Well logging evaluation method for coal bed gas typical reservoir industrial component analysis
By analyzing the correlation between the industrial components of coalbed methane reservoirs and logging data, a logging evaluation model was constructed, which solved the problem of inaccurate coalbed methane reservoir evaluation in existing technologies and achieved more accurate reservoir characteristic assessment and mining potential disclosure.
Patent Information
- Application Number
- CN202510951270.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-10
- Publication Date
- 2025-09-09
AI Technical Summary
Existing technologies ignore the impact of industrial components in coalbed methane reservoirs on reservoir characteristics, resulting in inaccurate reservoir evaluation and possible data distortion during the sampling process, affecting the assessment of mining potential.
By obtaining industrial component data and logging data of coal samples, analyzing their correlation coefficients and weights, building a logging evaluation model, identifying key evaluation parameters, and accurately reflecting the industrial component characteristics of coalbed methane reservoirs.
It improves the accuracy of reservoir evaluation and mining efficiency, reduces development costs, and optimizes resource utilization and mining processes.
Smart Images
Figure CN120608674A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of coalbed methane well logging engineering, in particular to a well logging evaluation method for industrial component analysis of typical coalbed methane reservoirs. Background Art
[0002] Coalbed methane, as an important natural gas resource, has attracted widespread attention due to its abundant reserves and clean energy characteristics. In order to improve the extraction efficiency of coalbed methane and reduce extraction costs, scientific and accurate reservoir evaluation methods are particularly important. However, single logging data is often not enough to fully reflect the complexity of the reservoir. Especially when facing different types of reservoirs, relying solely on logging data may not accurately reveal the industrial component characteristics of the reservoir and its relationship with the reservoir production capacity.
[0003] Traditional methods usually limit the evaluation of coalbed methane reservoirs to surface analysis of logging data, ignoring the important impact of reservoir industrial components on coalbed methane production potential. In particular, the industrial component parameters in coal samples are often not fully combined with logging data, resulting in limited accuracy and comprehensiveness of reservoir evaluation.
[0004] Among them, the limitations of the existing technology include at least the following problems: the existing technology ignores the impact of industrial components in coalbed methane reservoirs on reservoir characteristics. These industrial component parameters in coal samples not only directly reflect the physical and chemical properties of the coal seam, but are also closely related to key indicators such as the gas storage capacity, gas permeability, and gas production potential of the reservoir. For example, coal seams with higher ash content may have lower methane gas permeability, while high-volatile coal seams may have higher gas production. However, the existing technology has not fully considered the impact of these industrial components on the reservoir, and can often only provide shallow information at the geological level, which is difficult to deeply reflect the true mining potential of the coal seam. In addition, the sampling process will cause distortion and inaccuracy in the experimental data of the industrial components of coal rock, which may cause errors in the industrial component analysis results of the coalbed methane reservoir, affecting the accuracy of the reservoir evaluation. Summary of the Invention
[0005] In response to the shortcomings of the existing technology, the present invention provides a logging evaluation method for analyzing the industrial components of typical coalbed methane reservoirs, which solves the problem that the existing technology ignores the impact of industrial components in coalbed methane reservoirs on reservoir characteristics, resulting in the inability to accurately reflect the mining potential of the coal seam, and may cause distortion of coal rock industrial component data during the sampling process, affecting the accuracy of reservoir evaluation.
[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a logging evaluation method for analyzing the industrial components of typical coalbed methane reservoirs, comprising the following steps: obtaining coal sample industrial component data and logging data of the industrial components of the reservoir to be evaluated, the coal sample industrial component data including several component parameters, and the logging data including several logging parameters; based on the coal sample industrial component data and logging data of the industrial components of the reservoir to be evaluated, analyzing the correlation coefficient set of its corresponding component parameters, including the correlation coefficient between each component parameter and each logging parameter; based on the correlation coefficient set of each component parameter of the industrial components of the reservoir to be evaluated, analyzing the correlation weight set of its corresponding component parameters; based on the correlation weight set of each component parameter of the industrial components of the reservoir to be evaluated, analyzing the key evaluation parameter set of its corresponding component parameters; based on the key evaluation parameter set of each component parameter of the industrial components of the reservoir to be evaluated, constructing a logging evaluation model for its corresponding component parameters.
