Shale Secondary Porous Reservoir Quality Evaluation Methods and Electronic Equipment
By using a diagenetic pore evolution model and dynamic simulation driven by geological data, the problem of the unknowability of pore evolution in traditional reservoir quality evaluation has been solved, enabling quantitative evaluation of shale secondary porous reservoirs and accurate identification of high-quality reservoirs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA UNIV OF MINING & TECH (BEIJING)
- Filing Date
- 2025-12-23
- Publication Date
- 2026-07-31
AI Technical Summary
Traditional reservoir quality evaluation methods are unable to dynamically reproduce the pore evolution process, making it difficult to understand and predict the genesis and distribution of high-quality reservoirs.
A diagenetic porosity evolution model was adopted, combining dissolution-induced porosity increase and cementation-induced porosity decrease terms. The porosity generation and consumption process of shale secondary porous reservoirs was simulated through digital core pore networks. Geological evolution data was used as boundary conditions for the solution, quantifying the impact of diagenesis on porosity.
This study enables quantitative evaluation of the quality of secondary porous shale reservoirs, overcoming the limitations of traditional static descriptions, improving evaluation accuracy, and clarifying the core mechanism of pore evolution.
Smart Images
Figure CN121519922B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of oil and gas reservoir evaluation technology, and in particular to a method and electronic equipment for evaluating the quality of shale secondary porous reservoirs. Background Technology
[0002] The quality of a reservoir is a prerequisite for the accumulation of oil and gas. For deeply buried clastic and carbonate reservoirs, the quality of the reservoir space is often determined by secondary porosity. Traditional reservoir diagenesis studies mainly rely on static observation methods such as thin section identification and scanning electron microscopy. Researchers qualitatively infer a "diagenetic sequence" by observing dissolution phenomena and cement-filling relationships in existing rock samples, and use this to interpret the quality of reservoir properties.
[0003] The inventors discovered that this "static-driven" research paradigm has a fundamental limitation: namely, the unknowability of its process. Static rock thin sections can only show the final "pattern" left by millions of years of diagenetic evolution, but cannot "reproduce" the dynamic "process" that the pore structure undergoes under changing temperature, pressure, and fluid environments. For example, it is impossible to quantitatively determine the rate of feldspar dissolution, the specific period and volume of quartz enlargement.
[0004] Therefore, there is an urgent need for a new technology that can move from static description to dynamic reproduction in order to more scientifically understand and predict the formation and distribution of high-quality reservoirs. Summary of the Invention
[0005] This invention provides a method and electronic device for evaluating the quality of shale secondary porous reservoirs, in order to solve the problem that traditional reservoir quality evaluation methods are unable to dynamically reproduce the evolution of reservoir pores, and the process is unknowable, making it difficult to understand and predict the genesis and distribution of high-quality reservoirs.
[0006] In a first aspect, embodiments of the present invention provide a method for evaluating the quality of shale secondary porous reservoirs, including:
[0007] To obtain mineral parameters, reaction kinetic parameters, and geological data of shale secondary porous reservoir samples;
[0008] Based on geological data, determine geological evolution data;
[0009] Mineral parameters and reaction kinetic parameters are input into the diagenetic porosity evolution model. In the digital core pore network, geological evolution data is used as boundary conditions to solve the diagenetic porosity evolution model and obtain the reservoir quality evaluation results of shale secondary porosity reservoir samples.
[0010] The diagenetic porosity evolution model includes a dissolution-induced porosity term and a cementation-induced porosity term; the dissolution-induced porosity term describes the origin of secondary porosity in shale, while the cementation-induced porosity term describes the reasons for the depletion of secondary porosity in shale.
[0011] In one possible implementation, before inputting mineral parameters and reaction kinetic parameters into the diagenetic porosity evolution model, and solving the diagenetic porosity evolution model in a digital core pore network using geological evolution data as boundary conditions to obtain the reservoir quality evaluation results of shale secondary porous reservoir samples, the method further includes:
[0012] Based on geological evolution data as boundary conditions, determine the hydrocarbon inhibition factor corresponding to each evolution moment;
[0013] Based on the hydrocarbon inhibition factor corresponding to each evolution time, the effective reaction surface area of minerals at each evolution time in the shale secondary porous reservoir sample was determined;
[0014] The effective reaction surface area of minerals at each evolution stage is input into the diagenetic porosity evolution model.
[0015] In one possible implementation, the effective reaction surface area of minerals at each evolution time in shale secondary porous reservoir samples is determined based on the hydrocarbon inhibition factor corresponding to each evolution time, including:
[0016] Based on the hydrocarbon inhibition factor corresponding to each evolution time and the calculation formula of the effective reaction surface area of minerals, the effective reaction surface area of minerals at each evolution time in the shale secondary porous reservoir sample is determined.
[0017] The formula for calculating the effective reaction surface area of a mineral is as follows:
[0018]
[0019] In the formula, The effective reaction surface area of the mineral; The total surface area of the minerals; These are the hydrocarbon inhibition factors corresponding to each evolutionary time step.
