A multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction method and system

Abstract

The present invention relates to a method and system for evaluating and predicting shale oil enrichment areas with multiple sweet spots and multiple parameters, belonging to the technical field of unconventional oil and gas exploration and development, including: ΔlogR The oil content sweet spot parameter, i.e., free oil, is obtained by using the method and the formation free oil recovery model; the physical property sweet spot parameter, i.e., porosity, is obtained based on the multivariate linear regression analysis; the elastic energy sweet spot parameter, i.e., formation pressure coefficient, is predicted based on the Bowers method; the fracturability sweet spot, i.e., fracturability index, is obtained based on the brittleness index predicted by the comprehensive random forest algorithm and the Young's modulus obtained by the mathematical model; the sweet spot comprehensive evaluation index is obtained based on the hierarchical analysis calculation method, and the sweet spot comprehensive evaluation index is applied to key shale oil exploration wells. The present invention improves the accuracy of the evaluation results and provides favorable support for shale oil exploration and development.

Description

Technical Field

[0001] The present invention belongs to the technical field of unconventional oil and gas exploration and development, and in particular relates to a multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction method and system. Background Art

[0002] Shale oil "sweet spots" refer to shale oil-rich sections or areas in areas with excellent reservoir properties, oil content, brittleness, and geostress in shale formations. Combined with information such as oil testing, test production results, and production capacity, they can be selected to be suitable for exploration and development. The evaluation of shale oil "sweet spots" and the selection of enriched areas are the key and difficult points of the entire shale oil exploration research, and the evaluation results directly affect production decisions and development results.

[0003] Previous methods for evaluating and predicting shale oil-rich areas mainly include plane superposition method, seismic inversion method, machine learning method, well logging modeling method and other means. Among them, although the plane superposition method can quickly point out favorable areas, it may not be applicable to areas with thick strata, rapid lithological changes, and limited spatial distribution range; the scale of seismic inversion is usually large (meter level), and some evaluation parameters may not be inverted through seismic, resulting in limited overall evaluation accuracy; and machine learning algorithms require a large number of data samples and sufficient samples for testing. Well logging modeling usually refers to the construction of interpretation models for different evaluation parameters with the help of high-resolution logging data, and then evaluates shale oil-rich areas. However, due to the large number of evaluation parameters in shale oil-rich areas, how to optimize the main control parameters for shale oil-rich areas and conduct comprehensive evaluation remains to be solved.

[0004] Therefore, how to cover the main control factors of the "sweet spot" as much as possible and reflect the evaluation system and model of the comprehensive "sweet spot" is the key content of shale oil exploration research. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction method;

[0006] The present invention also provides a multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction system.

[0007] The technical problem to be solved by the present invention is: to overcome the problems of the existing shale oil rich area evaluation and prediction methods in the background technology, such as the limitations of the application scope, low evaluation accuracy, the need for a large number of samples, incomplete coverage of the main controlling factors of the sweet spots, etc., and to provide a shale oil rich area evaluation and prediction method with multiple sweet spots and multiple parameters. By adopting an improved hierarchical analysis calculation method to comprehensively analyze the oil content sweet spot parameters, physical property sweet spot parameters, elastic energy sweet spot parameters, and fracturability sweet spot parameters, a sweet spot comprehensive evaluation index is obtained, and the evaluation and prediction of shale oil rich areas with multiple sweet spots and multiple parameters is realized, providing favorable support for subsequent shale oil exploration and development.

[0008] The technical solution of the present invention is:

[0009] A multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction method, comprising:

[0010] based on ΔlogR The oil-bearing sweet spot parameter, i.e., free oil volume, is obtained by using the method and formation free oil recovery model.

[0011] Based on multiple linear regression analysis, the physical property sweet spot parameter, i.e., porosity, was obtained;

[0012] Predict the elastic energy sweet spot parameter, i.e., formation pressure coefficient, based on the Bowers method;

[0013] The fracturability sweet spot, i.e., the fracturability index, is obtained based on the brittleness index predicted by the comprehensive random forest algorithm and the Young's modulus obtained by the mathematical model;

[0014] The sweet spot comprehensive evaluation index is obtained based on the hierarchical analysis calculation method, and is applied to key shale oil exploration wells.

[0015] Preferably, according to the present invention, the improved ΔlogR method calculation formula of the variable proportional coefficient is as follows:

[0016] ;

[0017] In the formula, Δ logR is the difference in acoustic time difference and the amplitude of resistivity logging curve; R is the logging resistivity value, Ω·m; R 基线 is the logging resistivity value corresponding to the baseline, Ω·m; D is the proportionality coefficient, reflecting the resistivity in the calculation ΔlogR The proportion of the process, the value range is between 0-1; Δt is the logging acoustic wave time difference, μs / ft; Δt 基线 is the logging acoustic time difference value corresponding to the baseline, μs / ft, A, the correction constant;

[0018] According to the preferred embodiment of the present invention, the formation free oil recovery model is as follows:

[0019] The content of crude oil after gas chromatography recovery is equal to the free oil content S1, and the recovery coefficient k is obtained according to the free oil S1 content before and after recovery. The recovery coefficient k is proportional to the variable proportional coefficient. ΔlogR The oil-bearing sweet spot parameter, i.e. the free oil volume in well logging after recovery, is obtained by combining the two methods; the calculation formula is as follows:

[0020] ;

[0021] ;

[0022] In the formula, Q 游测 is the logging free oil content after recovery, mg / g; k is the recovery coefficient of free oil S1; ΔlogR S1 The amplitude difference between the acoustic time difference and the resistivity logging curve, N C1恢复前 is the molar amount of normal alkane with 1 carbon atom before recovery, N C1恢复后 is the molar amount of normal alkane with 1 carbon atom after recovery.

