Deep coal bed gas reservoir high brittleness sweet spot area prediction method, system and equipment

Abstract

The application discloses a deep coal bed gas reservoir high brittleness sweet spot area prediction method, system and equipment, relates to the oil and gas and coal bed gas geophysical exploration field, and the method comprises the steps of: taking dynamic elastic modulus as the independent variable, taking coal bed fracture pressure as the dependent variable, adopting a multiple linear regression method to perform fitting, determining a partial regression coefficient, and thereby constructing a brittleness index model; based on original data of a target area deep coal bed gas reservoir, adopting prestack simultaneous inversion technology and rock physical formula to perform inversion and calculation, obtaining a three-dimensional dynamic Young's modulus data body, a three-dimensional dynamic Poisson's ratio data body and a three-dimensional dynamic shear modulus data body, and substituting into the brittleness index model to obtain a three-dimensional brittleness index data body; according to a brittleness index threshold, dividing a region corresponding to the three-dimensional brittleness index data body to determine a high brittleness sweet spot area. The application can improve the accuracy of predicting the high brittleness sweet spot area of the deep coal bed gas reservoir.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration technology for oil and gas and coalbed methane, and in particular to a method, system and equipment for predicting the high brittle sweet spot of deep coalbed methane reservoirs. Background Technology

[0002] The fracturability of deep coalbed methane reservoirs is the key to their development success, and the evaluation of coal and rock brittleness (fragility) is the core basis for predicting whether a reservoir can form a complex fracture network through hydraulic fracturing. However, there is a clear logical evolutionary relationship and improvement needs in the technological development of this field.

[0003] In some cases, the most common approach is to directly apply the brittleness index model of shale gas, primarily using Young's modulus and Poisson's ratio for calculations. However, this approach has a fundamental flaw: the composition of coal and rock, dominated by organic matter, differs significantly from that of shale in terms of rock mechanical properties. Laboratory mechanical tests show that the strain at failure in coal and rock is much greater than that in shale, and their stress-strain curves and failure mechanisms are more complex. This fundamental difference leads to a systematic misjudgment of the fracturing capability of coal seams when directly applied to shale models, resulting in low accuracy in identifying high-brittle sweet spots in deep coalbed methane reservoirs. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, and equipment for predicting the high brittleness sweet spot region of deep coalbed methane reservoirs, which can improve the accuracy of predicting the high brittleness sweet spot region of deep coalbed methane reservoirs.

[0005] To achieve the above objectives, this application provides the following solution.

[0006] In a first aspect, this application provides a method for predicting the highly brittle sweet spot region of deep coalbed methane reservoirs, including: Using dynamic elastic modulus as the independent variable and coal seam fracturing pressure as the dependent variable, a multiple linear regression method was used to fit the data and determine the partial regression coefficients; the dynamic elastic modulus includes: dynamic Young's modulus, dynamic Poisson's ratio, and dynamic shear modulus. Based on the partial regression coefficients, a fragility index model is constructed; Obtain raw data of deep coalbed methane reservoirs in the target area; Based on the original data, inversion and calculation were performed using pre-stack synchronous inversion technology and rock physics formulas to obtain three-dimensional dynamic Young's modulus data volume, three-dimensional dynamic Poisson's ratio data volume and three-dimensional dynamic shear modulus data volume. Substituting the three-dimensional dynamic Young's modulus data volume, the three-dimensional dynamic Poisson's ratio data volume, and the three-dimensional dynamic shear modulus data volume into the brittleness index model, we obtain the three-dimensional brittleness index data volume. The regions corresponding to the three-dimensional brittleness index data volume are divided according to the brittleness index threshold to determine the high-brittleness dessert region.

[0007] Furthermore, using dynamic elastic modulus as the independent variable and coal seam fracturing pressure as the dependent variable, a multiple linear regression method was employed to fit the data and determine the partial regression coefficients, specifically including: The static rock test data and coal seam fracture pressure of the experimental well, as well as the sample dynamic rock test data of the corresponding reference well, are obtained. The static rock test data includes: static Young's modulus, static Poisson's ratio, and static shear modulus. The sample dynamic rock test data includes: sample dynamic Young's modulus, sample dynamic Poisson's ratio, and sample dynamic shear modulus. Based on the static rock test data and the sample dynamic rock test data, the dynamic-static conversion formula is obtained through cross plot analysis and regression fitting. The static rock test data is converted using the dynamic-static conversion formula to obtain initial dynamic rock test data; the initial dynamic rock test data includes: initial dynamic Young's modulus, initial dynamic Poisson's ratio, and initial dynamic shear modulus. The initial dynamic rock test data are normalized to obtain the dynamic elastic modulus; A multiple linear regression model is constructed using the dynamic elastic modulus as the independent variable and the coal seam fracturing pressure as the dependent variable. The partial regression coefficients are obtained by solving the multiple linear regression model.

[0008] Furthermore, the expression for the multiple linear regression model is: ; in, This refers to the coal seam fracturing pressure. The intercept is... The partial regression coefficients of the dynamic Young's modulus. For dynamic Young's modulus, The partial regression coefficient for the dynamic Poisson's ratio. For dynamic Poisson's ratio, The partial regression coefficients of the dynamic shear modulus. For dynamic shear modulus, This is random error.

