Oil and gas field geothermal resource evaluation method

Abstract

The invention relates to the technical field of geothermal resource evaluation, in particular to an oil and gas field geothermal resource evaluation method which comprises the following steps: S1, establishing a basic database of a thermal reservoir section of a research area; s2, establishing a three-dimensional space structure model of the thermal reservoir section of the research area; s3, establishing a lithofacies model of the thermal reservoir section of the research area; s4, establishing a phase control porosity model of the thermal reservoir section of the research area; s5, establishing a temperature field model of the thermal reservoir section of the research area; s6, establishing a heterogeneous water saturation model of the thermal reservoir section of the research area; and S7, carrying out space superposition on the models, obtained in the steps S2-S6, of the thermal reservoir section of the research area to obtain a geothermal resource evaluation model, then calculating the total geothermal resource quantity in the geothermal resource evaluation model, and finally obtaining the recoverable geothermal resource quantity. The method can accurately describe the physical and thermal properties of the geothermal reservoir, so that the geothermal resource evaluation result is closer to the actual situation.

Description

Technical Field

[0001] The present invention relates to the technical field of geothermal resource evaluation, and more specifically, to a method for evaluating geothermal resources in oil and gas fields. Background Art

[0002] As one of the new energy sources, geothermal energy has shown significant advantages due to its wide distribution, rich resource reserves, efficient utilization, clean renewability and good thermal energy continuity. Oil and gas field geothermal energy is an important part of geothermal resources. Large-scale development and efficient utilization of oil and gas field geothermal resources are of great significance to optimizing energy structure, ensuring energy security, achieving low-carbon transformation and sustainable development.

[0003] Geothermal resource evaluation is a key link in selecting high-quality geothermal resource targets, reducing development risks, and ensuring economic and social benefits. The evaluation of geothermal resources includes dynamic evaluation methods and static evaluation methods. Among them, for static evaluation methods, the most commonly used methods are the heat reservoir volume method and the Monte Carlo simulation based on the heat reservoir volume method.

[0004] The thermal reservoir volume method is widely used in practice and can be used to quickly estimate geothermal resources. However, this method also has some shortcomings: the thermal reservoir volume method assumes that the properties of the geothermal reservoir are uniform throughout the reservoir, but in fact, geothermal reservoirs are usually affected by tectonic activities, sedimentary environment and other factors and show significant heterogeneity. Therefore, the evaluation results of this method may lead to a serious overestimation of geothermal resources, especially for thermal reservoirs with heterogeneous lithology and water saturation not 100% in oil and gas fields.

[0005] Monte Carlo simulation based on the thermal reservoir volume method can provide a quantitative estimate of the uncertainty of geothermal resource assessment results, but this method also has some shortcomings: the assessment results of this method are heavily dependent on the probability distribution of the input parameters. If these distributions are not accurately determined, the evaluation results will also be affected. In addition, the results of Monte Carlo simulation are very sensitive to the initial parameter assumptions. If the initial assumptions of the geological model or parameters are not accurate enough, the simulation results may deviate from the actual situation. Summary of the invention

[0006] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a method for evaluating geothermal resources in oil and gas fields, which can accurately describe the physical and thermal properties of geothermal reservoirs and make the geothermal resource evaluation results closer to the actual situation.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0008] A method for evaluating geothermal resources in oil and gas fields is provided, comprising the following steps:

[0009] S1. Collect seismic data, well logging interpretation data, sedimentary data and indoor lithofacies test data in the study area, and establish a basic database of the thermal reservoir section in the study area;

[0010] S2. Based on the basic database, a fault model and a layer model are established, and then the two are spatially superimposed to obtain a three-dimensional spatial structural model of the thermal reservoir section in the study area;

[0011] S3. Based on the sedimentary data and the three-dimensional spatial structural model, a digital phase model of the thermal reservoir section in the study area is obtained; then, the digital phase model is used as a control boundary, and a sequential indicative sedimentary phase model is obtained through statistical inference; the lithofacies logging curve is obtained by preprocessing the indoor lithofacies test data and the logging interpretation data; then, based on the processed lithofacies logging curve, under the constraint of the sequential indicative sedimentary phase model, the lithofacies model of the thermal reservoir section in the study area is obtained through statistical inference;

[0012] S4. For the area with no wells or few wells in the thermal reservoir section of the study area, the porosity data volume is obtained by using the seismic data and the logging interpretation data and the genetic inversion method; then, the porosity data volume is used as a constraint to obtain the phase-controlled porosity model of the thermal reservoir section of the study area by statistical inference;

[0013] S5. Establishing a regression equation based on the lithofacies logging curve and the logging interpretation data; using the regression equation and combining the lithofacies model of step S3 to obtain a temperature field model of the thermal reservoir section in the study area;

[0014] S6. Identify the well logging interpretation data and establish a sedimentary sand body gas-water transition zone model; establish a water saturation attribute distribution model through statistical inference of the well logging interpretation data; spatially superimpose the sedimentary sand body gas-water transition zone model with the water saturation attribute distribution model, and then spatially superimpose it with the lithofacies model of step S3 to obtain a heterogeneous water saturation model of the thermal reservoir section in the study area;

[0015] S7. Spatially superimpose the various models of the thermal reservoir section in the study area finally obtained in steps S2 to S6 to obtain a geothermal resource evaluation model of the thermal reservoir section in the study area based on grid attributes, then calculate the total geothermal resources in the geothermal resource evaluation model, and then obtain the recoverable geothermal resources of the thermal reservoir section in the study area through the total geothermal resources.

