• 专利附录
  • 专利说明
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    • 专利摘要:
    A method and system for modeling saturation in a reservoir, comprising obtaining capillary pressure data that represents the capillary pressure in a reservoir, obtaining permeability data that represents the permeability in the reservoir, determining a number of pore grooves represented by the capillary pressure data, the creation of a set of hyperbolic tangents whose number is equal to the number of pore grooves, the combination of the set of hyperbolic tangents to create a curve to approach determine the capillary pressure data and to define a set of hyperbolic tangent parameters, the combination of at least one of the hyperbolic tangent parameters with the permeability data to define a saturation height function, the modeling of a saturation in the tank using the saturation height function, and the display of the Saturation is based on the saturation height function.
    公开号:FR3034894A1
    申请号:FR1553043
    申请日:2015-04-09
    公开日:2016-10-14
    发明作者:Sylvain Wlodarczyk；Keith Pinto；Olivier Marche
    申请人:Services Petroliers Schlumberger SA；
    IPC主号:
    专利说明:

    [0001] BACKGROUND [0001] To create accurate models of oilfield reservoir, water and hydrocarbon saturation can be predicted at a given point in the oilfield reservoir. [0002] Saturation data may be available at the well scale, where they can be accurately derived from petrophysical drilling report data using different process flows and industry standards. However, it may be desirable to calculate tank-scale saturation, where some tank properties are known. In such cases, a saturation pattern can be obtained by using a saturation height function. However, saturation models can be based on a saturation height function for single pore throat systems, or if multiple pore throat modeling is possible, on unstable models that depend on the number of data points used. and selecting the best approximation intervals. Summary Embodiments of the disclosure may provide a computer system, a non-transient computer-readable medium, and a method of saturation modeling in a tank. For example, the method includes obtaining capillary pressure data that represents the capillary pressure in the reservoir and obtaining permeability data that represents the permeability in the reservoir. The method may also include determining a number of pore grooves represented by the capillary pressure data, and creating hyperbolic tangents based on the capillary pressure data whose number is equal to the number of pore grooves. The method may further include combining hyperbolic tangents to create a curve for approaching the capillary pressure data and defining hyperbolic tangent parameters, and combining at least one of the hyperbolic tangent parameters with the permeability data for set a saturation height function. The method may further include modeling saturation in the reservoir using the saturation height function, and displaying the saturation pattern based on the saturation height function. In another embodiment, said at least one hyperbolic tangent parameter has a linear relationship with the logarithm of the permeability obtained. In another embodiment, each of the respective hyperbolic tangents is created for only one of the respective pore grooves, so that never two of the hyperbolic tangents are created for the same pore grooves. In another embodiment, the hyperbolic tangents are defined by the following equation: ## EQU1 ## ) with the constraints: 5 wn> 0, Vn E [1, N] n, NEN an + i <a, n, Vn e [1, N-1] n, NEN where P represents a logarithmic transformation of a pressure normalized capillary and N represents the number of hyperbolic tangents. [0007] In another embodiment, the hyperbolic tangent parameter tn has a linear relationship with the log of the permeability obtained as defined by the following equation: tn = kn.log (K) kn + Where K represents the permeability data obtained. [0008] In another embodiment, the saturation height function is defined by the following equation: f (P, K, an, wn, kn,) = al + alv (an ± i - an). tanh (wn (P - log (K) kn + i)). In another embodiment, the combination of the set of hyperbolic tangents to create the curve to approach the capillary pressure data and define the set of hyperbolic tangent parameters comprises the use of a method of 20 least squares nonlinear. In another embodiment, the modeling of the saturation in the reservoir comprises the modeling of the saturation on the basis of a combination of the saturation height function and one or more property (s) of the saturation. tank. In another embodiment, said one or more reservoir property (s) comprises (include) porosity, height above free water, or a combination thereof. In another embodiment, the non-transitory computer-readable medium stores instructions that, when executed by one or more processor (s) of a computer system, cause the computer system to perform operations. . For example, the operations may include obtaining capillary pressure data that represents the capillary pressure in the reservoir, and obtaining permeability data that represents the permeability in the reservoir. The operation may also include determining a number of pore grooves represented by the capillary pressure data, and creating hyperbolic tangents based on the capillary pressure data whose number is equal to the number of grooves of the pore. The operations may further include the combination of hyperbolic tangents to create a curve for approaching the capillary pressure data and for defining hyperbolic tangent parameters, and the combination of at least one of the hyperbolic tangent parameters with the permeability data. to set a saturation height function. The operations may further include modeling saturation in the tank using the saturation height function, and displaying the saturation pattern based on the saturation height function. In another embodiment, the computer system may comprise one or more processor (s), and a memory system comprising one or more medium (s) readable by a non-transient computer (s) storing instructions which, when executed by one or more processor (s) of a computer system, cause the computer system to perform operations. For example, the operations may include obtaining capillary pressure data that represents the capillary pressure in the reservoir, and obtaining permeability data that represents the permeability in the reservoir. The operation may also include determining a number of pore grooves represented by the capillary pressure data, and creating hyperbolic tangents based on the capillary pressure data whose number is equal to the number of pore grooves. The operations may further comprise the combination of hyperbolic tangents to create a curve for approaching the capillary pressure data and for defining hyperbolic tangent parameters, and the combination of at least one of the hyperbolic tangent parameters with the permeability data. to set a saturation height function. Operations may further include modeling saturation in the reservoir using the saturation height function, and displaying the saturation pattern based on the saturation height function. This summary is provided for the purpose of introducing a selection of concepts which are described in more detail below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor to be used as a guide to limit the scope of the claimed subject matter. Brief Description of the Drawings [0015] The accompanying