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    • 专利摘要:
    method of evaluating connectivity between sections in a hydrocarbon reservoir, and system of evaluating connectivity between sections in a hydrocarbon reservoir crossed by a link less than a well. a method is disclosed to assess connectivity between sections in a hydrocarbon reservoir. the hydrocarbon samples are collected at different depths in at least one well the fluorescence intensity determines the actual heavy end concentrations of the hydrocarbons for the corresponding different depths. the actual heavy end concentrations of hydrocarbons for corresponding different depths are determined and the actual heavy end concentrations of the hydrocarbons are compared with the estimated heavy end concentrations to assess connectivity between sections of the hydrocarbon reservoir.
    公开号:BR112014025835B1
    申请号:R112014025835-0
    申请日:2013-03-07
    公开日:2021-03-23
    发明作者:Andrew E. Pomerantz；Youxiang Zuo；Oliver C. Mullins
    申请人:Schlumberger Technology B.V.；
    IPC主号:
    专利说明:

    [0001] [0001] Reservoir connectivity analysis determines whether reservoir fluids flow from one reservoir section to another reservoir section and where to drill a well. Reservoir connectivity can be assessed by taking and analyzing oil samples from a well, using Downhole Fluid Analysis (DFA), including optical spectroscopy to determine the concentration of the heavy tip at different depths in the well and comparing with a model FloryHuggins-Zuo Equation of State (FHZ EOS). This analysis is based on the interpretation of optical absorption in the visible or near infrared region (known as "color") and is related to the relative content of asphaltenes or colored resins in the oil. It is difficult, however, to quantify the asphaltene content in some oils. Some systems measure the relative asphaltene content using the color and fluorescence of heavy oil, both of which are suitable for oils with a high concentration of heavy end. These methods may not be feasible for oils with a low concentration of heavy ends, such as gas condensates. summary
    [0002] [0002] The above summary is provided to present a selection of concepts in a simplified way that are described in the detailed description. The summary is not intended to identify the main characteristics or essential characteristics of the claimed object, nor is it intended to be used as an aid in determining the scope of the claimed matter.
    [0003] [0003] The method evaluates the connectivity between the sections in a hydrocarbon reservoir, crossed by a well. A plurality of hydrocarbons are collected over corresponding depths within at least one well. The fluorescence intensity is used to determine a plurality of actual hydrocarbon heavy end concentrations for different corresponding depths, based on the plurality of hydrocarbon samples. A plurality of hydrocarbon heavy-end concentrations for the corresponding depths are determined. The plurality of actual heavy end concentrations of the hydrocarbons is compared with the plurality of the estimated heavy end concentrations of hydrocarbons to assess connectivity between adjacent sections of the hydrocarbon reservoir.
    [0004] [0004] In one example, the hydrocarbon is formed as a condensate of gas, wet gas or volatile oil. In another example, the hydrocarbon is formed as a fluid containing <1% by weight of asphaltene. Hydrocarbons can also be formed as an optical density absorption fluid (optical density, OD) ˂ 0.1 in visible or infrared spectral regions. In another example, the hydrocarbon is formed as a fluid having existing heavy extremities as isolated molecules. In another example, the hydrocarbon is formed as a fluid having a fluorescence proportional to the content of the heavy end.
    [0005] [0005] In one example, a plurality of estimated heavy tip concentrations have a gradient over the depth of the well indicative of connectivity between sections of the hydrocarbon reservoir. The plurality of hydrocarbon samples can be collected using a downhole tool within the well. In another example, the plurality of the estimated heavy end concentrations are determined using a FloryHuggins-Zuo Equation of State (FHZ EOS) model. Concentrations of heavy extremities may be based on concentrations of aromatic compounds and resins, which may have a chemical composition similar to that of the perylene molecule.
    [0006] [0006] The system for assessing connectivity between sections in a hydrocarbon reservoir traversed by at least one well that includes at least one tool to collect a plurality of hydrocarbon samples over corresponding depths within at least one well. The fluorescence intensity is used to determine the actual hydrocarbon heavy end concentrations. A processor cooperates with at least one tool and determines the estimated heavy end concentrations and compares the plurality of actual hydrocarbon heavy end concentrations with the hydrocarbon concentrations with the estimated heavy concentrations to assess connectivity between sections of the hydrocarbon reservoir.
    [0007] [0007] Non-transitory computer-readable medium is also disclosed and is executable on a processor to perform steps to assess connectivity between sections in a hydrocarbon reservoir and traversed by a well and for which fluorescence intensity has been used to determine the actual heavy end concentrations of gas condensates for different corresponding depths. It compares actual hydrocarbon heavy end concentrations with estimated heavy end concentrations. Brief Description of the Figures
    [0008] [0008] Fig. 1 A is a schematic diagram of an oil reservoir fluid analysis tool that can be used with the methodology in accordance with a non-limited modality.
    [0009] [0009] Fig. 1B is a schematic diagram of a fluid analysis module suitable for use with the tool in Fig. 1A.
    [0010] [0010] Fig. 2 is a flowchart showing a workflow for assessing connectivity in accordance with an unrestricted mode.
    [0011] [0011] Fig. 3 is a graph showing a linear relationship in the comparison of fluorescence and color for light oil, which demonstrates that fluorescence can be interpreted as a measure of the content of the heavy end in the oil.
    [0012] [0012] Fig. 4 is a graph showing fluorescence data measured at different depths with DFA and the straight line indicating the FHZ EOS forecast indicating that the reservoir is connected and in balance. Detailed Description
    [0013] [0013] The present description is made with reference to the attached figures, in which exemplary modalities are shown. However, many different modalities can be used, and, therefore, the description should not be interpreted as limited to the modalities established here. Instead, these modalities are provided for this disclosure to be in-depth and complete. Similar numbers refer to similar elements everywhere.
    [0014] [0014] Referring to Figs. 1A and 1B, there is an illustrated tool for controlling a fluid analysis module (data fluid Analysis, DFA) from the downhole reservoir and in FIG. 1B, an exemplary embodiment of the fluid analysis module of Fig. 1A that includes several probes, including a probe to measure fluorescence data and enable connectivity assessment, gas condensate reservoir using the EOS FHZ. Substantial details of the EOS FHZ are described in the commonly assigned International Patent Application Publication No. WO 2012/042397 based on US US Interim Patent Application Series No. 61 / 387,066 and commonly assigned US Interim Patent Application US Series No. 61/587, 846.