[0016] Furthermore, the seismic data includes seismic interpretation fault files and seismic interpretation thermal reservoir segment layer data; the well logging interpretation data includes well layering data;

[0017] Step S2 includes the following steps:

[0018] S21. Performing well logging correction on the seismic interpretation fault file, and then establishing the fault model based on the well logging corrected seismic interpretation fault file;

[0019] S22. defining the horizontal and vertical resolutions of the grid cells of the fault model;

[0020] S23. Generate the layer model by Kriging interpolation method according to the well layer data and the thermal reservoir layer layer data interpreted by seismic analysis;

[0021] S24. Spatially superimpose the layer model and the fault model to obtain the three-dimensional spatial structural model.

[0022] Furthermore, the sedimentary data include single well sedimentary microfacies data and sedimentary microfacies diagrams of each time unit of the thermal reservoir segment; the indoor lithofacies test data include a classification table of thermal reservoir segments containing porosity and permeability ranges of different lithofacies; the well logging interpretation data include well logging interpretation porosity curves and well logging interpretation permeability curves;

[0023] The step S3 comprises the following steps:

[0024] S31. Loading the sedimentary microphase map of each time unit of the thermal reservoir segment and digitizing it, so that each microphase is assigned a code;

[0025] S32. assigning the codes in step S31 to the grid cells of the three-dimensional space construction model in step S2 to obtain the digital phase model;

[0026] S33. Using the digital phase model as the control boundary, using the sequential indication algorithm under phase control, and performing random data simulation on the single well sedimentary microfacies data, the sequential indication sedimentary phase model under phase control is obtained;

[0027] S34. By using the thermal reservoir segment classification table containing porosity and permeability ranges of different lithofacies, setting the porosity and permeability classification standards of different lithofacies in the thermal reservoir segment, and determining the lithofacies parameter classification standards of the well logging interpretation porosity curve and the well logging interpretation permeability curve;

[0028] S35. According to the lithofacies parameter classification standard, the porosity and permeability of each lithofacies are set by using a truncated Gaussian function to obtain the lithofacies logging curve corresponding to the logging interpretation porosity curve and the logging interpretation permeability curve, and the lithofacies logging curve is coarsened to obtain the coarsening result of the lithofacies logging curve;

[0029] S36. Based on the coarsening results of the lithofacies logging curves, under the constraints of the sequential indication sedimentary facies model, a plurality of initial lithofacies models are randomly generated using a sequential indication algorithm, and a most of algorithm is used to achieve grid-level allocation of the initial lithofacies model under sedimentary facies control, thereby obtaining a grid-level lithofacies model under sedimentary facies control conditions.

[0030] Further, the seismic data includes seismic multi-attribute bodies; the well logging interpretation data includes well logging interpretation porosity curves;

[0031] Step S4 includes the following steps:

[0032] S41. Perform multi-attribute fusion on the earthquake multi-attribute body to obtain an earthquake multi-attribute fusion body;

[0033] S42. Using the seismic multi-attribute fusion body to carve the spatial distribution characteristics of the seismic attributes of the thermal reservoir segment to identify the spatial distribution of the thermal reservoir segment;

[0034] S43. Using principal component analysis to reduce the dimension of the seismic multi-attribute body, a seismic attribute body having a correlation with the well logging interpretation porosity greater than 0.9 and a cumulative contribution rate greater than 90% is obtained;

[0035] S44. Generate a genetic inversion porosity data volume based on the spatial distribution of the thermal reservoir segment in the area with no or few wells by using the porosity curve interpreted by the well logging and the seismic attribute volume obtained in step S43 through a genetic inversion method;

[0036] S45. Using the porosity data volume as a constraint parameter, a sequential indicator algorithm is used to generate multiple porosity models, and then the arithmetic average of the multiple porosity models is taken to obtain the phase-controlled porosity model.

[0037] Furthermore, the seismic multi-attribute body includes a seismic amplitude attribute body, a seismic sweet spot attribute body, a reflection intensity attribute body, a graphic equalization attribute body, a denoising attribute body, a structural smoothing attribute body, a root mean square attribute body, a filtering attribute body, and an equal frequency attribute body.

[0038] Further, the logging interpretation data includes logging curve data with depth, natural gamma value and density value;

[0039] Step S5 includes the following steps:

[0040] S51. Based on the lithofacies logging curve of step S3, using the logging curve data with depth, natural gamma value and density value, obtain the natural gamma value and density value of the lithofacies at the corresponding depth, and then obtain the temperature value of the lithofacies at the corresponding depth;

[0041] S52. Perform regression analysis using the data obtained in step S51 to establish a regression equation;

[0042] S53. Using the regression equation and combining it with the lithofacies model of step S3, the temperature field model is obtained.

[0043] Further, in step S52, the regression equation is:

[0044] T=0.058*DEPTH+0.068*GR-18.032*DEN-14.889;

[0045] Where, T represents temperature; DEPTH represents depth; GR represents the natural gamma value in the logging interpretation data; and DEN represents the density value in the logging interpretation data.

[0046] Furthermore, the well logging interpretation data includes gas reservoir profile data interpreted by well logging and water saturation data interpreted by well logging;

[0047] Step S6 includes the following steps:

[0048] S61. Using the gas reservoir profile data interpreted by the well logging, identifying the depth of the gas-water interface in each layer of the thermal reservoir section;

[0049] S62. Using the depth of the gas-water interface in each layer, the upper and lower boundaries of the gas-water transition zone are identified, and a gas-water transition zone model of a sedimentary sand body having a gas-water boundary is established;

[0050] S63. Using the water saturation data interpreted by the well logging, a sequential indicator algorithm is used to generate a plurality of initial water saturation attribute distribution body models, and then arithmetic averages are taken for the plurality of initial water saturation attribute distribution body models to obtain a water saturation attribute distribution body model;

[0051] S64. spatially superimposing the water saturation attribute distribution model and the sedimentary sand body gas-water transition zone model to obtain a water saturation model;

[0052] S65. Spatially superimpose the water saturation model and the lithofacies model of step S3 to obtain the heterogeneous water saturation model.