[0009] Furthermore, based on the aforementioned partial regression coefficients, a fragility index model is constructed, specifically including: The standard deviation of the dynamic rock test data and the standard deviation of the coal seam fracturing pressure are used to standardize the partial regression coefficients to obtain standardized partial regression coefficients. Calculate the weighting coefficients based on the standardized partial regression coefficients; Based on the aforementioned weighting coefficients, a fragility index model is constructed.

[0010] Furthermore, the formula for calculating the weighting coefficient is as follows: ; in, For weighting coefficients, when for hour, The weighting coefficients for the dynamic Young's modulus, when for hour, The weighting coefficients for the dynamic Poisson ratio, when for hour, The weighting coefficient for dynamic shear modulus; For standardized partial regression coefficients, when for hour, The standardized partial regression coefficients of the dynamic Young's modulus, when for hour, The standardized partial regression coefficient of the dynamic Poisson ratio, when for hour, The standardized partial regression coefficients of the dynamic shear modulus; The standardized partial regression coefficients of the dynamic Young's modulus; The standardized partial regression coefficient of the dynamic Poisson's ratio; The standardized partial regression coefficients of the dynamic shear modulus are given.

[0011] Furthermore, the expression for the fragility index model is: ; in, This is a three-dimensional brittleness index data volume; These are the weighting coefficients for the dynamic Young's modulus; This is a three-dimensional dynamic Young's modulus data volume; The weighting coefficients for the dynamic Poisson ratio; It is a three-dimensional dynamic Poisson's ratio data volume; The weighting coefficient for dynamic shear modulus; It is a three-dimensional shear modulus data volume.

[0012] Furthermore, based on the original data, pre-stack synchronous inversion technology and rock physics formulas are used for inversion and calculation to obtain three-dimensional dynamic Young's modulus data volume, three-dimensional dynamic Poisson's ratio data volume, and three-dimensional dynamic shear modulus data volume, specifically including: Based on the original data, pre-stack synchronous inversion technology is used to perform inversion to obtain the P-wave velocity data volume, S-wave velocity data volume and density data volume of the target region. Based on the P-wave velocity data volume, the S-wave velocity data volume, and the density data volume, the initial three-dimensional dynamic Young's modulus data volume, the initial three-dimensional dynamic Poisson's ratio data volume, and the initial three-dimensional dynamic shear modulus data volume are calculated using rock physics formulas. The initial three-dimensional dynamic Young's modulus data volume, the initial three-dimensional dynamic Poisson's ratio data volume, and the initial three-dimensional dynamic shear modulus data volume are normalized to obtain the three-dimensional dynamic Young's modulus data volume, the three-dimensional dynamic Poisson's ratio data volume, and the three-dimensional dynamic shear modulus data volume.

[0013] Furthermore, the brittleness index thresholds, from largest to smallest, include: a first brittleness index threshold. Second fragility index threshold Third fragility index threshold Fourth fragility index threshold ; The region corresponding to the three-dimensional brittleness index data volume is divided according to the brittleness index threshold to determine the high-brittleness dessert region, specifically including: like Then, the region corresponding to the three-dimensional crispness index data volume is determined to be the first type of dessert region; the first type of dessert region is the high crispness dessert region; wherein, This is a three-dimensional brittleness index data volume; like Then the region corresponding to the three-dimensional brittleness index data volume is determined to be the second type of dessert region; like Then the region corresponding to the three-dimensional brittleness index data volume is determined to be the third type of dessert region; like Then the region corresponding to the three-dimensional brittleness index data volume is determined to be the fourth type of dessert region; like If so, the region corresponding to the three-dimensional brittleness index data volume is determined to be the fifth type of dessert area.

[0014] Secondly, this application provides a system for predicting the high brittleness sweet spot region of deep coalbed methane reservoirs, including: The fitting module is used to fit the dynamic elastic modulus as the independent variable and the coal seam fracturing pressure as the dependent variable using a multiple linear regression method to determine the partial regression coefficients; the dynamic elastic modulus includes: dynamic Young's modulus, dynamic Poisson's ratio and dynamic shear modulus. A construction module is used to construct a fragility index model based on the partial regression coefficients; The acquisition module is used to acquire raw data of deep coalbed methane reservoirs in the target area; The inversion and calculation module is used to perform inversion and calculation based on the original data using pre-stack synchronous inversion technology and rock physics formulas to obtain three-dimensional dynamic Young's modulus data volume, three-dimensional dynamic Poisson's ratio data volume and three-dimensional dynamic shear modulus data volume. The brittleness index module is used to substitute the three-dimensional dynamic Young's modulus data volume, the three-dimensional dynamic Poisson's ratio data volume, and the three-dimensional dynamic shear modulus data volume into the brittleness index model to obtain the three-dimensional brittleness index data volume. The segmentation module is used to segment the region corresponding to the three-dimensional brittleness index data volume according to the brittleness index threshold, and to determine the high brittleness dessert area.

[0015] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for predicting the high brittle sweet spot region of deep coalbed methane reservoirs.