[0023] Preferably, according to the present invention, the physical property sweet spot parameter, i.e., porosity, is obtained based on multivariate linear regression analysis and is expressed as:

[0024] ;

[0025] In the formula, is the porosity, %; AC is the acoustic time difference logging value, μs / ft; DEN is the density logging value, g / cm 3 ; a is the conversion coefficient of acoustic time difference logging value, b is the conversion coefficient of density logging value, and c is the comprehensive correction coefficient of porosity.

[0026] Preferably, according to the present invention, the elastic energy sweet spot parameter, i.e., the formation pressure coefficient, is predicted based on the Bowers method and is expressed as:

[0027] Formation pressure coefficients for normally compacted and undercompacted formations The calculation formula is as follows:

[0028] ;

[0029]

[0030] Fluid expansion formation pressure coefficient The calculation formula is as follows:

[0031] ;

[0032] ;

[0033] Where, v is the speed of sound waves, m / s; A, B is the empirical coefficient of the study area, a constant; σ is the vertical effective stress, MPa; is the formation pore pressure of normally compacted and undercompacted formations, MPa; is the fluid-expanded formation pore pressure, MPa; P 0 is the overburden pressure, MPa; v max is the speed of sound wave at the inflection point, m / s; W is the unloading (elastic) coefficient, dimensionless; P m is the hydrostatic pressure, MPa.

[0034] According to the preferred embodiment of the present invention, based on the brittleness index predicted by the comprehensive random forest algorithm and the Young's modulus obtained by the mathematical model; comprising: using the random forest algorithm (RF) to predict the brittleness index BI For prediction, the selected calculation model is as follows:

[0035] ;

[0036] In the formula, V 石英 , V 长石 , V 黄铁矿 , V 白云石 , V 方解石 , V 黏土 , V TOC are the contents of quartz, feldspar, pyrite, dolomite, calcite, clay and TOC, wt.%;

[0037] The shear wave time difference of the shale formation is estimated based on the P-wave time difference and density logging data. The calculation formula is:

[0038] ;

[0039] In the formula, DTS is the shear wave time difference of shale formation, μs / ft; DTC is the P-wave time difference logging value, μs / ft; A It is the reciprocal of the rock density logging value, g / cm 3 ;

[0040] The Young's modulus is calculated based on the logging data, and the correlation analysis is performed with the measured data of Young's modulus. The calculation formula is:

[0041] ;

[0042] In the formula, E is Young's modulus, Δt s is the shear wave logging value, μs / ft; Δtp is the longitudinal wave logging value, μs / ft; ρ is the density logging value, g / cm 3 ;

[0043] Fracability Index FI It is expressed as:

[0044] ;

[0045] ;

[0046] ;

[0047] In the formula, FI is the fracturability index, dimensionless; BI n is the brittleness index after positive normalization, dimensionless; BI is the brittleness index at a certain depth point, dimensionless; BI max , B.I. min are the maximum and minimum values ​​of the brittleness index in the studied interval, dimensionless; E n is the normalized Young's modulus, dimensionless; E is the Young's modulus at a certain depth, dimensionless; BI max , B.I. min are the maximum and minimum values ​​of Young's modulus in the studied layer, respectively, and are dimensionless.

[0048] Preferably, according to the present invention, a sweet spot comprehensive evaluation index of oil content sweet spot parameter, physical property sweet spot parameter, elastic energy sweet spot parameter, and fracturability sweet spot is obtained based on a hierarchical analysis calculation method; the expression is as follows:

[0049] ;

[0050] In the formula, U is the comprehensive evaluation index of dessert, dimensionless; U 1 is the amount of free oil after recovery after positive normalization, dimensionless; U 2 is the porosity after positive normalization, dimensionless; U 3 is the formation pressure coefficient after forward normalization, dimensionless; U 4 is the normalized fracturability index, dimensionless; w 1. w 2. w 3 and w 4 are U 1. U 2.U 3 and U A weight factor of 4; Q 游测 is the amount of free oil after recovery, mg / g; Q 游测max , Q 游测min are the maximum and minimum values ​​of free oil after recovery, mg / g; is the porosity, %; , are the maximum and minimum values ​​of porosity, %; C p is the formation pressure coefficient, dimensionless; C pmax , C pmin are the maximum and minimum values ​​of the formation pressure coefficient, dimensionless; FI is the fracturability index, dimensionless; FI max , FI min are the maximum and minimum values ​​of the fracturability index, dimensionless.

[0051] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction method are implemented.

[0052] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction method.

[0053] A multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction system, comprising:

[0054] The oily dessert parameter acquisition module is configured as follows: based on the improved ΔlogR The oil-bearing sweet spot parameter, i.e., free oil volume, is obtained by using the method and formation free oil recovery model.