drawings, which are incorporated in and constitute a part of this description, illustrate embodiments of the present teachings. These and other aspects and advantages in the embodiments of the disclosure will become apparent and will be more readily appreciated from the following description of various embodiments, taken in conjunction with the accompanying drawings, in which: which: [0016] Figure 1 illustrates an example of a system that includes different management components for managing different aspects of a geological environment according to one embodiment. FIG. 2 illustrates a flowchart of a saturation modeling method in a reservoir according to one embodiment. [0018] Figure 3 illustrates a model of hyperbolic tangents in a capillary pressure and water saturation system according to one embodiment. Figure 4 illustrates a model of hyperbolic tangents in a capillary pressure and water saturation system according to one embodiment. Figure 5 illustrates a model of hyperbolic tangents in a capillary pressure and water saturation system according to one embodiment. [0021] Figure 6 illustrates capillary pressure data of a multiple pore groove system according to one embodiment. Figure 7 illustrates a curve approximating capillary pressure data according to one embodiment. [0023] Figure 8 illustrates hyperbolic tangents corresponding to pore grooves according to one embodiment. Figure 9 illustrates capillary pressure curves and permeability values according to one embodiment. Figure 10 illustrates hyperbolic tangents and unknown parameter values according to one embodiment. Figure 11 is a schematic view of a computer system according to one embodiment. It should be noted that some details of the drawings have been simplified and are shown in a manner to facilitate understanding of the present teachings rather than maintaining strict structural precision, detail and scale. These drawings and figures are intended to be explanatory and not restrictive. Detailed Description [0028] Reference will now be made in detail to the various embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings and figures. Embodiments are described below to provide a more complete understanding of the components, methods, and devices disclosed herein. The examples are given for illustrative and not restrictive purposes. However, it will be apparent to those skilled in the art that the invention can be practiced without these specific details. In other examples, well-known methods, procedures, components, circuits and networks have not been described in detail so as not to unnecessarily complicate aspects of the embodiments. From one end to the other of the specification and the claims, the following terms carry the meaning explicitly associated here unless the context clearly indicates otherwise. The phrases "in some embodiments" and "in one embodiment" employed do not necessarily refer to the same mode (s) of realization, although this may be the case. In addition, the phrases "in another embodiment" and "in some other embodiments" employed herein do not necessarily refer to a different embodiment, although this may be the case. As described below, it is possible to easily combine different embodiments without departing from the scope or spirit of the present disclosure. As used herein, the term "or" is an inclusive operator, and is equivalent to the term "and / or" unless the context clearly indicates otherwise. The term "on the basis of" is not exclusive and allows to rely on undescribed additional factors unless the context clearly indicates otherwise. In the specification, the use of "at least one of A, B, and C" includes embodiments containing A, B, or C, multiple examples of A, B, or C, or combinations of A / B, or C, B, A / C, B / C, A / B / B / B / C / C, A / B / C, etc. In addition, throughout the booklet, the meaning of "one", "one", and "the" includes several references. The meaning of "in" includes "in" and "on". [0031] It should also be understood that although the terms first, second, and so on. can be used here to describe different elements, these elements should not be limited by these terms. These terms are used for the purpose of distinguishing one element from another. For example, a first object or a first step could be called a second object or a second step, and similarly a second object or a second step could be called a first object or a first step, without departing from the first object or step. scope of the invention. The first object or step, and the second object or step, are both objects or steps, respectively, but they should not be considered as the same object or step. It should be further understood that the terms "includes", "including", 3034894 "includes" and / or "including", when used in this specification, specify the presence of characteristics, integers, steps, specified operations, elements, and / or components, but do not prohibit the presence or addition of one or more other characteristics, integers, steps, operations, elements, components, and / or groups of these. Furthermore, as used herein, the term "if" may be interpreted to mean "when" or "in the case where" or "in response to the determination of" or "in response to the detection of". , depending on the context. When referring here to any range of numerical values, said ranges should be understood to include each and every number and / or fraction between the indicated minimum and maximum of the range. For example, a range of 0.5% to 6% will expressly include intermediate values of 0.6%, 0.7% and 0.9%, up to 5.95%, 5.97% and 5%. 99% included. The same principle applies to every other numeric property and / or elementary range indicated here, unless the context clearly indicates otherwise. The attention will now be directed to the processing procedures, methods, techniques and processing flows according to several embodiments. Certain operations in the processing procedures, methods, techniques and process flows disclosed herein may be combined and / or the order of certain operations may be changed. FIG. 1 illustrates an example of a system 100 comprising different management components 110 for managing different aspects of a geological environment 150 (for example, an environment comprising a sedimentary basin, a reservoir 151, one or more fault lines ( s) 20 153-1, one or more geological body (s) 153-2, etc.). For example, the management components 110 may allow direct or indirect management of the detection, drilling, injection, extraction, etc., relative to the geological environment 150. Then, additional information relating to the geologic environment 150 may become available in return 160 (for example, optionally as an input to one or more of the management components 110). In the example shown in FIG. 1, the management components 110 comprise a seismic data component 112, an additional information component 114 (for example, well / log data), a component of processing 116, a simulation component 120, an attribute component 130, an analysis / visualization component 142 and a process stream component 144. In operation, seismic data and other information provided by the components 112 and 114 may be input into the simulation component 120. In an exemplary embodiment, the simulation component 120 may be based on entities 122. Entities 122 may comprise terrestrial entities or objects such as wells, surfaces, bodies, reservoirs, etc. In the system 100, the entities 122 may comprise virtual representations of