    [0015] [0015] The application of EOS FHZ is described below in relation to the subject in question of measuring concentrations of hydrocarbon heavy-end gas, such as gas condensates, to determine the connectivity of the reservoir. A system that measures asphaltene content using spectroscopy commonly assigned in US Patent Application US Series No. 13 / 446,975 and a serial number using fluorescence is disclosed in International Patent Application PCT / US2013 / 021274, based on the Application for US Provisional Patent US Series No. 61 / 585,934. The system and method as described below can be used in one or more holes that intersect a common formation. This analysis is not restricted to a single hole. Lateral connectivity often analyzed and a single-well or multi-well approach can be used.
    [0016] [0016] Gas condensates have little color, indicating that the relative concentration of the heavy end cannot be determined by optical spectroscopy. The application of EOS FHZ to assess the connectivity of the reservoir is based on measurements of relative concentration of the heavy extremities. In accordance with an example of non-limitation, hydrocarbons such as gas condensates are measured using fluorescence data to allow assessment of connectivity in gas condensates using the EOS FHZ reservoir.
    [0017] [0017] In one example the hydrocarbon is formed as a gas condensate and in another example, as a wet gas with a small amount of liquid present, ranging from a wet gas that is gas saturated with liquid vapor to a flow multi-phase with a volume of 90% gas in an example of non-limitation. In another example, the hydrocarbon is formed as a volatile oil or a fluid containing ˂ 1% by weight asphaltene. Hydrocarbons can also be formed with optical density absorption (OD) ˂ 0.1 in the visible or infrared spectral regions. A hydrocarbon can also be formed as a fluid having a heavy end exists as isolated molecules. Hydrocarbons can also be formed as a fluid having fluorescence that is proportional to the content of the heavy end.
    [0018] [0018] Fig.1 A illustrates an oil reservoir analysis system 8 that can be used in accordance with an example of no limitation of assessing reservoir connectivity. System 8 includes a drilling tool 10 suspended in the hole at the bottom end of a typical multi-conductor cable 15 which is usually wound in a suitable winch on the forming surface 12. The cable 15 is electrically coupled to a control system 18 in the forming surface and includes electronics and processing circuits for the processing tool. The drilling tool 10 includes an elongated body, 19, which carries a selectively extensible fluid admitting assembly 20 and a selectively extendable tool anchoring member 21 which are respectively disposed on opposite sides of the tool body. The intake fluid of the assembly 20 is equipped to selectively close or isolate selected parts of the wall of the well 12 such that fluid communication with the adjacent earth formation 14 is established.
    [0019] [0019] The intake fluid of the set 20 and drilling tool 10 includes a flow line leading to a fluid analysis module 25. The forming fluid obtained by the intake fluid of the set 20 flows through the flow line and the fluid analysis module 25. The fluid can later be expelled through a port or it can be sent to one or more fluids collecting chambers 22 and 23 that can receive and retain the liquids obtained from the formation. With the intake fluid of assembly 20 sealingly engaging formation 14, a short rapid pressure drop can be used to break the plaster seal. The first fluid drawn into the tool can be highly contaminated with filtered sludge. As the tool continues to extract liquid from formation 14, it cleans the area near the intake fluid of assembly 20 and reservoir fluid becomes the dominant component. The time required for cleaning depends on many parameters, including formation permeability, fluid viscosity, pressure differences between the hole and the formation and difference in unbalanced pressure and its duration during drilling. Increasing the pump rate may shorten the cleaning time, but the rate is carefully controlled to preserve pressure build-up conditions.
    [0020] [0020] The fluid analysis module 25 includes components to measure the temperature and pressure of the fluid in the flow line. The module can work in conjunction with a circuit and the device that measures the fluorescence intensity in different samples of gas condensates, which are collected at different depths within the well. Reference is made to International Patent Application PCT / US2013 / 021274 by reference which discloses heavy fluorescence oil to measure the asphaltene content of heavy oil and includes various sensors and modules that measure the fluorescence of gas condensates. The fluid analysis module 25 also derives from properties that characterize the sample of forming fluid at the pressure and temperature of the flow line.
    [0021] [0021] In one embodiment, the fluid analysis module 25 measures absorption spectra and converts such measurements into concentrations of various alkane components and groups in the fluid sample. in an illustrative example, fluid analysis module 25 provides measurements of the concentrations (ie weight percent) of carbon dioxide (CO2), methane (CH4), ethane (C2H6), C3-C5 alkane group, the agglomerate hexane and heavier C6 (+) alkane components and asphaltene content. Fluid analysis module 25 measures crude fluid density (ρ) in flow line temperature and pressure, crude fluid viscosity (μ) in flow line temperature and pressure (cP), formation pressure and formation temperature .
    [0022] [0022] Fluid intake assembly control 20 and fluid analysis module 25 and the flow path for collecting chambers 22, 23 are maintained by control system 18. As will be appreciated by those skilled in the art, the fluid analysis module 25 and the electrical control system located on the surface18 include data processing functionality (for example, one or more microprocessors, associated memory and other hardware and / or software) to implement the methodology, as described in this document. The electrical control system 18 can also be realized by a distributed data processing system. The data measured by the drilling tool 10 is communicated (for example, in real time) through a communication link, for example, a satellite link, to a remote location for data analysis, which can be performed at a drilling station. work or other appropriate data processing system, for example, a computer cluster or grid computing.
    [0023] [0023] Sampling formation fluids by drilling tool 10 may be contaminated with the mud filtrate of a drilling fluid that infiltrates formation 14 during the drilling process. In some examples, formation fluids are withdrawn from formation 14 and pumped into the well or into a large waste chamber in the well bottom tool 10 until the fluid being removed becomes sufficiently clean. In a clean sample, the concentration of the sludge filtrate in the sample fluid is acceptably low so that the fluid substantially represents native (ie naturally occurring) fluid formation. In the illustrated example, exploration well tool 10 is provided with fluid collection chambers 22 and 23 to store collected fluid samples.
    [0024] [0024] System 8 of Fig. 1A makes in situ determinations regarding hydrocarbons having geological formations by sampling fluid wells from the reservoir at one or more measurement stations within well 12 and performs the analysis of the bottom-of-well liquid (for acronym in English for downhole fluid Analysis, DFA) of one or more samples of reservoir fluid for each measurement station (including compositional analysis such as estimating the concentrations of a plurality of compositional components of a given sample and other fluid properties) . In accordance with an example of non-limitation, gas condensate samples are collected from different depths. The analysis of the bottom-of-well liquid can be compared to a model equation of state (EOS) of the thermodynamic fluid behavior to characterize the liquid in the reservoir at different locations within the reservoir and to determine fluid production parameters, transport properties and other indicators commercially useful reservoir.