[0055] The physical property sweet spot parameter acquisition module is configured to: obtain the physical property sweet spot parameter, i.e., porosity, based on multivariate linear regression analysis;

[0056] The elastic energy sweet spot parameter acquisition module is configured to: predict the elastic energy sweet spot parameter, i.e., the formation pressure coefficient, based on the Bowers method;

[0057] The fracturability sweet spot acquisition module is configured to: obtain the fracturability sweet spot, i.e., the fracturability index, based on the brittleness index predicted by the comprehensive random forest algorithm and the Young's modulus obtained by the mathematical model;

[0058] The module for obtaining and applying the sweet spot comprehensive evaluation index is configured to obtain the sweet spot comprehensive evaluation index based on an improved hierarchical analysis calculation method, and apply the sweet spot comprehensive evaluation index to key shale oil exploration wells.

[0059] The beneficial effects of the present invention are:

[0060] 1. High evaluation accuracy: The evaluation method of the present invention mainly selects well logging data for analysis. Well logging data has the characteristics of strong continuity and high resolution, and conducts relevant field experiments to test the correlation. When the correlation is significant, the next step of analysis and research is carried out with high accuracy. In addition, the oil content sweet spot parameters, physical property sweet spot parameters, elastic energy sweet spot parameters, and fracturability sweet spot parameters are comprehensively considered, and key parameters with high correlation are selected. The improved evaluation calculation model is adopted. Compared with other experimental methods introduced in the background technology, this method greatly improves the accuracy of the evaluation results and provides favorable support for shale oil exploration and development.

[0061] 2. Economic and universal: Affected by the number of core samples, experimental costs and analysis time, experimental data results are usually difficult to promote and apply on a large scale. However, logging data has the characteristics of strong continuity and high resolution, and the oil field will obtain corresponding logging data during the drilling process. The logging data is easy to obtain. On the basis of the logging data, key layer samples are selected to carry out experiments and conduct correlation tests, which can greatly reduce the research cost. The entire evaluation and prediction process is complete, and many sweet spot evaluation parameters are considered. Only minor adjustments need to be made according to the shale properties in different regions. This shows that this method has good application prospects in the characterization of shale oil-rich areas, evaluation and prediction, and is suitable for promotion and application to other terrestrial / marine shales.

[0062] 3. Strong identification and representativeness: Previous studies on shale oil sweet spots have mainly focused on a single parameter or multiple parameters, without optimizing key parameters or comprehensively considering geological, engineering and economic factors. The lack of an accurate quantitative evaluation model has hindered the optimal selection of shale oil sweet spots and targeted engineering measures. The present invention evaluates and predicts shale oil-rich areas through multiple sweet spots and multiple parameters, comprehensively considers geological sweet spots, engineering sweet spots and economic factors, and more accurately predicts shale oil-rich areas, greatly improving the representativeness of the experimental results. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0064] Picture 1 It is a schematic flow chart of the multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction method of the present invention;

[0065] Picture 2 It is a schematic diagram of the relationship between the measured TOC and the predicted TOC of the present invention;

[0066] Picture 3 It is a schematic diagram of the relationship between the measured S1 and the predicted S1 of the present invention;

[0067] Picture 4 It is a schematic diagram of the relationship between the measured core porosity and the predicted porosity based on the conventional well logging method of the present invention;

[0068] Picture 5 It is a schematic diagram of the relationship between the measured formation pressure coefficient and the predicted formation pressure coefficient of the present invention;

[0069] Picture 6 Schematic diagram of the relationship between the measured Young's modulus and the predicted Young's modulus of the present invention;

[0070] Picture 7 This is a schematic diagram of single well evaluation of the comprehensive evaluation index of the key well sweet spot of the present invention. DETAILED DESCRIPTION

[0071] The present invention will be further defined below in conjunction with the accompanying drawings and embodiments, but is not limited thereto.

[0072] Example 1

[0073] A multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction method, such as Picture 1 As shown, including:

[0074] based on ΔlogR The oil-bearing sweet spot parameter, i.e., free oil volume, is obtained by using the method and formation free oil recovery model.

[0075] Based on multiple linear regression analysis, the physical property sweet spot parameter, i.e., porosity, was obtained;

[0076] Predict the elastic energy sweet spot parameter, i.e., formation pressure coefficient, based on the Bowers method;

[0077] The fracturability sweet spot, i.e., the fracturability index, is obtained based on the brittleness index predicted by the comprehensive random forest algorithm and the Young's modulus obtained by the mathematical model;

[0078] The sweet spot comprehensive evaluation index is obtained based on the hierarchical analysis calculation method, and is applied to key shale oil exploration wells.

