method based on pore network model|

法律状态:
2019-10-22| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|

---

2020-04-07| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|

---

2020-12-08| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|

---

2021-01-19| B16A| Patent or certificate of addition of invention granted [chapter 16.1 patent gazette]|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 02/07/2012, OBSERVADAS AS CONDICOES LEGAIS. |

优先权:

---

申请号 | 申请日 | 专利标题

---

US201161506680P| true| 2011-07-12|2011-07-12|

---

US61/506,680|2011-07-12|

---

PCT/US2012/045220|WO2013009512A2|2011-07-12|2012-07-02|Method for simulating fractional multi-phase/multi-component flow through porous media|

---