[0053] Further, step S7 includes the following steps:

[0054] S71. spatially superimpose the three-dimensional spatial structural model, lithofacies model, phase-controlled porosity model, temperature field model, and heterogeneous water saturation model to obtain a geothermal resource evaluation model based on grid attributes for the thermal reservoir segment in the study area;

[0055] S72. Establish a calculation formula for the amount of geothermal resources stored in the nth lithofacies matrix of the thermal reservoir section, and establish a calculation formula for the amount of geothermal resources stored in the nth lithofacies of the thermal reservoir section;

[0056] S73. Calculate the amount of geothermal resources stored in different lithofacies of each grid unit of the geothermal resource evaluation model according to the calculation formula of step S72 to obtain the amount of geothermal resources stored in each lithofacies matrix and the amount of geothermal resources stored in water; add up the amount of geothermal resources stored in various lithofacies matrices and the amount of geothermal resources stored in water to obtain the total amount of geothermal resources stored in the thermal reservoir section of the study area;

[0057] S74. According to the total geothermal resources and in combination with the recoverable coefficient of resources, the recoverable geothermal resources are obtained.

[0058] Further, in step S72, the calculation formula for the amount of geothermal resources stored in the nth lithofacies matrix of the thermal reservoir section is:

[0059]

[0060] In the formula, It indicates the amount of geothermal resources stored in the nth lithofacies matrix of the thermal reservoir section. represents the density corresponding to the nth rock in the thermal reservoir segment, represents the specific heat of the nth rock in the thermal reservoir segment, x i,j ,y i,j They represent the x- and y-direction steps of the (i, j) coordinate grid unit in the geothermal resource evaluation model, represents the porosity of the (i, j) coordinate grid cell in the nth lithofacies distribution of the thermal reservoir segment, represents the average heat reservoir temperature in the (i, j) coordinate grid unit in the nth lithofacies distribution of the heat reservoir segment, T 0 represents the annual average temperature of the study area, M i,j represents the thickness of the (i, j) coordinate grid unit in the nth lithofacies distribution of the thermal reservoir section; wherein the nth lithofacies corresponds to the lithofacies number in the lithofacies model;

[0061] The calculation formula for the amount of geothermal resources stored in water in the nth lithofacies of the thermal reservoir section is:

[0062]

[0063] In the formula, represents the amount of geothermal resources stored in the nth lithofacies of the thermal reservoir segment, ρ w represents the density of water in the thermal reservoir segment, c w represents the specific heat capacity of water in the thermal reservoir segment, x i,j ,y i,j They represent the x- and y-direction steps of the grid cell at the (i, j) coordinate in the geothermal resource evaluation model. represents the porosity of the (i, j) coordinate grid cell in the nth lithofacies distribution of the thermal reservoir segment, represents the water saturation in the (i, j) coordinate grid cell in the nth lithofacies distribution of the thermal reservoir segment, represents the average temperature of water in the (i, j) coordinate grid unit in the nth lithofacies distribution of the thermal reservoir segment, T 0 represents the annual average temperature of the study area, M i,j represents the thickness of the (i, j) coordinate grid unit in the nth lithofacies distribution of the thermal reservoir section; wherein the nth lithofacies corresponds to the lithofacies number in the lithofacies model;

[0064] In step S73, the total geothermal resources are calculated as follows:

[0065]

[0066] In the formula, Q s represents the total geothermal resources, represents the amount of geothermal resources stored in the nth lithofacies of the thermal reservoir section, It indicates the amount of geothermal resources stored in the nth lithofacies matrix of the thermal reservoir section;

[0067] In step S74, the calculation formula for the recoverable geothermal resources is:

[0068] Q e =Q s *R e ;

[0069] In the formula, Q e represents the recoverable geothermal resources, Q s represents the total geothermal resources, R e Indicates the recoverable coefficient of resources.

[0070] Compared with the prior art, the present invention has the following beneficial effects:

[0071] The present invention is a method for evaluating geothermal resources in oil and gas fields. It comprehensively utilizes seismic data, well logging interpretation data, sedimentary data and indoor lithofacies test data in the oil and gas field area. Based on these data, a three-dimensional spatial structural model, a lithofacies model, a phase-controlled porosity model, a temperature field model and a heterogeneous water saturation model of the thermal reservoir section in the study area are established, and the above three-dimensional spatial structural model and each model are spatially superimposed to obtain a geothermal resource evaluation model, so as to realize the lithology and attribute allocation of each grid unit in the oil and gas field. By gridding the thermal reservoir section in the study area, combining the density and specific heat data of different lithofacies and water, the amount of geothermal resources in each grid unit can be accurately calculated; by integrating and accumulating the calculation results in these grid units, the total amount of geothermal resources in the thermal reservoir section of the entire study area is obtained, and the recoverable geothermal resources in the thermal reservoir section of the study area can be obtained, providing a solid data foundation and scientific guidance for large-scale geothermal energy development in the future oil and gas field area. The present invention can accurately describe the physical and thermal properties of the geothermal reservoir, so that the geothermal resource evaluation results are closer to the actual situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A flow chart of a method for evaluating geothermal resources in oil and gas fields according to the present invention;

[0073] Figure 2 The three-dimensional space construction model in step S2 of the present invention;

[0074] Figure 3 The facies model in step S3 of the present invention;

[0075] Figure 4 A top view of the lithofacies model in step S3 of the present invention;

[0076] Figure 5 is the phase-controlled porosity model in step S4 of the present invention;

[0077] Figure 6 is the temperature field model in step S5 of the present invention;

[0078] Figure 7 is the heterogeneous water saturation model in step S6 of the present invention;

[0079] Figure 8 It is a top view of the heterogeneous water saturation model in step S6 of the present invention. DETAILED DESCRIPTION

[0080] The present invention is further described below in conjunction with specific implementation methods. The accompanying drawings are only used for exemplary descriptions and are only schematic diagrams, not actual drawings, and cannot be understood as limiting this patent; in order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the accompanying drawings may be omitted.