[0016] According to the specific embodiments provided in this application, this application has the following technical effects: This application, firstly, fits the dynamic elastic modulus (dynamic Young's modulus, dynamic Poisson's ratio, and dynamic shear modulus) of deep coalbed methane reservoirs with coal seam fracturing pressure using a multiple linear regression method. By using coal seam fracturing pressure as the dependent variable, the correlation between coal seam fracturing pressure and dynamic elastic modulus is clarified, making the physical meaning of the coal seam clear and avoiding interference from engineering factors and the subjectivity of empirical weights. Secondly, compared to calculations using only Young's modulus and Poisson's ratio, this application innovatively introduces dynamic shear modulus, expanding the physical parameter dimensions of the model. Since coal basins are formed under the mechanical action of multiple tectonic movements and shear backgrounds, the dynamic shear modulus considers the multidimensional forces in the coal basin, making the data input for the brittleness index model more comprehensive, thus enabling the brittleness index model to more accurately predict high-brittle sweet spots. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a method for predicting the high brittleness sweet spot region of a deep coalbed methane reservoir, as provided in this application.

[0019] Figure 2 A schematic diagram of the scatter plot fitting provided in this application.

[0020] Figure 3 A schematic diagram of the longitudinal wave velocity data volume provided in this application.

[0021] Figure 4 A schematic diagram of the transverse wave velocity data volume provided in this application.

[0022] Figure 5 A schematic diagram of the density data volume provided in this application.

[0023] Figure 6 A schematic diagram of the three-dimensional dynamic Young's modulus data volume provided in this application.

[0024] Figure 7 A schematic diagram of the three-dimensional dynamic Poisson's ratio data volume provided in this application.

[0025] Figure 8 A schematic diagram of the three-dimensional dynamic shear modulus data volume provided in this application.

[0026] Figure 9 This is a schematic diagram of the brittle planar distribution of coal seam No. 4+5 provided in an embodiment of this application.

[0027] Figure 10 This is a schematic diagram illustrating the classification of the sweet spot zones of coal seams 4+5 according to an embodiment of this application.

[0028] Figure 11 A schematic diagram of the functional modules of a high-brittleness sweet spot prediction system for deep coalbed methane reservoirs provided in this application.

[0029] Figure 12 This is a schematic diagram of the structure of a computer device provided in this application.

[0030] Figure labels: Fitting module-1; Construction module-2; Acquisition module-3; Inversion and calculation module-4; Fragility index module-5; Division module-6. Detailed Implementation

[0031] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0032] In some cases, most methods still suffer from two key shortcomings: First, regarding model parameters, they only utilize two physical parameters—Young's modulus and Poisson's ratio—for calculations. This omission leads to the model's inability to accurately reflect the fracturing behavior of coal and rock under actual geological conditions. This is particularly true for the multi-stage tectonic movements and shear backgrounds commonly experienced in China's coal-bearing basins, where the lack of shear modulus severely compromises the model's geological adaptability. Second, the weighting method lacks a clear physical calibration process. Even when multiple parameters are introduced, their weights are often based on statistical experience or simple mathematical fitting, failing to fully utilize the core rock mechanics principle that "lower fracturing pressure better characterizes coal and rock brittleness." This results in a weak physical foundation for the model and significant deviations between predicted results and actual fracturing behavior.

[0033] The existence of these technical defects severely restricts the accuracy and reliability of brittleness prediction in deep coalbed methane reservoirs, and there is an urgent need for a new technical solution that can simultaneously solve the problems of parameter completeness and clear physical meaning.

[0034] In view of this, this application aims to solve the above problems by introducing the physical property parameter of shear modulus and providing a method for predicting the high brittle sweet spot region of deep coalbed methane reservoirs by integrating shear modulus and directly calibrating the weight based on coal seam fracture pressure, in order to improve the accuracy of predicting the high brittle sweet spot region of deep coalbed methane reservoirs.

[0035] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] In one exemplary embodiment, such as Figure 1 As shown, a method for predicting the high brittle sweet spot region of deep coalbed methane reservoirs is provided. This method is executed by computer equipment, specifically by a computer device such as a terminal or server alone, or by a terminal and a server together. In this embodiment, the method is described using a server as an example, including the following steps S1 to S6.

[0037] Step S1: Using dynamic elastic modulus as the independent variable and coal seam fracturing pressure as the dependent variable, a multiple linear regression method is used to fit the data and determine the partial regression coefficients; the dynamic elastic modulus includes: dynamic Young's modulus, dynamic Poisson's ratio, and dynamic shear modulus.

[0038] As one possible approach, step S1 specifically includes steps S11 to S16.

[0039] Step S11: Obtain static rock test data and coal seam fracture pressure from the experimental well, as well as sample dynamic rock test data from the corresponding reference well; the static rock test data includes: static Young's modulus, static Poisson's ratio, and static shear modulus; the sample dynamic rock test data includes: sample dynamic Young's modulus, sample dynamic Poisson's ratio, and sample dynamic shear modulus.

[0040] Specifically, the static rock test data and coal seam fracturing pressure are data obtained from mechanical tests conducted in the laboratory; meanwhile, the dynamic rock test data of the corresponding reference well are the measured values ​​in the well.

[0041] Step S12: Based on static rock test data and sample dynamic rock test data, obtain the dynamic-static conversion formula through cross plot analysis and regression fitting.

[0042] Specifically, through cross plot analysis and regression fitting, dynamic and static parameter conversion formulas applicable to this region are established (e.g.: E s = a × E d + b ,like Figure 2 As shown, the coefficients of the fitted formula are obtained by fitting a scatter plot. a and b ,in, E s This is the static Young's modulus. a and b For coefficients, E d (This is the dynamic Young's modulus of the sample). Other moduli are obtained using the same method to obtain the dynamic-to-static conversion coefficients. Using this formula, all static mechanical parameters measured in the laboratory are uniformly converted into dynamic values ​​to ensure consistency with subsequent seismic inversion results in terms of scale.