[0020] In one possible implementation, geological evolution data is used as boundary conditions in a digital core pore network to solve a diagenetic pore evolution model, yielding reservoir quality evaluation results for shale secondary porous reservoir samples, including:
[0021] Using geological evolution data as boundary conditions, the diagenetic pore evolution model is solved in the digital core pore network to obtain the evolution history of reservoir rock physical properties corresponding to shale secondary pore reservoir samples.
[0022] Based on the evolution history of the physical properties of the reservoir rocks, the reservoir quality evaluation results of the shale secondary porosity reservoir samples were obtained.
[0023] In one possible implementation, reservoir quality assessment results for shale secondary porosity reservoir samples are obtained based on the evolution history of reservoir rock physical properties, including:
[0024] Based on the evolution history of the physical properties of reservoir rocks, determine the dynamic evolution curves of porosity, permeability, and the effective reaction surface area of minerals;
[0025] The dissolution window period is determined based on the porosity dynamic evolution curve, and the cementation window period is determined based on the permeability dynamic evolution curve. The dissolution window period represents the porosity formation stage, and the cementation window period represents the porosity reduction stage.
[0026] Based on the dissolution window period, cementation window period, and changes in the effective reaction surface area of minerals, a map of reservoir physical properties is drawn.
[0027] Areas in the reservoir physical property distribution map where porosity and permeability meet preset conditions are designated as high-quality reservoir areas.
[0028] In one possible implementation, after identifying regions in the reservoir physical property distribution map where porosity and permeability meet preset conditions as high-quality reservoir regions, the method further includes:
[0029] Overlay the reservoir physical property distribution map with the oil and gas saturation distribution map;
[0030] The high-quality reservoir areas that overlap with the high oil and gas saturation areas in the oil and gas saturation distribution map are designated as sweet spots.
[0031] In one possible implementation, the diagenetic porosity evolution model is as follows:
[0032] ;
[0033] In the formula, j Indicates different types of diagenetic reactions; Indicates the first j The change in porosity caused by diagenetic reactions; Indicates the erosion and porosity enhancement term; This indicates the cementation-reduced pore term.
[0034] In one possible implementation, the dissolution porosity enhancement term is determined by the following formula:
[0035]
[0036] In the formula, For the first j Molar volume of dissolvable minerals; For the first j The dissolution reaction rate of soluble minerals; For the first j The effective reaction surface area of dissolvable minerals; For the first j The rate constant of the dissolution reaction of soluble minerals is determined by temperature; Hydrogen ion activity; For the first j Saturation index of dissolvable minerals;
[0037] The cementation porosity reduction term is determined by the following formula:
[0038]
[0039] In the formula, For the first j Molar volume of cementitious minerals; For the first j The cementation reaction rate of cementing minerals; For the first j The effective reaction surface area of cementing minerals; For the first j The cementation reaction rate constant of cementing minerals is determined by temperature; For the first j The saturation index of cementing minerals.
[0040] In one possible implementation, geological evolution data is determined based on geological data, including:
[0041] Input geological data into the basin simulation model;
[0042] In the basin simulation model, the temperature and pressure paths, fluid injection, and fluid chemical composition evolution of the reservoirs where the shale secondary porous reservoir samples are located are analyzed to obtain geological evolution data.
[0043] In a second aspect, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.
[0044] In this embodiment of the invention, the diagenetic porosity evolution model includes a dissolution-induced porosity-increasing term and a cementation-induced porosity-reducing term. These two terms can quantify the impact of diagenesis on porosity, clarify the core mechanism of porosity evolution, and improve the accuracy of evaluation. Based on geological data, geological evolution data is determined and used as boundary conditions, providing dynamic environmental constraints to support the setting of model boundary conditions. Finally, by solving the model, the dynamic evolution history of reservoir porosity is reproduced, enabling quantitative evaluation of reservoir quality and overcoming the limitations of traditional static descriptions. Attached Figure Description
[0045] Figure 1 This is a flowchart illustrating the implementation of the shale secondary porosity reservoir quality evaluation method provided in this embodiment of the invention.
[0046] Figure 2 This is a schematic diagram of the structure of the shale secondary porous reservoir quality evaluation device provided in an embodiment of the present invention. Detailed Implementation
[0047] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
[0048] Figure 1 This is a flowchart illustrating the implementation of the shale secondary porosity reservoir quality evaluation method provided in this embodiment of the invention. Figure 1 As shown, the method includes:
[0049] Step 110: Obtain the mineral parameters, reaction kinetic parameters, and geological data of the shale secondary porous reservoir sample.
[0050] In this embodiment, the mineral parameters of the shale secondary porous reservoir sample reflect the basic mineral characteristics. These parameters may include the minerals in the shale secondary porous reservoir sample, the molar volume, molar mass, total surface area, and mineral distribution characteristic parameters, such as pore throat radius distribution, pore volume fraction, and connectivity parameters, to clarify the distribution state of minerals in the pore throat.
[0051] Among them, minerals can be divided into two categories: one category is minerals that play a dissolving role and are used to generate secondary pores, such as dissolving feldspar and calcite; the other category is minerals that play a cementing role and are used to destroy secondary pores, such as quartz and clay.
