A system and method for calculating the range of heating modification and permeability of a shale oil reservoir

Abstract

The application discloses a shale oil reservoir heating reconstruction range and permeability calculation system and method, relates to the technical field of shale oil reservoir development, and comprises a data acquisition module, a reservoir model construction module, a temperature-pressure coupling calculation module, a reconstruction range solving module, a permeability inversion module and a result output checking module. Through acquisition of shale oil reservoir basic geological parameters and heating working condition parameters, a shale reservoir multi-physical field coupling numerical model considering heat-induced cracking and organic matter pyrolysis is constructed, well bottom pressure dynamic response is solved based on a numerical inversion algorithm, measured pressure data is iteratively fitted, the reservoir heating reconstruction range is accurately calculated, real-time permeability parameters of the reconstruction area are obtained through inversion, and the calculation result is checked and corrected through multiple working conditions. The shale oil reservoir heating reconstruction range and permeability calculation system and method can effectively guide shale oil heating development parameter optimization of a target block, and improve reservoir reconstruction effect and oil and gas production capacity.

Description

Technical Field

[0001] This invention relates to the field of shale oil reservoir development technology, and in particular to a system and method for calculating the range and permeability of shale oil reservoir heating stimulation. Background Technology

[0002] In the process of shale oil reservoir heating development, the heating stimulation range and the permeability of the stimulated area are core parameters for evaluating reservoir stimulation effectiveness, optimizing heating process parameters, and predicting oil and gas production capacity. Accurately obtaining these two parameters can effectively avoid reservoir damage and energy waste caused by overheating, as well as insufficient reservoir stimulation and low production capacity caused by underheating.

[0003] Current technologies have several shortcomings: First, traditional calculation methods often use a single temperature field for simulation, neglecting the multi-field coupling effects of reservoir temperature, pressure, seepage, and thermal fracturing, and failing to consider the dynamic changes in pore permeability caused by shale organic matter pyrolysis and mineral phase transformation, resulting in significant deviations between calculation results and actual field conditions. Second, they cannot accurately define the effective boundary range of heating stimulation, making it difficult to distinguish the differences in reservoir parameters between the stimulated zone, transition zone, and unstimulated zone. Third, conventional permeability calculations often use static fitting formulas, which cannot adapt to the dynamic evolution of permeability "valley-peak" during heating, and cannot achieve real-time dynamic inversion. Fourth, they lack an integrated calculation system, resulting in fragmented data acquisition, model building, parameter solving, and result verification, low automation, and poor computational efficiency, failing to meet the needs of real-time optimization and development in the field.

[0004] Therefore, there is an urgent need to design an integrated, high-precision, and highly adaptable calculation system and method for the heating stimulation range and permeability of shale oil reservoirs, in order to solve the technical pain points of existing technologies, such as low calculation accuracy, poor dynamic adaptability, and inability to quantitatively evaluate the stimulation effect. Summary of the Invention

[0005] The purpose of this invention is to provide a calculation system and method for the heating stimulation range and permeability of shale oil reservoirs, enabling accurate definition of the stimulation range and dynamic inversion of permeability under in-situ heating conditions. Through multi-physics coupling modeling and numerical iterative fitting, the calculation accuracy is significantly improved. At the same time, an integrated intelligent calculation system is constructed to improve the efficiency of field applications, providing technical support for optimizing the heating development process of shale oil reservoirs and accurately predicting production capacity.

[0006] To achieve the above objectives, the present invention provides a calculation system for the heating stimulation range and permeability of shale oil reservoirs, including a data acquisition module, a reservoir model construction module, a temperature-pressure coupled calculation module, a stimulation range solution module, a permeability inversion module, and a result output verification module; The data acquisition module collects on-site geological exploration data, heating equipment operating data, and downhole monitoring real-time data, and outputs standardized, denoised, and dimensionlessly consistent reservoir basic parameters, heating operating parameters, and dynamic measured parameters, providing the original data base for backend modeling and calculation; The reservoir model construction module constructs a heating physical model of a horizontal well in a heterogeneous shale oil reservoir based on various parameters output by the data acquisition module. Combining the thermal fracturing mechanism of shale and the pyrolysis kinetics of organic matter, a temperature-pressure-seepage multi-physics coupled mathematical model is established, and the boundary conditions and initial conditions of the model are defined. The temperature-pressure coupled calculation module uses the finite element difference method for numerical discretization, transforms the time-domain pressure equation into the Laplace domain equation through the Laplace transform, and combines the Stehfest numerical inversion algorithm to solve the temperature field distribution and pressure dynamic response data of different heating times and different reservoir locations in the real domain. The module for determining the scope of the modification is preset with the effective temperature threshold and permeability mutation threshold of the reservoir. Combined with the calculation results of the temperature field and pressure field, it identifies the temperature and permeability mutation boundaries of the reservoir through the gradient discrimination method, accurately defines the radial range of heating modification and the effective vertical modification thickness, and divides the core modification area, transition area and unmodified area. The permeability inversion module constructs a dynamic correlation formula between heating temperature, thermal fracturing development degree and reservoir permeability. Using measured bottom hole pressure data as constraints, iterative fitting algorithm is used for inversion correction to obtain real-time permeability parameters for different stimulation areas and different heating stages. The results output verification module outputs and stores the visualized transformation range and permeability calculation results. At the same time, it combines on-site production capacity measurement data and core experimental data to perform multi-dimensional verification, correct model parameters and calculation errors, and output the final accurate calculation results.

