A method for simulating migration and dissolution route of ancient acidic fluid in oil and gas reservoir

Abstract

This invention discloses a method for simulating the migration and dissolution routes of paleoacidic fluids in oil and gas reservoirs, comprising: obtaining a structural map of the top surface of the target reservoir; obtaining a lithofacies distribution map of the top surface of the target reservoir; calculating the physical property data of different lithofacies combinations; combining the obtained lithofacies distribution map of the top surface of the target reservoir and the obtained physical property data of different lithofacies combinations, and using a random interpolation method to obtain a physical property distribution map of the top surface of the target reservoir; constructing a paleoacidic fluid migration potential map by combining the obtained structural map of the top surface of the target reservoir and the obtained physical property distribution map of the top surface of the target reservoir; and using hydrocarbon expulsion and oil and gas migration forces to drive the paleoacidic fluids and conduct paleoacidic fluid migration and dissolution route simulation. This invention effectively solves the shortcomings of existing technologies, has low simulation costs, and comprehensively considers the influence of structure, lithofacies, and fluid potential on fluid migration, improving the simulation effect and accuracy, and is beneficial to research and production applications in the field of oil and gas exploration.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas reservoir prediction technology, specifically a method for simulating the migration and dissolution routes of ancient acidic fluids in oil and gas reservoirs. Background Technology

[0002] Reservoir quality is a crucial factor influencing the effectiveness of oil and gas resource exploration and development. During diagenesis, readily soluble components such as feldspar, calcite, and some rock fragments are dissolved by paleoacidic fluids, forming secondary porosity and significantly improving reservoir quality. Clearly defining the migration and dissolution routes of paleoacidic fluids in oil and gas reservoirs is essential for predicting the spatial distribution of high-quality reservoirs. However, the migration and dissolution paths of paleoacidic fluids within formations are complex processes influenced by multiple factors, including tectonic morphology, fractures, and formation permeability. Therefore, accurately determining the migration and dissolution routes of paleoacidic fluids remains a challenge in the current field of oil and gas exploration.

[0003] Currently, the main methods for studying the migration of ancient fluids in strata fall into two categories: physical experimental simulation and numerical simulation. In terms of physical experiment simulation: Patent application number 202121551318.0 discloses an underground fluid transport simulation experimental system, including a formation model, a liquid collector, a first storage tank containing sufficient water or dyed fracturing fluid, and a second storage tank containing sufficient dyed solution or petroleum; Patent application number 201520582469.0 relates to an experimental device for sealing the lateral migration of oil and gas in fracture zones, including a fluid injection data recording and analysis system, an oil transfer tank, a simulation system, a fluid output metering and analysis system, and a data processing and analysis system, connected in sequence, and an outer casing. The main body includes a disk and a fracture zone. The disk is cut by the fracture zone located inside it to form a fractured anticline structure with a descending left disk and an ascending right disk; Patent application number 202311395000.1 discloses a multiphase fluid transport distribution monitoring device based on acoustic-electric combination, including a fluid injection control module, a fluid displacement transport simulation module, a data dynamic monitoring and acquisition analysis module, and a recovery weighing module. The aforementioned physical simulation devices or technologies have high simulation costs. Furthermore, due to limitations in the size of the simulation devices, the types and scales of strata and fractures that can be simulated are limited. Therefore, the applicability of their simulation results in oil and gas exploration is not high. In numerical simulation: based on the fluid potential theory proposed by Hubbert et al. (1953), the fluid potential is obtained by calculating the sum of mechanical energy during the paleofluid migration process. Combined with the differences in fluid potential in different units, the migration direction of the paleofluid is evaluated. This method requires a large amount of test data and does not consider the influence of fractures on fluid migration. Commercial software such as FLAC 2D / 3D is also commonly used for fluid migration simulation. For example, Wei Xiaoyan et al. (2022) and Liu Chuandong et al. (2023) used FLAC2D and FLAC3D simulation software to mesh the simulated target area, set simulation parameters, initial conditions, and boundary conditions, and simulate the fluid field to obtain the migration route of the paleofluid. The simulation results of this method are greatly affected by the simulation parameters. For strata with complex lithology, fine meshing is required, leading to a rapid decrease in simulation efficiency. Furthermore, patent application number 201010219162.6 relates to a method for generating oil and gas migration paths, including generating three-dimensional geological body layer structure information, obtaining reservoir porosity information, obtaining reservoir interconnections with porosity greater than the lower limit of porosity, obtaining oil and gas migration nodes within them, and generating oil and gas migration path information. This method lacks consideration of fluid migration potential and structure, both of which have a significant impact on the accuracy of fluid migration simulation results, especially in areas with well-developed structures. The method of patent 201010219162.6 may not be applicable in such areas.