[0007] Furthermore, each component parameter is the moisture value Mad, the ash value Aad, the volatile matter value Vad, and the fixed carbon value FCad in sequence, and each logging parameter is specifically the acoustic wave time difference value AC, the natural gamma value GR, the apparent density value DEN, and the deep lateral resistivity value LLD.
[0008] Furthermore, the specific steps of analyzing the correlation coefficient set of each component parameter of the industrial component of the reservoir to be evaluated are as follows: each component parameter of the industrial component of the reservoir to be evaluated is respectively analyzed with each logging parameter to obtain the correlation coefficient between its corresponding component parameter and each logging parameter; the correlation coefficient between each component parameter of the industrial component of the reservoir to be evaluated and each logging parameter is marked as the correlation coefficient set of its corresponding component parameter.
[0009] Furthermore, the specific steps of analyzing the relevant weight set of each component parameter of the industrial component of the reservoir to be evaluated are as follows: summing the correlation coefficients of each component parameter of the industrial component of the reservoir to be evaluated and each logging parameter to obtain the correlation coefficients and values of its corresponding component parameters; and ratioing the correlation coefficients of each component parameter of the industrial component of the reservoir to be evaluated and each logging parameter to the corresponding correlation coefficients and values to obtain the relevant weight values of each logging parameter of its corresponding component parameters, that is, the relevant weight set.
[0010] Furthermore, the specific steps of analyzing the key evaluation parameter set of each component parameter of the industrial component of the reservoir to be evaluated are as follows: determine whether the relevant weight value of each logging parameter of each component parameter of the industrial component of the reservoir to be evaluated is higher than a preset threshold; if it is higher than the preset threshold, then (the logging parameter) is marked as a key evaluation parameter; if it is not higher than the preset corresponding threshold, then it is not marked.
[0011] Furthermore, the well logging evaluation model for the ash value of the industrial components of the reservoir to be evaluated is as follows: ;in, To evaluate the regression intercept term of the ash value of the industrial components of the reservoir, 、 、 、 The following are the correlation coefficients between the ash value for evaluating reservoir industrial components and each logging parameter.
[0012] The present invention has the following beneficial effects: (1) The logging evaluation method for the industrial component analysis of typical coalbed methane reservoirs obtains the industrial component data of coal samples and the related logging data, analyzes the correlation between each component parameter and the logging parameter, and constructs an accurate logging evaluation model, so as to be able to conduct in-depth analysis of various industrial components of coalbed methane reservoirs to ensure that the relationship between each industrial component parameter and the logging parameter is fully explored, thereby making the industrial component analysis results of coalbed methane reservoirs more accurate, avoiding the distortion of coal rock industrial component data, and thus improving the accuracy of reservoir evaluation, and improving mining efficiency and resource utilization.
[0013] (2) The logging evaluation method for the analysis of the industrial components of typical coalbed methane reservoirs performs a multi-dimensional correlation analysis on the industrial component data and logging data of the coal samples, calculates the weight value of each logging parameter, and determines the key evaluation parameter set based on these weight values, thereby effectively identifying and quantifying the impact of various key logging parameters on the industrial components of the coalbed methane reservoir. This refines the reservoir evaluation process, thereby enabling the mining potential of the reservoir to be revealed at a deeper level, and improves the calculation accuracy and reliability of the evaluation model, thereby improving the accuracy of coalbed methane development, while reducing development costs and optimizing resource allocation during the development process.
[0014] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Figure 1 This is a flow chart of a well logging evaluation method for analyzing industrial components of a typical coalbed methane reservoir according to the present invention.
[0016] Figure 2 The present invention is a flowchart of the specific steps of analyzing the relevant weight set of each component parameter of the industrial component of the reservoir to be evaluated in a well logging evaluation method for analyzing the industrial components of a typical coalbed methane reservoir of the present invention. DETAILED DESCRIPTION
[0017] See also Figure 1The embodiment of the present invention provides a technical solution: a well logging evaluation method for analyzing the industrial components of a typical coalbed methane reservoir, comprising the following steps: obtaining coal sample (laboratory) industrial component data and well logging data of the industrial components of the reservoir to be evaluated (typical of coalbed methane), the coal sample industrial component data including several component parameters, and the well logging data including several well logging parameters; based on the coal sample industrial component data and well logging data of the industrial components of the reservoir to be evaluated, analyzing the correlation coefficient set of the corresponding component parameters, including the correlation coefficient between each component parameter and each well logging parameter; based on the correlation coefficient set of each component parameter of the industrial components of the reservoir to be evaluated, analyzing the correlation weight set of the corresponding component parameters; based on the correlation weight set of each component parameter of the industrial components of the reservoir to be evaluated, analyzing the key evaluation parameter set of the corresponding component parameters; based on the key evaluation parameter set of each component parameter of the industrial components of the reservoir to be evaluated, constructing a well logging evaluation model for the corresponding component parameters.
