Quantitative prediction method for coal rock gas favorable area

Abstract

The invention discloses a quantitative prediction method for a coal-rock gas favorable area. The quantitative prediction method comprises the following steps: S1, collecting coal-rock underground core samples and cover rock core samples of a coal seam which is deeper than 2000m in different areas of a coal-containing basin; s2, measuring the vitrinite reflectivity, the vitrinite content, the porosity and the permeability of the underground rock core sample; s3, measuring the porosity, the permeability, the breakthrough pressure and the diffusion coefficient of the cap rock core sample, and statistically analyzing contribution weights of five evaluation indexes of the coal seam thickness, the porosity, the permeability, the breakthrough pressure and the diffusion coefficient to the sealing capacity based on the measured data to obtain a sealing index S; s4, statistically analyzing the contribution weights of the coal seam thickness, the vitrinite reflectivity, the vitrinite content, the porosity and the permeability measured in the step S2 and the sealing index S to the sealing capacity, and establishing a quantitative calculation formula of an enrichment index E; and S5, performing coal rock gas favorable area evaluation based on the enrichment index E.

Description

Technical Field

[0001] This invention relates to the field of oil and gas extraction technology, and in particular to a quantitative prediction method for favorable coal and rock gas areas. Background Technology

[0002] As a new and clean energy source, coalbed methane has attracted considerable attention against the backdrop of global energy transition and environmental protection. China's coalbed methane resources exceed 30 × 10⁻⁶. 12 m 3 The region is rich in resources, mainly concentrated in the Ordos Basin, Sichuan Basin, and Junggar Basin. In recent years, the Ordos Basin has seen breakthroughs in the exploration and development of the Benxi Formation No. 8 coal seam, which is deeper than 2000m. In 2021, the Jishen 6-7 Ping 01 well in the southeastern Jixian area achieved a daily gas production of 10.1 × 10⁻⁶ m³. 4 m 3 High-yield industrial gas flow represents a major breakthrough in deep coal and rock gas production. Wells such as Nalin 1H and M172H, deployed in the central and eastern parts of the basin, have achieved daily production of (5~10) × 10⁻⁶ m³ / h. 4 m 3 The high-yield industrial gas flow has laid the foundation for the commercial development of deep coalbed methane, demonstrating the enormous exploration potential of coalbed methane in the Ordos Basin. Therefore, conducting predictions of favorable coalbed methane areas is of great significance for screening sweet spots and formulating development plans for deep coalbed methane.

[0003] Current research indicates that coal gas enrichment is primarily controlled by its hydrocarbon generation conditions (coal seam thickness, vitrinite content, and maturity), reservoir space (porosity and permeability), and caprock sealing capacity. However, existing studies generally focus on the analysis of single controlling factors and are qualitative, lacking accuracy and specific evaluation methods for favorable zones tailored to the characteristics of coal gas reservoirs. To address this, this invention, based on the main controlling factors of differential enrichment in coal gas and the coupling of multiple factors in reservoir formation, comprehensively optimizes parameters such as coal seam thickness, maturity, vitrinite content, porosity, permeability, reservoir-caprock combination, and sealing capacity to conduct a quantitative evaluation of favorable parameters for coal gas areas in the Ordos Basin. Establishing a quantitative prediction method for favorable coal gas areas enables convenient and rapid prediction of these areas, which is of great significance for large-scale exploration and development of coal gas. Summary of the Invention

[0004] To address the aforementioned shortcomings of existing methods for analyzing and predicting favorable coal-rock gas areas, this invention provides a quantitative prediction method for favorable coal-rock gas areas.

[0005] The quantitative prediction method for favorable coal-rock gas areas provided by this invention mainly involves a comprehensive analysis of the coal-rock gas accumulation patterns, including research on hydrocarbon generation potential, reservoir evaluation, preservation conditions, and key control factors of accumulation, as well as a comprehensive study of accumulation and enrichment models and prediction of favorable areas. The specific steps are as follows: S1. Sample collection: Collect coal core samples and caprock core samples from coal seams deeper than 2000m in different areas of the coal-bearing basin.