[0079] Example 2

[0080] The difference between the multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction method described in Example 1 is that:

[0081] The improved ΔlogR method calculation formula of the variable proportional coefficient is as follows:

[0082] ;

[0083] In the formula, Δ logR is the difference in acoustic time difference and the amplitude of resistivity logging curve; R is the logging resistivity value, Ω·m; R 基线 is the logging resistivity value corresponding to the baseline, Ω·m; D is the proportionality coefficient, reflecting the resistivity in the calculation ΔlogR The proportion of the process, the value range is between 0-1; Δt is the logging acoustic wave time difference, μs / ft; Δt 基线 is the logging acoustic time difference value corresponding to the baseline, μs / ft, A, the correction constant;

[0084] ΔlogR The method was first established by EXXON / ESSO Oil Company as a method for evaluating TOC based on well logging. However, it is easily interfered by human factors in the actual calculation process and is usually difficult to achieve high prediction accuracy. ΔlogR Further optimize the method and establish ΔlogR The calculation model of TOC and S1 of the method, Δ logR TOC and ΔlogR S1 .

[0085] The formation free oil recovery model is as follows:

[0086] When crude oil is tested by gas chromatography (GC), some light hydrocarbons (mainly below C8) have been lost, and the remaining normal alkanes (C 8+ ) follows the "e" exponential relationship with the molar amount. The "e" exponential light hydrocarbon recovery method can be used to recover the loss of different normal alkane components in crude oil in the formation from a molecular perspective. Taking the crude oil in the work area as the benchmark, the molar amount of normal alkanes with carbon atoms greater than 8 and their carbon numbers are fitted to the "e" exponential function. This is used to recover the free hydrocarbon components (carbon atoms <34). The recovered components are converted into mass and then summed to obtain the content of normal alkanes in the formation. The content recovered by crude oil gas chromatography is equated to the free oil amount S1, and the recovery coefficient k is obtained based on the free oil S1 content before and after recovery. The recovery coefficient k is compared with the variable proportional coefficient ΔlogR The oil-bearing sweet spot parameter, i.e. the free oil volume in well logging after recovery, is obtained by combining the two methods; the calculation formula is as follows:

[0087] ;

[0088] ;

[0089] In the formula, Q 游测 is the logging free oil content after recovery, mg / g; k is the recovery coefficient of free oil S1; ΔlogR S1 The amplitude difference between the acoustic time difference and the resistivity logging curve, N C1恢复前 is the molar amount of normal alkane with 1 carbon atom before recovery, N C1恢复后 is the molar amount of normal alkane with 1 carbon atom after recovery.

[0090] Based on the multivariate linear regression analysis, the physical property sweet spot parameter, i.e., porosity, is obtained, which is expressed as:

[0091] ;

[0092] In the formula, is the porosity, %; AC is the acoustic time difference logging value, μs / ft; DEN is the density logging value, g / cm 3 ; a is the conversion coefficient of acoustic time difference logging value, b is the conversion coefficient of density logging value, and c is the comprehensive correction coefficient of porosity.

[0093] Based on the Bowers method, the elastic energy sweet spot parameter, i.e., the formation pressure coefficient, is predicted and expressed as:

[0094] Formation pressure coefficients for normally compacted and undercompacted formations The calculation formula is as follows:

[0095] ;

[0096]

[0097] Fluid expansion formation pressure coefficient The calculation formula is as follows:

[0098] ;

[0099] ;

[0100] Where, v is the speed of sound waves, m / s; A, B is the empirical coefficient of the study area, a constant; σ is the vertical effective stress, MPa; is the formation pore pressure of normally compacted and undercompacted formations, MPa; is the fluid-expanded formation pore pressure, MPa; P 0 is the overburden pressure, MPa; v max is the speed of sound wave at the inflection point, m / s; W is the unloading (elastic) coefficient, dimensionless; P m is the hydrostatic pressure, MPa.

[0101] Young's modulus obtained by the brittleness index predicted by the comprehensive random forest algorithm and the mathematical model; including: using the random forest algorithm (RF) to predict the brittleness index BI For prediction, the selected calculation model is as follows:

[0102] ;

[0103] In the formula, V 石英 , V 长石 , V 黄铁矿 , V 白云石 , V 方解石 , V 黏土 , V TOC are the contents of quartz, feldspar, pyrite, dolomite, calcite, clay and TOC, wt.%;

[0104] The shear wave time difference of the shale formation is estimated based on the P-wave time difference and density logging data. The calculation formula is:

[0105] ;

[0106] In the formula, DTS is the shear wave time difference of shale formation, μs / ft; DTC is the P-wave time difference logging value, μs / ft; A It is the reciprocal of the rock density logging value, g / cm 3 ;

[0107] The Young's modulus is calculated based on the logging data, and the correlation analysis is performed with the measured data of Young's modulus. The calculation formula is:

[0108] ;

[0109] In the formula, E is Young's modulus, Δt s is the shear wave logging value, μs / ft; Δt pis the longitudinal wave logging value, μs / ft; ρ is the density logging value, g / cm 3 ;

[0110] Fracability Index FI It is expressed as:

[0111] ;

[0112] ;

[0113] ;

[0114] In the formula, FI is the fracturability index, dimensionless; BI n is the brittleness index after positive normalization, dimensionless; BI is the brittleness index at a certain depth point, dimensionless; BI max , B.I. min are the maximum and minimum values ​​of the brittleness index in the studied interval, dimensionless; E n is the normalized Young's modulus, dimensionless; E is the Young's modulus at a certain depth, dimensionless; BI max , B.I. min are the maximum and minimum values ​​of Young's modulus in the studied layer, respectively, and are dimensionless.