[0007] Preferably, the specific contents of the data acquisition module are as follows: Basic geological parameters include reservoir thickness, porosity, initial permeability, rock thermal conductivity, organic matter content, maturity, and reservoir stress parameters. Heating operating parameters include heating power, heating duration, heating temperature, horizontal well type parameters, and heating wellbore location parameters; The on-site measured dynamic parameters include real-time bottom hole pressure, measured reservoir temperature, and dynamic data on oil and gas production during the heating process.

[0008] Preferably, the specific contents of the reservoir model construction module are as follows: Based on measured reservoir thickness, bedding distribution, vertical rhythm characteristics, and planar heterogeneity coefficient, combined with horizontal well diameter, wellbore trajectory, heating section length, and spatial location parameters, a three-dimensional reservoir heterogeneity physical model is reconstructed. To address the physicochemical changes throughout the entire process of in-situ heating of shale, this study couples five core mechanisms: rock thermoelastic strain, high-temperature porosity deformation, dynamic propagation of thermally induced cracks, organic matter pyrolysis loss, and fluid seepage in porous media, and constructs a set of multi-physics field coupled mathematical fundamental equations. Based on the preprocessed geological and working condition parameters, differentiated values ​​are assigned to the basic parameters of the entire reconstructed model. The reconstructed three-dimensional reservoir model was subjected to structured mesh generation using the finite element difference method. Initial conditions are set and boundary conditions are defined for the reconstructed three-dimensional reservoir model, and pre-correction and initialization are performed.

[0009] Preferably, the specific contents of the temperature-pressure coupling calculation module are as follows: The reservoir temperature field is solved by using the heat conduction control equation that considers the pyrolysis of organic matter and the seepage of porous media. This is suitable for in-situ heating of heterogeneous shale reservoirs and takes into account the heat conduction of the solid skeleton, the heat transfer of fluid convection, and the endothermic effect of organic matter pyrolysis. The reservoir pressure-seepage coupling solution adopts the temperature-pressure bidirectional coupling seepage equation, which considers the influence of temperature-induced pore deformation and fracture propagation on the seepage field and is adapted to the dynamic pressure evolution characteristics of the entire heating process. The pressure time-domain conversion uses the Stehfest numerical inversion core calculation formula to achieve high-precision conversion from Lagrange domain pressure analytical solution to real-domain time-domain pressure data.

[0010] The preferred module for determining the scope of modification is as follows: A two-parameter gradient discrimination formula is used to accurately determine the modification boundary. By using temperature gradient and permeability gradient as dual threshold constraints, the modified area and the unmodified area are accurately distinguished. The specific formula is as follows: ; in, The radial distance of the reservoir. The temperature gradient threshold. The interface that simultaneously satisfies the dual gradient condition, which is the permeability gradient threshold, is the effective heating and transformation boundary of the reservoir.

[0011] The preferred specific content of the penetration rate inversion module is as follows: A dynamic correlation formula was constructed between heating temperature, thermal fracturing development degree, and reservoir permeability. The specific formula is as follows: ; in, The initial permeability of the reservoir. The low-temperature porosity compressibility coefficient, The organic matter pyrolysis damage coefficient, Contribution coefficient to fracture permeability rate; The least squares iterative inversion objective function is adopted, and the model parameters are continuously corrected iteratively using the field measured pressure data as constraints, to minimize the error between the calculated and measured values. The specific formula is as follows: ; in, Let the iterative error objective function be... To calculate the pressure for the numerical model, For on-site pressure measurement, The number of data samples to be monitored.

[0012] Preferably, the specific working content of the result output verification module is as follows: Collect all intermediate data and final results from system modeling, solving, range determination, and parameter inversion, and build a traceable standardized database to achieve full-process data retention and reuse; A triple verification mechanism is adopted to verify the error of core parameters such as the heating modification range and the zoned permeability. Accurately pinpoint various sources of error, such as model mechanism, dynamic parameters, boundary conditions, and iterative fitting, and correct key sensitive parameters; The structured output of usable engineering results is adapted to the needs of on-site development and application. The calibrated optimal model parameters are fed back to the front-end reservoir model construction module to achieve closed-loop iteration.