[0006] S2. Determine the vitrinite reflectance, vitrinite content, porosity, and permeability of the downhole core samples.

[0007] S3. Determine the porosity, permeability, breakthrough pressure, and diffusion coefficient of the caprock core samples. Based on the correlation between the measured porosity, permeability, breakthrough pressure, and diffusion coefficient of the caprock core samples and the actual sealing capacity, statistical analysis methods are used to clarify the contribution weight of each evaluation index parameter (thickness, porosity, permeability, breakthrough pressure, and diffusion coefficient) to the sealing capacity, thus obtaining the sealing index. S The calculation formula is as follows;

[0008] In the formula, S For closed-end index, n X represents the number of evaluation indicators. k Let be the normalized parameter of the k-th evaluation index. A k is the weight of the k-th evaluation indicator.

[0009] Based on a large amount of measured data, it was calculated that the sealing index of sandstone roof is relatively low, all less than 0.5; the sealing index of mudstone roof is relatively high, about 1.0; and limestone has the strongest sealing ability, with a sealing index of about 1.5.

[0010] S4. Using statistical analysis methods, analyze the contribution weights of six evaluation indicators to sealing capacity: coal seam thickness, vitrinite reflectance measured in step S2, vitrinite content, porosity and permeability, and sealing index S. Introduce weight calibration factors, indicator interaction terms, nonlinear correction terms, and constraints to establish a quantitative calculation formula for enrichment index E.

[0011] In the formula, E The enrichment index has a value range of [0, +∞). The larger the value, the more significant the enrichment effect.

[0012] n The number of evaluation indicators is a positive integer, and its value ranges from 1 to 2. n ≥2 (to avoid the limitations of single-parameter evaluation), and n Less than or equal to n max , n max To preset the maximum number of evaluation parameters, we can prevent parameter redundancy from causing a decrease in computational efficiency.

[0013] X k Let be the normalization parameter for the k-th evaluation index, with a value range of [0, 1]. This parameter is derived from the original index data. x k The result was obtained using an improved standardized algorithm; the specific calculation formula is as follows: ,in, and These are the minimum and maximum values ​​of the k-th indicator, respectively.

[0014] X i Let be the normalized parameter of the i-th evaluation index.

[0015] A k Let be the weight of the k-th evaluation indicator, with a value range of [0, 1], and satisfy the constraint condition. To ensure the rationality of weight allocation, the Analytic Hierarchy Process (AHP) is used to solve the problem, which solves the technical problem of the strong subjectivity of traditional weight setting.

[0016] A i Let i be the weight of the i-th evaluation index. α k is the weight calibration factor for the k-th evaluation indicator, with a value range of [0.8, 1.2], used to correct the basic weights. A k Subjective bias, calculated using the following formula: This enables adaptive adjustment of the calibration factor.

[0017] d k Let be the weight stability factor for the k-th evaluation index, with a value range of [0.95, 1.05]. This factor is used to compensate for random errors during the weight calibration process, and its calculation formula is: ,in, Let be the standard deviation of the weight of the k-th indicator. This is the average of the standard deviations of all indicator weights, further improving the accuracy of weight calibration.

[0018] β ki Let be the interaction coefficient between the k-th and i-th evaluation indicators, with a value range of [0, 0.5]. This coefficient characterizes the synergistic / antagonistic effects between different evaluation indicators. Synergistic effects are indicated by... When antagonistic The enrichment index was obtained from orthogonal experimental data. The specific calibration method is as follows: (1) Design an orthogonal experimental scheme, with the kth and ith evaluation indicators as the core influencing factors, and the remaining evaluation indicators set as fixed benchmark values. Each factor is set with 3-5 levels (covering its actual value range); (2) Repeat each orthogonal experiment 3 times and collect the measured values ​​of the enrichment index corresponding to each group of experiments. Simultaneously, calculate the theoretical enrichment index value when only the k-th and i-th indicators are considered individually. , (3) Calculate the interaction difference If Δ E ki >0, judged as synergistic effect, according to The maximum value of the interaction difference across all experimental groups; if Δ E ki ≤0 indicates no synergistic effect (i.e., antagonistic effect), and is calibrated. β ki =0; (4) Take the average of the calibration results of all orthogonal experimental groups to obtain the final result. β ki This ensures the reliability and repeatability of the calibration results.