[0115] Based on the hierarchical analysis calculation method, the sweet spot comprehensive evaluation index of oil content sweet spot parameters, physical property sweet spot parameters, elastic energy sweet spot parameters, and fracturability sweet spot is obtained; the expression is as follows:

[0116] ;

[0117] In the formula, U is the comprehensive evaluation index of dessert, dimensionless; U 1 is the amount of free oil after recovery after positive normalization, dimensionless; U 2 is the porosity after positive normalization, dimensionless; U 3 is the formation pressure coefficient after forward normalization, dimensionless; U 4 is the normalized fracturability index, dimensionless; w 1. w 2. w 3 and w 4 are U 1. U 2. U 3 andU A weight factor of 4; Q 游测 is the amount of free oil after recovery, mg / g; Q 游测max , Q 游测min are the maximum and minimum values ​​of free oil after recovery, mg / g; is the porosity, %; , are the maximum and minimum values ​​of porosity, %; C p is the formation pressure coefficient, dimensionless; C pmax , C pmin are the maximum and minimum values ​​of the formation pressure coefficient, dimensionless; FI is the fracturability index, dimensionless; FI max , FI min are the maximum and minimum values ​​of the fracturability index, dimensionless.

[0118] Based on an improved hierarchical analysis calculation method, the sweet spot comprehensive evaluation index is obtained and applied to key shale oil exploration wells. Substituting the corresponding logging curve data into the above sweet spot comprehensive evaluation index model can obtain the corresponding well sweet spot comprehensive evaluation index single well interpretation curve, and the RESFORM software can be used to obtain the single well sweet spot comprehensive interpretation. The sweet spot comprehensive evaluation index logging curve has obvious abnormal amplitude difference in the shale oil rich zone. U The part greater than 0.5 can be regarded as a shale oil-rich area.

[0119] Example 3

[0120] The difference between the multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction method described in Example 2 is that:

[0121] 1. Basic Experiment

[0122] (1) TOC, rock pyrolysis and X-ray diffraction (XRD) experiments The TOC experiment was carried out with the aid of a CS-230 carbon-sulfur analyzer. For the specific experimental process, please refer to the national standard "Determination of Total Organic Carbon in Sedimentary Rocks" (GB / T19145-2022); the conventional pyrolysis experiment was carried out with the aid of a Rock-Eval6 source rock analyzer. For the specific experimental process, please refer to the national standard "Rock Pyrolysis Analysis" (GB / T18602-2012); for the specific experimental process of the XRD experiment, please refer to the industry standard "X-ray Diffraction Analysis Method for Clay Minerals and Common Non-Clay Minerals in Sedimentary Rocks" (SY / T5163-2010).

[0123] (2) NMR porosity measurement experiment

[0124] The model of the MRI equipment is MicroMR23-060H-1, and the test parameters are set as follows: echo interval TE is 0.06 ms, waiting time TW is 1500 ms, number of echoes (NECH) is 3000, 90 pulse width (P90) is 7.2 μs, and number of scans (NS) is 8.

[0125] (3) Young’s modulus test

[0126] The diameter and height of the 25 mm cylindrical sample were measured and recorded using a vernier caliper. The sample was then placed on the platform of the TAW-1000 microcomputer-controlled electro-hydraulic servo rock triaxial tester, and the corresponding confining pressure was set according to the sample depth. Other parameters were set and the loading experiment was carried out. The experiment was stopped when the rock sample broke, and the data was exported and calculated to obtain the Young's modulus (Table 1).

[0127] Table 1 Test results of Young's modulus and Poisson's ratio of typical continental shale oil samples;

[0128]

[0129] 2. Well logging evaluation of oily sweet spot parameters

[0130] Select a variable scale factor ΔlogR The method uses the variable proportional coefficient to measure TOC and the resistivity and acoustic time difference logging values ​​at the same depth. ΔlogR The TOC logging prediction model can be obtained by using the TOC logging prediction method:

[0131] ;

[0132] like Picture 2 After testing the correlation as shown, similarly, using the variable scale coefficient ΔlogR The method is used to calculate the resistivity and acoustic time difference logging values ​​of the measured conventional pyrolysis free oil S1 and the corresponding depth, and then the prediction model of S1 logging is obtained:

[0133] ;

[0134] like Picture 3 After the correlation test shown in the figure, the obtained S1 calculation model can be used to correct the light hydrocarbons to realize the free oil volume logging evaluation model after recovery in the study area:

[0135] ;

[0136] In the formula, Q 游测is the logging free oil content after recovery, mg / g; k is the recovery coefficient of formation S1; ΔlogR S1 The difference in acoustic time and amplitude of resistivity logging curves.

[0137] 3. Well logging evaluation of physical property sweet spot parameters

[0138] With the help of core calibration logging, the vertical porosity variation characteristics of a single well were evaluated by establishing a correlation between the logging curve and the measured core porosity. Overall, the measured porosity has a high correlation with the corresponding AC and DEN, but a low correlation with CNL. Therefore, SPSS statistical analysis software was used to establish a multivariate linear regression model of porosity for AC and DEN, and the following analysis was performed: Picture 4 The correlation test shown. The corresponding model formula can be expressed as:

[0139] ;

[0140] In the formula, is the porosity, %; AC is the acoustic time difference logging value, μs / ft; DEN is the density logging value, g / cm 3 .