[0043] Step S13: Use the dynamic-static conversion formula to convert the static rock test data to obtain the initial dynamic rock test data; the initial dynamic rock test data includes: initial dynamic Young's modulus, initial dynamic Poisson's ratio and initial dynamic shear modulus.

[0044] Step S14: Normalize the initial dynamic rock test data to obtain the dynamic elastic modulus.

[0045] Specifically, the initial dynamic Young's modulus, initial dynamic shear modulus, and initial dynamic Poisson's ratio (given that Poisson's ratio is negatively correlated with brittleness, we use...) are used to determine these parameters. The values ​​are converted and normalized to obtain the dynamic elastic modulus (dynamic Young's modulus, dynamic Poisson's ratio, and dynamic shear modulus).

[0046] Step S15: Construct a multiple linear regression model with dynamic elastic modulus as the independent variable and coal seam fracturing pressure as the dependent variable.

[0047] Specifically, a multiple linear regression analysis was conducted using dynamic Young's modulus, dynamic Poisson's ratio, and dynamic shear modulus as independent variables, and measured coal seam fracturing pressure as the dependent variable, to establish a multiple linear regression model.

[0048] As an feasible approach, the expression for the multiple linear regression model is: .

[0049] in, This refers to the coal seam fracturing pressure. The intercept is... The partial regression coefficients of the dynamic Young's modulus. For dynamic Young's modulus, The partial regression coefficient for the dynamic Poisson's ratio. For dynamic Poisson's ratio, The partial regression coefficients of the dynamic shear modulus. For dynamic shear modulus, This is random error.

[0050] Specifically, here, fracture pressure serves as the reverse benchmark for brittleness: The lower the value, the easier the coal and rock are to fracture under formation conditions, i.e., the higher the brittleness. The purpose of this regression is to find which combinations of elastic parameters can effectively reduce the fracture pressure of coal seams. The greater the contribution of the elasticity parameter, the larger the absolute value of its partial regression coefficient, and the negative sign should be (i.e., as the parameter increases, ...). (Decrease).

[0051] Step S16: Solve the multiple linear regression model to obtain the partial regression coefficients.

[0052] Step S2: Construct a fragility index model based on the partial regression coefficients.

[0053] As one feasible approach, step S2 specifically includes steps S21 to S23: Step S21: Standardize the partial regression coefficients using the standard deviation of dynamic rock test data and the standard deviation of coal seam fracturing pressure to obtain standardized partial regression coefficients.

[0054] Specifically, to eliminate the influence of dimensions and determine weights, it is necessary to calculate the standardized partial regression coefficients (usually denoted as...). For the first... Each independent variable has its standardized partial regression coefficient. It can be derived from its partial regression coefficient Standard deviation of independent variable and the standard deviation of the dependent variable The calculation shows that: .

[0055] Step S22: Calculate the weight coefficients based on the standardized partial regression coefficients.

[0056] Specifically, the absolute values ​​of the standardized partial regression coefficients are taken (since their signs already reflect the direction of action) and normalized to obtain the weight coefficients of each parameter (i.e., dynamic Young's modulus, dynamic Poisson's ratio, and dynamic shear modulus) in the brittleness index.

[0057] As an feasible approach, the formula for calculating the weighting coefficient is as follows: .

[0058] in, For weighting coefficients, when for hour, The weighting coefficients for the dynamic Young's modulus, when for hour, The weighting coefficients for the dynamic Poisson ratio, when for hour, The weighting coefficient for dynamic shear modulus; For standardized partial regression coefficients, when for hour, The standardized partial regression coefficients of the dynamic Young's modulus, when for hour, The standardized partial regression coefficient of the dynamic Poisson ratio, when for hour, The standardized partial regression coefficients of the dynamic shear modulus; The standardized partial regression coefficients of Young's dynamic modulus; The standardized partial regression coefficient of the dynamic Poisson's ratio; These are the standardized partial regression coefficients of the dynamic shear modulus. And the weighting coefficients satisfy... .

[0059] Step S23: Construct a fragility index model based on the weighting coefficients.

[0060] As an implementable approach, the fragility index model is expressed as follows: .

[0061] in, This is a three-dimensional brittleness index data volume; These are the weighting coefficients for the dynamic Young's modulus; This is a three-dimensional dynamic Young's modulus data volume; The weighting coefficients for the dynamic Poisson ratio; It is a three-dimensional dynamic Poisson's ratio data volume; The weighting coefficient for dynamic shear modulus; It is a three-dimensional shear modulus data volume.

[0062] Step S3: Obtain raw data of deep coalbed methane reservoirs in the target area.

[0063] Specifically, the raw data includes: geological and logging data and seismic data. Seismic data preparation: Collecting three-dimensional pre-stack seismic gathers (CRP gathers) of deep coalbed methane reservoirs in the target area and corresponding seismic wavelets with angular characteristics. Geological and logging data preparation: Collecting P-wave velocity, S-wave velocity, density curves at well points, as well as interpretation results of precise geological horizons.