    [0025] [0025] For example, the EOS model can provide a phase envelope that can be used to interactively vary the rate at which samples are collected and to avoid entering the two-phase region. In another example, EOS can evaluate the production methodologies for the reservoir. Such properties can include density, viscosity and volume of gas formed from a liquid after expansion at a temperature and pressure. The characterization of the fluid sample in relation to its thermodynamic model can also be used as a reference to determine the validity of the sample obtained, either to maintain the sample, or if it is necessary to obtain another sample at the place of interest. More particularly, based on the thermodynamic model and information on formation pressures, sampling pressures and formation temperatures, if it is determined that the fluid sample obtained near or below the sample bubble point, a decision can be made to discard the sample and / or to obtain a sample at a slower rate (ie a lower pressure drop), so that the gas will not evolve outside the sample. Because knowledge of the exact dew point of a retrograde gas condensate in a formation may be desirable, a decision can be made, when conditions permit, to vary the pressure drop in an attempt to observe liquid condensation and therefore establish the actual saturation pressure.
    [0026] [0026] Fig. 1B illustrates an embodiment of the fluid analysis module 25 of Fig. 1A (marked) 25 ', including a probe 202 having a port 204 for admitting the forming fluid therein. A hydraulic mechanism extending 206 may be driven by a hydraulic system 220 to extend probe 202 to seal in a wrapping 14. In other embodiments, more than one probe may be used or inflatable packers may replace the probe (s) and function to establish fluid connections with the sample fluid samples and formation.
    [0027] [0027] An example of probe 202 is the Quicksilver Probe developed by Schlumberger Technology Corporation of Sugar Land, Texas, USA. Quicksilver Probe divides the fluid flow from the reservoir into two concentric zones, that is, a central zone isolated from a guard zone on the perimeter of the central zone. The two zones are connected to separate flow lines with independent pumps. Pumps can be run at different rates to explore contrast of filtered / fluid viscosity and anisotropy and reservoir permeability. Higher speed of entry into the guard zone directs the contaminated fluid into the guard zone of the flow line, while the clean liquid is dragged to the central zone. Fluid analyzers analyze the fluid in each flow line to determine the composition of the fluid in the respective flow lines. Pump rates can be adjusted based on such compositional analysis to archive and maintain desired levels of fluid contamination. Quicksilver Probe's operation efficiently separates contaminated fluids from the cleaner fluid at the beginning of the fluid extraction process, which results in getting clean fluid in much less time compared to traditionally formed testing tools.
    [0028] [0028] The fluid analysis module 25 'includes a flow line 207 that carries the forming fluid from port 204 through a fluid analyzer 208. The fluid analyzer 208 may include a light source that directs light to a sapphire prism placed adjacent to the fluid flow of the flow line. The reflection of such light is analyzed by a gas refractometer and dual fluorescent detectors. The gas refractometer qualitatively identifies the fluid phase in the flow line. Dual fluorescent detectors detect bubbles of free gas and retrograde liquid elimination to accurately detect the flow of single-phase fluid in flow line 207. The type of fluid is also identified. The resulting information phase can be used to define the difference between retrograde condensates and volatile oils, which can have a similar gas-to-oil ratio (GORs) and crude oil densities. It can also be used to monitor phase separation in real time and to ensure single phase sampling. The 208 fluid analyzer also includes two spectrometers - a filter matrix spectrometer and a grid-type spectrometer. The 208 fluid analyzer or other associated modules also include devices to measure fluorescence intensity in samples of gas condensates at different depths within the well.
    [0029] [0029] The filter matrix spectrometer of analyzer 208 includes a light broadband source providing light broadband that passes along optical guides and through an optical chamber in the flow line to an array of optical density detectors that are designed to detect narrow frequency bands (commonly referred to as channels) in the visible and near infrared spectrum, as described in U.S. Patent No. 4,994,671. The filter matrix spectrometer also employs optical filters that provide for color identification (also referred to as "optical density" or "OD") of the fluid in the flow line. Such color measurements support identification fluid, determination of asphaltene content and pH measurement. This 208-grade fluid analyzer spectrometer is designed to detect channels in the near infrared spectrum (between 1600-1800 nm) where the reservoir fluid has absorption characteristics that reflect the molecular structure.
    [0030] [0030] The fluid analyzer 208 also includes a pressure sensor for measuring the pressure of the forming fluid in the flow line 207, a temperature sensor for measuring the temperature of the fluid in the flow line 207, and the density of a sensor to measure the density of the crude fluid of the fluid in the flow line 207. In addition to density, the density sensor can also provide a measure of the crude fluid viscosity of the oscillation frequency quality factor. In one embodiment, the fluid analyzer 208 is the commercially available in situ fluid analyzer from Schlumberger Technology Corporation. The flow line sensors of the 208 fluid analyzer can be replaced or supplemented with other types of suitable measurement sensors (eg NMR sensors, capacitance sensors, etc.). Pressure sensors and / or temperature sensors for measuring pressure and temperature of the liquid entrained into flow line 207 can also be part of probe 202.
    [0031] [0031] A pump 228 is fluidly coupled to flow line 207 and is controlled to draw formation fluid into flow line 207 and to provide fluid formation to collection chambers 22 and 23 (FIG.1 A ) via valve 229 and flow path 231 (Fig. 1B).
    [0032] [0032] The fluid analysis module 25 'includes a data processing system 213 that receives and transmits control and data signals to the other components of module 25' to control the operations of module 25 '. The data processing system 213 also communicates with the fluid analyzer 208 to receive, store and process the measurement data generated therein. In one embodiment, the data processing system 213 processes the output of measurement data by the fluid analyzer 208 to derive and store measurements of the hydrocarbon composition of fluid samples analyzed in situ by the fluid analyzer 208, including: line temperature flow; flow line pressure; density of the crude fluid (p) in the temperature flow line; pressure flow line; gross fluid viscosity (μ) at temperature and pressure flow line; (e.g. weight percentages) of concentrations of carbon dioxide (CO2), methane (CH4), ethane (C2H6), C3-C5 alkane group, the hexane agglomerate and heavier components of C6 (+) alkane and content asphaltene; gas / oil ratio (GOR); and other parameters (such as API gravity, oil formation volume ratio (B0), etc.).