[0004] In summary, existing physical experiment simulation techniques suffer from drawbacks such as high simulation costs and limited simulation types and scales. Furthermore, due to the limited amount of test data, the contradiction between mesh generation and computational efficiency, and the incomplete consideration of simulation factors, the simulation effects and applicability of existing numerical simulation techniques are also unsatisfactory. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide a method for simulating the migration and dissolution routes of paleoacidic fluids in oil and gas reservoirs that overcomes or at least partially solves the above problems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for simulating paleoacidic fluid migration and dissolution routes in oil and gas reservoirs, the method comprising the following steps:

[0008] S1. Using core, logging and seismic data of the target area, conduct joint well and seismic calibration, carry out inter-well stratigraphic correlation and seismic stratigraphic top surface interpretation, and obtain the structural map of the top surface of the target reservoir.

[0009] S2. Based on the stratigraphic correlation results, combined with the drilling core, logging and well logging data of the target area, obtain the lithofacies distribution map of the top surface of the target reservoir;

[0010] S3. Collect core samples, test the physical properties of different types of lithofacies in the target reservoir, and calculate the physical property data of different lithofacies combinations.

[0011] S4. Combining the lithofacies distribution map of the target reservoir top surface obtained in step S2 and the physical property data of different types of lithofacies combinations obtained in step S3, use the random interpolation method to obtain the physical property distribution map of the target reservoir top surface.

[0012] S5. Combining the structural map of the top surface of the target reservoir obtained in step S1 and the physical property distribution map of the top surface of the target reservoir obtained in step S4, construct a paleoacidic fluid migration potential map, and use hydrocarbon expulsion and oil and gas migration forces to drive the paleoacidic fluid, and carry out paleoacidic fluid migration and dissolution route simulation.

[0013] Optionally, step S1 includes:

[0014] S101. Based on the stratigraphic unconformity characteristics of the target area, establish stratigraphic correlation marker layers by combining core, well logging and seismic data;

[0015] S102. Select wells that have penetrated the target reservoir in the research target area as backbone wells, establish seismic synthetic records, identify the marker layers of seismic data, and carry out the tracking and interpretation of seismic horizons in the research target area.

[0016] S103. Using the Kriging interpolation method, trend fitting is performed on the data between seismic interpretation grids to obtain the structural map of the top surface of the target reservoir.

[0017] Optionally, step S2 includes:

[0018] S201. Based on the stratigraphic correlation results and seismic data, identify the stratigraphic erosion lines of the target reservoir group and determine the distribution area of ​​the target reservoir in the stratigraphic erosion residual zone.

[0019] S202. Within the erosion residual zone, using core, logging and well logging data, determine the lithofacies unit of the target reservoir by taking the group of residual strata at the top of the target reservoir as the unit;

[0020] S203. Based on the lithofacies type of the target reservoir, lithofacies assemblage units are divided according to the vertical proportion of different lithofacies. Based on sedimentary principles and Wolseau's facies law, a lithofacies distribution map of the top surface of the target reservoir is drawn.

[0021] Optionally, step S3 includes:

[0022] S301. Collect typical samples from the core section, test the physical properties of different types of lithofacies in the target reservoir, perform statistical analysis on the test results, and obtain the average, maximum and minimum values ​​of the physical properties.