[0064] Step S4: Based on the original data, inversion and calculation are performed using pre-stack synchronous inversion technology and rock physics formulas to obtain three-dimensional dynamic Young's modulus data volume, three-dimensional dynamic Poisson's ratio data volume, and three-dimensional dynamic shear modulus data volume.

[0065] As an feasible approach, step S4 specifically includes: based on the original data, performing inversion using pre-stack synchronous inversion technology to obtain the P-wave velocity data volume of the target region (i.e., Figure 3 Longitudinal wave velocity volume), transverse wave velocity data volume (i.e.) Figure 4 Medium transverse wave velocity volume) and density data volume (i.e. Figure 5 (Medium-density volume); Based on the P-wave velocity data volume, S-wave velocity data volume, and density data volume, rock physics formulas are used to calculate the initial three-dimensional dynamic Young's modulus data volume, the initial three-dimensional dynamic Poisson's ratio data volume, and the initial three-dimensional dynamic shear modulus data volume; these data volumes are then normalized to obtain the three-dimensional dynamic Young's modulus data volume (i.e., medium-density volume); Figure 6 Young's modulus data volume), three-dimensional dynamic Poisson's ratio data volume (i.e. Figure 7 Poisson's ratio data volume) and three-dimensional dynamic shear modulus data volume (i.e. Figure 8 Medium shear modulus data body).

[0066] Specifically, initial model construction: using the well logging data and geological strata prepared in step S3, a three-dimensional low-frequency initial model of P-wave velocity, S-wave velocity and density is established.

[0067] Perform synchronous inversion: The pre-stack synchronous inversion algorithm based on the Zoeppritz equation is adopted. The seismic gathers are used as input and the three-dimensional low-frequency initial model is used as constraints to invert the P-wave velocity data volume, S-wave velocity data volume and density data volume of the target area.

[0068] Elastic parameter conversion: Using the P-wave velocity data volume, S-wave velocity data volume and density data volume of the target area obtained by the above inversion, the initial three-dimensional dynamic Young's modulus data volume and the initial three-dimensional dynamic Poisson's ratio data volume are calculated point by point according to the rock physics formula.

[0069] Shear modulus calculation: According to the formula ,in, For density data volume, The initial three-dimensional dynamic shear modulus data volume is calculated from the shear wave velocity data volume. This step yields a dynamic parameter volume, and since the brittleness index model has already been calibrated based on dynamic values, no further dynamic-to-static conversion is required.

[0070] Normalization: Finally, the initial three-dimensional dynamic Young's modulus data volume, the initial three-dimensional dynamic Poisson's ratio data volume, and the initial three-dimensional dynamic shear modulus data volume are normalized.

[0071] Step S5: Substitute the three-dimensional dynamic Young's modulus data volume, the three-dimensional dynamic Poisson's ratio data volume, and the three-dimensional dynamic shear modulus data volume into the brittleness index model to obtain the three-dimensional brittleness index data volume.

[0072] Step S6: Divide the region corresponding to the three-dimensional brittleness index data volume according to the brittleness index threshold to determine the high brittleness dessert area.

[0073] Specifically, brittleness attribute extraction: along the top and bottom interfaces of the target coal seam, extract the average or maximum value of its brittleness index from the three-dimensional brittleness index data volume to generate a coal seam brittleness plane distribution map.

[0074] Sweet spot evaluation and delineation: Combining geological understanding and fracturing engineering requirements, a brittleness index threshold is set, and high-brittleness sweet spot areas are delineated on the plan view. High-brittleness sweet spot areas correspond to favorable regions with low fracturing pressure and ease of fracturing stimulation, providing a direct basis for drilling trajectory design and fracturing scheme optimization.

[0075] As an implementable approach, the fragility index thresholds, from largest to smallest, include: the first fragility index threshold. Second fragility index threshold Third fragility index threshold Fourth fragility index threshold .

[0076] Among them, if Therefore, the region corresponding to the three-dimensional crispness index data volume is determined to be the first type of dessert region; the first type of dessert region is the high crispness dessert region; among which, It is a three-dimensional brittleness index data volume.

[0077] like If so, the region corresponding to the three-dimensional brittleness index data volume is determined to be the second type of dessert region.

[0078] like If so, the region corresponding to the three-dimensional brittleness index data volume is determined to be the third type of dessert area.

[0079] like If so, the region corresponding to the three-dimensional brittleness index data volume is determined to be the fourth type of dessert area.

[0080] like If so, the region corresponding to the three-dimensional brittleness index data volume is determined to be the fifth type of dessert area.

[0081] Based on the method for predicting the high-brittle sweet spot region of deep coalbed methane reservoirs provided in this application, a specific embodiment is provided for a certain work area: I. Data Preparation and Model Building (corresponding to steps S1, S2, and S3): Laboratory petrological test data and coal seam fracture pressure from 8 key wells in the work area were collected, along with corresponding dynamic rock test data and raw data from deep coalbed methane reservoirs.

[0082] Establish dynamic-static conversion formulas: for example, E s =0.75× E d +0.5 (GPa), and complete the dynamic conversion of all static parameters.

[0083] Multiple regression analysis was conducted using dynamic rock test data as the independent variable and coal seam fracturing pressure as the dependent variable. The regression analysis confirmed that the increase in dynamic Young's modulus and dynamic shear modulus was significantly correlated with the decrease in fracturing pressure (with negative coefficients), while the increase in dynamic Poisson's ratio led to an increase in fracturing pressure (with a positive coefficient), which is entirely in line with theoretical expectations.