[0013] To achieve the above objectives, the present invention also provides a method for calculating the range and permeability of shale oil reservoir heating stimulation, comprising the following steps: S1. Acquire basic geological parameters, heating condition parameters, and field-measured dynamic parameters of the target shale oil reservoir through the data acquisition module. Remove abnormal data, correct the mean, unify the parameter dimensions, and complete data preprocessing. Select medium- and low-maturity shale oil reservoirs as the calculation objects and determine the heating development process type. S2. By constructing a reservoir model, the physical model of horizontal well heating is simplified to fit the actual working conditions of in-situ heating. A coupled mathematical model of heat conduction, heat convection, thermal fracturing, and fluid seepage in shale reservoir is established. The initial conditions of the model are set as the original temperature of the reservoir, the original pore pressure, and the initial permeability. The boundary conditions are a closed outer boundary and a wellbore isothermal heating inner boundary. S3. Through the temperature-pressure coupling calculation module, the coupled mathematical model is discretized by finite element difference, the time-domain partial differential equation is converted into the Lagrange domain ordinary differential equation, the analytical solution of the bottom hole pressure in the Lagrange domain is solved, and then the temperature distribution data and pressure dynamic response data at different heating times and different reservoir spatial locations in the real domain are obtained through the Stehfest numerical inversion algorithm. S4. Using the modification range solution module, retrieve the effective modification temperature threshold and permeability mutation threshold of the reservoir determined by the indoor core experiment, traverse the entire reservoir calculation data, identify the temperature gradient mutation interface and permeability mutation interface, use these as boundaries to determine the horizontal radial modification radius and vertical modification thickness of the reservoir, divide the modification core area, transition area and unmodified area, and complete the quantitative calculation of the modification range. S5. Through the permeability inversion module, a dynamic evolution model of temperature-fracture density-permeability is established to fit the dynamic change law of permeability during shale heating; with the field measured bottom hole pressure and production data as constraints, the least squares iterative algorithm is used to continuously correct the model parameters and invert the real-time permeability values ​​of each reformed area. S6. Through the result output verification module, the calculated transformation range and permeability parameters are compared and verified with the core thermal shock test data and field well test data to correct the model system error. Finally, the spatial range of reservoir heating transformation, zoned permeability parameters and dynamic evolution law are visualized and output to complete the calculation.

[0014] Therefore, the shale oil reservoir heating stimulation range and permeability calculation system and method described above have the following advantages compared with the prior art: 1. This invention constructs a multi-physics field coupled calculation model of temperature-pressure-thermal fracturing-seepage, which fully considers the influence of shale organic matter pyrolysis, mineral phase transformation and heterogeneity on reservoir stimulation, closely matches the real working conditions of in-situ heating, and solves the problem of low accuracy of traditional single-field simulation calculation. The calculation accuracy of permeability and stimulation range is improved by more than 30%. 2. By adopting the Stehfest numerical inversion algorithm combined with iterative fitting constraints, the reservoir pressure can be dynamically and accurately solved and the permeability can be dynamically inverted. It can adapt to the dynamic evolution law of permeability throughout the heating process, breaking through the limitations of traditional static permeability calculation, and can output the modification parameters of different heating stages in real time. 3. By using the dual threshold gradient discrimination method of temperature and permeability to define the transformation boundary and accurately divide the reservoir transformation zone, the problem of vague definition and lack of quantitative standards in the existing technology of transformation scope is solved, which can provide accurate quantitative basis for the optimization of heating process parameters, well network deployment and production capacity prediction. 4. An integrated intelligent system for data acquisition, modeling, calculation, verification, and output has been constructed, enabling automated and intelligent calculation of shale oil reservoir heating and transformation parameters. This significantly improves on-site calculation efficiency and is adaptable to various in-situ heating and development scenarios for medium- and low-maturity shale oil reservoirs, demonstrating strong versatility and practicality.

[0015] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0016] Figure 1 This is an overall framework diagram of a calculation system for the heating stimulation range and permeability of shale oil reservoirs according to the present invention; Figure 2 This is a visual interface diagram of a calculation system for the heating stimulation range and permeability of shale oil reservoirs according to the present invention; Figure 3 This invention provides a calculation system for the heating and permeability range of shale oil reservoirs, based on the present invention, which plots permeability variations at different radial distances. Figure 4 This is a bar chart comparing the radii of different heating durations in a shale oil reservoir heating stimulation calculation system of the present invention, which calculates the range and permeability of the reservoir. Figure 5 This is a flowchart of a method for calculating the range and permeability of shale oil reservoir heating stimulation according to the present invention. Detailed Implementation

[0017] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of this invention is usually placed when in use. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0018] Example like Figures 1-4 As shown, the present invention provides a calculation system for the heating stimulation range and permeability of shale oil reservoirs, including a data acquisition module, a reservoir model construction module, a temperature-pressure coupling calculation module, a stimulation range solution module, a permeability inversion module, and a result output verification module. The data acquisition module collects on-site geological exploration data, heating equipment operating data, and downhole monitoring real-time data, and outputs standardized, denoised, and dimensionlessly consistent reservoir basic parameters, heating operating parameters, and dynamic measured parameters, providing the original data base for backend modeling and calculation; The specific contents of the data acquisition module are as follows: Basic geological parameters include reservoir thickness, porosity, initial permeability, rock thermal conductivity, organic matter content, maturity, and reservoir stress parameters. Heating operating parameters include heating power, heating duration, heating temperature, horizontal well type parameters, and heating wellbore location parameters; The on-site measured dynamic parameters include real-time bottom hole pressure, measured reservoir temperature, and dynamic data on oil and gas production during the heating process.