[0081] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, it is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limitations on this patent. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0082] Example 1

[0083] like Figures 1 to 8 The first embodiment of a method for evaluating geothermal resources in oil and gas fields according to the present invention is shown, and includes the following steps:

[0084] S1. Collect seismic data, well logging interpretation data, sedimentary data and indoor lithofacies test data in the study area, and establish a basic database of the thermal reservoir section in the study area;

[0085] S2. Based on the basic database, a fault model and a layer model are established, and then the two are spatially superimposed to obtain a three-dimensional spatial structural model of the thermal reservoir section in the study area;

[0086] S3. Based on the sedimentary data and the three-dimensional spatial structural model, a digital phase model of the thermal reservoir section in the study area is obtained; then, the digital phase model is used as the control boundary, and a sequential indicative sedimentary phase model is obtained through statistical inference; the lithofacies logging curve is obtained by preprocessing the indoor lithofacies test data and the logging interpretation data; then, based on the processed lithofacies logging curve, under the constraint of the sequential indicative sedimentary phase model, the lithofacies model of the thermal reservoir section in the study area is obtained through statistical inference;

[0087] S4. For areas with no or few wells in the thermal reservoir section of the study area, the porosity data volume is obtained by using seismic data and well logging interpretation data and genetic inversion method; then, the porosity data volume is used as a constraint to obtain the phase-controlled porosity model of the thermal reservoir section of the study area through statistical inference;

[0088] S5. Establish a regression equation based on the lithofacies logging curve and the logging interpretation data; use the regression equation and the lithofacies model of step S3 to obtain a temperature field model of the thermal reservoir section in the study area;

[0089] S6. Identify the well logging interpretation data and establish a sedimentary sand body gas-water transition zone model; establish a water saturation attribute distribution model through statistical inference of the well logging interpretation data; spatially superimpose the sedimentary sand body gas-water transition zone model with the water saturation attribute distribution model, and then spatially superimpose it with the lithofacies model of step S3 to obtain a heterogeneous water saturation model of the thermal reservoir section in the study area;

[0090] S7. Spatially superimpose the various models of the thermal reservoir section in the study area finally obtained in steps S2 to S6 to obtain a geothermal resource evaluation model of the thermal reservoir section in the study area based on grid attributes, then calculate the total geothermal resources in the geothermal resource evaluation model, and then obtain the recoverable geothermal resources of the thermal reservoir section in the study area through the total geothermal resources.

[0091] The present invention comprehensively utilizes seismic data, well logging interpretation data, sedimentary data and indoor lithofacies test data in the oil and gas field area, and based on these data, establishes a three-dimensional spatial structural model, a lithofacies model, a phase-controlled porosity model, a temperature field model, and a heterogeneous water saturation model of the thermal reservoir section in the study area, and spatially superimposes the above three-dimensional spatial structural model and each model to obtain a geothermal resource evaluation model, so as to realize the lithology and attribute allocation of each grid unit in the oil and gas field. By gridding the thermal reservoir section in the study area, combining the density and specific heat data of different lithofacies and water, the amount of geothermal resources in each grid unit can be accurately calculated; by integrating and accumulating the calculation results in these grid units, the total amount of geothermal resources in the thermal reservoir section of the entire study area is obtained, and the recoverable geothermal resources in the thermal reservoir section of the study area can be obtained, providing a solid data foundation and scientific guidance for large-scale geothermal energy development in the future oil and gas field area. The present invention can accurately describe the physical and thermal properties of the geothermal reservoir, so that the geothermal resource evaluation results are closer to the actual situation.

[0092] Example 2

[0093] This embodiment is similar to Embodiment 1, except that in this embodiment:

[0094] In step S1, the seismic data includes seismic interpretation fault files, seismic interpretation thermal reservoir segment level data, and seismic multi-attribute bodies;

[0095] Well logging interpretation data include well stratification data, well logging interpretation porosity curve, well logging interpretation permeability curve, well logging curve data with depth, natural gamma value and density value, well logging interpretation gas reservoir profile data, well logging interpretation water saturation data;

[0096] Sedimentary data include single well sedimentary microfacies data and sedimentary microfacies maps of each time unit in the thermal reservoir section;

[0097] Indoor lithofacies test data include classification tables of thermal reservoir sections with porosity and permeability ranges for different lithofacies, density data and specific heat capacity data for different lithofacies and water at corresponding temperatures of thermal reservoir sections;

[0098] The above data are converted into a standard Petrel input file to establish a basic database of the thermal reservoir section in the study area.

[0099] Specifically, step S2 includes the following steps:

[0100] S21. Performing well logging correction on the seismic interpretation fault file, and then establishing a fault model based on the well logging corrected seismic interpretation fault file;

[0101] S22. Define the horizontal and vertical resolutions of the grid cells of the fault model, that is, define the step size of the grid cells; in this embodiment, the step size of the grid cells may be set to 150m×150m, and the step size of the grid cells in the vertical direction may be set to 3m;

[0102] S23. Generate a layer model based on the well layer data and the thermal reservoir layer data interpreted by seismic analysis by using the Kriging interpolation method;

[0103] The thermal reservoir section layer data interpreted by seismic analysis include the top and bottom surface data of the thermal reservoir section interpreted by seismic analysis, and therefore, the generated layer model includes the top and bottom surface models;

[0104] S24. Spatially superimpose the top and bottom surface models with the fault model to obtain a three-dimensional spatial structural model. Figure 2 shown.