    [0033] [0033] Temperature and pressure of the flow line are measured by the temperature sensor and pressure sensor, respectively, of the fluid analyzer 208 (and / or probe 202). In one embodiment, the output of temperature sensors and pressure sensors are monitored continuously before, during and after sample acquisition to derive the temperature and pressure of the fluid in flow line 207. Pressure formation can be measured by the sensor fluid analyzer pressure gauge 208 in conjunction with downhole fluid sampling and analysis at a particular metering station after build-up of the flow line for forming pressure.
    [0034] [0034] GOR is determined by measuring the amount of methane and liquid petroleum components using near infrared absorption peaks. The ratio of peak methane to peak oil in a single phase live oil is directly related to Gor. The fluid analysis module 25 'can also detect and / or measure other fluid properties of a sample of a certain crude oil, including the formation of retrograde condensation, asphaltene precipitation, and / or gas evolution.
    [0035] [0035] The fluid analysis module 25 'also includes a bus tool 214 that communicates data signals and control signals between the data processing system 213 and the control system located on the surface 18 of Fig.1A. The busbar tool 214 can also charge the electrical power supply of signals generated by the power source located on the surface to supply the fluid analysis module 25 'and the module 25' can include a supply transforming / regulating power supply power supply 215 to transform the power supply signals provided via tool bar 214 to appropriate levels suitable for use by the electrical components of the module 25 '.
    [0036] [0036] Although the components of Fig. 1B are shown and described as being communicatively coupled and arranged in a particular configuration, persons ordinarily skilled in the art will appreciate that the components of the fluid analysis module 25 'can be communicatively coupled and / or arranged differently than depicted in FIG. 1B without departing from the scope of this disclosure. In addition, the methods, apparatus and systems described herein are not limited to a specific type of transport, but can instead be applied in relation to different types of transport, including, for example, spiral tubes, electrical cable, wired drill pipe, and / or other transport mechanisms known in the industry.
    [0037] [0037] In accordance with the present disclosure, the system of Figs. 1A and 1B can operate using the methodology shown in flowchart 300 of Fig. 2 to assess connectivity based on analysis of the downhole fluid (DFA) of reservoir fluid samples and more particularly gas condensates as explained in detail bellow. As will be appreciated by those skilled in the art, the electrical control system located on surface 18 and the fluid analysis module 25 of the well tool 10 each include data processing functionality (for example, one or more microprocessors, memory and other hardware and / or software) that cooperate to implement the method, as described in this document. The electrical control system 18 can also be performed by a distributed data processing system or workstation or another suitable data processing system (such as a computer cluster or computer grid).
    [0038] [0038] Below is more details of the method used to assess the connectivity of condensed gas reservoirs. A brief description of the asphaltene structure and methodology is described for the first time in order to better understand the process as described when FHZ EOS is applied to heavy-end condensed gas.
    [0039] [0039] Asphaltenes are a class of solubility of crude oils that are soluble in aromatic solvents but insoluble in n-alkanes. Asphaltenes are solids that precipitate when an excess of n-heptane or pentane is added to the crude oil. Asphaltene molecules are polar with relatively high molecular weights (approximately 700 to approximately 1,000 g / mol) and a population density of approximately 1.2 g / cc and can precipitate from actual crude oil production. During oil production, asphaltenes are often destabilized and precipitate because of changes in temperature, pressure and / or the chemical composition of crude oil.
    [0040] [0040] Reservoir fluids can be classified using nanoscience to measure compositional classifications in oil columns. Compartmentalization, connectivity, gradient fluids and viscosity can be materially affected by small amounts of asphaltene. The viscosity of the oil depends on the asphaltene index. The magnitude of the fluid gradients depends on the asphaltene aggregation structures, which depends on the asphaltene content, among other variables. Asphaltene gradient measurements are indicative of connectivity and compartmentalization, but small amounts of asphaltene do not cause a reservoir to be compartmentalized. Fluid analysis of rock bottom sampling can allow an accurate measurement of the variation of the asphaltene content through the oil column. The asphaltene content and distribution is gathered using a combination of sensors and the measured gradients and coloration, fluid density, gas / oil ratio (GOR), hydrocarbon composition, fluorescence intensity and viscosity are measured in situ. Fluid property measurements are performed at the bottom of the well and transmitted to the surface in real time, as IFA data and together with the asphaltene state equation (EOS) models. As a result, it is possible to determine the complexity of the reservoir such as flow barriers and fluid compartmentalization. Because asphaltenes are solid, they are treated with a colloidal EOS, such as Flory-Huggins-Zuo (FHZ) EOS. Downhole fluid analysis data are processed with the FHZ EOS as applied and a determination made whether asphaltene is distributed in thermodynamic equilibrium through a reservoir.
    [0041] [0041] Fig. 2 is a high level flowchart 300 showing an example method that assesses connectivity in gas condensate reservoirs. The method assesses connectivity between adjacent sections in a hydrocarbon reservoir, crossed by a well. Gas condensates have little color and the relative concentration of the heavy end cannot be determined by optical spectroscopy. The conventional application of FloryHuggins-Zuo Equation of State (EOS FHZ) to assess reservoir connectivity is based on measurements of relative concentration on the heavier side. As a result, the EOS FHZ had not been applied to gas condensates because the necessary measurements were not available.
    [0042] [0042] Flowchart 300 shown in Fig. 2 is a high level flow diagram, of a method that assesses connectivity between adjacent sections in a hydrocarbon reservoir, crossed by a well. After the start (block 302), a plurality of gas condensate samples are collected at different corresponding depths within the well (block 304). The fluorescence intensity is used to determine a plurality of actual heavy final concentrations of gas condensates for different corresponding depths, based on the plurality of condensed gas samples (block 306). A plurality of estimated heavy part concentrations of gas condensates for different corresponding depths are determined (block 308). The plurality of estimated heavy-end concentrations of gas condensates are compared with the plurality of estimated heavy-end concentrations of gas condensates to assess connectivity between adjacent sections of the hydrocarbon reservoir (block 310). The method ends at block 312.
    [0043] [0043] In one example, the plurality of estimated heavy end concentrations have a gradient over the depth of the well indicative of connectivity between adjacent sections of the hydrocarbon reservoir. As noted above, the EOS FHZ model is used to determine the plurality of concentrations estimated on the heavier side, which can be based on concentrations of aromatics and resins.