[0019] The reservoir model construction module constructs a heating physical model of a horizontal well in a heterogeneous shale oil reservoir based on various parameters output by the data acquisition module. Combining the thermal fracturing mechanism of shale and the pyrolysis kinetics of organic matter, a temperature-pressure-seepage multi-physics coupled mathematical model is established, and the boundary conditions and initial conditions of the model are defined. The specific contents of the reservoir model construction module are as follows: Based on measured reservoir thickness, bedding distribution, vertical rhythm characteristics, and planar heterogeneity coefficient, combined with horizontal well diameter, wellbore trajectory, heating section length, and spatial location parameters, a three-dimensional heterogeneous physical model of the reservoir is reconstructed. The simplified assumptions of traditional homogeneous reservoirs are abandoned, and different lithological, physical, and thermal parameters are assigned to different sub-layers to accurately characterize the geological characteristics of shale reservoirs, which are highly heterogeneous, low porosity and low permeability, and well-developed bedding. This model is fully matched to the in-situ heating development conditions of horizontal wells in the field. This study addresses the physicochemical changes throughout the in-situ heating process of shale, coupling five core mechanisms: rock thermoelastic strain, high-temperature porosity deformation, dynamic propagation of thermally induced fractures, organic matter pyrolysis loss, and fluid seepage in porous media. A multi-physics coupled mathematical equation set is constructed to achieve bidirectional dynamic coupling of temperature, porosity, permeability, stress, and pressure, completely eliminating calculation errors caused by traditional single-field simulations and static parameter assignments. The equation set includes the following formulas: Thermoelastic strain formula for reservoir rocks (characterizing high-temperature skeleton expansion deformation): ; in, For the thermoelastic strain of the rock, The coefficient of thermal expansion of the rock. For real-time reservoir temperature, The original temperature of the reservoir; Reservoir temperature-dependent porosity dynamic evolution formula (adapted to pore opening and closing and deformation characteristics during heating process): ; in, For real-time porosity, Initial porosity, The organic matter pyrolysis pore damage coefficient. This refers to the threshold temperature for the pyrolysis of organic matter. Thermodynamic Evolution Formula for Thermally Induced Crack Density (Quantifying the Development Degree of High-Temperature Cracks and Calculating Support Permeability): ; in, Temperature-related crack density, The original crack surface density, This is the thermal development coefficient of the crack. This is a step function; the calculation of crack development is initiated after the temperature exceeds the threshold. Based on the preprocessed geological and operational parameters, differentiated values ​​are assigned to the fundamental parameters of the reconstructed model across the entire domain; these include key sensitive parameters such as rock thermal conductivity, fluid viscosity, rock specific heat capacity, organic matter pyrolysis kinetic coefficient, stress sensitivity coefficient, thermal expansion coefficient, and pore damage coefficient. Independent parameter groups are set for high-organic-matter layers, tight interlayers, and layers with varying permeability in the reservoir, accurately reproducing the heterogeneous physical properties of the shale reservoir and ensuring the model's realism. The reconstructed three-dimensional reservoir model was structured and meshed using the finite element difference method. Mesh refinement was applied to the core heating and near-wellbore disturbance areas, improving the mesh accuracy to 0.5m×0.5m. For the undisturbed reservoirs at the far end, a sparse mesh transition was used to balance numerical calculation accuracy and overall computational efficiency, achieving high-precision numerical discretization of the entire reservoir domain. The initial conditions and boundary conditions are set for the reconstructed 3D reservoir model, and pre-correction and initialization are performed as follows: The initial field parameters of the model are uniformly assigned as the original reservoir temperature, original formation pressure, initial porosity, initial permeability, and original fracture density; the outer boundary of the reservoir is set as a closed, adiabatic rigid boundary without seepage or heat exchange to eliminate boundary interference; the inner boundary of the heated wellbore is set as a constant temperature and constant power dynamic heating boundary to fit the continuous heating and dynamic disturbance conditions of on-site in-situ heating. Based on indoor core thermal simulation experiments and high-temperature physical property testing data, sensitive dynamic parameters such as thermal expansion coefficient, pore damage coefficient, and crack development coefficient are pre-corrected to eliminate inherent systematic errors in the model. Finally, a standardized multiphysics numerical calculation model containing complete physical mechanisms, coupled equations, grid system, and boundary initial values ​​is output, providing a high-precision calculation carrier for subsequent temperature-pressure coupled solutions.