[0105] Specifically, step S3 includes the following steps:

[0106] S31. Load the sedimentary microfacies map of each time unit of the thermal reservoir segment and digitize it, so that each microfacies is assigned a code;

[0107] S32. assigning the codes in step S31 to the grid cells of the three-dimensional space structure model in step S24 to obtain a digital phase model;

[0108] S33. Using the digital facies model as the control boundary and the sequential indication algorithm under facies control, a sequential indication sedimentary facies model under facies control is obtained by performing random data simulation on the sedimentary microfacies data of a single well;

[0109] S34. By using the classification table of thermal reservoir sections containing porosity and permeability ranges of different lithofacies, setting the porosity and permeability classification standards of different lithofacies of the thermal reservoir sections, and determining the lithofacies parameter classification standards of the well logging interpretation porosity curve and the well logging interpretation permeability curve;

[0110] In step S34, the classification table of thermal reservoir sections including porosity and permeability ranges of different lithofacies may be as shown in Table 1:

[0111] Table 1 Classification of thermal reservoir sections with porosity and permeability ranges for different lithofacies

[0112]

[0113] The lithofacies parameter division standards of the well logging interpretation porosity curve and the well logging interpretation permeability curve are determined as shown in Table 2:

[0114] Table 2 Lithofacies parameter classification standards

[0115]

[0116] S35. According to the lithofacies parameter classification standard, the porosity and permeability of each lithofacies are set by using a truncated Gaussian function to obtain a lithofacies logging curve corresponding to the logging interpretation porosity curve and the logging interpretation permeability curve, and the lithofacies logging curve is coarsened to obtain a lithofacies logging curve coarsening result;

[0117] S36. Based on the coarsening result of the lithofacies logging curve, under the constraint of the sequential indication sedimentary facies model in step S33, a plurality of initial lithofacies models are randomly generated using the sequential indication algorithm, and the grid level allocation of the initial lithofacies model under sedimentary facies control is realized using the most of algorithm to obtain a grid-level lithofacies model under sedimentary facies control; Figure 3 and Figure 4 It should be noted that the most of algorithm refers to the process of uniformly assigning the largest proportion of discrete data values ​​on the well trajectory passing through the grid when assigning values ​​to the three-dimensional grid.

[0118] Specifically, step S4 includes the following steps:

[0119] S41. fusing the earthquake multi-attribute body to obtain the earthquake multi-attribute fusion body;

[0120] Among them, the seismic multi-attribute body includes the seismic amplitude attribute body, the seismic sweet spot attribute body, the reflection intensity attribute body, the graphic equalization attribute body, the denoising attribute body, the structural smoothing attribute body, the root mean square attribute body, the filtering attribute body, and the equal frequency attribute body; the seismic multi-attribute fusion body is the multi-attribute fusion of the above nine seismic attribute bodies;

[0121] S42. Use seismic multi-attribute fusion to carve the spatial distribution characteristics of seismic attributes of the thermal reservoir segment to identify the spatial distribution of the thermal reservoir segment;

[0122] S43. The principal component analysis method is used to reduce the dimension of the seismic multi-attribute, that is, the above nine seismic attribute bodies, to obtain seismic attribute bodies with a correlation with the well logging interpretation porosity greater than 0.9 and a cumulative contribution rate greater than 90%; for example, the seismic attribute bodies finally selected include the seismic sweet spot attribute body, the reflection intensity attribute body and the root mean square attribute body;

[0123] S44. Generate a genetic inversion porosity data volume based on the spatial distribution of the thermal reservoir segment in the area with no or few wells by using the porosity curve interpreted by logging and the seismic attribute volume obtained in step S43 through a genetic inversion method;

[0124] S45. Using the porosity data volume as a constraint parameter, a sequential indicator algorithm is used to generate multiple porosity models, and then the arithmetic average of the multiple porosity models is taken to obtain a phase-controlled porosity model. Figure 5 shown.

[0125] In step S4, it should be noted that the area with no wells or few wells in the thermal reservoir section of the study area refers to the area with a well density of less than 2-3 wells / square kilometer.

[0126] Specifically, step S5 includes the following steps:

[0127] S51. Based on the lithofacies logging curve of step S35, using the logging curve data with depth, natural gamma value and density value, obtain the natural gamma value and density value of the lithofacies at the corresponding depth, and then obtain the temperature value of the lithofacies at the corresponding depth;

[0128] S52. Perform regression analysis using the data obtained in step S51 to establish a regression equation;

[0129] The regression equation is:

[0130] T=0.058*DEPTH+0.068*GR-18.032*DEN-14.889;

[0131] In the formula, T represents temperature; DEPTH represents depth; GR represents the natural gamma value in the logging interpretation data; DEN represents the density value in the logging interpretation data;

[0132] S53. Using the regression equation and the lithofacies model of step S36, a temperature field model is obtained; it should be noted that in the regression equation, the GR and DEN corresponding to different rocks are determined in the well logging curve data, that is, only the grids where different lithofacies are located need to provide corresponding depth information, and the temperature corresponding to different rocks at different depths in the thermal reservoir section of the study area can be obtained according to the regression equation, and then the temperature field model can be obtained. Figure 6 shown.