[0141] 4. Elastic energy sweet spot parameter logging evaluation

[0142] The Bowers method is used to evaluate and predict the formation pore pressure, and the AC logging data is corrected to fully remove the influence of TOC on the formation pressure calculation results. The measured data are brought into the two models, and the empirical coefficient A=6.4388×10 -4 , B=4.55, unloading (elastic) coefficient W=1.03, thus the formation pressure logging evaluation model of this area is obtained as follows, and the following is carried out: Picture 5 Correlation test shown:

[0143] Sedimentary loading model:

[0144] ;

[0145] Sediment unloading model:

[0146] ;

[0147] ;

[0148] 5. Well logging evaluation of fracturability sweet spot parameters

[0149] The brittleness index is predicted by using the random forest method (RF), and the selected calculation model is as follows:

[0150] ;

[0151] In the formula, V quartz, V Feldspar, V Pyrite, V dolomite, V 方解石 , V 黏土 , V TOC The contents of quartz, feldspar, pyrite, dolomite, calcite, clay and TOC, wt.%, respectively.

[0152] The shear wave time difference of the shale formation is estimated based on the P-wave time difference and density logging data. The calculation formula is:

[0153] ;

[0154] In the formula, DTS is the shear wave time difference logging value, μs / ft; DTC is the P-wave time difference logging value, μs / ft; A It is the reciprocal of the rock density logging value, g / cm 3 .

[0155] The Young's modulus is calculated based on the logging data, and the correlation analysis is performed with the measured data of Young's modulus. The calculation formula is:

[0156] ;

[0157] In the formula, Δt s is the shear wave logging value, μs / ft; Δt p is the longitudinal wave logging value, μs / ft; ρ is the density logging value, g / cm 3 .

[0158] By comparing the experimentally measured Young's modulus with the Young's modulus calculated according to the formula, it can be seen that there is an obvious positive correlation between the two (such as Picture 6 As shown in Figure 2). It can be inferred that the calculation model results of Young's modulus have high reliability and credibility.

[0159] VI. Comprehensive evaluation model and prediction of shale oil-rich areas

[0160] The improved analytic hierarchy process is to compare the relative importance of each evaluation index, quantify the relative importance and form a comparison matrix, and further calculate the comparison matrix to obtain the construction judgment matrix. The specific steps of the improved analytic hierarchy process to obtain the weight coefficients of different evaluation parameters are:

[0161] (1) Construct the comparison matrix judgment value. Taking this study as an example, if parameter i is not as important as parameter j, the judgment value is 0; if parameter i is equally important as parameter j, the judgment value is 1; if parameter i is more important than parameter j, the judgment value is 2.

[0162] (2) Establish a comparison matrix. Rank the relative importance of different “sweet spot” evaluation parameters: free oil volume > porosity = formation pressure coefficient > fracturability index. The reason is that the primary task of predicting the "sweet spot" of shale oil is to ensure that there is shale oil enrichment in the studied layer or region. Therefore, free oil content is the most important factor in evaluating the "sweet spot" of shale oil; secondly, porosity is an evaluation index indicating the storage capacity of shale oil. Sometimes, the storage capacity is large but not necessarily contains oil, and may also contain water. Therefore, the importance of porosity to the "sweet spot" of shale oil is lower than that of free oil content; similarly, layers or regions with high oil content usually have high formation pressure coefficients, but the cause of abnormal high pressure is not necessarily only hydrocarbon generation and pressurization, so the importance of formation pressure coefficient to the "sweet spot" of shale oil is also lower than that of free oil content and is considered to be the same as porosity; the fracturability index usually indicates the difficulty of exploration and development of shale oil reservoirs. Considering that the research on fracturability is generally good and conducive to reservoir transformation, the importance of the fracturability index to the "sweet spot" of shale oil is lower than that of the other three evaluation parameters. In order to quantify the relative importance of the four parameters, different parameters are assigned corresponding judgment values ​​(Table 2).

[0163] Table 2 Judgment values ​​of different parameters in the comparison matrix;

[0164]

[0165] (3) Obtaining the construction judgment matrix A Based on the establishment of the comparison matrix, according to the r in Table 2 i and r j The size of a in the following formula is determined by i Value. That is, when r i >r j When i =r i -r j ; When r i =r j When i =1; when r i <r j When i =1 / (r j -r i ). Thus, the judgment matrix can be constructed by comparing the matrix:

[0166] ;

[0167] (4) Find the antisymmetric matrix B of the matrix A. For a in the above formula i The values ​​are logarithmic and the calculation process is as follows:

[0168] ;

[0169] (5) Find the optimal transfer matrix C of matrix B. For b in the above formula i The values ​​are converted as follows:

[0170] ;

[0171] ;

[0172] (6) According to the above formula, we can get c i Value, calculate the matrix A to obtain the quasi-optimal consistent matrix A ' , the calculation process is as follows:

[0173] ;

[0174] (7) Obtain the matrix A ' The characteristic vector of is normalized to obtain the weight coefficients of different parameters, among which the free oil content w 1=0.534, porosity w 2=0.197, formation pressure coefficient w 3=0.197, fracturability index w 4=0.072. The weight coefficient is brought into the model to obtain the comprehensive evaluation index of the shale oil "sweet spot" in the study area: U Model:

[0175] ;

[0176] Substituting the logging data of key exploration wells into the above model, the comprehensive evaluation diagram of a single well can be obtained as follows: Picture 7 As shown in Figure 2, based on the distribution characteristics of oil content, physical properties, elastic energy and index U, three shale oil "sweet spot" intervals can be selected as follows: Picture 7 As shown in the dotted box, comprehensive evaluation and prediction of multiple sweet spots and multiple parameters in shale oil-rich areas are achieved.