[0023] S302. Based on the lithofacies assemblage type of the target reservoir, and combined with the proportion of each lithofacies in different lithofacies assemblages, obtain the maximum, minimum, and average values ​​of the physical properties of different lithofacies assemblages. The calculation formulas are as follows:

[0024] Por max =Por max-facies1 *V facies1 +Por max-facies2 *V facies2 +……+Por max-faciesn *V faciesn

[0025] Por min =Por min-facies1 *V facies1 +Por min-facies2 *V facies2 +……+Por min-faciesn *V faciesn

[0026] Por aver =Por aver-facies1 *V facies1 +Por aver-facies2 *V facies2 +……+Por aver-faciesn *V faciesn

[0027] Among them, Por max Por min Por aver These represent the maximum, minimum, and average values ​​of physical properties for different lithofacies assemblages; Por max-facies1 Por min-facies1 Por aver-facies1 Por max-facies2 Por min-facies2 Por aver-facies2 Por max-faciesn Por min-faciesn Por aver-faciesn These represent the maximum, minimum, and average porosity values ​​for the first, second, and nth lithofacies types, respectively; V facies1 V facies2 V faciesn These represent the percentages of the first, second, and nth lithofacies types, respectively.

[0028] Optionally, step S4 may utilize random interpolation methods including:

[0029] Construct a set of well point coordinate variables, extract x, y, and porosity data, and read the lithofacies distribution grayscale image, where: x is the abscissa matrix of the well point and y is the ordinate matrix of the well point;

[0030] Define an interpolation grid, perform Kriging interpolation for each lithofacies type, and constrain the maximum, minimum, and average porosity within the lithofacies type.

[0031] Optionally, step S5 includes:

[0032] S501. The top surface physical property distribution map and top surface structural map of the target reservoir are processed into grayscale. The two grayscale maps are overlaid using a Python program, and a paleoacidic fluid migration potential map is constructed.

[0033] S502. A simulation program for ancient acidic fluid driven by hydrocarbon expulsion and oil and gas migration forces was constructed using Python.

[0034] S503. Use Python programs to output the paleoacidic fluid migration paths and active regions of the target reservoir in the research area.

[0035] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0036] This invention constructs a paleoacidic fluid migration potential map based on the top surface structural map, top surface lithofacies distribution map, and top surface physical property distribution map of the target reservoir. It utilizes hydrocarbon expulsion and oil and gas migration forces to drive the paleoacidic fluid, accurately simulating the migration and dissolution routes of the paleoacidic fluid. This invention effectively solves the shortcomings of existing technologies, has low simulation costs, and comprehensively considers the influence of structure, lithofacies, and fluid potential on fluid migration, improving the simulation effect and accuracy, which is beneficial to research and production applications in the field of oil and gas exploration. Attached Figure Description

[0037] Figure 1 A schematic flowchart of a method for simulating the migration and dissolution routes of ancient acidic fluids in oil and gas reservoirs, provided in an embodiment of this application;

[0038] Figure 2 This is a seismic interpretation diagram of the regional unconformity interface in an embodiment of this application;

[0039] Figure 3 This is a reservoir top surface structure diagram according to an embodiment of this application;

[0040] Figure 4 This is a lithological distribution diagram of the reservoir top surface according to an embodiment of this application;

[0041] Figure 5 This is a reservoir property distribution diagram according to an embodiment of this application;

[0042] Figure 6 This is a reservoir paleoacidic fluid migration potential diagram according to an embodiment of this application;

[0043] Figure 7 This describes the reservoir paleoacidic fluid migration path in an embodiment of this application. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0045] Please see Figure 1 This embodiment provides a method for simulating the migration and dissolution routes of paleoacidic fluids in oil and gas reservoirs. The method includes the following steps:

[0046] S1. Using core, logging and seismic data of the target area, conduct joint well and seismic calibration, carry out inter-well stratigraphic correlation and seismic stratigraphic top surface interpretation, and obtain the structural map of the top surface of the target reservoir.

[0047] Step S1 includes:

[0048] S101. Based on the stratigraphic unconformity characteristics of the target area, establish stratigraphic correlation marker layers by combining core, well logging and seismic data.

[0049] S102. Select wells that have penetrated the target reservoir in the research target area as backbone wells, establish seismic synthetic records, identify the marker layers of the seismic data, and carry out the tracking and interpretation of seismic horizons in the research target area.

[0050] S103. Using the Kriging interpolation method, trend fitting is performed on the data between seismic interpretation grids to obtain the structural map of the top surface of the target reservoir.