    [0044] [0044] As noted above, the connectivity assessment system includes at least one tool that collects the plurality of gas condensate samples and uses the fluorescence intensity to determine the plurality of actual heavy final concentrations of gas condensates for the different corresponding depths, based on the plurality of gas condensate samples. The processor shown in Fig. 1 A cooperates with a tool, as described above with respect to Fig. 1 A and 1B and determines the plurality of the estimated heavy end concentrations of gas condensates for the corresponding different depths and makes the comparison .
    [0045] [0045] Non-transient computer-readable medium corresponds, in one example to electronics and processing, and is executable on a processor to perform the steps to assess connectivity between adjacent sections in a hydrocarbon reservoir traversed by the well from which a plurality of gas condensate samples were collected at corresponding to different depths within the well. The fluorescence intensity has been used to determine a plurality of actual heavy final concentrations of gas condensates for different corresponding depths, based on the plurality of gas condensate samples. The different steps are performed, determining the plurality of the final heavy estimation concentrations of gas condensates for the corresponding corresponding depths and comparing the plurality of the real final heavy gas condensate concentrations with the plurality of the final heavy estimate concentrations of gas condensates. gas to assess connectivity between adjacent sections of the hydrocarbon reservoir.
    [0046] [0046] Now follow more details of the methodology according to an example of non-limitation. For example, gas condensate samples can be collected at various depths using an MDT ™ modular forming dynamics tester as available from Schlumberger Technology Corporation Sugarland, Texas, USA. This tester measures the reservoir pressure and collects representative samples of multilayer fluid, providing permeability and anisotropy data through different pressure transient assays at different intervals. This type of MDT tester can include basic components of an electronic power module (for Electronic Power Module, MRPC), hydraulic power module (for Hydraulic Power module, MRHY), single probe module (for the Single Probe Module, MRPS) and modular sample chambers (for the Modular Sample Chambers, MRSC). The fluorescence of these samples is measured using downhole fluid analysis (for Downhole Fluid Analysis, DFA) in order to determine a plurality of the actual heavy final concentrations of gas condensates for different corresponding depths, based on plurality of gas condensate samples. Deep-hole fluid sampling as described above can be performed in an example using a fixed telephony formation test and tool (in the acronym in English for Wirteline Formation testing and Sampling Tool, WFT) to acquire samples at different depths with good vertical sampling resolution. Any contamination can be corrected using an Oil-based Contamination Monitor (OCM) which is also available from Schlumberger Technology Corporation.
    [0047] [0047] In one example, the OCM technique uses an optical device to monitor color accumulation during sampling and provide a real-time analysis of sample contamination. It processes the data to predict how long it will take to reach an acceptably low level of contamination. For example, the reservoir fluid replaces the filtrate in a flow line and the optical density (OD) of a methane signal proportionally increases the methane content of the oil in an unbounded example. Methane detection is used for lightly colored condensates and crude petroleum oils. In these fluids, color build-up is difficult to detect, but the high methane content makes a reliable methane-based OCM algorithm possible. The methane content can also be used to determine the gas / oil ratio (gas / oil ratio, GOR) of the sample. For example, when a sample of formation fluid is taken from a well that is drilled with oil-based mud (the acronym for Oil-Based Mud, OBM), contamination of the sample by OBM filtrate can affect the exact measurement of the sample PVT properties. Contamination of the OBM sample in situ can be predicted in real time by the Live Fluid Analyzer (LFA).
    [0048] [0048] After a correction made by the contamination, the flowering intensity is interpreted proportional to the estimated heavy tip concentrations of condensates in relation to the other condensates in the field. Gas condensates mainly contain a light end with little or no asphaltenes. Most of these compounds are saturated hydrocarbons, which do not fluoresce. Condensates have some compounds that contain aromatic carbon as aromatic compounds or resins in the SARA classification and fluoresce. These are similar compounds that lead to fluorescence in black oils, which also contain asphaltenes, which predominantly serve to satiate, that is, prevent fluorescence. In black oils, an increase in asphaltenes results in quenching fluorescence and less more. Gas condensates do not contain fluorescence coolers, however and in gas condensates an increase in heavy ends, such as aromatics and resins, results in more fluorophores and therefore more fluorescence. The fluorescence of light oils is modeled as linearly proportional to the concentration of the heavy end, because there is no cooler to interrupt linearity.
    [0049] [0049] Crude oil can be divided into four classes: saturated, aromatic, resins and asphaltenes. Saturated ones generally do not participate in fluorescence. Aromatic compounds and resins are fluorophores but they are not coolers, which absorb incident photons and emit fluorescent photons, but they do not react with themselves to cool. Asphaltenes are coolers but not fluorophores, in that they do not fluoresce at concentrations found in most crude oils, but they cool the fluorescence of resins and aromatic compounds.
    [0050] [0050] Fig. 3 is a graph that compares fluorescence with the color of light oil that still has a measurable color. This figure shows, on the vertical axis, the fluorescence intensity in channel 1 and on the horizontal axis the optical density at 570 nanometers (nm). The linear relationship between fluorescence and color demonstrates that fluorescence can be interpreted as a relative measure of the final content weighed in this oil. For oils that are even lighter, absorption color measurement is not robust (very little incident light is absorbed, so a small absorption signal is measured over a large transmission background), while fluorescence is more robust (the fluorescence signal may be small, but it is measured over a very small background). This makes fluorescence suitable for measuring the content of heavy parts and gas condensates.
    [0051] [0051] As noted above, the compensatory classification model of the heavy end in the gas condensate reservoir is processed according to EOS FHZ. For more information, as an example of FHZ EOS are explained below. The compositional classification in familiar black oils results from contrasts in density and solubility between heavy extremities such as asphaltenes and the rest of the oil as described by EOS FHZ. Heavy ends and gas condensates such as aromatics and resins likewise have contrasts in density and solubility from the rest of the condensate. Therefore, the heavy ends and gas condensates exhibit the compositional classification as described by FHZ EOS. Heavy ends, black oils and heavy mobile oils have asphaltene molecules, nanoaggregated asphaltene and / or asphaltene clusters. These particles have sizes of at least 1.5 nanometers (nm) as measured by laboratory standards and confirmed by the analysis of the compositional classifications of the FHZ EOS reservoir. Heavy ends and gas condensates such as aromatics and resins are smaller molecules that do not form aggregates. They should have particle sizes around 1 nanometer. For example, it was found in one case that fluorescence results in a particular light oil mainly from a single compound called perylene, which is about 1 nm in size and is expected to be the representative of fluorophore in condensates of gas. It should be understood that perylene analysis is an example that can be used according to an example of non-limitation.