[0019] l ki Let be the interaction strength coefficient between the k-th and i-th evaluation indicators, with a value range of [0.8, 1.2]. Its core definition is to quantify the interaction strength coefficient between the k-th and i-th evaluation indicators. k The and the first i The strength of synergistic / antagonistic effects among the evaluation indicators, and their correlation with the interaction coefficient. β ki The coefficients show a positive correlation; the stronger the interaction, the closer it is to 1.2, and the weaker the interaction, the closer it is to 0.8. The specific definition and quantification logic are as follows: Coefficient l ki Interaction coefficient β ki The strength supplement is used to address the issue of relying solely on... β ki For technical issues where the strength of synergy cannot be distinguished, its value is determined by the significance of the interaction, and the specific calculation formula is as follows: , where max( β ki ) is the interaction pair for all indicators β ki The maximum value; when β k When =0 (antagonistic effect), l ki =0.8 (no interaction strength); when β ki =max(β ki (At the time of strongest synergy) l ki =1.2 (strongest interaction intensity); when it is between the two, the above calculation formula is used to calculate the interaction intensity accurately and ensure the distinguishability of different interaction levels.

[0020] c The correction coefficient, with a value range of [0.9, 1.1], is obtained by fitting experimental data and is used to calibrate the overall systematic error of the formula. The fitting sample size is no less than 30 groups to ensure the reliability of the correction coefficient. The specific method for fitting experimental data is as follows: 1. Select at least 30 experimental samples under different working conditions, and collect the original data of each evaluation index for each sample. x k And the corresponding enrichment index was accurately measured using experimental methods. 2. Calculate the normalization parameter for each sample group according to the definition of each parameter in the formula. X k Basic weights A k Weight calibration factor α k Weight stability factor d k Interaction coefficient β ki Interaction intensity coefficient l ki 1. Dispersion-related parameters and environmental impact correction coefficient ζ; 2. Substitute the calculated parameters into the enrichment index E calculation formula in step S4 to calculate the theoretical value of the enrichment index for each sample group. 4. For dependent variable, Using as the independent variable, the least squares method is used for linear fitting, and the fitting equation is: ,in 5. Perform a significance test on the fitting results (confidence level not less than 95%). If the residuals are... The value approaches 0, and the goodness of fit R0 is... 2 If the value is ≥0.9, then the fitted value is used. c This serves as the final overall correction coefficient. If the above conditions are not met, increase the experimental sample size (no less than 40 groups) and repeat the above steps until the requirements are met, ensuring... c It can effectively calibrate the systematic error of the formula.

[0021] The average of the normalized parameters of all evaluation indicators, i.e. It is used to characterize the overall level of the normalization parameter and serves as a benchmark value for calculating the discreteness.

[0022] ε is the local minimum constant, which takes the value ε = 10. -6 This is used to avoid calculation errors when the denominator is 0, and to prevent loss of calculation precision due to excessively small values.

[0023] η is the dispersion adjustment coefficient, with a value range of [0.9, 1.1]. Its meaning is: based on the dispersion of the normalized parameters of all evaluation indicators, it adaptively adjusts the strength of the nonlinear correction term to achieve accurate correction for data with different dispersion levels. The core function of this coefficient is to solve the problem of insufficient or excessive correction strength of the nonlinear correction term when the dispersion of the normalized parameters varies greatly in different samples. Its value is related to the dispersion coefficient of the normalized parameters. CV Directly related; where the coefficient of variation is... , For all normalized parameters X k standard deviation This is the average of all normalized parameters; the specific calculation formula is as follows: , where max( CV ) represents the maximum value of the discrete coefficients across all experimental samples; when CV When =0 (all normalized parameters are not discrete and take consistent values), or =0.9 (minimum correction strength); when CV =max( CV When the dispersion is at its maximum, or =1.1 (maximum correction strength); when it is between the two, it is calculated according to the above calculation formula to ensure that the strength of the nonlinear correction term matches the data dispersion and improve the adaptability of the formula to data with different degrees of dispersion.