real physical entities that are reconstructed to simulation fms. Entities 122 may include entities based on data acquired by detection, observation, etc. (for example, seismic data 112 and other information 114). An entity may be characterized by one or more properties (for example, a geometric pillar grid entity of a terrestrial model may be characterized by a porosity property). These properties can represent one or more measure (s) (for example, acquired data), calculations, and so on. In an exemplary embodiment, the simulation component 120 may operate in conjunction with a software infrastructure such as an object-oriented infrastructure. In such an infrastructure, entities may include entities based on predefined classes to facilitate modeling and simulation. An example commercially available object-oriented infrastructure is the MICROSOFT® .NET® (Redmond, Washington) infrastructure, which provides a set of extensible object classes. In the .NET® infrastructure, an object class includes a reusable code module and associated structure data. Object classes can be used to instantiate object instances for use by a program, script, and so on. For example, classes of boreholes may define objects to represent boreholes based on well data. In the example shown in FIG. 1, the simulation component 120 can process information to conform to one or more attribute (s) specified by the attribute component 130, which can understand an attribute library. Such processing may be performed prior to entry into the simulation component 120 (for example, consider the processing component 116). For example, the simulation component 120 may perform operations on input information based on one or more attribute (s) specified by the attribute component 130. In an exemplary mode of embodiment, the simulation component 120 may construct one or more models of the geological environment 150, which may be based on a simulation of the behavior of the geological environment 150 (for example, in response to one or more actions, whether natural or artificial). In the example shown in Figure 1, the analysis / visualization component 142 may allow interaction with a model or model-based results (eg, simulation results, etc.). For example, an output from the simulation component 120 may be input to one or more other processing streams, as indicated by a process stream component 144. For example, the simulation component 120 may include one or more characteristics of a simulator such as the ECLIPSETM tank simulator (Schlumberger Limited, Houston, Texas), INTERSECTTm tank simulator (Schlumberger Limited, Houston, Texas), etc. For example, a simulation component, a simulator, etc. may include features for implementing one or more non-mesh techniques (e.g., to solve one or more equations, etc.). For example, a reservoir or reservoirs may / may be simulated with respect to one or more improved recovery technique (s) (for example, consider a heat treatment such as SAGD, etc.). In an exemplary embodiment, the management components 110 may include features of a commercially available infrastructure such as the PETREL® seismic simulation software infrastructure (Schlumberger Limited, Houston, Texas). The PETREL® infrastructure provides components that optimize exploration and development operations. The PETREL® infrastructure includes seismic simulation software components that are capable of generating information to be used to increase tank performance, for example, by improving the productivity of engaged teams. Using such an infrastructure, different professionals (for example, geophysicists, geologists and reservoir engineers) can develop collaborative process flows and integrate operations to streamline processes. Such an infrastructure may be considered an application and may be considered a data-driven application (eg, where data is inputted to modeling, simulation, etc.). In an exemplary embodiment, different aspects of the management components 110 may include additional products or extension modules that operate according to specifications of an infrastructure environment. For example, a commercially available infrastructure environment marketed as the OCEAN® Infrastructure Environment (Schlumberger Limited, Houston, Texas) allows for the integration of additional products (or plug-ins) into a stream. PETREL® infrastructure treatment. The OCEAN® infrastructure environment develops .NET® tools (Microsoft Corporation, Redmond, Washington) and provides stable and user-friendly interfaces for efficient development. In an exemplary embodiment, different components may be implemented as additional products (or extension modules) that conform to and operate according to specifications of an infrastructure environment (e.g. application programming interface (API), etc.). FIG. 1 also shows an example of an infrastructure 170 that includes a model simulation layer 180 in conjunction with an infrastructure service layer 190, an infrastructure core layer 195, and a module layer. 175. The infrastructure 170 may include the commercially available OCEAN® infrastructure, where the Model 180 simulation layer is the software package centered on the commercially available PETREL® model that hosts the OCEAN® infrastructure applications. In an exemplary embodiment, the PETREL® software can be considered a data-driven application. PETREL® software can include an infrastructure to build and visualize a model. For example, an infrastructure may include features to implement one or more mesh generation techniques. For example, an infrastructure may include an input component for receiving information from an interpretation of seismic data, one or more attribute (s) based at least in part on seismic data, log data. , image data, etc. Such an infrastructure may include a mesh generation component that processes the input information, optionally in conjunction with other information, to generate a mesh. 10044] In the example shown in Figure 1, the model simulation layer 180 can provide domain objects 182, act as a data source 184, provide a rendering 186 and provide different user interfaces 188. Rendering 186 can provide a graphical environment in which applications can display their data as user interfaces 188 can provide a common look and feel for user application interface components. For example, domain objects 182 may comprise entity objects, property objects and optionally other objects. Entity objects can be used to geometrically represent wells, surfaces, bodies, reservoirs, etc., while the property objects can be used to provide property values as well as data versions and display data. settings. For example, an entity object may represent a sink, where a property object provides log information as well as version information and display information (for example, displaying the well as part of a model). In the example shown in Figure 1, data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be find at the same physical site or at different physical sites and are accessible through one or more networks. The model simulation layer 180 may be configured to model projects. As a result, a particular project can be stored, where stored project information can include entries, a template, results, and cases. So, when completing a modeling session, a user can store a project. At a later time, it is possible to access and restore the project using the model simulation layer 180, which can recreate elements of the objects of the relevant domain.