    [0052] [0052] As described above, the compositional classifications predicted by FHZ EOS with approximately 1 nm are compared with the fluorescence of different depths, measured by the DFA. An agreement between the forecast and the measure validates the EOS FHZ assumptions, showing the balance for a connected reservoir.
    [0053] [0053] Fig. 4 shows a graph with the depth in meters, on a vertical axis and the fluorescence intensity on the horizontal axis as an example of workflow. Diamond points are fluorescence data measured at different depths with DFA. The solid line is the FHZ EOS forecast. The good agreement shown in this graph suggests that this reservoir is connected and in balance.
    [0054] [0054] In the numerical model, the FHZ EOS simulates over time the equilibrium concentration of heavy-ended gas condensates as a function of depth or location within the well. Fluid samples are acquired from at least one well and analyzed to measure concentration and quantify gas condensates on the heavier side. The simulated concentration as a function of location compared to the concentration measured as a function of location allows an analysis of conductivity in the reservoir, which is used to determine where wells should be drilled and how the reservoir should be managed. In one example, it is the liquid reservoir within the well determined to be connected and in a state of equilibrium when the differences between the simulated equilibrium concentration as a function of the location within the well and the measured concentration such as the end gas condensates are below a concentration threshold and compartmentalized when above the limit.
    [0055] [0055] The chemical composition of oil varies in different parts of a connected reservoir. This change in composition with position (for example, depth) in the reservoir is referred to as compositional classification. The magnitude of this compositional classification (that is, the difference in the composition of the two fluids collected from different depths can be measured with the analysis of the bottom-of-well liquid (DFA) and predicted with the mathematical equation of the state model (EOS).) The EOS model is based on assumptions that the reservoir is connected and on thermodynamic equilibrium. If the magnitude of the compositional classification as a measure corresponds to the expected composition of the classification, then the assumptions of the EOS model are confirmed. In the event that the magnitude of the composition category measured does not match the predictions of the EOS model, it can be assumed that there is reservoir compartmentalization or that the reservoir fluids are not in equilibrium. Many different forces can contribute to a lack of thermodynamic balance, such as tar mats, washing water, biodegradation and real-time charging. The methodology as described helps to determine whether the reservoir is compartmentalized or in a state of thermodynamic non-equilibrium, to assist in development decisions.
    [0056] [0056] The equation of the state model (EOS) describes the thermodynamic behavior of the fluid and provides for the characterization of the reservoir fluid in different locations within the reservoir. With the fluid reservoir characterized in relation to its thermodynamic behavior, fluid production parameters, transport properties and other commercially useful indicators of the reservoir can be calculated.
    [0057] [0057] The modeling step described above uses the Flory-Huggins-Zuo (FHZ) EOS model, which derives from compositional classifications and other property gradients (eg pressure and temperature gradients) and describes the volumetric behavior of the mixture of oil and gas (and possibly water) in reservoir fluids, depending on the depth of the reservoir of interest. The compositional classifications derived from the EOS FHZ model include mass fractions, mole fractions, molecular weights and specific gravities for a set of pseudocomponents of the formation fluid. Such pseudocomponents include a heavy pseudocomponent representing asphaltenes in the formation fluid, a second distillate pseudocomponent that represents the fraction of non-asphaltene liquid in the formation fluid and a third pseudocomponent lumen that presents gases in the formation fluid. The pseudocomponent, derived from the EOS FHZ model, can also represent single-numbered carbon components (in the acronym for Single Carbon Number, SCAN) and other fractions or protuberances of the forming fluid (such as a fraction of water) as desired. The EOS FHZ model can predict compositional classifications with depth that take into account the impacts of gravitational forces, chemical forces, thermal diffusion, etc. as taught in International Patent Application Publication WO 2011/007268. Other applications for EOS FHZ have been described in US patents US 7,822,554 and 7,920,970, in US Patent Application Publication No. 2009/0248310 and 2009/0312997, Publication of International Patent Application No. WO 2009/138911 and WO 2011/030243 and US Patent Application Publication US 12 / 752,967, and International Patent Application PCT / IB2011 / 051740.
    [0058] [0058] Connectivity can be indicated by moderately decreasing values with GOR depth, a continuous increase in asphaltene content as a function of depth, and / or a continuous increase in fluid density and / or fluid viscosity as a function of depth. On the other hand, compartmentalization and / or imbalance can be indicated by the discontinuous GOR (or lesser GOR is found greater in the column), and / or discontinuous asphaltene content (or if greater asphaltene content is found greater in the column), and / or fluid density and / or fluid viscosity.
    [0059] [0059] EOS fluid property predictions are based on the assumption that reservoir fluids within the well are connected and in a state of thermodynamic equilibrium. Thus, the fluid properties measured by the analysis of the well-bottom liquid can be accessed to confirm that they correspond to this expected architecture. More specifically, a probability that the reservoir is connected and in a steady state can be indicated, moderately decreasing GOR values with depth, a continuous increase in asphaltene content as a function of depth, and / or a continuous increase in fluid density and / or fluid viscosity as a function of depth. In addition, a probability that the reservoir is connected and in an equilibrium state can be indicated by consistencies (ie, small differences) between the predicted fluid properties of the EOS model (particularly GOR, asphaltene content, fluid density and fluid viscosity) and corresponding downhole fluid analysis measurements.
    [0060] [0060] On the other hand, the probability that the reservoir is compartmentalized and / or in an unbalanced state can be indicated by discontinuous GOR (or if lower GOR is higher in the column), discontinuous asphaltene content (or if more high asphaltene is higher in the column), and / or discontinuous fluid density and / or fluid viscosity (or if higher fluid density and / or fluid viscosity is found higher in the column). A probability that the reservoir is compartmentalized and / or in an unbalanced state can be indicated by inconsistencies (ie large differences) between the predictions of EOS fluid properties (particularly GOR, asphaltene content, fluid density and fluid viscosity) ) and corresponding downhole fluid analysis measurements.
    [0061] [0061] Measurements of the composition of samples of hydrocarbon fluids are derived from translation of output data from a fluid analyzer. In the preferred mode, this translation employs an empirical relationship that relates fluorescence to the measurement of the concentration of a high molecular weight fraction of the fluid reservoir of the form: IF = C1 * φα + C2, (1) where IF is the measured fluorescence of the formation fluid; φα is the volume fraction corresponding to the high molecular weight fraction; and C1 and C2 are constants derived from empirical data.