[0052] Mineral parameters can be obtained through core experimental analysis and field measurements, specifically as follows:
[0053] Select 3-5 core samples from the target reservoir that are free of fractures and have uniform lithology, i.e. shale secondary porous reservoir samples. The diameter of the samples can be 2.5cm / 5cm, the length can be 5-10cm, and they should cover the main porosity range.
[0054] The sample was prepared into a 30 μm thick rock section. Using a polarizing microscope and a scanning electron microscope, the mineral types, such as quartz, feldspar, and clay, were identified. The volume fraction of each mineral was determined, that is, the proportion of each mineral in the dissolved minerals and cementing minerals.
[0055] The inherent parameters of minerals are determined experimentally, such as by using X-ray diffraction combined with literature calibration, to obtain the molar volume, molar mass, and total surface area of minerals.
[0056] In addition, high-pressure mercury intrusion and nuclear magnetic resonance experiments can be used to obtain the pore throat radius distribution, pore volume fraction and connectivity parameters, so as to clarify the distribution state of minerals in the pore throat.
[0057] The reaction kinetic parameters of various ores in shale secondary porous reservoir samples are used to support the quantitative calculation of diagenetic reaction rates. These parameters may include prefactors, activation energies, reaction orders, ion activities, saturation exponents, and empirical coefficients, etc. Among them, prefactors and activation energies are used to derive reaction rate constants.
[0058] After the pre-factor and activation energy were determined by indoor melting and fire cementation simulation experiments, the reaction rate constant was derived by combining the Arrhenius equation.
[0059] The calculation formula is as follows:
[0060]
[0061] In the formula, The reaction rate constant; Foremost factor; It is the gas constant; Absolute temperature; It is the activation energy.
[0062] The reaction order and ion activity were obtained by fitting the reaction order through batch reaction experiments, and the hydrogen ion activity in the fluid was determined by ion chromatography.
[0063] The saturation index is calculated using geochemical software in conjunction with fluid chemical composition data, and serves as a criterion for determining whether a dissolution or cementation reaction has occurred.
[0064] Empirical coefficients are used to calibrate the relationship between fracture density and stress difference. They are determined by referring to literature on similar reservoirs in the reference area and combining the results of imaging logging fracture density and stress simulation.
[0065] Geological data for shale secondary porous reservoir samples were obtained through a combination of methods, including field surveys, drilling logging, and experimental analysis. This data includes basic geological data, thermodynamic data, and fluid and hydrocarbon-related data, which were acquired through the following methods:
[0066] Basic geological data: Stratigraphic thickness, lithological sequence, and sedimentary discontinuity periods are obtained through field profile measurements, well core logging, and well logging curves; the sedimentary age of the strata is determined by combining paleontological fossil dating and zircon U-Pb dating to establish a temporal framework; and the basin's tectonic evolution stages and fault activity times are sorted out through seismic profile interpretation.
[0067] Thermodynamic data: By collecting core samples, the homogenization temperature and salinity of fluid inclusions were determined using a microthermometer; the current geothermal flow value was calculated by bottom hole temperature logging, and the paleothermal flow trend was determined by referring to the regional geothermal history; thermal properties such as thermal conductivity and specific heat capacity of rocks of the same lithology were measured or referenced.
[0068] Fluid and oil / gas related data: Determine the hydrocarbon generation and expulsion time of source rocks through Rock-Eval pyrolysis data; collect reservoir water samples and determine fluid ion concentrations; infer fracture widths from well logging data and obtain current formation pressures by combining well test data.
[0069] Step 120: Determine the geological evolution data based on the geological data.
[0070] In this embodiment, geological evolution data can be obtained by inputting geological data into a basin simulation model. The basin simulation model includes a burial history module and a thermal history module.
[0071] Geological evolution data can include the reservoir's temperature and pressure paths, fluid charge history, and the evolution of fluid chemical composition throughout its geological history, obtained using basin simulation models.
[0072] The burial history module is used to calculate the deposition rate based on the stratum thickness and deposition time, and this deposition rate is used to calculate the thermal history.
[0073] The thermal history module assigns paleothermal flow values according to the basin's tectonic stages, such as the extensional and stable periods, and uses a one-dimensional vertical heat conduction model to correlate burial depth with heat conduction efficiency.
[0074] Subsequently, the target reservoir is subdivided vertically into 5-10m / layers to match the logging resolution, and horizontally into 100-500m / grids according to the basin area to balance calculation efficiency and accuracy.
[0075] Based on the output of the thermal history module, the absolute temperature is calculated by combining the surface temperature to generate a temperature-pressure evolution curve and obtain the corresponding temperature-pressure history. The Rock-Eval pyrolysis data of the source rock are input to determine the hydrocarbon generation and expulsion time, set the permeability threshold of the transport layer, and simulate the time, direction and intensity of fluid injection to realize the simulation of fluid injection history. Water-rock reaction kinetic parameters, such as feldspar dissolution rate constant and fluid-mineral partition coefficient, are input to calculate the chemical composition data such as fluid ion concentration at different geological times to obtain the fluid chemical evolution history.