[0018] The key evaluation parameter set for ash content includes acoustic transit time (AC), natural gamma (GR), apparent density (DEN), and deep lateral resistivity (LLD). The well logging evaluation model for ash content of industrial components in the reservoir to be evaluated is as follows: ;in, To evaluate the regression intercept term of the ash value of the industrial components of the reservoir, 、 、 、 The following are the correlation coefficients between the ash value for evaluating reservoir industrial components and each logging parameter.
[0019] The well logging evaluation model for the fixed carbon value of the industrial components of the reservoir to be evaluated is as follows: .
[0020] The well logging evaluation model for the volatile content of industrial components in the reservoir to be evaluated is as follows: .
[0021] The well logging evaluation model for the moisture value of the industrial components of the reservoir to be evaluated is as follows: .
[0022] Each component parameter is the moisture value Mad, ash value Aad, volatile matter value Vad, and fixed carbon value FCad, and each logging parameter is specifically (including but not limited to) the acoustic wave time difference value AC, the natural gamma value GR, the apparent density value DEN, and the deep lateral resistivity value LLD.
[0023] The moisture value Mad, ash value Aad, volatile matter value Vad, and fixed carbon value FCad are all obtained through laboratory sampling.
[0024] The natural gamma value GR is obtained by direct measurement using a gamma ray detection instrument during the drilling process and uploaded to the database.
[0025] The apparent density value DEN is obtained by a gamma-ray density logging tool, which calculates the volume density value of the rock formation by measuring the change in electron density in the rock formation and uploads it to the database.
[0026] The acoustic time difference value AC is obtained by an acoustic logging tool, which calculates the value by emitting acoustic waves and recording the time it takes for the acoustic waves to propagate in the formation, and then uploads it to the database.
[0027] The deep lateral resistivity value (LLD) is obtained by a resistivity logging tool. This tool calculates the resistivity value of the rock formation by measuring the resistance of current propagation in the formation and uploads it to the database.
[0028] Specifically, if Figure 2 As shown, the specific steps for analyzing the correlation coefficient set of each component parameter of the industrial component of the reservoir to be evaluated are as follows: each component parameter of the industrial component of the reservoir to be evaluated is respectively subjected to a phase relationship analysis (such as the Pearson correlation coefficient) with each well logging parameter to obtain the correlation coefficient between its corresponding component parameter and each well logging parameter (for example, the correlation value between the moisture value and the acoustic wave time difference value of the industrial component of the reservoir to be evaluated, i.e., the correlation coefficient between the moisture value and the acoustic wave time difference value, is calculated based on the Pearson correlation coefficient); the correlation coefficient between each component parameter of the industrial component of the reservoir to be evaluated and each well logging parameter is marked as the correlation coefficient set of its corresponding component parameter.
[0029] The specific steps of analyzing the correlation weight set of each component parameter of the industrial component of the reservoir to be evaluated are as follows: summing the correlation coefficients of each component parameter of the industrial component of the reservoir to be evaluated and each logging parameter to obtain the correlation coefficients and values of its corresponding component parameters; and respectively ratioing the correlation coefficients of each component parameter of the industrial component of the reservoir to be evaluated and each logging parameter with the corresponding correlation coefficients and values to obtain the correlation weight value of each logging parameter of its corresponding component parameters, that is, the correlation weight set.
[0030] The specific steps for analyzing the key evaluation parameter set of each component parameter of the reservoir industrial component to be evaluated are as follows: determine whether the relevant weight value of each logging parameter of each component parameter of the reservoir industrial component to be evaluated is higher than a preset threshold; if it is higher than the preset threshold, then mark (the logging parameter) as a key evaluation parameter; if it is not higher than the preset corresponding threshold, then do not mark it.