    [0062] [0062] GOR is determined by measuring the amount of methane and liquid petroleum components using near infrared absorption peaks. The ratio of peak methane to peak oil in a single live crude oil phase is directly related to Gor.
    [0063] [0063] In the preferred modality, the solubility model treats the liquid in the reservoir as a mixture of two parts: a part of solute (the high molecular weight fraction) and the oil mixture (or mass reservoir fluid that includes the lower molecular weight fractions, as well as the high molecular weight fraction). The properties of the oil mixture can be measured by analyzing the well-bottom liquid and / or estimated by an EOS model. It is assumed that the reservoir fluids are connected (that is, there is a lack of compartmentalization) and in thermodynamic equilibrium. In this approach, the relative concentration (volume fraction) of the part of the solute as a function of depth is given by:
    [0064] [0064] The density ρm of the oil mixture at one or more depths can be measured by analyzing the downhole fluid (or laboratory analysis of the reservoir fluids collected at a certain depth under reservoir conditions). It can also be derived from the output of the EOS model.
    [0065] [0065] The molar volume vm for the oil mixture at a certain depth can be provided by the solution of the EOS model or another suitable approach.
    [0066] [0066] The solubility parameter ðm for the oil mixture at a given depth can be derived from an empirical correlation for the pm density of the oil mixture at a given depth. For example, the solubility parameter ðm (in (MPa) 0.5) can be derived from: ðm = 17,347 / 7 ρm + 2,904 (3) where ρm is the density of the oil mixture, at a given depth in g / cm3.
    [0067] [0067] A linear function of the shape of Eq. (4) can be used to correlate a property of the oil mixture (such as density ρm, molar volume vm, to solubility parameter ðm, viscosity, etc.) as a function of the depth by : α = cΔh + αref where one is the property (such as pm density, molar volume vm the solubility parameter ð m) of the oil mixture, c is a coefficient, αref is the property of the oil mixture at a reference depth, and Δh is the difference in height in relation to the reference depth.
    [0068] [0068] Solubility parameter ða (in MPa0.5) of the part of the solute at a given depth can be derived from a temperature gradient determined in relation to a reference measurement station by: δα (T) = δα (T0) [1-1.07 × 10-3 (ΔT)] where T0 is the temperature at the reference measurement station (for example, T0 = 298.15 K), T is the temperature at a given depth, ΔT = T - T0, and δα (Τ0) is a solubility parameter (in MPa0.5) for the part of solute in T0 (eg, δα (Τ0) = 21.85 MPa0.5).
    [0069] [0069] The impact of pressure on the solubility parameter for the solute part is small and insignificant. The temperature gradient in the well can be measured by a distributed fiber optic temperature sensor. Alternatively, the temperature in the well can be measured by analyzing well fluids at various stations. A linear function of the form of Eq. (4) can be used to derive the temperature gradient between stations as a function of depth.
    [0070] [0070] Both pressure and temperature can be accounted for in determining the solubility parameter δα (P, Τ) for the solute part such as:
    [0071] [0071] The partial density (in kg / m3) of the solute part can be derived from a constant that can vary for different solute part classes, such as 1.05 kg / m3 for resins, 1.15 kg / m3 for asphaltene nanoaggregates e.1.2 for asphaltene groups,
    [0072] [0072] Once the above properties are obtained, the remaining adjustable parameter in Eq. (2) is the molar volume of the solute part. The molar volume of the solute part varies for different classes of high molecular weight fraction. For example, resins have a lower molar volume than asphaltene nanoaggregates, which have a lower molar volume than asphaltene clusters. The model assumes that the molar volume of the solute part is constant as a function of depth. A spherical model is preferably used to estimate the molar volume of the solute part by: V = 1/6 * π * d3 * Na where V is the molar volume, d is the molecular diameter and Na is the Avogadro constant.
    [0073] [0073] For example, for the class where part of the solute includes resins (with little or no asphaltene nanoaggregates and asphaltene clusters), the molecular diameter d can vary in a range of 1.25 ± 0.15nm. For the class where the solute leaves includes asphaltene nanoaggregates (with little or no asphaltene resins and clusters), the molecular diameter d can vary within a range of 1.8 ± 0.2nm. For the class where the solute leaves includes asphaltene clusters (with little or no resins and asphaltene nanoaggregates), the molecular diameter d can vary within a range of 4.0 ± 0.5nm. For the class where the solute is a mixture of nanoaggregated resins and asphaltene (with little or no asphaltene clusters), the molecular diameter d may vary in a range corresponding to such resins and nanoaggregates (for example, between 1.25 nm and 1.8 nm ). These diameters are exemplary in nature and can be adjusted as desired.
    [0074] [0074] In this way, Eq. (1) can be used to determine a family of curves for one or more classes of the part of the solute. For example, the classes of the solute part may include resins, asphaltane nanoaggregates, asphaltane groupings and combinations thereof. A class of solute part can include resins with almost no nanoaggregate or asphaltane assembly. Another class of the solute part can include resins and asphaltane nanoaggregates with almost no grouping. A further class of solute part can include asphaltane groupings with almost no resin and asphaltane nanoaggregates. The family of curves represents an estimate of the concentration of the class of the solute part as a function of height. Each curve of the respective family is derived from a molecular diameter that falls within the range of diameters for the corresponding class of the solute part. A solution can be solved by fitting the curves to the corresponding measurements of the concentration of the respective class of the solute part at varying depths as derived from the fluid analysis of the downhole to determine an even better matching curve. For example, the family of curves for the solute part class includes resins (with almost no nanoaggregates and asphaltane assemblies) can be fitted to measures of resin concentrations at varying depth. In another example, the family of curves for the solute part class including asphaltane nanoaggregates (with almost no asphaltane resins and groupings) can fit for measurements of asphaltene nanoaggregate concentrations at varying depth.
    [0075] [0075] Yet another example, the family of curves for the solute part class including asphaltene resins and nanoaggregates (with almost no asphaltane groupings) can fit the measurements of mixed resins and asphaltene nanoaggregate concentrations in depth to vary . In still another example, the family of curves for the class of the solute part including asphaltene clusters (with almost no resins and asphaltane nanoaggregates) can fit the measurements of concentrations of the asphaltene clusters in varying depth. If an even better fit is identified, the estimated properties and / or measures of the best combining solute grades (or other appropriate properties) can be used for reservoir analysis. If no adjustment was possible, then the reservoir liquids could not be in equilibrium or more complex formulation may be required to describe the petroleum liquid in the reservoir.