    [0002] In the example shown in FIG. 1, the geological environment 150 may comprise layers (for example, a stratification) comprising a reservoir 151 and one or more other characteristic (s), such as the fault 153-1, the geological body 153-2, etc. For example, the geological environment 150 may be equipped with any of a variety of sensors, detectors, actuators, and the like. For example, the equipment 152 may include a communication circuit for receiving and transmitting information relating to one or more network (s) 155. This information may include information associated with a drilling equipment 154, which may be a piece of equipment. to acquire information, help to recover resources, etc. Other equipment 156 may be located at a distance from a well site and comprise a capture, detection, transmission or other circuit. This equipment may include a storage and communication circuit for storing and communicating data, instructions, etc. For example, one or more satellite (s) may be provided for communications, data acquisition, etc. For example, Figure 1 shows a satellite in communication with the network 155 that can be configured for communications, note that the satellite may include a circuit for imaging (eg, spatial, spectral, temporal, radiometric, etc.). ).
    [0003] Fig. 1 also shows the geological environment 150 as optionally comprising a well-associated equipment 157 and 158 comprising a substantially horizontal portion which is capable of cutting one or more fractures (s) 159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (eg, hydraulic fractures) or a combination of natural and man-made fractures. For example, a well may be drilled for a laterally extending reservoir. In this example, lateral variations of properties, constraints, etc. may exist, where an assessment of these variations may assist planning, operations, etc. to develop a laterally extending reservoir (for example, by fracturing, injection, extraction, etc.). For example, the equipment 157 and / or 158 may comprise components, a system, systems, etc. to perform fracturing, seismic detection, seismic data analysis, evaluation of one or more fractures, etc. As mentioned, the system 100 may be used to execute one or more processing streams. A treatment flow may be a treatment comprising a number of processing steps. A processing step may act on data, for example, creating new data, updating existing data, etc. For example, one or more input (s) can be made and one or more result (s) generated, for example, based on one or more algorithm (s). For example, a system may include a workflow editor for creating, editing, executing, etc. a flow of treatment. In this example, the process stream editor may provide a selection of one or more predefined processing step (s), one or more custom processing step (s), etc. For example, a process flow may be a process stream to be implemented in the PETREL® software, for example, which operates on seismic data, on one or more seismic attribute (s), etc. For example, a process flow may be a treatment to be implemented in the OCEAN® infrastructure. For example, a process stream may include one or more processing steps that access a module such as an extension module (eg, external executable code, etc.). As described above, the system 100 may be used to simulate or model a geological environment 150 and / or a reservoir 151. A reservoir modeling is often based on saturation data as a component. In some embodiments, the system 100 may be based on a saturation model as a component of the reservoir model 151. [0051] Figure 2 illustrates a flowchart of a reservoir saturation modeling method 200. As illustrated in FIG. 2, the method 200 can begin by obtaining petrophysical data at operation 210. For example, at step 210, petrophysical data from the reservoir can be collected or received. Petrophysical data may include capillary pressure data and permeability data. In some embodiments, the petrophysical data may also include porosity, height data above the free water level and rock type. In step 220, a number of pore grooves can be determined from the resulting petrophysical data. For example, a number of pore grooves can be determined from the capillary pressure data obtained. In other embodiments, the number of pore grooves in the system can be predetermined. Once the number of pore grooves has been determined, a set of hyperbolic tangents whose number is equal to the number of pore grooves can be determined in step 230. [0054] At the operation 240, the set of hyperbolic tangents can be used to create a curve to approximate the obtained petrophysical data and to define a set of hyperbolic tangent parameters. For example, the set of hyperbolic tangents can be used to create a curve to determine the obtained capillary pressure data and to define a set of hyperbolic tangent parameters associated with said curve. Once the hyperbolic tangent parameters are defined, at least one hyperbolic tangent parameter 10 can be combined with the petrophysical data obtained to define dependencies for a saturation height function at operation 250. For example at least one hyperbolic tangent parameter may be combined with the permeability data obtained to define a permeability dependency for some of the parameters that define a saturation height function. In step 260, the saturation height function can be combined with petrophysical data to model a saturation in the reservoir. For example, saturation of water and hydrocarbon in a reservoir can be calculated from the saturation height function using permeability data, porosity data, and height above the free water level. . In some embodiments, the saturation height function can also be combined with rock type data. For example, the saturation height function can be limited to a single type of rock or only one type of rock can be adopted for the tank model. At operation 270, the saturation pattern can be displayed. For example, at operation 270, the saturation pattern or changes in the saturation pattern can be displayed. In other embodiments, the saturation pattern may be displayed as part of the larger reservoir model. As described above, a saturation data model can be used to predict saturation of water and hydrocarbon at a given point in an oil field reservoir. For example, a saturation data