[0133] Specifically, step S6 includes the following steps:

[0134] S61. Using the gas reservoir profile data interpreted from well logging, identify the depth of the gas-water interface in each layer of the thermal reservoir section;

[0135] S62. Using the depth of the gas-water interface in each layer, the upper and lower boundaries of the gas-water transition zone are identified, and a gas-water transition zone model of a sedimentary sand body with a gas-water boundary is established;

[0136] S63. Using the water saturation data interpreted by well logging, a sequential indicator algorithm is used to generate a plurality of initial water saturation attribute distribution body models, and then arithmetic averages the plurality of initial water saturation attribute distribution body models to obtain a water saturation attribute distribution body model;

[0137] S64. spatially superimpose the water saturation attribute distribution model and the sedimentary sand body gas-water transition zone model to obtain a water saturation model;

[0138] S65. Spatially superimpose the water saturation model and the lithofacies model of step S36 to obtain a heterogeneous water saturation model. Figure 7 and Figure 8 shown.

[0139] Example 3

[0140] This embodiment is similar to Embodiment 1 or 2, except that, in this embodiment:

[0141] Step S7 includes the following steps:

[0142] S71. spatially superimpose the three-dimensional spatial structural model of step S24, the lithofacies model of step S36, the phase-controlled porosity model of step S45, the temperature field model of step S53, and the heterogeneous water saturation model of step S65 to obtain a geothermal resource evaluation model based on grid attributes for the thermal reservoir segment in the study area;

[0143] S72. Establish a calculation formula for the amount of geothermal resources stored in the nth lithofacies matrix of the thermal reservoir section, and establish a calculation formula for the amount of geothermal resources stored in the nth lithofacies of the thermal reservoir section; wherein,

[0144] The calculation formula for the amount of geothermal resources stored in the nth lithofacies matrix of the thermal reservoir section is:

[0145]

[0146] In the formula, It indicates the amount of geothermal resources stored in the nth lithofacies matrix of the thermal reservoir section. represents the density corresponding to the nth rock in the thermal reservoir segment, represents the specific heat of the nth rock in the thermal reservoir segment, x i,j ,y i,j They represent the x- and y-direction steps of the (i, j) coordinate grid unit in the geothermal resource evaluation model, represents the porosity of the (i, j) coordinate grid cell in the nth lithofacies distribution of the thermal reservoir segment, represents the average heat reservoir temperature in the (i, j) coordinate grid unit in the nth lithofacies distribution of the heat reservoir segment, T 0 represents the annual average temperature of the study area, M i,j represents the thickness of the (i, j) coordinate grid unit in the nth lithofacies distribution of the thermal reservoir segment; where the nth lithofacies is the lithofacies number in the corresponding lithofacies model;

[0147] The calculation formula for the amount of geothermal resources stored in water in the nth lithofacies of the thermal reservoir section is:

[0148]

[0149] In the formula, represents the amount of geothermal resources stored in the nth lithofacies of the thermal reservoir segment, ρ w represents the density of water in the thermal reservoir segment, c w represents the specific heat capacity of water in the thermal reservoir segment, x i,j ,y i,j They represent the x- and y-direction steps of the grid cell at the (i, j) coordinate in the geothermal resource evaluation model. represents the porosity of the (i, j) coordinate grid cell in the nth lithofacies distribution of the thermal reservoir segment, represents the water saturation in the (i, j) coordinate grid cell in the nth lithofacies distribution of the thermal reservoir segment, represents the average temperature of water in the (i, j) coordinate grid unit in the nth lithofacies distribution of the thermal reservoir segment, T 0 represents the annual average temperature of the study area, M i,j represents the thickness of the (i, j) coordinate grid unit in the nth lithofacies distribution of the thermal reservoir segment; where the nth lithofacies is the lithofacies number in the corresponding lithofacies model;

[0150] It should be noted that in the formula of step S72, the density data and specific heat capacity data are directly involved in the calculation of geothermal resources, and the density and specific heat capacity data of water corresponding to each type of rock phase are different, and the density and specific heat capacity data of water are obtained from the density data and specific heat capacity data of different rock phases and water at the corresponding temperature of the thermal reservoir section in the indoor rock phase test data;

[0151] Among them, the density data and specific heat capacity data of different lithofacies and water at the corresponding temperature of the thermal reservoir section are shown in Table 3:

[0152] Table 3 Density and specific heat capacity data of different lithofacies and water at corresponding temperatures in the thermal reservoir section

[0153]

[0154] S73. Calculate the amount of geothermal resources stored in different lithofacies of each grid unit of the geothermal resource evaluation model according to the calculation formula of step S72 to obtain the amount of geothermal resources stored in each lithofacies matrix and the amount of geothermal resources stored in water; add up the amount of geothermal resources stored in various lithofacies matrices and the amount of geothermal resources stored in water to obtain the total amount of geothermal resources stored in the thermal reservoir section of the study area; wherein,

[0155] The total geothermal resources can be calculated as follows:

[0156]

[0157] In the formula, Q s represents the total geothermal resources, represents the amount of geothermal resources stored in the nth lithofacies of the thermal reservoir section, It indicates the amount of geothermal resources stored in the nth lithofacies matrix of the thermal reservoir section;

[0158] S74. According to the total geothermal resources and combined with the resource recoverability coefficient, the recoverable geothermal resources are obtained; wherein,

[0159] The calculation formula for recoverable geothermal resources is:

[0160] Q e =Q s *R e ;

[0161] In the formula, Q e represents the recoverable geothermal resources, Q s represents the total geothermal resources, R e Indicates the recoverable coefficient of resources.