    [0076] [0076] Other appropriate structural models can be used to estimate and vary the molar volume for different classes of the solute part. It is also possible that Eq. (2) can be simplified by ignoring the first and second terms of the exponent, which gives an analytical model of the form:
    [0077] [0077] An illustrative example of an EOS FHZ model now follows and assumes that there are fluids in the reservoir, with a number of components of composition and that the fluids in the reservoir are connected (that is, there is a lack of compartmentalization) and in a state of thermodynamic equilibrium.In addition, it is assumed that there is no phenomenon of adsorption, or any type of chemical reactions in the reservoir. The mass flow (J) of composition component i that crosses the boundary of an elemental volume of the porous medium is expressed as:
    [0078] [0078] The average fluid velocity (u) is estimated by
    [0079] [0079] According to Darcy's law, the baro-phenomenological diffusion coefficients must meet the following restriction:
    [0080] [0080] if the pore size is very far from the average free path of the molecules, the mobility of the components, due to an external pressure field, is very close to the total mobility. The massive chemical potential is a function of the mole fraction (x), pressure, and temperature.
    [0081] [0081] At constant temperature, the derivative of the massive chemical potential (uj) has two contributions:
    [0082] [0082] In the ideal case, the phenomenological coefficients (L) can be related to the effective practical diffusion coefficients (Di eff):
    [0083] [0083] The conservation of mass for component i in the reservoir fluid, which governs the distribution of components in porous media, is expressed as:
    [0084] [0084] Eq. (16) can be used to solve a wide range of problems. This is a dynamic model that is changing with time t.
    [0085] [0085] Considering that the mechanical balance of the fluid column has been achieved:
    [0086] [0086] The vertical distribution of the components can be calculated by solving the following set of equations:
    [0087] [0087] If the horizontal components of external flows are significant, the equations along the other axis have to be solved as well. Along an "x" horizontal axis the equations become:
    [0088] [0088] The mechanical balance of the fluid column
    [0089] [0089] The contribution of the pressure gradient of the thermal diffusion (so-called Soret contribution) are given by:
    [0090] [0090] And the contribution of the pressure gradient of external flows is expressed as
    [0091] [0091] Assuming an isothermal reservoir and ignoring the external flow, it results in the following equation:
    [0092] [0092] Eq. (25) can be rewritten as:
    [0093] [0093] Eq. (26) and (27) can be solved to predict compositions and the volumetric behavior of mixtures of oil and gas in the reservoir. Flash calculations are used to solve for the fugacity (fi) of the components of the oil and gas mixtures in equilibrium.
    [0094] [0094] Many modifications and other modalities of the invention will come to the mind of one versed in the technique that has the benefit of the teachings presented in the previous descriptions and in the associated figures. Consequently, it is understood that the various modifications and modalities are intended to be included within the scope of the added claims.
    权利要求:
    Claims (8)
    [0001]
    METHOD OF ASSESSING CONNECTIVITY BETWEEN SECTIONS IN A HYDROCARBONET RESERVOIR, characterized by the fact that it comprises: collecting a plurality of hydrocarbon samples at different corresponding depths within the hydrocarbon reservoir (304); use the fluorescence intensity to determine a plurality of estimated heavy end concentrations of hydrocarbons for the corresponding different depths based on the plurality of hydrocarbon samples (306); determining a plurality of estimated heavy end concentrations of hydrocarbons for the corresponding different depths (308); and comparing the plurality of the estimated heavy end concentrations of the hydrocarbons with the plurality of the estimated heavy end concentrations of the hydrocarbons to assess the connectivity between the sections of the hydrocarbon reservoir (310).
    [0002]
    Method according to claim 1, characterized by the fact that the hydrocarbon comprises at least one of a gas condensate, a wet gas, a volatile oil, fluid having existing heavy ends as isolated molecules, fluid with ˂ 0.1 density absorption optics (OD) in visible or near-infrared spectral regions, fluid with fluorescence that is proportional to the content of the heavy end, and a liquid that contains ˂ 1% by weight of asphaltane.
    [0003]
    Method according to claim 1, characterized by the fact that the plurality of the estimated heavy end concentrations of the hydrocarbons has a gradient over the depth of a downhole indicator of connectivity between sections of the hydrocarbon reservoir.
    [0004]
    Method according to claim 1, characterized in that determining the plurality of estimated heavy end concentrations comprises using a Flory-Huggins-Zuo Equation of State (FHZ EOS) model.
    [0005]
    SYSTEM TO ASSESS CONNECTIVITY BETWEEN SECTIONS IN A HYDROCARBONET RESERVOIR THROUGH AT LEAST ONE WELL, the system characterized by the fact that it comprises: at least one tool (10) for collect a plurality of hydrocarbon samples over corresponding different depths within at least one well (304), and use the fluorescence intensity to determine a plurality of estimated heavy end concentrations of hydrocarbons for the corresponding different depths based on the plurality of hydrocarbon samples (308); and a processor (213) that cooperates with said at least one tool (10) for determining a plurality of estimated heavy end concentrations of hydrocarbons for the corresponding different depths (308); and compare the plurality of the estimated heavy end concentrations of the hydrocarbons with the plurality of the estimated heavy end concentrations to assess the connectivity between the sections of the hydrocarbon reservoir (310).
    [0006]
    System according to claim 5, characterized by the fact that the plurality of the estimated heavy end concentrations has a gradient over the depth of the well indicative of connectivity between sections of the hydrocarbon reservoir.
    [0007]
    System according to claim 5, characterized by the fact that said processor (213) determines the plurality of estimated heavy end concentrations using a Flory-Huggins-Zuo Equation of State (FHZ EOS) model.
    [0008]
    System according to claim 5, characterized by the fact that the concentrations of the heavy end are based on concentrations of aromatic compounds and resins.
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    法律状态:
    2018-12-04| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|
    2020-02-18| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|
    2020-09-29| B07A| Application suspended after technical examination (opinion) [chapter 7.1 patent gazette]|
    2021-01-12| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|
    2021-03-23| 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 07/03/2013, OBSERVADAS AS CONDICOES LEGAIS. |
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    US201261657070P| true| 2012-06-08|2012-06-08|
    US61/657,070|2012-06-08|
    PCT/US2013/029549|WO2013184190A1|2012-06-08|2013-03-07|Assessing reservoir connectivity in hydrocarbon reservoirs|
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