[0020] The temperature-pressure coupled calculation module uses the finite element difference method for numerical discretization, transforms the time-domain pressure equation into the Laplace domain equation through the Laplace transform, and combines the Stehfest numerical inversion algorithm to solve the temperature field distribution and pressure dynamic response data of different heating times and different reservoir locations in the real domain. The specific contents of the temperature-pressure coupling calculation module are as follows: The reservoir temperature field is solved using the heat conduction control equations that consider organic matter pyrolysis and porous media seepage, adapting to the in-situ heating conditions of heterogeneous shale reservoirs. This approach takes into account the heat conduction of the solid framework, fluid convection heat transfer, and the endothermic effect of organic matter pyrolysis. The specific formulas are as follows: ; in, The density of the shale reservoir rock skeleton. For the specific heat capacity of the rock skeleton, Heating time The equivalent thermal conductivity of the reservoir is... For reservoir fluid density, Specific heat capacity of the fluid The fluid seepage velocity vector. For the heat source of organic matter pyrolysis, Power consumption and heat term for thermally induced cracking of rocks; Solving the reservoir pressure-seepage coupling equation adopts a two-way temperature-pressure coupling seepage equation, considering the influence of temperature-induced pore deformation and fracture propagation on the seepage field, and adapting to the dynamic pressure evolution characteristics of the entire heating process. The specific formula is as follows: ; in, For real-time reservoir porosity, Real-time permeability correlated with temperature. For reservoir fluid viscosity, For reservoir pore pressure, For wellbore source and sink; The pressure time-domain transformation uses the Stehfest numerical inversion core calculation formula to achieve high-precision transformation from Lagrange domain pressure analytical solutions to real-domain time-domain pressure data. The specific formula is as follows: ; in, For the real-domain and time-domain bottom hole pressure, For the analytical solution of the pressure in the Lagrange domain, The inversion order, ranging from 8 to 12. These are the weighting coefficients for Stehfest.

[0021] The module for determining the scope of the modification is preset with the effective temperature threshold and permeability mutation threshold of the reservoir. Combined with the calculation results of the temperature field and pressure field, it identifies the temperature and permeability mutation boundaries of the reservoir through the gradient discrimination method, accurately defines the radial range of heating modification and the effective vertical modification thickness, and divides the core modification area, transition area and unmodified area. The specific contents of the modification range solution module are as follows: A two-parameter gradient discrimination formula is adopted to accurately determine the modification boundary. By using temperature gradient and permeability gradient as dual threshold constraints, the modified area and the unmodified area are accurately distinguished, which solves the problems of fuzzy boundary and difficulty in quantification in traditional methods. The specific formula is as follows: ; in, The radial distance of the reservoir. The temperature gradient threshold. The interface that simultaneously satisfies the dual gradient condition, which is the permeability gradient threshold, is the effective heating and transformation boundary of the reservoir.

[0022] The permeability inversion module constructs a dynamic correlation formula between heating temperature, thermal fracturing development degree and reservoir permeability. Using measured bottom hole pressure data as constraints, iterative fitting algorithm is used for inversion correction to obtain real-time permeability parameters for different stimulation areas and different heating stages. The specific contents of the penetration rate inversion module are as follows: A dynamic correlation formula was constructed between heating temperature, thermal fracturing development degree, and reservoir permeability. The specific formula is as follows: ; in, The initial permeability of the reservoir. The low-temperature porosity compressibility coefficient, The organic matter pyrolysis damage coefficient, Contribution coefficient to fracture permeability rate; The least squares iterative inversion objective function is adopted, with on-site measured pressure data as constraints. The model parameters are continuously iteratively corrected to minimize the error between the calculated and measured values, ensuring the accuracy of permeability inversion. The specific formula is as follows: ; in, Let the iterative error objective function be... To calculate the pressure for the numerical model, For on-site pressure measurement, The number of data samples to be monitored.

[0023] The result output verification module outputs and stores the visualized transformation range and permeability calculation results. At the same time, it combines the on-site production capacity measurement data and core test data to perform multi-dimensional verification, correct model parameters and calculation errors, and output the final accurate calculation results. The specific tasks of the result output verification module are as follows: Collect all intermediate data and final results from system modeling, solving, range determination, and parameter inversion, and build a traceable standardized database to achieve full-process data retention and reuse; A triple verification mechanism (core test, field well test, and production capacity dynamics) is adopted to verify the error of core parameters such as heating transformation range and zoned permeability. A 10% error threshold is preset, and calculation anomalies are judged if the threshold is exceeded. Accurately locate various error sources such as model mechanism, dynamic parameters, boundary conditions, and iterative fitting, correct key sensitive parameters, eliminate system bias, and improve the calculation and adaptation accuracy of heterogeneous reservoirs; The structured output includes engineering deliverables such as modification radius, vertical modification thickness, zoning boundaries, dynamic permeability, field distribution cloud map, and error convergence report, which are adapted to the needs of on-site development and application. The calibrated optimal model parameters are fed back to the front-end reservoir model construction module to achieve closed-loop iteration and continuously optimize the system's calculation accuracy.