[0024] The optimized nonlinear correction term has a value range of (0, 1] and is used to correct the calculation deviation caused by excessive dispersion of the normalization parameter. Compared with the original correction term, a new dispersion adjustment coefficient η is added to adapt to data with different dispersion levels and improve the stability and accuracy of the formula.

[0025] ζ is the environmental impact correction coefficient, ranging from [0.9, 1.1], used to compensate for the influence of environmental factors (such as temperature and humidity) on the enrichment index calculation. It is obtained by calibration from environmental monitoring data. The specific method is as follows: 1. Determine the core environmental factors affecting the enrichment effect (such as temperature). T ,depth H 1) Set the monitoring range for each environmental factor (covering the actual application environment range of this method), and set 5-6 monitoring levels for each environmental factor; 2) Keep the original data and other parameters of all evaluation indicators unchanged, and only change the environmental factor levels. Conduct 3 parallel experiments under each set of environmental conditions and collect the measured values ​​of the enrichment index. Simultaneously, the theoretical value of the enrichment index under standard environmental conditions (preset reference temperature and humidity) is calculated. 3. Calculate the initial values ​​of the correction factors for each set of environmental conditions. 4. Using environmental factors (such as temperature and humidity) as independent variables, Establish a multiple linear regression model with [variable name] as the dependent variable. ,in a , b , c 5. To validate the regression model, select 10 validation samples under different environmental conditions and substitute them into the model for calculation. g If the relative error between the calculated value and the measured calibration value is ≤5%, then the regression model is determined to be valid. g The calibration model; in practical applications, by monitoring environmental factor parameters in real time and substituting them into the model, the corresponding values ​​can be obtained. g This will enable precise compensation for environmental impacts.

[0026] S5. Based on existing exploration results and actual coal and shale gas production, establish evaluation criteria for favorable coal and shale gas areas based on the enrichment index E, as detailed in Table 1.

[0027] Table 1. Evaluation Criteria for Favorable Coal Mine Areas Based on Enrichment Index E

[0028] Compared with the prior art, the advantages of the present invention are: (1) The method of the present invention uses parameters such as coal thickness, maturity, vitrinite content, porosity, permeability, reservoir-cap combination and sealing capacity to make quantitative prediction of favorable coal areas. That is, it integrates the three main controlling factors of coal enrichment and accumulation: hydrocarbon generation conditions, reservoir conditions and preservation conditions. The method is simple to operate, accurate and efficient.

[0029] (2) The method of this invention is based on the basic geological parameters, well logging data, and conventional experimental analysis results commonly used in coal gas exploration and development. It avoids the high costs, long cycles, demanding experimental conditions, and complex operations associated with multiple rounds of field testing, high-precision seismic interpretation, and complex geological modeling. Through the quantitative calculation and prediction model of this invention, the distribution range, enrichment level, and resource scale of favorable coal gas areas can be accurately determined. This facilitates large-scale, multi-block, and multi-level quantitative prediction of favorable coal gas areas, solving the problem of traditional methods' difficulty in achieving large-scale and accurate prediction. This has significant guiding value for target area selection, resource potential evaluation, and development scheme optimization in coal gas exploration and development. It can effectively reduce exploration and development risks, improve resource utilization efficiency, and promote efficient and large-scale exploration and development of coal gas, possessing significant economic value and engineering application significance.

[0030] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0031] Figure 1 This is a planar distribution map of enrichment indices in the central and eastern parts of the Ordos Basin.