[0051] S2. Based on the stratigraphic correlation results, combined with the drilling core, logging and well logging data of the target area, obtain the lithofacies distribution map of the top surface of the target reservoir.

[0052] Step S2 includes:

[0053] S201. Based on the stratigraphic correlation results and seismic data, identify the stratigraphic erosion lines of the target reservoir group and determine the distribution area of ​​the target reservoir in the stratigraphic erosion residual zone.

[0054] S202. Within the erosion residual zone, using core, logging, and well logging data, determine the lithofacies units of the target reservoir, taking the group of residual strata at the top of the target reservoir as the unit.

[0055] S203. Based on the lithofacies type of the target reservoir, lithofacies assemblage units are divided according to the vertical proportion of different lithofacies. Based on sedimentary principles and Wolseau's facies law, a lithofacies distribution map of the top surface of the target reservoir is drawn.

[0056] S3. Collect core samples, test the physical properties of different types of lithofacies in the target reservoir, and calculate the physical property data of different lithofacies combinations.

[0057] Step S3 includes:

[0058] S301. Collect typical samples from the core section, test the physical properties of different types of lithofacies in the target reservoir, perform statistical analysis on the test results, and obtain the average, maximum and minimum values ​​of the physical properties.

[0059] S302. Based on the lithofacies assemblage type of the target reservoir, and combined with the proportion of each lithofacies in different lithofacies assemblages, obtain the maximum, minimum, and average values ​​of the physical properties of different lithofacies assemblages. The calculation formulas are as follows:

[0060] Por max =Por max-facies1 *V facies1 +Por max-facies2 *V facies2 +……+Pormax-faciesn *V faciesn

[0061] Por min =Por min-facies1 *V facies1 +Por min-facies2 *V facies2 +……+Por min-faciesn

[0062] *V faciesn

[0063] Por aver =Por aver-facies1 *V facies1 +Por aver-facies2 *V facies2 +……+Por aver-faciesn

[0064] *V faciesn

[0065] Among them, Por max Por min Por aver These represent the maximum, minimum, and average values ​​of physical properties for different lithofacies assemblages; Por max-facies1 Por min-facies1 Por aver-facies1 Por max-facies2 Por min-facies2 Por aver-facies2 Por max-faciesn Por min-faciesn Por aver-faciesn These represent the maximum, minimum, and average porosity values ​​for the first, second, and nth lithofacies types, respectively; V facies1 V facies2 V faciesn These represent the percentages of the first, second, and nth lithofacies types, respectively.

[0066] S4. Combining the lithofacies distribution map of the target reservoir top surface obtained in step S2 and the physical property data of different types of lithofacies combinations obtained in step S3, use the random interpolation method to obtain the physical property distribution map of the target reservoir top surface.

[0067] Step S4 utilizes random interpolation methods, including:

[0068] Construct a set of well point coordinate variables

[0069]

[0070] Extract x, y, and porosity data, where: x is the abscissa matrix of well points, and y is the ordinate matrix of well points; x = Well_Location_data[:,0]

[0071] y = Well_Location_data[:,1]

[0072] porosity=Well_Location_data[:,2]

[0073] Read the grayscale image of lithofacies distribution (e.g., 'lithofacies_map.png').

[0074] image_path='lithofacies_map.png'

[0075] lithofacies_img=Image.open(image_path).convert('L')

[0076] lithofacies_map=np.array(lithofacies_img)

[0077] Define the interpolation grid

[0078] gridx=np.linspace(min(x),max(x),lithofacies_map.shape[1])

[0079] gridy=np.linspace(min(y),max(y),

[0080] lithofacies_map.shape[0])

[0081] grid_x,grid_y=np.meshgrid(gridx,gridy)

[0082] For each lithofacies type (grayscale value), Kriging interpolation is performed, and the maximum, minimum, and average porosity are constrained within that lithofacies type.

[0083]

[0084] S5. Combining the structural map of the top surface of the target reservoir obtained in step S1 and the physical property distribution map of the top surface of the target reservoir obtained in step S4, construct a paleoacidic fluid migration potential map, and use hydrocarbon expulsion and oil and gas migration forces to drive the paleoacidic fluid, and carry out paleoacidic fluid migration and dissolution route simulation.