[0024] like Figure 5 As shown, a method for calculating the range and permeability of shale oil reservoir heating stimulation includes the following steps: S1. Acquire basic geological parameters, heating condition parameters, and field-measured dynamic parameters of the target shale oil reservoir through the data acquisition module. Remove abnormal data, correct the mean, unify the parameter dimensions, and complete data preprocessing. Select medium- and low-maturity shale oil reservoirs as the calculation objects and determine the heating development process type. S2. By constructing a reservoir model, the physical model of horizontal well heating is simplified to fit the actual working conditions of in-situ heating. A coupled mathematical model of heat conduction, heat convection, thermal fracturing, and fluid seepage in shale reservoir is established. The initial conditions of the model are set as the original temperature of the reservoir, the original pore pressure, and the initial permeability. The boundary conditions are a closed outer boundary and a wellbore isothermal heating inner boundary. S3. Through the temperature-pressure coupling calculation module, the coupled mathematical model is discretized by finite element difference, the time-domain partial differential equation is converted into the Lagrange domain ordinary differential equation, the analytical solution of the bottom hole pressure in the Lagrange domain is solved, and then the temperature distribution data and pressure dynamic response data at different heating times and different reservoir spatial locations in the real domain are obtained through the Stehfest numerical inversion algorithm. S4. Using the modification range solution module, retrieve the effective modification temperature threshold and permeability mutation threshold of the reservoir determined by the indoor core experiment, traverse the entire reservoir calculation data, identify the temperature gradient mutation interface and permeability mutation interface, use these as boundaries to determine the horizontal radial modification radius and vertical modification thickness of the reservoir, divide the modification core area, transition area and unmodified area, and complete the quantitative calculation of the modification range. S5. Through the permeability inversion module, a dynamic evolution model of temperature-fracture density-permeability is established to fit the dynamic change law of permeability during shale heating; with the field measured bottom hole pressure and production data as constraints, the least squares iterative algorithm is used to continuously correct the model parameters and invert the real-time permeability values ​​of each reformed area. S6. Through the result output verification module, the calculated transformation range and permeability parameters are compared and verified with the core thermal shock test data and field well test data to correct the model system error. Finally, the spatial range of reservoir heating transformation, zoned permeability parameters and dynamic evolution law are visualized and output to complete the calculation.

[0025] In the specific implementation process, the low-maturity shale oil reservoir of the second member of the Nenjiang Formation in the Cretaceous of the Songliao Basin was selected as the target block. The system and method of this invention were used to calculate the heating stimulation range and permeability. The specific steps are as follows: Data acquisition and preprocessing: The target reservoir thickness was 28m, the initial permeability was 0.023mD, the organic matter content was 3.2%, the vitrinite reflectance was 0.45%, and the rock thermal conductivity was 2.1W / (m·℃). In-situ radio frequency heating was used in the horizontal well with a heating power of 80kW and a continuous heating time of 15 days. The wellbore heating temperature was 320℃. The measured bottom hole pressure data and reservoir temperature monitoring data were collected every 24 hours during the heating process. Abnormal fluctuation data were removed, and data standardization preprocessing was completed.

[0026] Model Construction: A horizontal well heating physical model of the heterogeneous shale reservoir in the target block was constructed. The model size was set to 100m horizontally and 30m vertically to fit the actual reservoir boundary. A coupled mathematical model of heat conduction, thermal fracturing, and fluid seepage was established. The initial reservoir temperature was set to 25℃, the original formation pressure to 18.5MPa, the outer boundary to be a closed non-seepage boundary, and the wellbore to be a constant-temperature heating boundary.

[0027] Multi-field coupling solution: The coupled model is divided into finite element differential meshes with a mesh accuracy of 1m×1m; the Laplace transform is used to transform the equation domain and solve the analytical solution of the Laplace domain pressure. The Stehfest numerical inversion algorithm is used to calculate the temperature and pressure field distribution data of the entire reservoir after 15 days of heating, and to obtain the dynamic response law of temperature and pressure at different radial and longitudinal positions.

[0028] Delineation of the modification scope: Based on indoor core thermal shock experiments, the effective modification temperature threshold for the shale in this block was determined to be 280℃, and the permeability mutation threshold was determined to be 0.08mD. The mutation boundary was identified by gradient discrimination method, and the effective horizontal radial modification radius was calculated to be 22.6m, and the effective vertical modification thickness was calculated to be 26.3m. Among them, the radial radius of the core modification zone is 15.2m, and the width of the transition zone is 7.4m, thus accurately delineating the modification scope.

[0029] Permeability inversion correction: Substituting the dynamic evolution calculation formula of permeability, combined with the characteristics of shale permeability evolution with temperature in this block, and using measured bottom hole pressure data as constraints, the model parameters are continuously corrected through the least squares iterative objective function. The average permeability of the core reformed area is 0.36 mD, the average permeability of the transition area is 0.11 mD, and the permeability of the unreformed area remains at the initial 0.023 mD. The error between the permeability calculation results and the field well test data is less than 8%.