[0162] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for evaluating geothermal resources in oil and gas fields, characterized in that: The steps include: S1. Collect seismic data, well logging interpretation data, sedimentary data and indoor lithofacies test data in the study area, and establish a basic database of the thermal reservoir section in the study area; S2. Based on the basic database, a fault model and a layer model are established, and then the two are spatially superimposed to obtain a three-dimensional spatial structural model of the thermal reservoir section in the study area; S3. Based on the sedimentary data and the three-dimensional spatial structural model, a digital phase model of the thermal reservoir section in the study area is obtained; then, the digital phase model is used as a control boundary to obtain a sequential indicative sedimentary phase model through statistical inference; and a lithofacies logging curve is obtained by preprocessing the indoor lithofacies test data and logging interpretation data; Then, based on the processed lithofacies logging curves, under the constraints of the sequential indicative sedimentary facies model, a lithofacies model of the thermal reservoir section in the study area is obtained by statistical inference; S4. For the area with no wells or few wells in the thermal reservoir section of the study area, the porosity data volume is obtained by using the seismic data and the logging interpretation data and the genetic inversion method; then, the porosity data volume is used as a constraint to obtain the phase-controlled porosity model of the thermal reservoir section of the study area by statistical inference; S5. Establishing a regression equation based on the lithofacies logging curve and the logging interpretation data; using the regression equation and combining the lithofacies model of step S3 to obtain a temperature field model of the thermal reservoir section in the study area; S6. Identify the well logging interpretation data and establish a sedimentary sand body gas-water transition zone model; establish a water saturation attribute distribution model through statistical inference of the well logging interpretation data; spatially superimpose the sedimentary sand body gas-water transition zone model with the water saturation attribute distribution model, and then spatially superimpose it with the lithofacies model of step S3 to obtain a heterogeneous water saturation model of the thermal reservoir section in the study area; S7. Spatially superimpose the various models of the thermal reservoir section in the study area finally obtained in steps S2 to S6 to obtain a geothermal resource evaluation model of the thermal reservoir section in the study area based on grid attributes, then calculate the total geothermal resources in the geothermal resource evaluation model, and then obtain the recoverable geothermal resources of the thermal reservoir section in the study area through the total geothermal resources.

2. The method for evaluating geothermal resources in oil and gas fields according to claim 1, characterized in that: The seismic data includes seismic interpretation fault files and seismic interpretation thermal reservoir segment layer data; the well logging interpretation data includes well layer data; Step S2 includes the following steps: S21. Performing well logging correction on the seismic interpretation fault file, and then establishing the fault model based on the well logging corrected seismic interpretation fault file; S22. defining the horizontal and vertical resolutions of the grid cells of the fault model; S23. Generate the layer model by Kriging interpolation method according to the well layer data and the thermal reservoir layer layer data interpreted by seismic analysis; S24. Spatially superimpose the layer model and the fault model to obtain the three-dimensional spatial structural model.

3. The method for evaluating geothermal resources in oil and gas fields according to claim 1, characterized in that: The sedimentary data include single well sedimentary microfacies data and sedimentary microfacies diagrams of each time unit of the thermal reservoir section; the indoor lithofacies test data include a classification table of thermal reservoir sections containing porosity and permeability ranges of different lithofacies; the well logging interpretation data include well logging interpretation porosity curves and well logging interpretation permeability curves; The step S3 comprises the following steps: S31. Loading the sedimentary microphase map of each time unit of the thermal reservoir segment and digitizing it, so that each microphase is assigned a code; S32. assigning the codes in step S31 to the grid cells of the three-dimensional space construction model in step S2 to obtain the digital phase model; S33. Using the digital phase model as the control boundary, using the sequential indication algorithm under phase control, and performing random data simulation on the single well sedimentary microfacies data, the sequential indication sedimentary phase model under phase control is obtained; S34. By using the thermal reservoir segment classification table containing porosity and permeability ranges of different lithofacies, setting the porosity and permeability classification standards of different lithofacies in the thermal reservoir segment, and determining the lithofacies parameter classification standards of the well logging interpretation porosity curve and the well logging interpretation permeability curve; S35. According to the lithofacies parameter classification standard, the porosity and permeability of each lithofacies are set by using a truncated Gaussian function to obtain the lithofacies logging curve corresponding to the logging interpretation porosity curve and the logging interpretation permeability curve, and the lithofacies logging curve is coarsened to obtain the coarsening result of the lithofacies logging curve; S36. Based on the coarsening results of the lithofacies logging curves, under the constraints of the sequential indication sedimentary facies model, a plurality of initial lithofacies models are randomly generated using a sequential indication algorithm, and a most of algorithm is used to achieve grid-level allocation of the initial lithofacies model under sedimentary facies control, thereby obtaining a grid-level lithofacies model under sedimentary facies control conditions.

4. The method for evaluating geothermal resources in oil and gas fields according to claim 1, characterized in that: The seismic data includes seismic multi-attribute bodies; the well logging interpretation data includes well logging interpretation porosity curves; Step S4 includes the following steps: S41. Perform multi-attribute fusion on the earthquake multi-attribute body to obtain an earthquake multi-attribute fusion body; S42. Using the seismic multi-attribute fusion body to carve the spatial distribution characteristics of the seismic attributes of the thermal reservoir segment to identify the spatial distribution of the thermal reservoir segment; S43. Using principal component analysis to reduce the dimension of the seismic multi-attribute body, a seismic attribute body having a correlation with the well logging interpretation porosity greater than 0.9 and a cumulative contribution rate greater than 90% is obtained; S44. Generate a genetic inversion porosity data volume based on the spatial distribution of the thermal reservoir segment in the area with no or few wells by using the porosity curve interpreted by the well logging and the seismic attribute volume obtained in step S43 through a genetic inversion method; S45. Using the porosity data volume as a constraint parameter, a sequential indicator algorithm is used to generate multiple porosity models, and then the arithmetic average of the multiple porosity models is taken to obtain the phase-controlled porosity model.

5. The method for evaluating geothermal resources in oil and gas fields according to claim 4, characterized in that: The seismic multi-attribute body includes a seismic amplitude attribute body, a seismic sweet spot attribute body, a reflection intensity attribute body, a graphic equalization attribute body, a denoising attribute body, a structural smoothing attribute body, a root mean square attribute body, a filtering attribute body, and an equal frequency attribute body.