[0085] Step S5 includes:

[0086] S501. The top surface physical property distribution map and the top surface structural map of the target reservoir are processed into grayscale. The two grayscale maps are superimposed using a Python program. The grayscale value variation range of the physical property distribution map is about 1 / 8 of the grayscale value variation range of the structural map. A paleoacidic fluid migration potential map is constructed.

[0087] S502. A simulation program for ancient acidic fluids driven by hydrocarbon expulsion and oil and gas migration forces is constructed using Python. The specific simulation program is as follows:

[0088] #1. Import the necessary databases

[0089] import numpy as np

[0090] import cv2

[0091] import matplotlib.pyplot asplt

[0092] #2. Define gradient calculation based on paleofluid potential map

[0093]

[0094] #3. Define gradient calculation based on paleofluid potential map

[0095]

[0096]

[0097]

[0098] #4. Simulation process based on function calls

[0099] elevation_map=cv2.imread('elevation_map.png',cv2.IMREAD_GRAYSCALE)

[0100] num_starting_points = int(input("Please enter the number of oil and gas migration starting points:"))

[0101] paths=simulate_oil_gas_migration(elevation_map,num_starting_points)

[0102] S503. Use the plt.imshow function of the Python program to study the paleoacidic fluid migration path and active area of ​​the target reservoir in the target area.

[0103] This embodiment constructs a paleoacidic fluid migration potential map based on the top surface structural map, top surface lithofacies distribution map, and top surface physical property distribution map of the target reservoir. It utilizes hydrocarbon expulsion and oil and gas migration forces to drive the paleoacidic fluid, accurately simulating the migration and dissolution routes of the paleoacidic fluid. This invention effectively solves the shortcomings of existing technologies, has low simulation costs, and comprehensively considers the influence of structure, lithofacies, and fluid potential on fluid migration, improving the simulation effect and accuracy, which is beneficial to research and production applications in the field of oil and gas exploration.

[0104] This embodiment takes the Mesozoic reservoirs in the Gubei area of ​​the Jiyang Depression as an example to provide a method for simulating the paleoacidic fluid migration and dissolution routes of oil and gas reservoirs, specifically including:

[0105] Step 1: Using core, well logging, and seismic data from the Mesozoic strata in the Gubei area of ​​the Jiyang Depression, conduct joint well-seismic calibration. Using wells that penetrate the Mesozoic strata as the backbone, create synthetic seismic records, interpret the stratigraphic interface at the top of the Mesozoic strata, and use the seismic interpretation results to draw a top structural map. This includes the following steps:

[0106] Step 1.1, as follows Figure 2 As shown, based on the angular unconformities caused by multiple regional tectonic movements in the Mesozoic strata of the Jiyang Depression, the characteristics of regional stratigraphic unconformities were determined, and stratigraphic correlation marker layers were established by combining core, well logging, and seismic data.

[0107] Step 1.2: Using wells that have penetrated the Mesozoic strata as the backbone wells, establish a seismic composite record. Using the unconformity at the top of the Mesozoic strata as a constraint, mark the seismic data marker layers and carry out seismic horizon tracking and interpretation.

[0108] Step 1.3: Using the interpreted seismic tracking results of the Mesozoic strata top surface, draw a structural map of the Mesozoic strata top surface. During the drawing process, the Kriging interpolation method is used to perform trend fitting on the data between the seismic interpretation grid lines to obtain the structural map of the reservoir top surface, such as... Figure 3 As shown.

[0109] Step 2: Using 34 cored wells, including Y155, BG402, and GB107, in the Gubei area of ​​the Jiyang Depression as benchmark wells, and combining logging, well logging, and core data, a lithofacies distribution map of the upper Mesozoic strata is drawn. This specifically includes the following steps:

[0110] Step 2.1: Based on the well-to-well stratigraphic correlation results and seismic data, identify the stratigraphic erosion lines of the target reservoir group and determine the distribution area of ​​the target reservoir in the stratigraphic erosion residual zone, such as... Figure 4 As shown, four erosion lines were identified in the Gubei area: the Fangzi Formation and Santai Formation of the Jurassic period in the Mesozoic era, and the Mengyin Formation and Xiwa Formation of the Cretaceous period.