model can be created using reservoir properties such as permeability, porosity, height above the free water level, and a saturation height function. In some embodiments, porosity, permeability, and rock type data may be obtained from seismic data and / or well data. Similarly, the saturation height function may be a function of capillary pressure, water saturation and permeability data. In some embodiments, the petrophysical data for these oilfield properties are obtained from an analysis of core samples representative of the oilfield reservoir. [0059] As used herein, the term "capillary pressure" refers to the difference in capillary forces created by two or more immiscible fluids within voids of a rock. Capillary pressure data can be measured experimentally or can be received in the model. For example, capillary pressure can be measured through porous plate experiments, centrifugation or mercury injection. Capillary pressure data may include measurement of saturation at a different pressure level and / or height (e). In some embodiments, a laboratory record of capillary pressure versus wetting phase saturation or nonwetting phase saturation is obtained and is used to establish the saturation height function. In another embodiment, the capillary pressure data obtained by experimentation is normalized before the capillary pressure data is used to establish the saturation height function. Normalization can allow the saturation height function to be used with the tank with different fluid systems, such as gas / water, oil / water and oil / water / gas. In one embodiment, the measured capillary pressure data is representative of the capillary pressure of the oil field reservoir or a portion thereof. For example, capillary pressure data in terms of height may represent a maximum thickness of the reservoir to be modeled. As used herein, the term "water saturation" refers to a portion of a porosity of the substrate filled with e. in. In one embodiment, water saturation data can be obtained by experimentation. For example, water saturation can be obtained by capillary pressure experiments: saturation in the non-wetting phase (in the case of mercury injection) can be calculated as being the volume occupied by the non-wetting phase ( measured as the volume injected during the experiment) on the total pore volume. In some embodiments, the water saturation data is normalized. In one embodiment, the measured water saturation data is representative of the water saturation of the oil field reservoir or a portion thereof. As used herein, the term "permeability" refers to the ability of a substrate to transmit a fluid. In one embodiment, permeability data can be obtained by experimentation. For example, permeability data can be derived from measured pressures prior to the entry of a substrate sample and after the exit of the substrate using a known viscosity fluid. In the case of gas, corrections, such as a correction for the Klinkenberg effect, may be included. In one embodiment, the measured permeability data is representative of the permeability of the oil field reservoir or part thereof. In one embodiment, the saturation height function is based on two equations for determining capillary pressure data measured from the reservoir: a first equation for solving a set of unknown parameters using capillary pressure data. measured, and a second equation that uses the unknown parameters resolved to apply a set of hyperbolic tangents for the purpose of determining capillary pressure data obtained from a single pore throat or multiple pore groove system. In one embodiment, these equations approach capillary pressure data measured from the reservoir using a non-linear least squares constraint method. In another embodiment, these equations approach capillary pressure data and saturation data measured from the reservoir using a non-linear least squares constraint method. For example, a first equation (equation 1) can use a set M of measured water saturation and capillary pressure data. In one embodiment, the water saturation and capillary pressure data are obtained by analysis and experimentation based on core samples from the reservoir. In another embodiment, the water saturation and capillary pressure data are normalized, and the normalized capillary pressure is transformed to the logarithm of the capillary pressure before being incorporated into equation 1. [0064] In a In the embodiment, equation 1 uses the M set of water saturation and capillary pressure saturation data measured in a non-linear least squares method to find unknown parameters (an, wn, tn) of a model that minimizes an error E between the data and a model of capillary pressure f. In one embodiment, the first equation corresponds to the following equation: Equation 1: 30 E Lii = 1 (Imeasi f (Pmeasila W nInitn)) 2 where Smeas and Pmeas represent water saturation and pressure data capillary and an, wn, tn are the unknown parameters to be solved. In another embodiment, a second equation incorporates previously unknown resolved parameters (an, wn, tn) in a model that defines a set N of hyperbolic tangents. For example, in one embodiment, the second equation corresponds to the following equation: Equation 2: f (Plan) wnitn) = al aN EnN, 1 year + i - an) .tanh (wn. (P - ta) ) With the constraints:> 0, tin E [1, N] n, N c N an + i <an, Vn E [1, N -1] n, NEN where P is the logarithmic transform of the normalized capillary pressure and N is the number of hyperbolic tangents determined for the model.
    [0004] In one embodiment, the number of hyperbolic tangents of the model in equation 2 is predetermined. For example, Figure 6 illustrates capillary pressure data from a 3-groove pore system, therefore, Equations 1 and 2 will be parameterized with N = 3.
    [0005] In one embodiment, the scaling factors (a, fi-an) of each hyperbolic tangent in the set N are related to each other such that the sum of the hyperbolic tangents is delimited between 2a1 and 2aN. . The binding can force the partition of hyperbolic tangents among different pore throats. For example, forcing a hyperbolic tangent by pore throat instead of a hyperbolic tangent on 3 pore gorges and two other hyperbolic tangents without any contribution. In other words, as illustrated in Figure 8, each hyperbolic tangent may be limited to a pore throat.