[0084] Weights are determined based on regression coefficients: =0.50, =0.30, =0.20. Establish the WBI model for this region: The model shows that, in this work area, Young's modulus contributes the most to reducing fracture pressure and increasing brittleness.

[0085] II. Seismic Inversion and Parameter Calculation (corresponding to step S4): Prepare 3D pre-stack seismic gathers, wavelets, well logging curves, and stratigraphic data.

[0086] Pre-stack synchronous inversion technology was used to obtain P-wave velocity data volume, S-wave velocity data volume, and density data volume.

[0087] The calculations were performed using rock physics formulas and then normalized to obtain three-dimensional dynamic Young's modulus data volume, three-dimensional dynamic Poisson's ratio data volume, and three-dimensional dynamic shear modulus data volume.

[0088] III. Crispness Prediction and Dessert Selection (corresponding to steps S5 and S6): Substitute the three-dimensional dynamic Young's modulus data volume, the three-dimensional dynamic Poisson's ratio data volume, and the three-dimensional dynamic shear modulus data volume into the brittleness index model to generate the three-dimensional brittleness index data volume.

[0089] like Figure 9 As shown, a plan view of the brittleness of coal seam 4+5 is plotted. In the figure, black dots represent well locations, blue lines represent extensional strike-slip faults, red lines represent compressional strike-slip faults, green lines represent normal faults, and pink lines represent reverse faults. The results show that the high brittleness value area ( The area is distributed in a north-south strip in the middle of the work area. The predicted fracture pressure in this area is low. Therefore, the three-dimensional map corresponding to the high brittleness value area in the plan view is identified as the high brittleness sweet spot area.

[0090] like Figure 10 As shown, the high-brittleness sweet spot region is delineated. In the figure, black dots represent well locations, blue lines represent extensional strike-slip faults, red lines represent compressional strike-slip faults, green lines represent normal faults, and pink lines represent reverse faults. Based on the height of the three-dimensional brittleness index data volume, if... The region corresponding to the three-dimensional fragility index data volume is determined to be the first type of dessert region (i.e., type I dessert region in the figure), meaning the first type of dessert region is a high-fragility dessert region; if Then the region corresponding to the three-dimensional brittleness index data volume is determined to be the second type of dessert region (i.e., the type II dessert region in the figure); if Then the region corresponding to the three-dimensional brittleness index data volume is determined to be the third type of dessert area (i.e., type III dessert area in the figure); if Then the region corresponding to the three-dimensional brittleness index data volume is determined to be the fourth type of dessert area (i.e., the IV type of dessert area in the figure); if The region corresponding to the three-dimensional brittleness index data volume is then identified as the fifth type of sweet spot region (i.e., the V type sweet spot region in the figure). Verification shows that this sweet spot region closely matches production well areas exhibiting low fracturing pressure and high conductivity in known fracturing operations, thus validating the effectiveness and accuracy of this method.

[0091] The beneficial effects of the method for predicting the brittle sweet spot region of deep coalbed methane reservoirs proposed in this application are mainly reflected in: 1. This paper presents a method for predicting the high brittleness sweet spot in deep coalbed methane reservoirs with clear physical meaning and higher accuracy. This method introduces dynamic shear modulus and uses experimental data on coal seam fracturing pressure to drive weight allocation, achieving high-precision three-dimensional prediction of the brittleness index and providing a reliable basis for selecting optimal fracturing sweet spots. Compared with existing technologies, it has the following significant advantages: solid theoretical foundation and clear physical meaning of the model: For the first time, laboratory-measured coal seam fracturing pressure is used as the calibration benchmark (i.e., dependent variable) of the multiple linear regression model. Utilizing the core rock mechanics principle that "the lower the fracturing pressure, the better the brittleness," a quantitative and inverse mathematical correlation is established between the brittleness index and the inherent fracturing characteristics of the rock through multiple linear regression. The physical meaning is clear, avoiding interference from engineering factors and the subjectivity of empirical weights.

[0092] 2. A more comprehensive brittleness index model with stronger geological adaptability: Based on traditional elastic moduli (Young's modulus and Poisson's ratio), a dynamic shear modulus is innovatively introduced. This, along with the dynamic Young's modulus and dynamic Poisson's ratio, constitutes a dynamic elastic modulus, thus constructing a brittleness index model involving all three. This allows the brittleness index model to simultaneously reflect the compressive, deformation, and shear failure resistance of coal and rock, making it particularly suitable for coal-bearing basins in China that have experienced intense shear tectonic activity. It provides a more comprehensive characterization of the brittle or ductile behavior of coal and rock, with high prediction accuracy and strong practicality.

[0093] 3. Employing pre-stack synchronous inversion and dynamic / static parameter conversion techniques ensures the accuracy of the conversion from seismic data to rock mechanics parameters. The final result is a three-dimensional brittleness index data volume, which enables visualization of the spatial distribution of brittleness and direct delineation of sweet spots. This data is used to guide the efficient development of deep coalbed methane, demonstrating significant practical value. It achieves three-dimensional, quantitative prediction of the spatial distribution of brittleness in deep coalbed methane reservoirs, and the prediction results are closely related to the actual formation fracturing pressure. This provides direct and reliable geological basis for horizontal well trajectory optimization and fracturing engineering scheme design, thereby improving the development efficiency and economic benefits of deep coalbed methane.