[0111] Step 2.2: Within the erosion residual area, using core, well logging, and well logging data, based on the erosion residual areas determined by the erosion lines of the Fangzi Formation, Santai Formation, Mengyin Formation, and Xiwa Formation in the Gubei area, the lithofacies assemblage type is determined by taking the Xiwa Formation as the unit in the Xiwa Formation erosion residual area, the Mengyin Formation erosion residual area as the unit in the Mengyin Formation erosion residual area, the Santai Formation erosion residual area as the unit in the Santai Formation erosion residual area, and the Fangzi Formation erosion residual area as the unit in the Fangzi Formation erosion residual area. The main lithofacies types of the Mesozoic strata in the Gubei area are igneous rock lithofacies, conglomerate lithofacies, sandstone lithofacies, and mudstone lithofacies.

[0112] Step 2.3: Based on the lithofacies types of the Mesozoic strata in the Gubei area, the following five lithofacies assemblages are divided according to the vertical proportion of different lithofacies: igneous and clastic lithofacies (igneous lithofacies content >15%), conglomerate and sandstone lithofacies (conglomerate content >30%), sandstone lithofacies (sandstone content >50%, conglomerate content <30%, igneous lithofacies content <15%), sandstone and mudstone lithofacies (50% > mudstone content >15%), and mudstone and sandstone lithofacies (mudstone content >50%). Based on sedimentary principles and Wolseau's facies rule, a lithofacies distribution map of the Mesozoic strata in the Gubei area is drawn, as shown below. Figure 4 As shown.

[0113] Step 3: Collect typical samples from the Mesozoic core section in the Gubei area of ​​the Jiyang Depression, test the physical properties of different lithofacies types of the target reservoir, and calculate the physical property data of different lithofacies combinations; specifically including the following steps:

[0114] Step 3.1: Collect 30 samples each of igneous rock, conglomerate, sandstone, and mudstone from the core section. Measure their porosity using a conventional gas analyzer. Perform statistical analysis on the test results to obtain the average, maximum, and minimum porosity values.

[0115] Step 3.2: Using the proportions of different lithofacies in the five lithofacies assemblages (igneous and clastic, conglomerate and sandstone, sandstone, sandstone and mudstone, and mudstone and sandstone) statistically analyzed at each well point, and combining the statistical characteristics (mean, maximum, and minimum) of the physical properties of different lithofacies, determine the mean, maximum, and minimum values ​​of the lithofacies composition properties at each well point. The calculation formula is as follows:

[0116] Por max =Por max-facies1 *V facies1 +Por max-facies2 *V facies2 +Por max-facies3 *V facies3 +Por max-facies4 *V facies4

[0117] Por min =Por min-facies1 *V facies1 +Por min-facies2 *V facies2 +Por min-facies3 *V facies3 +Por min-facies4 *V facies4

[0118] Por aver =Por aver-facies1 *V facies1 +Por aver-facies2 *V facies2 +Por aver-facies3 *V facies3 +Por aver-facies4 *V facies4

[0119] Among them, Por max-facies1 Por min-facies1 Por aver-facies1 Por max-facies2 Por min-facies2 Por aver-facies2 Por max-facies3 Por min-facies3 Por aver-facies3 Por max-facies4 Por min-facies4 Por aver-facies4 These represent the maximum, minimum, and average porosity values ​​for igneous rock facies, conglomerate facies, sandstone facies, and mudstone facies, respectively; V facies1 V facies2 V facies3 V facies4 These represent the percentages of igneous rock facies, conglomerate facies, sandstone facies, and mudstone facies, respectively.

[0120] Step 4: Combining the lithofacies distribution map obtained in Step 2 and the physical property data of different lithofacies obtained in Step 3, a random interpolation method is used to obtain the physical property distribution map of the top surface of the Mesozoic reservoir in the Gubei area of ​​the Jiyang Depression (e.g., Figure 5 As shown in the figure, the algorithm flow of the random interpolation method is as follows:

[0121] #1. Constructing a set of well point coordinate variables

[0122]

[0123] #2. Extract x, y, and porosity data

[0124] x = Well_Location_data[:,0]

[0125] y = Well_Location_data[:,1]

[0126] porosity=Well_Location_data[:,2]

[0127] #3. Read the grayscale image of lithofacies distribution (e.g., 'lithofacies_map.png').