[0030] Results Verification Output: The calculated modification range and permeability parameters are compared and verified with core experiments and field production data. After correcting systematic errors, the spatial distribution map of reservoir heating modification, zonal permeability parameters, and dynamic evolution curves are visualized and output to complete this calculation.

[0031] Therefore, this invention employs the above-mentioned calculation system and method for the heating stimulation range and permeability of shale oil reservoirs, enabling precise definition of the stimulation range and dynamic inversion of permeability under in-situ heating conditions. Through multi-physics coupling modeling and numerical iterative fitting, the calculation accuracy is significantly improved. At the same time, an integrated intelligent computing system is constructed to enhance the efficiency of field applications, providing technical support for optimizing shale oil reservoir heating development processes and accurately predicting production capacity.

[0032] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A system for calculating the range and permeability of shale oil reservoir heating stimulation, characterized in that, It includes a data acquisition module, a reservoir model construction module, a temperature-pressure coupling calculation module, a stimulation range solution module, a permeability inversion module, and a result output verification module; The data acquisition module collects on-site geological exploration data, heating equipment operating data, and downhole monitoring real-time data, and outputs standardized, denoised, and dimensionlessly consistent reservoir basic parameters, heating operating parameters, and dynamic measured parameters, providing the original data base for backend modeling and calculation; The reservoir model construction module constructs a heating physical model of a horizontal well in a heterogeneous shale oil reservoir based on various parameters output by the data acquisition module. Combining the thermal fracturing mechanism of shale and the pyrolysis kinetics of organic matter, a temperature-pressure-seepage multi-physics coupled mathematical model is established, and the boundary conditions and initial conditions of the model are defined. The temperature-pressure coupled calculation module uses the finite element difference method for numerical discretization, transforms the time-domain pressure equation into the Laplace domain equation through the Laplace transform, and combines the Stehfest numerical inversion algorithm to solve the temperature field distribution and pressure dynamic response data of different heating times and different reservoir locations in the real domain. The module for determining the scope of the modification is preset with the effective temperature threshold and permeability mutation threshold of the reservoir. Combined with the calculation results of the temperature field and pressure field, it identifies the temperature and permeability mutation boundaries of the reservoir through the gradient discrimination method, accurately defines the radial range of heating modification and the effective vertical modification thickness, and divides the core modification area, transition area and unmodified area. The permeability inversion module constructs a dynamic correlation formula between heating temperature, thermal fracturing development degree and reservoir permeability. Using measured bottom hole pressure data as constraints, iterative fitting algorithm is used for inversion correction to obtain real-time permeability parameters for different stimulation areas and different heating stages. The results output verification module outputs and stores the visualized transformation range and permeability calculation results. At the same time, it combines on-site production capacity measurement data and core experimental data to perform multi-dimensional verification, correct model parameters and calculation errors, and output the final accurate calculation results.

2. The calculation system for the heating stimulation range and permeability of shale oil reservoirs according to claim 1, characterized in that: The specific contents of the data acquisition module are as follows: Basic geological parameters include reservoir thickness, porosity, initial permeability, rock thermal conductivity, organic matter content, maturity, and reservoir stress parameters. Heating operating parameters include heating power, heating duration, heating temperature, horizontal well type parameters, and heating wellbore location parameters; The on-site measured dynamic parameters include real-time bottom hole pressure, measured reservoir temperature, and dynamic data on oil and gas production during the heating process.

3. The calculation system for the heating stimulation range and permeability of shale oil reservoirs according to claim 2, characterized in that: The specific contents of the reservoir model construction module are as follows: Based on measured reservoir thickness, bedding distribution, vertical rhythm characteristics, and planar heterogeneity coefficient, combined with horizontal well diameter, wellbore trajectory, heating section length, and spatial location parameters, a three-dimensional reservoir heterogeneity physical model is reconstructed. To address the physicochemical changes throughout the in-situ heating process of shale, this study couples five core mechanisms: rock thermoelastic strain, high-temperature porosity deformation, dynamic propagation of thermally induced cracks, organic matter pyrolysis loss, and fluid seepage in porous media, and constructs a set of multi-physics field coupled mathematical fundamental equations. Based on the preprocessed geological and working condition parameters, differentiated values ​​are assigned to the basic parameters of the entire reconstructed model. The reconstructed three-dimensional reservoir model was subjected to structured mesh generation using the finite element difference method. Initial conditions are set and boundary conditions are defined for the reconstructed three-dimensional reservoir model, and pre-correction and initialization are performed.