[0076] That is, it operates step by step according to the logic of burial history → thermal history → temperature and pressure history → fluid filling history → fluid chemical evolution history, so as to realize the coupling and linkage of each module.
[0077] During the linkage process, the accuracy of the obtained data is determined through paleotemperature verification, pressure verification, fluid charging timeline verification, and fluid composition verification. Specifically, paleotemperature verification involves comparing the simulated paleotemperature-geological time curve with the measured homogenization temperature of fluid inclusions, with an error of less than 5°C; pressure verification involves comparing the simulated current formation pressure with the measured well logging pressure, with an error of less than 10%; fluid charging timeline verification requires that the simulated fluid charging time match the evolution stage of reservoir hydrocarbon maturity; and fluid composition verification ensures that the simulated fluid ion concentration trend is consistent with the measured values of current reservoir water samples, with an allowable deviation within 20%.
[0078] If a certain verification fails to meet the standard, prioritize adjusting sensitive parameters such as paleothermal flux and compaction coefficient, and repeat the model run until all indicators meet the error requirements.
[0079] After the model is validated, three types of core geological evolution data are output, including temperature and pressure evolution data, such as the curves of paleotemperature and paleopressure of the reservoir from the sedimentary stage to the present day as a function of geological time, i.e., temperature and pressure path; fluid charging evolution data, such as the number of fluid charging periods, charging time, charging intensity and spatial distribution characteristics; and fluid chemical evolution data, such as the time series table of fluid chemical components at different geological times.
[0080] Step 130: Input the mineral parameters and reaction kinetic parameters into the diagenetic porosity evolution model. In the digital core pore network, use the geological evolution data as boundary conditions to solve the diagenetic porosity evolution model and obtain the reservoir quality evaluation results of the shale secondary porosity reservoir sample.
[0081] The diagenetic porosity evolution model includes a dissolution-induced porosity term and a cementation-induced porosity term; the dissolution-induced porosity term describes the origin of secondary porosity in shale, while the cementation-induced porosity term describes the reasons for the depletion of secondary porosity in shale.
[0082] In this embodiment, the diagenetic porosity evolution model is as follows:
[0083] ;
[0084] In the formula, j Indicates different types of diagenetic reactions; Indicates the first j The change in porosity caused by diagenetic reactions; Indicates the erosion and porosity enhancement term; This indicates the cementation-reduced pore term.
[0085] Accordingly, the dissolution and porosity enhancement term is determined by the following formula:
[0086]
[0087] In the formula, For the first j Molar volume of dissolvable minerals; For the first j The dissolution reaction rate of soluble minerals; For the first j The effective reaction surface area of dissolvable minerals; For the first j The rate constant of the dissolution reaction of soluble minerals is determined by temperature; Hydrogen ion activity; For the first j Saturation index of dissolvable minerals;
[0088] The cementation porosity reduction term is determined by the following formula:
[0089]
[0090] In the formula, For the first j Molar volume of cementitious minerals; For the first j The cementation reaction rate of cementing minerals; For the first j The effective reaction surface area of cementing minerals; For the first j The cementation reaction rate constant of cementing minerals is determined by temperature; For the first j The saturation index of cementing minerals.
[0091] The dissolution-induced porosity increase term and the cementation-induced porosity decrease term were determined based on the reaction kinetics model and the secondary enlargement model of transition state theory. These terms quantitatively describe how secondary porosity is created and the process of filling and reducing fractures.
[0092] Digital core pore networks replicate the pore throat distribution, mineral grain contact relationships, and pore connectivity of real cores. They can accurately reproduce the microscopic spatial scene of secondary pore generation and consumption, avoiding errors caused by traditional macroscopic models ignoring the microscopic pore throat structure.
[0093] In this embodiment, the digital core pore network provides a microscopic scene and computing platform for the diagenetic pore evolution model, while geological evolution data provides the model with spatiotemporal dynamic boundaries, jointly supporting the accurate simulation of the pore evolution process. During the solution process, the diagenetic pore evolution model defines the physicochemical rules of pore evolution, with dissolution-induced pore-increasing terms and cementation-induced pore-reducing terms as its core. Geological evolution data, through boundary condition loading, provides the model with environmental parameters such as temperature, pressure, and fluid that change with geological time, driving the diagenetic pore evolution model to dynamically reproduce the pore generation and consumption processes of different geological periods within the microscopic scene of the digital core, ultimately outputting reservoir property evolution results that closely match reality.
[0094] Therefore, this invention, by including a dissolution-induced porosity-increasing term and a cementation-induced porosity-reducing term in the diagenetic porosity evolution model, can quantify the impact of diagenesis on porosity, clarify the core mechanism of porosity evolution, and improve the accuracy of evaluation. Based on geological data, geological evolution data is determined and used as boundary conditions, providing dynamic environmental constraints to support the setting of model boundary conditions. Finally, by solving the model, the dynamic evolution history of reservoir porosity is reproduced, enabling quantitative evaluation of reservoir quality and overcoming the limitations of traditional static descriptions.