6. The method for evaluating geothermal resources in oil and gas fields according to claim 1, characterized in that: The logging interpretation data include logging curve data with depth, natural gamma value and density value; Step S5 includes the following steps: S51. Based on the lithofacies logging curve of step S3, using the logging curve data with depth, natural gamma value and density value, obtain the natural gamma value and density value of the lithofacies at the corresponding depth, and then obtain the temperature value of the lithofacies at the corresponding depth; S52. Perform regression analysis using the data obtained in step S51 to establish a regression equation; S53. Using the regression equation and combining it with the lithofacies model of step S3, the temperature field model is obtained.

7. The method for evaluating geothermal resources in oil and gas fields according to claim 6, characterized in that: In step S52, the regression equation is: T=0.058*DEPTH+0.068*GR-18.032*DEN-14.889; Where, T represents temperature; DEPTH represents depth; GR represents the natural gamma value in the logging interpretation data; and DEN represents the density value in the logging interpretation data.

8. The method for evaluating geothermal resources in oil and gas fields according to claim 1, characterized in that: The well logging interpretation data include gas reservoir profile data interpreted by well logging and water saturation data interpreted by well logging; Step S6 includes the following steps: S61. Using the gas reservoir profile data interpreted by the well logging, identifying the depth of the gas-water interface in each layer of the thermal reservoir section; S62. Using the depth of the gas-water interface in each layer, the upper and lower boundaries of the gas-water transition zone are identified, and a gas-water transition zone model of a sedimentary sand body having a gas-water boundary is established; S63. Using the water saturation data interpreted by the well logging, a sequential indicator algorithm is used to generate a plurality of initial water saturation attribute distribution body models, and then arithmetic averages are taken for the plurality of initial water saturation attribute distribution body models to obtain a water saturation attribute distribution body model; S64. spatially superimposing the water saturation attribute distribution model and the sedimentary sand body gas-water transition zone model to obtain a water saturation model; S65. Spatially superimpose the water saturation model and the lithofacies model of step S3 to obtain the heterogeneous water saturation model.

9. The method for evaluating geothermal resources in oil and gas fields according to claim 1, characterized in that: Step S7 includes the following steps: S71. spatially superimpose the three-dimensional spatial structural model, lithofacies model, phase-controlled porosity model, temperature field model, and heterogeneous water saturation model to obtain a geothermal resource evaluation model based on grid attributes for the thermal reservoir segment in the study area; S72. Establish a calculation formula for the amount of geothermal resources stored in the nth lithofacies matrix of the thermal reservoir section, and establish a calculation formula for the amount of geothermal resources stored in the nth lithofacies of the thermal reservoir section; S73. Calculate the amount of geothermal resources stored in different lithofacies of each grid unit of the geothermal resource evaluation model according to the calculation formula of step S72 to obtain the amount of geothermal resources stored in each lithofacies matrix and the amount of geothermal resources stored in water; The total geothermal resources in the thermal reservoir section of the study area are obtained by accumulating the geothermal resources in various lithofacies matrices and the geothermal resources in water. S74. According to the total geothermal resources and in combination with the recoverable coefficient of resources, the recoverable geothermal resources are obtained.

10. The method for evaluating geothermal resources in oil and gas fields according to claim 9, characterized in that: In step S72, the calculation formula for the amount of geothermal resources stored in the nth lithofacies matrix of the thermal reservoir section is: In the formula, It indicates the amount of geothermal resources stored in the nth lithofacies matrix of the thermal reservoir section. represents the density corresponding to the nth rock in the thermal reservoir segment, represents the specific heat of the nth rock in the thermal reservoir segment, x i,j ,y i,j They represent the x- and y-direction steps of the (i, j) coordinate grid unit in the geothermal resource evaluation model, represents the porosity of the (i, j) coordinate grid cell in the nth lithofacies distribution of the thermal reservoir segment, represents the average heat reservoir temperature in the (i, j) coordinate grid unit in the nth lithofacies distribution of the heat reservoir section, T0 represents the annual average temperature in the study area, M i,j represents the thickness of the (i, j) coordinate grid unit in the nth lithofacies distribution of the thermal reservoir section; wherein the nth lithofacies corresponds to the lithofacies number in the lithofacies model; The calculation formula for the amount of geothermal resources stored in water in the nth lithofacies of the thermal reservoir section is: In the formula, represents the amount of geothermal resources stored in the nth lithofacies of the thermal reservoir segment, ρ w represents the density of water in the thermal reservoir segment, c w represents the specific heat capacity of water in the thermal reservoir segment, x i,j ,y i,j They represent the x- and y-direction steps of the grid cell at the (i, j) coordinate in the geothermal resource evaluation model. represents the porosity of the (i, j) coordinate grid cell in the nth lithofacies distribution of the thermal reservoir segment, represents the water saturation in the (i, j) coordinate grid cell in the nth lithofacies distribution of the thermal reservoir segment, represents the average temperature of water in the (i, j) coordinate grid unit in the nth lithofacies distribution of the thermal reservoir section, T0 represents the annual average temperature of the study area, M i,j represents the thickness of the (i, j) coordinate grid unit in the nth lithofacies distribution of the thermal reservoir section; wherein the nth lithofacies corresponds to the lithofacies number in the lithofacies model; In step S73, the total geothermal resources are calculated as follows: In the formula, Q s represents the total geothermal resources, represents the amount of geothermal resources stored in the nth lithofacies of the thermal reservoir section, It indicates the amount of geothermal resources stored in the nth lithofacies matrix of the thermal reservoir section; In step S74, the calculation formula for the recoverable geothermal resources is: Q e =Q s *R e ; In the formula, Q e represents the recoverable geothermal resources, Q s represents the total geothermal resources, R e Indicates the recoverable coefficient of resources.

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