[0177] Example 3

[0178] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction method described in Example 1 or 2 are implemented.

[0179] Example 4

[0180] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction method described in Example 1 or 2.

[0181] Example 5

[0182] A multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction system, comprising:

[0183] The oily dessert parameter acquisition module is configured as follows: based on the improved ΔlogR The oil-bearing sweet spot parameter, i.e., free oil volume, is obtained by using the method and formation free oil recovery model.

[0184] The physical property sweet spot parameter acquisition module is configured to: obtain the physical property sweet spot parameter, i.e., porosity, based on multivariate linear regression analysis;

[0185] The elastic energy sweet spot parameter acquisition module is configured to: predict the elastic energy sweet spot parameter, i.e., the formation pressure coefficient, based on the Bowers method;

[0186] The fracturability sweet spot acquisition module is configured to: obtain the fracturability sweet spot, i.e., the fracturability index, based on the brittleness index predicted by the comprehensive random forest algorithm and the Young's modulus obtained by the mathematical model;

[0187] The module for obtaining and applying the sweet spot comprehensive evaluation index is configured to obtain the sweet spot comprehensive evaluation index based on an improved hierarchical analysis calculation method, and apply the sweet spot comprehensive evaluation index to key shale oil exploration.

Claims

1. A multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction method, characterized in that: include: Based on the ΔlogR method and the formation free oil recovery model, the oil-bearing sweet spot parameter, i.e., free oil content, is obtained; Based on multiple linear regression analysis, the physical property sweet spot parameter, i.e., porosity, was obtained; Predict the elastic energy sweet spot parameter, i.e., formation pressure coefficient, based on the Bowers method; The fracturability sweet spot, i.e., the fracturability index, is obtained based on the brittleness index predicted by the comprehensive random forest algorithm and the Young's modulus obtained by the mathematical model; Obtain the sweet spot comprehensive evaluation index based on the hierarchical analysis calculation method, and apply the sweet spot comprehensive evaluation index to key shale oil exploration wells; Based on the hierarchical analysis calculation method, the sweet spot comprehensive evaluation index of oil content sweet spot parameters, physical property sweet spot parameters, elastic energy sweet spot parameters, and fracturability sweet spot is obtained; the expression is as follows: Wherein, U is the comprehensive evaluation index of the sweet spot, dimensionless; U1 is the free oil volume after positive normalization, dimensionless; U2 is the porosity after positive normalization, dimensionless; U3 is the formation pressure coefficient after positive normalization, dimensionless; U4 is the fracturability index after positive normalization, dimensionless; w1, w2, w3, and w4 are the weight coefficients of U1, U2, U3, and U4 respectively; Q 游测 is the recovered free oil content, mg / g; Qyoutestmax and Qyoutestmin are the maximum and minimum values of the recovered free oil content, mg / g respectively; is the porosity, %; are the maximum and minimum values of the porosity, % respectively; Cp is the formation pressure coefficient, dimensionless; Cpmax and Cpmin are the maximum and minimum values of the formation pressure coefficient, dimensionless respectively; FI is the fracturability index, dimensionless; FImax and FImine are the maximum and minimum values of the fracturability index, dimensionless respectively; The steps to obtain the weight coefficients of different evaluation parameters are: (1) Constructing comparison matrix judgment value; (2) Establish a comparison matrix; rank the relative importance of different "sweet spots" evaluation parameters: free oil volume > porosity = formation pressure coefficient > fracturability index; (3) Obtaining the construction judgment matrix A On the basis of establishing the comparison matrix, the value of aij in the following formula is determined according to the size of ri and rj, that is, when ri>rj, aij=ri-rj; when ri=rj, aij=1; when ri<rj, aij=1 / (rj-ri); thus, the construction judgment matrix is ​​obtained through the comparison matrix: (4) To obtain the antisymmetric matrix B of the matrix A, take the logarithm of the aij values ​​in the above formula. The calculation process is as follows: (5) Obtain the optimal transfer matrix C of matrix B and transform the bij value in the above formula as follows: (6) According to the above formula, the cij value is obtained, and the matrix A is calculated to obtain the quasi-optimal consistent matrix A'. The calculation process is as follows: (7) Obtain the eigenvector of the matrix A' and normalize it to obtain the weight coefficients of different parameters.

2. The multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction method according to claim 1 is characterized in that: The improved ΔlogR method calculation formula of the variable proportional coefficient is as follows: Wherein, ΔlogR is the amplitude difference between the acoustic time difference and the resistivity logging curve; R is the logging resistivity value, Ω·m; Rbaseline is the logging resistivity value corresponding to the baseline, Ω·m; D is the proportionality coefficient, which reflects the proportion of resistivity in the calculation of ΔlogR, and the value range is between 0-1; Δt is the logging acoustic time difference value, μs / ft; Δt 基线 is the logging acoustic time difference value corresponding to the baseline, μs / ft, A, and the correction constant.