[0095] In an optional embodiment, before inputting mineral parameters and reaction kinetic parameters into the diagenetic porosity evolution model in step 130, and using geological evolution data as boundary conditions in the digital core pore network to solve the diagenetic porosity evolution model and obtain the reservoir quality evaluation results of the shale secondary porous reservoir sample, the method further includes:
[0096] Based on geological evolution data as boundary conditions, hydrocarbon inhibition factors corresponding to each evolution moment are determined.
[0097] Based on the hydrocarbon inhibition factors corresponding to each evolution stage, the effective reaction surface area of minerals at each evolution stage in the shale secondary porous reservoir sample was determined.
[0098] The effective reaction surface area of minerals at each evolution stage is input into the diagenetic porosity evolution model.
[0099] Another problem in traditional reservoir diagenesis research is the ongoing debate regarding the timing of "sweet spot" formation: whether pore formation or injection occurred first. For example, does early hydrocarbon injection inhibit cementation and thus protect porosity, or did the reservoir first undergo dissolution to create space for hydrocarbon accumulation? Traditional methods struggle to quantitatively verify this, resulting in our understanding of "sweet spot" formation remaining at the level of qualitative speculation with limited predictive power.
[0100] To address this issue, this invention creatively introduces a time-varying hydrocarbon inhibition factor to describe the inhibitory effect of oil and gas charging on cementation reactions. The evolution history of this hydrocarbon inhibition factor is determined by the fluid charging history simulated by the basin simulation model, i.e., the saturation evolution.
[0101] The inhibitory effect of hydrocarbon injection on cementation is dynamically quantified by hydrocarbon inhibitory factors, the temporal coupling relationship is clarified, and the surface area of mineral reaction is accurately corrected to improve the scientific nature of the model solution.
[0102] Accordingly, based on the hydrocarbon inhibition factor corresponding to each evolution time and the calculation formula for the effective reaction surface area of minerals, the effective reaction surface area of minerals at each evolution time in the shale secondary porous reservoir sample is determined.
[0103] The formula for calculating the effective reaction surface area of a mineral is as follows:
[0104]
[0105] In the formula, The effective reaction surface area of the mineral; The total surface area of the minerals; These represent the hydrocarbon inhibition factors corresponding to each evolutionary stage. When oil and gas are not injected... =0, cementation is proceeding normally; after oil and gas injection... →1, the effective reaction surface area approaches zero, and the cementing effect is greatly suppressed.
[0106] In an optional embodiment, step 130, using geological evolution data as boundary conditions in the digital core pore network to solve the diagenetic pore evolution model and obtain the reservoir quality evaluation results of the shale secondary porous reservoir sample, may include:
[0107] Step 131: Using geological evolution data as boundary conditions, solve the diagenetic pore evolution model in the digital core pore network to obtain the evolution history of reservoir rock physical properties corresponding to the shale secondary porous reservoir sample.
[0108] Step 132: Based on the evolution history of the physical properties of the reservoir rocks, obtain the reservoir quality evaluation results of the shale secondary porous reservoir samples.
[0109] In this embodiment, the diagenetic porosity evolution model is solved to obtain the evolution history of the reservoir rock physical properties corresponding to the shale secondary porosity reservoir sample. By analyzing the evolution history of the reservoir rock physical properties, corresponding evaluation indicators are obtained, and the reservoir quality evaluation results of the shale secondary porosity reservoir sample are obtained based on the evaluation indicators.
[0110] In an optional embodiment, step 132, which involves obtaining the reservoir quality evaluation result of the shale secondary porosity reservoir sample based on the evolution history of the reservoir rock's physical properties, may include:
[0111] Based on the evolution history of the physical properties of reservoir rocks, dynamic evolution curves of porosity, permeability, and effective reaction surface area of minerals were determined.
[0112] The dissolution window period is determined based on the porosity dynamic evolution curve, and the cementation window period is determined based on the permeability dynamic evolution curve. The dissolution window period represents the porosity formation stage, and the cementation window period represents the porosity reduction stage.
[0113] Based on the dissolution window period, cementation window period, and changes in the effective reaction surface area of minerals, a map of the distribution of reservoir physical properties is drawn.
[0114] Areas in the reservoir physical property distribution map where porosity and permeability meet preset conditions are designated as high-quality reservoir areas.
[0115] In this embodiment, the porosity dynamic evolution curve, permeability dynamic evolution curve, and effective reaction surface area change curve of minerals are extracted from the evolution history of the physical properties of reservoir rocks. Specifically, the corresponding porosity and permeability values, as well as the effective reaction surface area data of minerals at each time step, are extracted according to geological time steps.
[0116] Outliers are removed from the extracted data, and missing data is filled in by interpolation to ensure the continuity of the data sequence.
[0117] Using geological time as the x-axis and porosity, permeability, and effective reactive surface area of minerals as the y-axis, data was fitted using professional data analysis software to generate smooth dynamic evolution curves. The curves were then standardized to ensure readability and comparability, resulting in the desired curves.