    [0006] In one embodiment, the constraints present in equation 2 are configured to limit hyperbolic tangents to realistic capillary pressure curves and improve the stability of the model. For example, hyperbolic tangents can be sorted according to the number of pore grooves in the system, with the "first" hyperbolic tangent starting on the left side. Each pore throat and the corresponding combined hyperbolic tangent can be determined as a monotonous decreasing function. For example, Figures 3, 4, and 5 illustrate a model of hyperbolic tangents in a capillary pressure and water saturation system according to one embodiment. Figure 3 illustrates a single hyperbolic tangent 310 in a capillary pressure and water saturation system created using equation 2 above with included constraints. The x-axis represents the capillary pressure and the y-axis represents the saturation of water. Figure 4 illustrates two hyperbolic tangents 320 and 330 created using equation 2 above with constraints included. As illustrated in Figure 4, a third hyperbolic tangent 340 is the sum of the hyperbolic tangents 320 and 330 and represents a double pore throat system. Figure 5 illustrates two hyperbolic tangents 350 and 360 created without the constraints in equation 2 above, and a third hyperbolic tangent 370 which is the sum of hyperbolic tangents 350 and 360. As illustrated in FIG. 5, the third hyperbolic tangent 370 can not represent a realistic capillary pressure curve because the underlying unstressed hyperbolic tangents 350 and 360 go in different directions. A hyperbolic tangent can not represent a realistic capillary pressure layer either if it results in a non-monotonic decreasing function. In one embodiment, a nonlinear optimization routine is used to find the best approximation parameters. For example, a nonlinear optimization routine configured to handle linear inequality constraints, such as sequential quadratic programming, may be used to find the best approximation parameters. Figures 6, 7, and 8 illustrate a capillary pressure model according to embodiments of the present disclosure. Figure 6 illustrates capillary pressure data from a multiple pore groove system. Figure 7 illustrates a better approximation curve 410 on the capillary pressure data. As illustrated in FIG. 7, the best approximation curve 410 is the sum of three hyperbolic tangents 420, 430 and 440. FIG. 8 illustrates the three hyperbolic tangents 420, 430 and 440 shifted to show which hyperbolic tangent corresponds to what a pore gorge. As illustrated in FIGS. 6 to 8, a capillary pressure model incorporating equations 1 and 2 shows a good approximation with the measured capillary pressure data of the wells, and a number of hyperbolic tangents N can be established to determine the number of pore throats in the system. In some embodiments, a good approximation is determined by the degree of error in equation 1: the lowest error in equation 1 means the best approximation, while a higher error value indicates a lower quality of the approximation. In one embodiment, a saturation height function is created by combining the capillary pressure model of Equations 1 and 2 in conjunction with equations that incorporate other physical properties of the reservoir. For example, a capillary pressure model can be created using Equations 1 and 2 to determine measured capillary pressure data while simultaneously using two other equations to incorporate permeability data to create a saturation height function. In one embodiment, the unknown parameters of equations 1 and 2 have a linear relationship with the logarithm of the measured permeability for the reservoir. Therefore, in some embodiments, the unknown parameters of Equations 1 and 2 can be used to predict a saturation height function in terms of permeability and capillary pressure. Figures 9 and 10 illustrate relationships between the capillary pressure, the permeability and the unknown parameter tn, according to one embodiment. In particular, Figure 9 illustrates different capillary pressure curves at different values of a permeability K. Similarly, Figure 10 illustrates different models of a hyperbolic tangent created using Equations 1 and 2 according to different parameter values. unknown tn. As illustrated in Figures 9 and 10, there is a strong linear relationship between the log of permeability and the unknown parameter tn. For example, the linear relationship between the logarithm of permeability and the unknown parameter tn can be defined according to the following equation: Equation 3: tn = kn.log (K) + kn + 1 where K represents the measured permeability. In some embodiments, a strong linear relationship is represented by a higher value of R2, a linear correlation coefficient between log (K) and the parameters of equation 3. [0076] In a As a result, equation 3 can be used to define a fourth equation for a saturation height function that integrates permeability information. For example, equation 3 can be substituted in equation 1 to create the following equation: Equation 4: f (P, K, an, mn, kn) = al + aN + - an) .tanh (wn (P - kn.log (K) + kn + 1)) [0077] Therefore, in one embodiment, equation 4 represents a saturation model height function using simultaneously capillary pressure data and core permeability measurements. In one embodiment, saturation data for an oilfield reservoir is modeled using the saturation height function of Equation 4 to predict saturation of water and hydrocarbon at a point. given in an oil field tank. In one embodiment, a saturation data model can be created using reservoir properties such as permeability, porosity, height above the free water level, and saturation height function. In some embodiments, porosity, permeability, and rock type data are obtained from seismic data and wells. For example, a reservoir model can be defined by Sw = Fn 5 (z, K), where Sw is the saturation of water and hydrocarbon at a point in the reservoir, (z) is the height above the reservoir. free water level, and (K) is the permeability. Each of these equations can be limited to a specific type of rock. In some embodiments, the methods of the present disclosure may be executed by a computer system. Figure 11 illustrates an example of a computer system 500 of this type, according to some embodiments. The computer system 500 may include a computer or a computer system 501A, which may be an individual computer system 501A or an arrangement of distributed computer systems. The computer system 501A includes one or more analysis modules 502 that are configured to perform different tasks according to some embodiments, such as one or more disclosed method (s). s) here. To perform these different tasks, the analysis module 502 operates independently, or in coordination with one or more processor (s) 504, which is (or are) connected to one or more storage medium (s). 506. The processor (s) 504 is (or are) also connected to a network interface 507 to allow the computer system 501A to communicate on a data network 509 with one or more system (s). ) computers and / or additional computer system (s), such as 501B, 501C, and / or 501D (note that the computer systems 501B, 501C and / or 501D may or may not share the same architecture 501A computer system, and may be located in different physical locations, for example, the computer systems 501A and 501B may be located in a processing facility, while in communication with one or more system (s). ) of computers such as 25 501C and / or 501D which is (are) located ( s) in one or more data center (s), and / or located in different countries on different continents). A processor may comprise a microprocessor, a micro-controller, a processor module or subsystem, a programmable integrated circuit, a programmable gate array, or other control or computing device. The storage media 506 may be implemented in the form of one or more storage medium (s) readable by a computer or readable by a machine. Note that although in the exemplary embodiment shown in Figure 11 storage medium 506 is shown as being within computer system 501A, in some embodiments, media storage 506 may be distributed within and / or across multiple internal and / or external enclosures of the computer system 501A and / or additional computing systems. The storage media 506 may comprise one or more different memory form (s), including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable ROMs, and EPROMs), erasable and electrically programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed disks, floppy disks and removable disks, other magnetic media including tape, optical media such as compact discs (CDs) or digital video discs (DVDs), BLUERAY® discs or other types of optical storage, or other types of storage devices. Note that the instructions discussed above may be provided on a computer readable or machine readable storage medium, or may be provided on multiple computer readable or machine readable storage media distributed in an extended system. possibly comprising several nodes.