[0128] image_path='lithofacies_map.png'

[0129] lithofacies_img=Image.open(image_path).convert('L')

[0130] lithofacies_map=np.array(lithofacies_img)

[0131] #4. Define the interpolation grid

[0132] gridx=np.linspace(min(x),max(x),lithofacies_map.shape[1])

[0133] gridy=np.linspace(min(y),max(y),

[0134] lithofacies_map.shape[0])

[0135] grid_x,grid_y=np.meshgrid(gridx,gridy)

[0136] #5. For each lithofacies type (grayscale value), perform Kriging interpolation, and constrain the maximum, minimum, and average porosity within that lithofacies type.

[0137]

[0138]

[0139] Step 5: Combining the structural map of the target reservoir top surface obtained in Step 1 and the physical property distribution map of the target reservoir top surface obtained in Step 4, construct a paleoacidic fluid migration potential map. Utilize hydrocarbon expulsion and oil and gas migration forces to drive the paleoacidic fluid and conduct paleoacidic fluid and dissolution path simulations. This specifically includes the following steps:

[0140] Step 5.1: The top surface structural map of the reservoir is processed into grayscale. The minimum elevation value in the structural map is set to 160, the maximum elevation value is set to 0, and the grayscale value of the non-simulated area is set to 255. The top surface physical property distribution map of the reservoir is also processed into grayscale. Since buoyancy is mainly affected by the difference in structural elevation, the difference in physical properties only affects the fluid migration path in areas with similar elevations. Therefore, when processing the grayscale distribution map of the top surface physical property, the minimum physical property value is set to 20, the maximum physical property value is set to 0, and the grayscale value of the non-simulated area is set to 255. The two grayscale maps are then overlaid using a Python program to construct a paleoacidic fluid migration potential map, as shown below. Figure 6 As shown.

[0141] Step 5.2: Use Python to build a simulation program for hydrocarbon expulsion and oil and gas migration driven paleoacidic fluids. The specific simulation program is as follows:

[0142] #1. Import the necessary databases

[0143] import numpy as np

[0144] import cv2

[0145] import matplotlib.pyplot asplt

[0146] #2. Define gradient calculation based on paleofluid potential map

[0147]

[0148] #3. Define gradient calculation based on paleofluid potential map

[0149]

[0150]

[0151] #4. Simulation process based on function calls: `elevation_map = cv2.imread('elevation_map.png', cv2.IMREAD_GRAYSCALE)`

[0152] num_starting_points = int(input("Please enter the number of oil and gas migration starting points:"))

[0153] paths=simulate_oil_gas_migration(elevation_map,num_starting_points)

[0154] Step 5.3: Use Python's plt.imshow to output the paleoacidic fluid migration paths and activity areas of the Mesozoic reservoirs in the Gubei area of ​​the Jiyang Depression, such as... Figure 7 As shown.

[0155] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for simulating the migration and dissolution routes of paleoacidic fluids in oil and gas reservoirs, characterized in that, The method includes the following steps: S1. Using core, logging and seismic data of the target area, conduct joint well and seismic calibration, carry out inter-well stratigraphic correlation and seismic stratigraphic top surface interpretation, and obtain the structural map of the top surface of the target reservoir. S2. Based on the stratigraphic correlation results, combined with the drilling core, logging and well logging data of the target area, obtain the lithofacies distribution map of the top surface of the target reservoir; S3. Collect core samples, test the physical properties of different types of lithofacies in the target reservoir, and calculate the physical property data of different lithofacies combinations. S4. Combining the lithofacies distribution map of the target reservoir top surface obtained in step S2 and the physical property data of different types of lithofacies combinations obtained in step S3, use the random interpolation method to obtain the physical property distribution map of the target reservoir top surface. S5. Combining the structural map of the top surface of the target reservoir obtained in step S1 and the physical property distribution map of the top surface of the target reservoir obtained in step S4, construct a paleoacidic fluid migration potential map, and use hydrocarbon expulsion and oil and gas migration forces to drive the paleoacidic fluid, and carry out paleoacidic fluid migration and dissolution route simulation.