[0118] For the porosity dynamic evolution curve, the period in the curve where porosity increases significantly (e.g., the growth rate > 0.5% / Ma) and the duration exceeds a preset threshold (e.g., 1 Ma) is identified as the dissolution window period. That is, based on... Perform identification.
[0119] For the dynamic evolution curve of permeability, the period in which permeability decreases significantly, such as a decrease of >10% / Ma, is screened. The cementation window period is then determined by combining the inflection point and slope change of the curve. That is, based on... Perform identification.
[0120] The temporal information of the dissolution window and cementation window is spatiotemporally matched with the numerical data of the effective reaction surface area variation curve of minerals; using a planar profile grid as a carrier, the numerical values of porosity, permeability, and effective reaction surface area are mapped into spatial distribution, and a reservoir physical property distribution map is generated by professional drawing software, including porosity level, permeability zone, and reaction activity distribution.
[0121] In the reservoir physical property distribution map, spatial regions that simultaneously meet the porosity and permeability threshold requirements are extracted and marked as high-quality reservoir regions.
[0122] This invention identifies the dissolution and cementation window periods through curve feature quantification, clarifying the core time period of pore evolution and solving the problem of ambiguity in traditional time series. Furthermore, it integrates multiple parameters to draw distribution maps, intuitively presenting the differences in reservoir properties and providing clear data support for regional evaluation. Finally, it filters target areas based on preset thresholds to avoid qualitative judgment errors and improve the objectivity and accuracy of identifying high-quality reservoirs.
[0123] In an optional embodiment, after identifying regions in the reservoir physical property distribution map where porosity and permeability meet preset conditions as high-quality reservoir regions, the method further includes:
[0124] The reservoir physical property distribution map is overlaid with the oil and gas saturation distribution map.
[0125] The high-quality reservoir areas that overlap with the high oil and gas saturation areas in the oil and gas saturation distribution map are designated as sweet spots.
[0126] The method provided in this embodiment can also determine the dessert area. First, a corresponding oil and gas saturation distribution map is constructed:
[0127] The hydrocarbon saturation distribution map is generated by measuring samples. Specifically, the samples are first calibrated to determine their hydrocarbon saturation. Then, using the sample's hydrocarbon saturation as the true value, the formation hydrocarbon saturation is derived by back-calculating the corresponding well data. Based on the formation hydrocarbon saturation, corresponding regional geological constraints such as structure, lithology, and reservoir thickness are constructed. Discrete well data are expanded into continuous planar or profile data, and results such as basin simulation of hydrocarbon charging intensity and trap conditions are overlaid. This process is repeated to obtain the final hydrocarbon saturation distribution map.
[0128] Then, using the spatial overlay function of geographic information system software or professional geological simulation software, the reservoir physical property distribution map is overlaid with the hydrocarbon saturation distribution map. During the overlay process, only the overlapping portion between high-quality reservoir areas and high hydrocarbon saturation areas can be retained, and this overlapping spatial range can be automatically identified and extracted. A high hydrocarbon saturation area refers to an area where the hydrocarbon saturation is greater than a preset hydrocarbon saturation threshold.
[0129] Finally, the overlapping areas selected after superposition are delineated and their attributes are labeled, and a spatial distribution map and quantitative parameter table of the dessert area are output to complete the definition of the dessert area.
[0130] Therefore, the embodiments of the present invention achieve dual screening of reservoir properties and oil and gas enrichment through spatial superposition, avoiding the bias of single index judgment and ensuring the effectiveness of sweet spot.
[0131] In summary, this embodiment of the invention includes a dissolution-induced porosity-enhancing term and a cementation-induced porosity-reducing term in the diagenetic porosity evolution model. These two terms can quantify the impact of diagenesis on porosity, clarify the core mechanism of porosity evolution, and improve the accuracy of evaluation. Based on geological data, geological evolution data is determined and used as boundary conditions, providing dynamic environmental constraints to support the setting of model boundary conditions. Finally, by solving the model, the dynamic evolution history of reservoir porosity is reproduced to achieve quantitative evaluation of reservoir quality, breaking through the limitations of traditional static descriptions. Furthermore, considering the critical temporal controversy regarding the formation of "sweet spots"—whether pore formation or injection should occur first—this embodiment also introduces a time-varying hydrocarbon inhibition factor to describe the inhibitory effect of hydrocarbon injection on cementation reactions, clarifying the temporal coupling relationship, accurately correcting the mineral reaction surface area, and improving the scientific rigor of the model solution.
[0132] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
[0133] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.
[0134] Figure 2 A schematic diagram of the shale secondary porous reservoir quality evaluation device provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below:
[0135] like Figure 2 As shown, the shale secondary porous reservoir quality evaluation device 2 includes:
[0136] The acquisition module 21 is used to acquire mineral parameters, reaction kinetic parameters, and geological data of the shale secondary porous reservoir sample.