[0031] In this implementation scheme, the industrial component data and logging data of the coalbed methane reservoir are deeply mined and accurately quantified, thereby significantly improving the accuracy and reliability of reservoir evaluation. Specifically, by performing Pearson correlation coefficient analysis on the correlation between each component parameter and the logging parameter, the degree of influence of different logging parameters on each industrial component can be accurately identified, providing a theoretical basis for the logging evaluation of each component parameter. In addition, by summing the correlation coefficients and ratioing them with their total values, the calculated correlation weight set can help identify the most influential logging parameters in reservoir evaluation, thereby more effectively determining which parameters are most critical to the characteristics of the coalbed methane reservoir. Finally, this step can eliminate irrelevant data that has little impact on reservoir evaluation and focus on key logging parameters, thereby reducing errors and redundancy and optimizing the logging evaluation process. At the same time, by setting a threshold to screen the relevant weight values, only the logging parameters that have significantly contributed to the evaluation are marked as key evaluation parameters, thereby simplifying the complexity of the model and making it more accurate and efficient.
[0032] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.
[0033] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.
Claims
1. A logging evaluation method for industrial component analysis of typical coalbed methane reservoirs, characterized in that: The following steps are involved: Obtaining coal sample industrial component data and well logging data of the industrial component of the reservoir to be evaluated, wherein the coal sample industrial component data includes a plurality of component parameters, and the well logging data includes a plurality of well logging parameters; Based on the coal sample industrial component data of the reservoir industrial component to be evaluated and the well logging data, the correlation coefficient set of the corresponding component parameters is analyzed, including the correlation coefficient between each component parameter and each well logging parameter; Based on the correlation coefficient set of each component parameter of the industrial component of the reservoir to be evaluated, the correlation weight set of its corresponding component parameters is analyzed; Based on the relevant weight set of each component parameter of the industrial component of the reservoir to be evaluated, the key evaluation parameter set of the corresponding component parameter is analyzed; Based on the key evaluation parameter set of each component parameter of the industrial components of the reservoir to be evaluated, a well logging evaluation model of its corresponding component parameters is constructed.
2. The well logging evaluation method for analyzing industrial components of typical coalbed methane reservoirs according to claim 1, characterized in that: Each component parameter is the moisture value Mad, the ash value Aad, the volatile matter value Vad, and the fixed carbon value FCad, and each logging parameter is specifically the acoustic wave time difference value AC, the natural gamma value GR, the apparent density value DEN, and the deep lateral resistivity value LLD.
3. The well logging evaluation method for analyzing industrial components of typical coalbed methane reservoirs according to claim 1 is characterized in that: The specific steps for analyzing the correlation coefficient set of each component parameter of the industrial component of the reservoir to be evaluated are as follows: Conduct a correlation analysis between each component parameter of the industrial components of the reservoir to be evaluated and each logging parameter, and obtain the correlation coefficient between the corresponding component parameter and each logging parameter; The correlation coefficient between each component parameter of the industrial component of the reservoir to be evaluated and each logging parameter is marked as a correlation coefficient set of its corresponding component parameter.
4. The well logging evaluation method for analyzing industrial components of typical coalbed methane reservoirs according to claim 3 is characterized in that: The specific steps for analyzing the relevant weight set of each component parameter of the reservoir industrial component to be evaluated are as follows: Sum the correlation coefficients of each component parameter of the industrial component of the reservoir to be evaluated and each logging parameter to obtain the correlation coefficients and values of the corresponding component parameters; The correlation coefficients of each component parameter of the industrial component of the reservoir to be evaluated and each logging parameter are respectively ratioed with the corresponding correlation coefficients and values to obtain the correlation weight value of each logging parameter of its corresponding component parameter, that is, the correlation weight set.
5. The well logging evaluation method for analyzing industrial components of typical coalbed methane reservoirs according to claim 4 is characterized in that: The specific steps for analyzing the key evaluation parameter set for each component parameter of the reservoir industrial component to be evaluated are as follows: Determine whether the relevant weight value of each logging parameter of each component parameter of the industrial component of the reservoir to be evaluated is higher than a preset threshold; If it is higher than the preset threshold, it is marked as a key evaluation parameter; If it is not higher than the preset corresponding threshold, it will not be marked.
6. The well logging evaluation method for analyzing industrial components of typical coalbed methane reservoirs according to claim 2, characterized in that: The well logging evaluation model for the ash value of the industrial components of the reservoir to be evaluated is as follows: ； in, To evaluate the regression intercept term of the ash value of the industrial components of the reservoir, 、 、 、 The following are the correlation coefficients between the ash value for evaluating reservoir industrial components and each logging parameter.