[0094] Based on the same inventive concept, this application also provides a system for predicting the high brittleness sweet spot region of deep coalbed methane reservoirs. The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the high brittleness sweet spot region prediction system for deep coalbed methane reservoirs provided below can be found in the limitations of the high brittleness sweet spot region prediction method for deep coalbed methane reservoirs described above, and will not be repeated here.

[0095] In one exemplary embodiment, such as Figure 11 As shown, a system for predicting the high brittleness sweet spot region of deep coalbed methane reservoirs is provided, comprising: Fitting module 1 is used to fit the dynamic elastic modulus as the independent variable and the coal seam fracturing pressure as the dependent variable using a multiple linear regression method to determine the partial regression coefficients; the dynamic elastic modulus includes: dynamic Young's modulus, dynamic Poisson's ratio and dynamic shear modulus.

[0096] Module 2 is used to build a fragility index model based on partial regression coefficients.

[0097] Module 3 is used to acquire raw data of deep coalbed methane reservoirs in the target area.

[0098] Inversion and calculation module 4 is used to perform inversion and calculation based on the original data using pre-stack synchronous inversion technology and rock physics formulas to obtain three-dimensional dynamic Young's modulus data volume, three-dimensional dynamic Poisson's ratio data volume and three-dimensional dynamic shear modulus data volume.

[0099] The brittleness index module 5 is used to substitute the three-dimensional dynamic Young's modulus data volume, the three-dimensional dynamic Poisson's ratio data volume, and the three-dimensional dynamic shear modulus data volume into the brittleness index model to obtain the three-dimensional brittleness index data volume.

[0100] The segmentation module 6 is used to segment the region corresponding to the three-dimensional brittleness index data volume according to the brittleness index threshold, and to determine the high brittleness dessert area.

[0101] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 12 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores three-dimensional dynamic Young's modulus data, three-dimensional dynamic Poisson's ratio data, and three-dimensional dynamic shear modulus data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the aforementioned method for predicting the high-brittle sweet spot region of deep coalbed methane reservoirs.

[0102] Those skilled in the art will understand that Figure 12The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0103] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0104] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0105] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0106] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0107] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for predicting the highly brittle sweet spot region in deep coalbed methane reservoirs, characterized in that, The method for predicting the high-brittle sweet spot region of deep coalbed methane reservoirs includes: Using dynamic elastic modulus as the independent variable and coal seam fracturing pressure as the dependent variable, a multiple linear regression method was used to fit the data and determine the partial regression coefficients; the dynamic elastic modulus includes: dynamic Young's modulus, dynamic Poisson's ratio, and dynamic shear modulus. Based on the partial regression coefficients, a fragility index model is constructed; Obtain raw data of deep coalbed methane reservoirs in the target area; Based on the original data, inversion and calculation were performed using pre-stack synchronous inversion technology and rock physics formulas to obtain three-dimensional dynamic Young's modulus data volume, three-dimensional dynamic Poisson's ratio data volume and three-dimensional dynamic shear modulus data volume. Substituting the three-dimensional dynamic Young's modulus data volume, the three-dimensional dynamic Poisson's ratio data volume, and the three-dimensional dynamic shear modulus data volume into the brittleness index model, we obtain the three-dimensional brittleness index data volume. The regions corresponding to the three-dimensional brittleness index data volume are divided according to the brittleness index threshold to determine the high-brittleness dessert region.

2. The method for predicting the high-brittle sweet spot region of deep coalbed methane reservoirs according to claim 1, characterized in that, Using dynamic elastic modulus as the independent variable and coal seam fracturing pressure as the dependent variable, a multiple linear regression method was employed to fit the data and determine the partial regression coefficients, specifically including: The static rock test data and coal seam fracture pressure of the experimental well, as well as the sample dynamic rock test data of the corresponding reference well, are obtained. The static rock test data includes: static Young's modulus, static Poisson's ratio, and static shear modulus. The sample dynamic rock test data includes: sample dynamic Young's modulus, sample dynamic Poisson's ratio, and sample dynamic shear modulus. Based on the static rock test data and the sample dynamic rock test data, the dynamic-static conversion formula is obtained through cross plot analysis and regression fitting. The static rock test data is converted using the dynamic-static conversion formula to obtain initial dynamic rock test data; the initial dynamic rock test data includes: initial dynamic Young's modulus, initial dynamic Poisson's ratio, and initial dynamic shear modulus. The initial dynamic rock test data are normalized to obtain the dynamic elastic modulus; A multiple linear regression model is constructed using the dynamic elastic modulus as the independent variable and the coal seam fracturing pressure as the dependent variable. The partial regression coefficients are obtained by solving the multiple linear regression model.

3. The method for predicting the high-brittle sweet spot region of deep coalbed methane reservoirs according to claim 2, characterized in that, The expression for the multiple linear regression model is: ； in, This refers to the coal seam fracturing pressure. The intercept is... The partial regression coefficients of the dynamic Young's modulus. For dynamic Young's modulus, The partial regression coefficient for the dynamic Poisson's ratio. For dynamic Poisson's ratio, The partial regression coefficients of the dynamic shear modulus. For dynamic shear modulus, This is random error.