[0137] Evolution module 22 is used to determine geological evolution data based on geological data;
[0138] Solver module 23 is used to input mineral parameters and reaction kinetic parameters into the diagenetic porosity evolution model. In the digital core pore network, geological evolution data is used as boundary conditions to solve the diagenetic porosity evolution model and obtain the reservoir quality evaluation results of shale secondary porosity reservoir samples.
[0139] The diagenetic porosity evolution model includes a dissolution-induced porosity term and a cementation-induced porosity term; the dissolution-induced porosity term describes the origin of secondary porosity in shale, while the cementation-induced porosity term describes the reasons for the depletion of secondary porosity in shale.
[0140] In one possible implementation, the evolution module 22 is also used for:
[0141] Based on geological evolution data as boundary conditions, determine the hydrocarbon inhibition factor corresponding to each evolution moment;
[0142] Based on the hydrocarbon inhibition factor corresponding to each evolution time, the effective reaction surface area of minerals at each evolution time in the shale secondary porous reservoir sample was determined;
[0143] The effective reaction surface area of minerals at each evolution stage is input into the diagenetic porosity evolution model.
[0144] In one possible implementation, evolution module 22 is specifically used for:
[0145] Based on the hydrocarbon inhibition factor corresponding to each evolution time and the calculation formula of the effective reaction surface area of minerals, the effective reaction surface area of minerals at each evolution time in the shale secondary porous reservoir sample is determined.
[0146] The formula for calculating the effective reaction surface area of a mineral is as follows:
[0147]
[0148] In the formula, The effective reaction surface area of the mineral; The total surface area of the minerals; These are the hydrocarbon inhibition factors corresponding to each evolutionary time step.
[0149] In one possible implementation, the solver module 23 is specifically used for:
[0150] Using geological evolution data as boundary conditions, the diagenetic pore evolution model is solved in the digital core pore network to obtain the evolution history of reservoir rock physical properties corresponding to shale secondary pore reservoir samples.
[0151] Based on the evolution history of the physical properties of the reservoir rocks, the reservoir quality evaluation results of the shale secondary porosity reservoir samples were obtained.
[0152] In one possible implementation, the solver module 23 is specifically used for:
[0153] Based on the evolution history of the physical properties of reservoir rocks, determine the dynamic evolution curves of porosity, permeability, and the effective reaction surface area of minerals;
[0154] The dissolution window period is determined based on the porosity dynamic evolution curve, and the cementation window period is determined based on the permeability dynamic evolution curve. The dissolution window period represents the porosity formation stage, and the cementation window period represents the porosity reduction stage.
[0155] Based on the dissolution window period, cementation window period, and changes in the effective reaction surface area of minerals, a map of reservoir physical properties is drawn.
[0156] Areas in the reservoir physical property distribution map where porosity and permeability meet preset conditions are designated as high-quality reservoir areas.
[0157] In one possible implementation, the solver module 23 is also used for:
[0158] Overlay the reservoir physical property distribution map with the oil and gas saturation distribution map;
[0159] The high-quality reservoir areas that overlap with the high oil and gas saturation areas in the oil and gas saturation distribution map are designated as sweet spots.
[0160] In one possible implementation, the diagenetic porosity evolution model is as follows:
[0161] ;
[0162] In the formula, j Indicates different types of diagenetic reactions; Indicates the first j The change in porosity caused by diagenetic reactions; Indicates the erosion and porosity enhancement term; This indicates the cementation-reduced pore term.
[0163] In one possible implementation, the dissolution porosity enhancement term is determined by the following formula:
[0164]
[0165] In the formula, For the first j Molar volume of dissolvable minerals; For the first j The dissolution reaction rate of soluble minerals; For the first j The effective reaction surface area of dissolvable minerals; For the first j The rate constant of the dissolution reaction of soluble minerals is determined by temperature; Hydrogen ion activity; For the first j Saturation index of dissolvable minerals;
[0166] The cementation porosity reduction term is determined by the following formula:
[0167]
[0168] In the formula, For the first j Molar volume of cementitious minerals; For the first j The cementation reaction rate of cementing minerals; For the first j The effective reaction surface area of cementing minerals; For the first j The cementation reaction rate constant of cementing minerals is determined by temperature; For the first j The saturation index of cementing minerals.
[0169] In one possible implementation, evolution module 22 is specifically used for:
[0170] Input geological data into the basin simulation model;
[0171] In the basin simulation model, the temperature and pressure paths, fluid injection, and fluid chemical composition evolution of the reservoirs where the shale secondary porous reservoir samples are located are analyzed to obtain geological evolution data.
[0172] This invention also provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in the above method embodiments.
[0173] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.
[0174] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.