[0032] Figure 2 Map showing the favorable areas in the central and eastern parts of the Ordos Basin. Detailed Implementation

[0033] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0034] This embodiment applies the quantitative prediction method for favorable coal-gas areas of the present invention to the deep Benxi Formation coal seams in the Ordos Basin. Currently, several proven coal-gas reserves of hundreds of billions of cubic meters have been reported in this region, making it one of the basins with the greatest potential for coal-gas resources in China, with significant exploration potential. However, the following problems still exist during exploration: the exploration level of key blocks is high, while the exploration level of reserve exploration blocks is relatively low, and favorable zones are not yet clear; some exploration wells reveal uneven distribution of coal-gas thickness in reserve exploration areas, with some areas having a thickness of less than 5 meters, and the maturity of coal-gas also varies greatly, with both low and high maturity stages present. The reservoir properties and gas content also vary significantly, necessitating the development of favorable areas to provide direction for future production and development.

[0035] First, a comprehensive collection of relevant data for the study area was conducted, including source rock geochemical data, logging data, well logging data, reservoir physical property data, gas content test data, production reports, and analytical test data.

[0036] Key core wells were selected for systematic core observation and description. Based on the core observation results, a well logging identification standard for the study area was established using a rock-electrical method to identify coal and its overlying limestone, mudstone, or sandstone caprock. Coal thickness maps and caprock lithology distribution maps were then drawn.

[0037] Using conventional methods known in the industry, we conducted measurements of vitrinite reflectance, vitrinite content, porosity, and permeability of coal and rock samples, as well as porosity, permeability, breakthrough pressure, and diffusion coefficient of caprock samples.

[0038] Based on the collected data and experimental analysis results, the closure index of the method of this invention is substituted into the data. S The closure index is calculated using the formula. S Further, the enrichment index was adopted. EThe quantitative calculation formula involves substituting factors such as coal and rock thickness, maturity, vitrinite content, porosity, permeability, and sealing index. S Using parameters such as enrichment index, a plane distribution map of enrichment index is calculated and plotted. This embodiment obtains... Figure 1 The enrichment index plane distribution diagram is shown.

[0039] Based on the coal shale gas favorable area classification standard established by the method of this invention, the favorable coal shale gas areas in the study area are divided into Class I high-quality favorable areas and Class II medium-favorable areas. This embodiment obtains... Figure 2 The division results are shown.

[0040] Actual well testing results show that the S13H well in the Suide area, a favorable Class I area, is producing well, with a daily gas production of 10.44 × 10⁻⁶. 4 m 3 Well T124 in the Class II favorable area had relatively poor production, with a daily gas production of only 5.1 × 10⁻⁶. 4 m 3 This proves that the favorable area delineation results obtained using the method of this invention are consistent with geological realities, accurate and reliable, and can provide good guidance for the production and development of coal and rock gas.

[0041] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A quantitative prediction method for favorable coal-rock gas areas, characterized in that, Includes the following steps: S1. Collect coal and rock core samples from coal seams deeper than 2000m and caprock core samples from different areas of the coal-bearing basin; S2. Determine the vitrinite reflectance, vitrinite content, porosity, and permeability of downhole core samples; S3. Determine the porosity, permeability, breakthrough pressure, and diffusion coefficient of the caprock core samples. Based on the measured data, statistically analyze the contribution weights of these five evaluation indicators—coal seam thickness, porosity, permeability, breakthrough pressure, and diffusion coefficient—to the sealing capacity, and obtain the sealing index. S The calculation formula is as follows; In the formula, S For closed-end index, n X represents the number of evaluation indicators. k Let A be the normalized parameter of the k-th evaluation index. k The weight of the k-th evaluation indicator; S4. Statistically analyze the contribution weights of six evaluation indicators to sealing capacity: coal seam thickness, vitrinite reflectance measured in step S2, vitrinite content, porosity and permeability, and sealing index S. Establish a quantitative calculation formula for enrichment index E. In the formula, E The enrichment index, n X represents the number of evaluation indicators. k Let be the normalized parameter of the k-th evaluation index. A k The weight of the k-th evaluation indicator is... α k , where is the weight calibration factor for the k-th evaluation indicator; δ k , where is the weight stability factor for the k-th evaluation indicator; X i Let be the normalized parameter of the i-th evaluation index. A i Let i be the weight of the i-th evaluation index. β ki Let be the interaction coefficient between the k-th and i-th evaluation indicators; λ ki The interaction strength coefficient between the k-th and i-th evaluation indicators; γ For correction factor, The normalized parameter is the average of all evaluation indicators, and ε is the minimum constant, which takes the value of ε = 10. -6 ; η is the dispersion adjustment coefficient; This is the environmental impact correction factor; S5. Evaluation of favorable coal-rock gas areas based on enrichment index E, with the following evaluation criteria: When E≥0.8, it belongs to Class I, a high-quality and favorable area; When 0.5 ≤ E < 0.8, it belongs to Class II, a moderately favorable area; When E < 0.5, it belongs to Class III, which is a non-favorable area.