    [0007] This or these storage medium (s) readable by a computer or readable by a machine is (are) considered as part of an article (or an article of manufacture). An article or article of manufacture may refer to any single component (s) or multiple component (s) manufactured. The storage medium (s) may be located in the machine executing the machine-readable instructions, or may be located at a remote site from which instructions readable by a machine can be downloaded to a network to run. In some embodiments, the computer system 500 contains one or more modeling module (s) 508. In the computer system example 500, the computer system 501A includes the modeling module 508. In certain modes, In one embodiment, a single modeling module can be used to perform at least several aspects of one or more embodiments of the methods disclosed herein. In alternative embodiments, a plurality of modeling modules may be used to perform at least several aspects of the present methods. It will be appreciated that the computer system 500 is an exemplary computer system, and that the computer system 500 may comprise more or fewer components than shown, may combine additional components that are not represented in the computer system. the exemplary embodiment shown in Fig. 11, and / or that the computer system 500 may have a different configuration or arrangement of the components shown in Fig. 11. The various components shown in Figs. Figure 11 may be implemented in hardware equipment, software or a combination of hardware and software equipment, comprising one or more specific integrated signal processing and / or application circuit (s). In addition, aspects of the methods of processing described herein may be implemented by executing one or more functional modules in an information processing apparatus such as general purpose processors or processors. Application specific chips, such as ASIC, FPGA, PLD, or other suitable devices. These modules, combinations of these modules, and / or their combination with general purpose hardware equipment are included within the scope of protection of the invention. [0085] Interpretations, models and / or other geological interpretation aids can be refined in an iterative manner; this concept is applicable to the processes discussed here. This may include the use of feedback loops executed on an algorithmic basis, such as on a computing device (e.g., computer system 500, see Figure 11), and / or through manual control. performed by a user who can make determinations as to whether a given step, action, template, model, or set of curves has become sufficiently accurate to perform an evaluation of three-dimensional underground geological formations considered. The present disclosure has been described with reference to the embodiments. Although several embodiments have been shown and described, those skilled in the art will appreciate that changes can be made in these embodiments without betraying the principles and spirit of the foregoing detailed description. It is intended that the present disclosure be construed to embrace such modifications and alterations to the extent that they fall within the scope of the appended claims or equivalents thereof. 25
    权利要求:
    Claims (15)
    [0001]
    REVENDICATIONS1. A method of modeling saturation in a tank, comprising the steps of: obtaining capillary pressure data which represents the capillary pressure in the tank; obtaining permeability data which represents the permeability in the reservoir; determining a number of pore grooves represented by the capillary pressure data; creating hyperbolic tangents based on capillary pressure data whose number is equal to the number of pore grooves; combination of hyperbolic tangents to create a curve to approximate capillary pressure data and to define hyperbolic tangent parameters; combining at least one of the hyperbolic tangent parameters with the permeability data to define a saturation height function; modeling saturation in the reservoir using the saturation height function; and displaying the saturation pattern based on the saturation height function, each of the respective hyperbolic tangents being created for only one of the respective pore grooves, so that never two of the hyperbolic tangents are created for the same one of the pore.
    [0002]
    The method of claim 1, wherein said at least one hyperbolic tangent parameter has a linear relationship with the logarithm of the obtained permeability. 25
    [0003]
    3. A method according to claim 1 or 2, wherein the hyperbolic tangents are defined by the following equation: f (Plan, wrotn) = al aN + Eln ri (an -} - 1 year) .tanh (wn. P - ta)) with the constraints: wn> 0, Vil E [1, N] ri, N c N an + i <a ', Vn c [1, N - n, NEN where P represents the logarithmic transform of the normalized capillary pressure and N represents the number of hyperbolic tangents. 3034894 22
    [0004]
    4. The method of claim 3, wherein the hyperbolic tangent parameter tn has a linear relationship with the logarithm of the permeability obtained as defined by the following equation: tri = kii.lag (K) + k1 where K represents the permeability data obtained.
    [0005]
    5. The method according to claim 4, wherein the saturation height function is defined by the following equation: f (P, K, a ', w) = a1 + aN + En, _1 (an + i an) , tanh (w7, (P kn, .log (K) + len + i))
    [0006]
    The method according to any one of claims 1 to 5, wherein the combination of the set of hyperbolic tangents to create the curve for approaching the capillary pressure data and for defining the set of hyperbolic tangent parameters comprises the use of a nonlinear least squares process.