3. The multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction method according to claim 1 is characterized in that: The formation free oil recovery model is as follows: The content of crude oil after gas chromatography recovery is equal to the free oil content S1, and the recovery coefficient k is obtained according to the free oil S1 content before and after recovery. The recovery coefficient k is combined with the ΔlogR method of variable proportional coefficient to obtain the oil content sweet spot parameter, that is, the logging free oil content after recovery; the calculation formula is as follows: Q = k × (0.3173 × ΔlogR S1 -0.174) Where Q is the free oil content after recovery, mg / g; k is the free oil S1 recovery coefficient; ΔlogR S1 The amplitude difference between the acoustic time difference and the resistivity logging curve, N C1恢复前 is the molar amount of normal alkane with 1 carbon atom before recovery, N C1恢复后 is the molar amount of normal alkane with 1 carbon atom after recovery.

4. The multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction method according to claim 1 is characterized in that: Based on the multivariate linear regression analysis, the physical property sweet spot parameter, i.e., porosity, is obtained, which is expressed as: In the formula, is the predicted porosity, %; AC is the acoustic time difference logging value, μs / ft; DEN is the density logging value, g / cm 3 ; a is the conversion coefficient of acoustic time difference logging value, b is the conversion coefficient of density logging value, and c is the comprehensive correction coefficient of predicted porosity.

5. The multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction method according to claim 1 is characterized in that: Based on the Bowers method, the elastic energy sweet spot parameter, i.e., the formation pressure coefficient, is predicted and expressed as: Formation pressure coefficient C of normally compacted and undercompacted formations 71 The calculation formula is as follows: Fluid expansion formation pressure coefficient C 72 The calculation formula is as follows: Where v is the acoustic wave velocity, m / s; A and B are empirical coefficients of the study area, constants; σ is the vertical effective stress, MPa; P 71 is the pore pressure of normally compacted and undercompacted formations, MPa; P 72 is the pore pressure of the fluid-expanded formation, MPa; P0 is the overburden pressure, MPa; vmax is the acoustic velocity at the inflection point, m / s; W is the unloading coefficient, dimensionless; Pm is the hydrostatic pressure, MPa.

6. The multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction method according to claim 1 is characterized in that: The Young's modulus obtained based on the brittleness index predicted by the comprehensive random forest algorithm and the mathematical model; including: predicting the brittleness index BI with the help of the random forest algorithm, the selected calculation model is as follows: Where V 石英 、V 长石 、V 黄铁矿 、V 白云石 、V 方解石 、V 黏土 、V TOC are the contents of quartz, feldspar, pyrite, dolomite, calcite, clay and TOC, wt.% respectively; The shear wave time difference of the shale formation is estimated based on the P-wave time difference and density logging data. The calculation formula is: Where DTS is the shear wave time difference of the shale layer, μs / ft; DTC is the longitudinal wave time difference logging value, μs / ft; A is the reciprocal of the rock density logging value, g / cm 3 ; The Young's modulus is calculated based on the logging data, and the correlation analysis is performed with the measured data of Young's modulus. The calculation formula is: Where E is Young's modulus, Δts is the shear wave logging value, μs / ft; Δtp is the longitudinal wave logging value, μs / ft; ρ is the density logging value, g / cm 3 ; The fracturability index FI is expressed as: Wherein, FI is the fracturability index, dimensionless; BIn is the brittleness index after positive normalization, dimensionless; BI is the brittleness index at a certain depth point, dimensionless; BImax and BImin are the maximum and minimum values ​​of the brittleness index in the studied layer, dimensionless; En is the Young's modulus after positive normalization, dimensionless; E is the Young's modulus at a certain depth point, dimensionless; BImax and BImin are the maximum and minimum values ​​of the Young's modulus in the studied layer, dimensionless.

7. A multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction system, used to implement a multi-sweet spot and multi-parameter shale oil enrichment area evaluation and prediction method according to any one of claims 1 to 6, characterized in that: include: The oil content sweet spot parameter acquisition module is configured to: acquire the oil content sweet spot parameter, i.e., free oil content, based on the ΔlogR method and the formation free oil content recovery model; The physical property sweet spot parameter acquisition module is configured to: obtain the physical property sweet spot parameter, i.e., porosity, based on multivariate linear regression analysis; The elastic energy sweet spot parameter acquisition module is configured to: predict the elastic energy sweet spot parameter, i.e., the formation pressure coefficient, based on the Bowers method; The fracturability sweet spot acquisition module is configured to: obtain the fracturability sweet spot, i.e., the fracturability index, based on the brittleness index predicted by the comprehensive random forest algorithm and the Young's modulus obtained by the mathematical model; The module for obtaining and applying the sweet spot comprehensive evaluation index is configured to obtain the sweet spot comprehensive evaluation index based on an improved hierarchical analysis calculation method, and apply the sweet spot comprehensive evaluation index to key shale oil exploration wells.

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