2. The quantitative prediction method for favorable coal-rock gas areas as described in claim 1, characterized in that, α k The calculation formula is as follows: 。 3. The quantitative prediction method for favorable coal-rock gas areas as described in claim 1, characterized in that, δ k The calculation formula is as follows: In the formula, s k Let be the standard deviation of the weight of the k-th indicator. This is the average of the standard deviations of the weights of all indicators.

4. The quantitative prediction method for favorable coalbed methane areas as described in claim 1, characterized in that, β ki The value range is [0, 0.5], during synergistic effects. When antagonistic .

5. The quantitative prediction method for favorable coal-rock gas areas as described in claim 1, characterized in that, λ ki The calculation formula is as follows: Where, max( β ki ) is the interaction pair for all indicators β ki The maximum value.

6. The quantitative prediction method for favorable coal-rock gas areas as described in claim 1, characterized in that, γ The calculation steps are as follows: (1) Select at least 30 experimental samples under different working conditions, and collect the raw data of each evaluation index for each sample. x k And the corresponding enrichment index was accurately measured using experimental methods. ; (2) Calculate the normalization parameter for each group of samples. X k Basic weights A k Weight calibration factor α k Weight stability factor δ k Interaction coefficient β ki Interaction intensity coefficient λ ki , dispersion-related parameters and environmental impact correction coefficient ζ; (3) Substitute the calculated parameters into the enrichment index E calculation formula in step S4 to calculate the theoretical enrichment index value for each group of samples. ; (4) with For dependent variable, A linear fit is performed on the independent variable, and the fitting equation is: ; (5) Perform a significance test on the fitting results, with a confidence level of not less than 95%. If the residuals The value approaches 0, and the goodness of fit R0 is... 2 If the value is ≥0.9, then the fitted value is used. γ This serves as the final correction coefficient; if the above conditions are not met, increase the experimental sample size and repeat the above steps until the requirements are met, ensuring... γ It can effectively calibrate the systematic error of the formula.

7. The quantitative prediction method for favorable coal-rock gas areas as described in claim 1, characterized in that, The formula for calculating η is as follows: in, For discrete coefficients, s X For all normalized parameters X k standard deviation The average of all normalized parameters; max( CV ) represents the maximum value of the discrete coefficients among all experimental samples.

8. The quantitative prediction method for favorable coal-rock gas areas as described in claim 1, characterized in that, The calculation method is as follows: (1) Identify the core environmental factors that affect the enrichment effect, set the monitoring range for each environmental factor, and set 5-6 monitoring levels for each environmental factor; (2) Keep the original data of all evaluation indicators and other parameters unchanged, and only change the level of environmental factors. Conduct three parallel experiments under each environmental condition and collect the measured values ​​of the enrichment index. Simultaneously, the theoretical value of the enrichment index under standard environmental conditions was calculated. ; (3) Calculate the initial value of the correction coefficient under each set of environmental conditions. ; (4) Using environmental factor parameters as independent variables, Establish a multiple linear regression model with [variable name] as the dependent variable. ,in a , b , c These are the regression coefficients; (5) To validate the regression model, 10 validation samples under different environmental conditions were selected and substituted into the model for calculation. If the relative error between the calculated value and the measured calibration value is ≤5%, then the regression model is determined to be valid. The calibration model.

Images