2. The method for simulating paleoacidic fluid migration and dissolution routes in oil and gas reservoirs as described in claim 1, characterized in that, Step S1 includes: S101. Based on the stratigraphic unconformity characteristics of the target area, establish stratigraphic correlation marker layers by combining core, well logging and seismic data; S102. Select wells that have penetrated the target reservoir in the research target area as backbone wells, establish seismic synthetic records, identify the marker layers of seismic data, and carry out the tracking and interpretation of seismic horizons in the research target area. S103. Using the Kriging interpolation method, trend fitting is performed on the data between seismic interpretation grids to obtain the structural map of the top surface of the target reservoir.

3. The method for simulating paleoacidic fluid migration and dissolution routes in oil and gas reservoirs as described in claim 1, characterized in that, Step S2 includes: S201. Based on the stratigraphic correlation results and seismic data, identify the stratigraphic erosion lines of the target reservoir group and determine the distribution area of ​​the target reservoir in the stratigraphic erosion residual zone. S202. Within the erosion residual zone, using core, logging and well logging data, determine the lithofacies unit of the target reservoir by taking the group of residual strata at the top of the target reservoir as the unit; S203. Based on the lithofacies type of the target reservoir, lithofacies assemblage units are divided according to the vertical proportion of different lithofacies. Based on sedimentary principles and Wolseau's facies law, a lithofacies distribution map of the top surface of the target reservoir is drawn.

4. The method for simulating paleoacidic fluid migration and dissolution routes in oil and gas reservoirs as described in claim 1, characterized in that, Step S3 includes: S301. Collect typical samples from the core section, test the physical properties of different types of lithofacies in the target reservoir, perform statistical analysis on the test results, and obtain the average, maximum and minimum values ​​of the physical properties. S302. Based on the lithofacies assemblage type of the target reservoir, and combined with the proportion of each lithofacies in different lithofacies assemblages, obtain the maximum, minimum, and average values ​​of the physical properties of different lithofacies assemblages. The calculation formulas are as follows: By max =By max-facies1 *V facies1 +By max-facies2 *V facies2 +……+By max-faciesn *V faciesn By min =By min-facies1 *V facies1 +By min-facies2 *V facies2 +……+By min-faciesn *V faciesn By aver =By aver-facies1 *V facies1 +By aver-facies2 *V facies2 +……+By aver-faciesn *V faciesn Among them, Por max Por min Por aver These represent the maximum, minimum, and average values ​​of physical properties for different lithofacies assemblages; Por max-facies1 Por min-facies1 Por aver-facies1 Por max-facies2 Por min-facies2 Por aver-facies2 Por max-faciesn Por min-faciesn Por aver-faciesn These represent the maximum, minimum, and average porosity values ​​for the first, second, and nth lithofacies types, respectively; V facies1 V facies2 V faciesn These represent the percentages of the first, second, and nth lithofacies types, respectively.

5. The method for simulating paleoacidic fluid migration and dissolution routes in oil and gas reservoirs as described in claim 1, characterized in that, Step S4 utilizes random interpolation methods, including: Construct a set of well point coordinate variables, extract x, y, and porosity data, and read the lithofacies distribution grayscale image, where: x is the abscissa matrix of the well point and y is the ordinate matrix of the well point; Define an interpolation grid, perform Kriging interpolation for each lithofacies type, and constrain the maximum, minimum, and average porosity within the lithofacies type.

6. The method for simulating paleoacidic fluid migration and dissolution routes in oil and gas reservoirs as described in claim 1, characterized in that, Step S5 includes: S501. The top surface physical property distribution map and top surface structural map of the target reservoir are processed into grayscale. The two grayscale maps are overlaid using a Python program, and a paleoacidic fluid migration potential map is constructed. S502. A simulation program for ancient acidic fluid driven by hydrocarbon expulsion and oil and gas migration forces was constructed using Python. S503. Use Python programs to output the paleoacidic fluid migration paths and active regions of the target reservoir in the research area.

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