    [0007]
    The method of any one of claims 1 to 6, wherein the saturation modeling in the reservoir comprises saturation modeling based on a combination of the saturation height function and one or more several property (s) of the reservoir.
    [0008]
    8. Non-transient computer-readable medium storing instructions which, when executed by one or more processor (s) of a computer system, cause the computer system to perform operations, the operations including: obtaining data capillary pressure which represent the capillary pressure in a reservoir; obtaining permeability data which represents the permeability in the reservoir; determining a number of pore grooves represented by the capillary pressure data; Creating hyperbolic tangents on the basis of the capillary pressure data whose number is equal to the number of pore grooves; combination of hyperbolic tangents to create a curve to approximate capillary pressure data and to define hyperbolic tangent parameters; Combining at least one of the hyperbolic tangent parameters with the permeability data to define a saturation height function; modeling saturation in the reservoir using the saturation height function; and displaying the saturation pattern based on the saturation height function, each of the respective hyperbolic tangents being created for only one of the respective pore grooves, so that never two of the hyperbolic tangents are created for the same one of the pore. 10
    [0009]
    9. A non-transient computer readable medium according to claim 8, wherein the hyperbolic tangents are defined by the following equation: f (PI an) wn, tn) z --- al + aN + (4,4_1 - an .tanh (wne - tn)) with the constraints: w> 0, Vii E [1, N] n, NEN 15 an + i <an, Vn E [1, N - 11 n, NEN where P represents a transform logarithmic normalized capillary pressure and N represents the number of hyperbolic tangents.
    [0010]
    The non-transient computer readable medium of claim 9, wherein the hyperbolic tangent parameter tn has a linear relationship to the log of the permeability obtained as defined by the following equation: tri = kn.log (K) + 41 where K represents the permeability data obtained.
    [0011]
    11. A non-transient computer readable medium according to claim 10, wherein the saturation height function is defined by the following equation: f (P, K, a ', wn kn) = a + a N Eiiv1 (an +1 - an) -tanh (v, In (P - kn-log (K) k7 + 1)).
    [0012]
    Computer system, comprising: one or more processor (s); and a memory system comprising one or more non-transitory medium (s) readable by a computer storing instructions which, when executed by one or more processor (s) of a computer system. cause the computer system to perform operations, the operations comprising: obtaining capillary pressure data which represents the capillary pressure in a reservoir; Obtaining permeability data which represents the permeability in the reservoir; determining a number of pore grooves represented by the capillary pressure data; creating hyperbolic tangents based on capillary pressure data whose number is equal to the number of pore grooves; combination of hyperbolic tangents to create a curve to approximate capillary pressure data and to define hyperbolic tangent parameters; combining at least one of the hyperbolic tangent parameters with the permeability data to define a saturation height function; Modeling saturation in the reservoir using the saturation height function; and displaying the saturation pattern based on the saturation height function, each of the respective hyperbolic tangents being created for only one of the respective pore grooves, so that never two of the hyperbolic tangents are created for the same one of the pore.
    [0013]
    Computer system according to claim 12, wherein the hyperbolic tangents are defined by the following equation: f (P, ar 'w', tn) = ai + aN + En.1 (an + 1 year) .tanh ( (P - t)) with the constraints: W n> 0, Vn E [1, N] n, N c N an + i <an, Vn E [1, N - 1] n, NEN where P represents a logarithmic transform of a normalized capillary pressure and N represents the number of hyperbolic tangents. 30
    [0014]
    The computer system of claim 13, wherein the hyperbolic tangent parameter tn has a linear relationship with the logarithm of the permeability obtained as defined by the following equation: tn = kii.log (K) + kn, + 1 where K represents the permeability data obtained.
    [0015]
    15. The computer system of claim 14, wherein the saturation height function is defined by the following equation: f (P, K, an, wn, kn) == al + aN + - an) .tanh ( your (P - kn.log (K) len + i)).
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    同族专利:
    公开号 | 公开日
    FR3034894B1|2018-08-10|
    US20180119523A1|2018-05-03|
    WO2016164507A1|2016-10-13|
    EP3281043A1|2018-02-14|
    EP3281043A4|2019-01-09|
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    法律状态:
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    2016-10-14| PLSC| Publication of the preliminary search report|Effective date: 20161014 |
    2017-04-27| PLFP| Fee payment|Year of fee payment: 3 |
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    2022-01-07| ST| Notification of lapse|Effective date: 20211205 |
    优先权:
    申请号 | 申请日 | 专利标题
    FR1553043A|FR3034894B1|2015-04-09|2015-04-09|OIL STORAGE SATURATION TANK AND PERMEABILITY MODELING|
    FR1553043|2015-04-09|FR1553043A| FR3034894B1|2015-04-09|2015-04-09|OIL STORAGE SATURATION TANK AND PERMEABILITY MODELING|
    PCT/US2016/026311| WO2016164507A1|2015-04-09|2016-04-07|Oilfield reservior saturation and permeability modeling|
    US15/564,732| US20180119523A1|2015-04-09|2016-04-07|Oilfield Reservoir Saturation and Permeability Modeling|
    EP16777232.6A| EP3281043A4|2015-04-09|2016-04-07|Oilfield reservior saturation and permeability modeling|
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