4. The calculation system for the heating stimulation range and permeability of shale oil reservoirs according to claim 3, characterized in that: The specific contents of the temperature-pressure coupling calculation module are as follows: The reservoir temperature field is solved by using the heat conduction control equation that considers the pyrolysis of organic matter and the seepage of porous media. This is suitable for in-situ heating of heterogeneous shale reservoirs and takes into account the heat conduction of the solid skeleton, the heat transfer of fluid convection, and the endothermic effect of organic matter pyrolysis. The reservoir pressure-seepage coupling solution adopts the temperature-pressure bidirectional coupling seepage equation, which considers the influence of temperature-induced pore deformation and fracture propagation on the seepage field and is adapted to the dynamic pressure evolution characteristics of the entire heating process. The pressure time-domain conversion uses the Stehfest numerical inversion core calculation formula to achieve high-precision conversion from Lagrange domain pressure analytical solution to real-domain time-domain pressure data.

5. The calculation system for the heating stimulation range and permeability of shale oil reservoirs according to claim 4, characterized in that: The specific contents of the modification range solution module are as follows: A two-parameter gradient discrimination formula is used to accurately determine the modification boundary. By using temperature gradient and permeability gradient as dual threshold constraints, the modified area and the unmodified area are accurately distinguished. The specific formula is as follows: ； in, The radial distance of the reservoir. The temperature gradient threshold. The interface that simultaneously satisfies the dual gradient condition, which is the permeability gradient threshold, is the effective heating and transformation boundary of the reservoir.

6. The calculation system for the heating stimulation range and permeability of shale oil reservoirs according to claim 5, characterized in that: The specific contents of the penetration rate inversion module are as follows: A dynamic correlation formula was constructed between heating temperature, thermal fracturing development degree, and reservoir permeability. The specific formula is as follows: ； in, The initial permeability of the reservoir. The low-temperature porosity compressibility coefficient, The organic matter pyrolysis damage coefficient, Contribution coefficient to fracture permeability rate; The least squares iterative inversion objective function is adopted, and the model parameters are continuously corrected iteratively using the field measured pressure data as constraints, to minimize the error between the calculated and measured values. The specific formula is as follows: ； in, Let the iterative error objective function be... To calculate the pressure for the numerical model, For on-site pressure measurement, The number of data samples to be monitored.

7. The calculation system for the heating stimulation range and permeability of shale oil reservoirs according to claim 6, characterized in that: The specific tasks of the result output verification module are as follows: Collect all intermediate data and final results from system modeling, solving, range determination, and parameter inversion, and build a traceable standardized database to achieve full-process data retention and reuse; A triple verification mechanism is adopted to verify the error of core parameters such as the heating modification range and the zoned permeability. Accurately pinpoint various sources of error, such as model mechanism, dynamic parameters, boundary conditions, and iterative fitting, and correct key sensitive parameters; The structured output of usable engineering results is adapted to the needs of on-site development and application. The calibrated optimal model parameters are fed back to the front-end reservoir model construction module to achieve closed-loop iteration.

8. A method for calculating the range and permeability of shale oil reservoir heating stimulation, characterized in that: The method, employing the calculation system for the heating stimulation range and permeability of shale oil reservoirs as described in any one of claims 1-7, includes the following steps: S1. Acquire basic geological parameters, heating condition parameters, and field-measured dynamic parameters of the target shale oil reservoir through the data acquisition module. Remove abnormal data, correct the mean, unify the parameter dimensions, and complete data preprocessing. Select medium- and low-maturity shale oil reservoirs as the calculation objects and determine the heating development process type. S2. By constructing a reservoir model, the physical model of horizontal well heating is simplified to fit the actual working conditions of in-situ heating. A coupled mathematical model of heat conduction, heat convection, thermal fracturing, and fluid seepage in shale reservoir is established. The initial conditions of the model are set as the original temperature of the reservoir, the original pore pressure, and the initial permeability. The boundary conditions are a closed outer boundary and a wellbore isothermal heating inner boundary. S3. Through the temperature-pressure coupling calculation module, the coupled mathematical model is discretized by finite element difference, the time-domain partial differential equation is converted into the Lagrange domain ordinary differential equation, the analytical solution of the bottom hole pressure in the Lagrange domain is solved, and then the temperature distribution data and pressure dynamic response data at different heating times and different reservoir spatial locations in the real domain are obtained through the Stehfest numerical inversion algorithm. S4. Using the modification range solution module, retrieve the effective modification temperature threshold and permeability mutation threshold of the reservoir determined by the indoor core experiment, traverse the entire reservoir calculation data, identify the temperature gradient mutation interface and permeability mutation interface, use these as boundaries to determine the horizontal radial modification radius and vertical modification thickness of the reservoir, divide the modification core area, transition area and unmodified area, and complete the quantitative calculation of the modification range. S5. Through the permeability inversion module, a dynamic evolution model of temperature-fracture density-permeability is established to fit the dynamic change law of permeability during shale heating; with the field measured bottom hole pressure and production data as constraints, the least squares iterative algorithm is used to continuously correct the model parameters and invert the real-time permeability values ​​of each reformed area. S6. Through the result output verification module, the calculated transformation range and permeability parameters are compared and verified with the core thermal shock test data and field well test data to correct the model system error. Finally, the spatial range of reservoir heating transformation, zoned permeability parameters and dynamic evolution law are visualized and output to complete the calculation.

Images