4. The method for predicting the high-brittle sweet spot region of deep coalbed methane reservoirs according to claim 2, characterized in that, Based on the partial regression coefficients, a fragility index model is constructed, specifically including: The standard deviation of the dynamic rock test data and the standard deviation of the coal seam fracturing pressure are used to standardize the partial regression coefficients to obtain standardized partial regression coefficients. Calculate the weighting coefficients based on the standardized partial regression coefficients; Based on the aforementioned weighting coefficients, a fragility index model is constructed.

5. The method for predicting the high-brittle sweet spot region of deep coalbed methane reservoirs according to claim 4, characterized in that, The formula for calculating the weighting coefficient is as follows: ； in, For weighting coefficients, when for hour, The weighting coefficients for the dynamic Young's modulus, when for hour, The weighting coefficients for the dynamic Poisson ratio, when for hour, The weighting coefficient for dynamic shear modulus; For standardized partial regression coefficients, when for hour, The standardized partial regression coefficients of the dynamic Young's modulus, when for hour, The standardized partial regression coefficient of the dynamic Poisson ratio, when for hour, The standardized partial regression coefficients of the dynamic shear modulus; The standardized partial regression coefficients of the dynamic Young's modulus; The standardized partial regression coefficient of the dynamic Poisson's ratio; The standardized partial regression coefficients of the dynamic shear modulus are given.

6. The method for predicting the high-brittle sweet spot region of deep coalbed methane reservoirs according to claim 4, characterized in that, The expression for the brittleness index model is: ； in, This is a three-dimensional brittleness index data volume; These are the weighting coefficients for the dynamic Young's modulus; This is a three-dimensional dynamic Young's modulus data volume; The weighting coefficients for the dynamic Poisson ratio; It is a three-dimensional dynamic Poisson's ratio data volume; The weighting coefficient for dynamic shear modulus; It is a three-dimensional shear modulus data volume.

7. The method for predicting the high-brittle sweet spot region of deep coalbed methane reservoirs according to claim 1, characterized in that, Based on the original data, inversion and calculation were performed using pre-stack synchronous inversion technology and rock physics formulas to obtain three-dimensional dynamic Young's modulus data volume, three-dimensional dynamic Poisson's ratio data volume, and three-dimensional dynamic shear modulus data volume, specifically including: Based on the original data, pre-stack synchronous inversion technology is used to perform inversion to obtain the P-wave velocity data volume, S-wave velocity data volume and density data volume of the target region. Based on the P-wave velocity data volume, the S-wave velocity data volume, and the density data volume, the initial three-dimensional dynamic Young's modulus data volume, the initial three-dimensional dynamic Poisson's ratio data volume, and the initial three-dimensional dynamic shear modulus data volume are calculated using rock physics formulas. The initial three-dimensional dynamic Young's modulus data volume, the initial three-dimensional dynamic Poisson's ratio data volume, and the initial three-dimensional dynamic shear modulus data volume are normalized to obtain the three-dimensional dynamic Young's modulus data volume, the three-dimensional dynamic Poisson's ratio data volume, and the three-dimensional dynamic shear modulus data volume.

8. The method for predicting the high-brittle sweet spot region of deep coalbed methane reservoirs according to claim 1, characterized in that, The brittleness index thresholds, from largest to smallest, include: the first brittleness index threshold. Second fragility index threshold Third fragility index threshold Fourth fragility index threshold ; The region corresponding to the three-dimensional brittleness index data volume is divided according to the brittleness index threshold to determine the high-brittleness dessert region, specifically including: like Then, the region corresponding to the three-dimensional crispness index data volume is determined to be the first type of dessert region; the first type of dessert region is the high crispness dessert region; wherein, This is a three-dimensional brittleness index data volume; like Then the region corresponding to the three-dimensional brittleness index data volume is determined to be the second type of dessert region; like Then the region corresponding to the three-dimensional brittleness index data volume is determined to be the third type of dessert region; like Then the region corresponding to the three-dimensional brittleness index data volume is determined to be the fourth type of dessert region; like If so, the region corresponding to the three-dimensional brittleness index data volume is determined to be the fifth type of dessert area.

9. A system for predicting the high brittleness sweet spot region of deep coalbed methane reservoirs, characterized in that, include: The fitting module is used to fit the dynamic elastic modulus as the independent variable and the coal seam fracturing pressure as the dependent variable using the multiple linear regression method to determine the partial regression coefficients. The dynamic elastic modulus includes: dynamic Young's modulus, dynamic Poisson's ratio, and dynamic shear modulus; A construction module is used to construct a fragility index model based on the partial regression coefficients; The acquisition module is used to acquire raw data of deep coalbed methane reservoirs in the target area; The inversion and calculation module is used to perform inversion and calculation based on the original data using pre-stack synchronous inversion technology and rock physics formulas to obtain three-dimensional dynamic Young's modulus data volume, three-dimensional dynamic Poisson's ratio data volume and three-dimensional dynamic shear modulus data volume. The brittleness index module is used to substitute the three-dimensional dynamic Young's modulus data volume, the three-dimensional dynamic Poisson's ratio data volume, and the three-dimensional dynamic shear modulus data volume into the brittleness index model to obtain the three-dimensional brittleness index data volume. The segmentation module is used to segment the region corresponding to the three-dimensional brittleness index data volume according to the brittleness index threshold, and determine the high brittleness dessert area.

10. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for predicting the high brittle sweet spot region of a deep coalbed methane reservoir according to any one of claims 1-8.

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