Claims
1. A method for evaluating the quality of shale secondary porous reservoirs, characterized in that, include: Obtain mineral parameters, reaction kinetic parameters, and geological data of the shale secondary porous reservoir sample. Based on the geological data, determine the geological evolution data; The mineral parameters and reaction kinetic parameters are input into the diagenetic porosity evolution model. In the digital core pore network, the geological evolution data is used as boundary conditions to solve the diagenetic porosity evolution model and obtain the reservoir quality evaluation results of the shale secondary porosity reservoir sample. The diagenetic porosity evolution model includes a dissolution-induced porosity term and a cementation-induced porosity term; the dissolution-induced porosity term describes the origin of secondary porosity in shale, and the cementation-induced porosity term describes the reasons for the consumption of secondary porosity in shale. In the digital core pore network, the geological evolution data is used as boundary conditions to solve the diagenetic pore evolution model, obtaining the reservoir quality evaluation results of the shale secondary pore reservoir sample, including: Using the geological evolution data as boundary conditions, the diagenetic pore evolution model is solved in the digital core pore network to obtain the evolution history of the physical properties of the reservoir rock corresponding to the shale secondary pore reservoir sample. Based on the evolution history of the physical properties of the reservoir rocks, the reservoir quality evaluation results of the shale secondary porosity reservoir samples were obtained; Based on the evolution history of the physical properties of the reservoir rocks, the reservoir quality evaluation results of the shale secondary porosity reservoir samples are obtained, including: Based on the evolution history of the physical properties of the reservoir rocks, determine the dynamic evolution curves of porosity, permeability, and the effective reaction surface area of minerals. The dissolution window period is determined based on the porosity dynamic evolution curve; and the cementation window period is determined based on the permeability dynamic evolution curve; wherein, the dissolution window period represents the porosity formation stage, and the cementation window period represents the porosity reduction stage; Based on the dissolution window period, the cementation window period, and the change curve of the effective reaction surface area of minerals, a distribution map of reservoir physical properties is drawn. Regions in the reservoir physical property distribution map whose porosity and permeability meet preset conditions are designated as high-quality reservoir areas.
2. The method for evaluating the quality of shale secondary porous reservoirs according to claim 1, characterized in that, Before inputting the mineral parameters and reaction kinetic parameters into the diagenetic porosity evolution model, and using the geological evolution data as boundary conditions in the digital core pore network to solve the diagenetic porosity evolution model and obtain the reservoir quality evaluation results of the shale secondary porosity reservoir sample, the method further includes: Based on the geological evolution data as boundary conditions, the hydrocarbon inhibition factor corresponding to each evolution moment is determined; Based on the hydrocarbon inhibition factor corresponding to each evolution moment, the effective reaction surface area of minerals at each evolution moment in the shale secondary porous reservoir sample is determined; The effective reaction surface area of minerals at each evolution stage is input into the diagenetic porosity evolution model.
3. The method for evaluating the quality of shale secondary porous reservoirs according to claim 2, characterized in that, The determination of the effective reaction surface area of minerals at each evolution time in the shale secondary porous reservoir sample based on the hydrocarbon inhibition factor corresponding to each evolution time includes: Based on the hydrocarbon inhibition factor corresponding to each evolution time and the formula for calculating the effective reaction surface area of minerals, the effective reaction surface area of minerals at each evolution time in the shale secondary porous reservoir sample is determined. The effective reaction surface area of the mineral is calculated using the following formula: In the formula, The effective reaction surface area of the mineral; The total surface area of the minerals; These are the hydrocarbon inhibition factors corresponding to each evolutionary time step.
4. The method for evaluating the quality of shale secondary porous reservoirs according to claim 1, characterized in that, After designating areas in the reservoir physical property distribution map that meet preset conditions for porosity and permeability as high-quality reservoir areas, the method further includes: The reservoir physical property distribution map is overlaid with the oil and gas saturation distribution map; The high-quality reservoir areas that overlap with the high oil and gas saturation areas in the oil and gas saturation distribution map are designated as sweet spots.
5. The method for evaluating the quality of shale secondary porous reservoirs according to claim 1, characterized in that, The diagenetic porosity evolution model is as follows: ； In the formula, j Indicates different types of diagenetic reactions; Indicates the first j The change in porosity caused by diagenetic reactions; Indicates the erosion and porosity enhancement term; This indicates the cementation-reduced pore term.
6. The method for evaluating the quality of shale secondary porous reservoirs according to claim 1, characterized in that, The dissolution and porosity enhancement term is determined by the following formula: In the formula, For the first j Molar volume of dissolvable minerals; For the first j The dissolution reaction rate of soluble minerals; For the first j The effective reaction surface area of dissolvable minerals; For the first j The rate constant of the dissolution reaction of soluble minerals is determined by temperature; Hydrogen ion activity; For the first j Saturation index of dissolvable minerals; The cementation porosity reduction term is determined by the following formula: In the formula, For the first j Molar volume of cementitious minerals; For the first j The cementation reaction rate of cementing minerals; For the first j The effective reaction surface area of cementing minerals; For the first j The cementation reaction rate constant of cementing minerals is determined by temperature; For the first j The saturation index of cementing minerals.
7. The method for evaluating the quality of shale secondary porous reservoirs according to claim 1, characterized in that, The determination of geological evolution data based on the geological data includes: The geological data is input into the basin simulation model; In the basin simulation model, the temperature and pressure paths, fluid injection, and fluid chemical composition evolution of the reservoir where the shale secondary porous reservoir sample is located are analyzed to obtain geological evolution data.
8. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 7.