A shale reservoir classification and evaluation method based on machine learning and optimal fractal attributes

Abstract

The present invention discloses a shale reservoir classification and evaluation method based on machine learning and optimal fractal attributes, which relates to the technical field of shale reservoir evaluation and comprises the following steps: calculating fractal attribute indicators of a T2 spectrum, obtaining new cluster labels, obtaining optimal fractal attribute values, obtaining final classification labels, applying the final classification basis to all optimal fractal attribute values ​​of a target well, and obtaining a new classification result; the present invention performs multifractal calculations on original T2 spectrum data obtained by nuclear magnetic resonance logging, combines effective porosity data obtained from sealed coring data of corresponding well sections as a basis, divides effective porosities of different degrees into categories, performs systematic machine learning training on the fractal attributes corresponding to each category, obtains a set of classification intervals of fractal attribute values ​​based mainly on the fractal attribute with the greatest influence, and applies the intervals to all fractal attribute values ​​of the target layer section, thereby accurately classifying and evaluating shale reservoirs.

Description

Technical Field

[0001] The present invention relates to the technical field of shale reservoir evaluation, and in particular to a shale reservoir classification and evaluation method based on machine learning and optimal fractal attributes. Background Art

[0002] Shale reservoirs refer to rock layers composed mainly of fine-grained sediments (such as clay, mudstone and siltstone). They are rich in organic matter and can generate and store natural gas or oil. Shale reservoir classification and evaluation is a systematic analysis and classification based on factors such as the geological characteristics, physical properties and gas content of shale gas reservoirs. This process aims to identify and distinguish different types of shale reservoirs, and can provide a scientific basis for the exploration and development of shale gas, so as to make more effective resource assessment and development decisions.

[0003] Nuclear magnetic resonance logging technology has unique advantages in the evaluation of complex reservoirs. It can be used to evaluate the saturation of the study area. Nuclear magnetic resonance logging measures the relaxation signal of hydrogen atoms in the formation and can provide pore and fluid information that is independent of the lithology of the formation. A set of data obtained by measuring the transverse relaxation time (T2) distribution of the fluid in the rock pores is called the T2 spectrum. This data reflects the detailed information of the rock pore structure, including pore size, pore fluid type (oil, gas, water) and their saturation. T2 spectrum is often used to identify and evaluate the pore structure of the reservoir, including pore size distribution, pore fluid type and saturation. By analyzing the T2 spectrum, different fluids such as oil, gas and water can be distinguished because they exhibit different relaxation characteristics on the T2 spectrum. Therefore, nuclear magnetic resonance logging technology plays an important role in reservoir classification and evaluation.

[0004] Since the T2 spectral distribution obtained by nuclear magnetic resonance logging is closely related to the size and distribution of rock pores, and is closely related to lithology, pore fluid properties, wettability, and formation water salinity, the position of the oil peak in the T2 spectrum may vary from well to well, and the T2 cutoff value may not be the same. For low-permeability or tight reservoirs, the presence of (light) oil will cause the nuclear magnetic resonance T2 spectral distribution to become wider. Therefore, it is difficult to separate bound fluid from movable fluid on the T2 spectrum in some wells. As a result, the saturation calculated using nuclear magnetic resonance logging deviates from the actual situation, ultimately affecting the accuracy of reservoir evaluation and classification. Therefore, the present invention proposes a shale reservoir classification and evaluation method based on machine learning and optimal fractal properties to address the problems existing in the prior art. Summary of the Invention

[0005] In response to the above problems, the purpose of the present invention is to propose a shale reservoir classification and evaluation method based on machine learning and optimal fractal properties, so as to solve the problem that the existing shale reservoir classification and evaluation method uses nuclear magnetic resonance logging to calculate the saturation content, which has a certain deviation from the actual situation, resulting in the inability to accurately classify and evaluate shale reservoirs.

[0006] To achieve the purpose of the present invention, the present invention is implemented through the following technical solutions: a shale reservoir classification and evaluation method based on machine learning and optimal fractal attributes, comprising the following steps:

[0007] Step 1: Use nuclear magnetic resonance logging technology to obtain the original data of the target well depth T2 spectrum and perform parallel analysis and processing. Then, calculate the relevant fractal attribute values ​​of the analyzed and processed data to obtain the fractal attribute index of each group of T2 spectra;

[0008] Step 2: Obtain the movable porosity data of the target well and divide it into four categories as the original classification labels. Then, summarize the fractal attribute indicators and the original classification labels into a data table. Then, use the data table as the input data of the K-means clustering model, use machine learning methods to cluster the fractal attribute values, and obtain the corresponding new cluster labels.

[0009] Step 3: Use the random forest method to analyze the importance of fractal attribute indicators, and then use the fractal attribute with the highest importance in the analysis results as the final classification standard, that is, the optimal fractal attribute value;

[0010] Step 4: Based on the correspondence between the original classification labels and the new cluster labels, calculate the proportion of each type in each cluster label and the proportion of each type in all cluster labels. Comprehensively analyze the mapping relationship between the cluster labels and the original classification labels, and obtain the final label for each group of data based on this mapping relationship;

[0011] Step 5: Based on the final labels and the optimal fractal attribute values, the intervals of the optimal fractal attribute values ​​for each of the four types of labels are obtained. This interval is used as the final classification basis and applied to all the optimal fractal attribute values ​​of the target well to obtain a new shale reservoir classification and evaluation result based mainly on the optimal fractal attribute.

[0012] A further improvement is that in step 1, the specific step of analyzing and processing the original data of the target well depth T2 spectrum is: dimensionlessly processing the original T2 spectrum data through a dimensionless formula so that all the data converges within the interval [0,1].

[0013] A further improvement is that in step 1, the relevant fractal attribute values ​​of the analyzed data are calculated using the fractal dimension calculation method, and the fractal attribute indicators of the T2 spectrum include the singular intensity, offset, and maximum and minimum offset values ​​of the T2 spectrum.

[0014] A further improvement is that in step 2, the movable porosity data is obtained from the sealed coring data of the target well section, and the movable porosity data is divided into four categories from large to small.

[0015] A further improvement is that in step 2, the summarized data table is preprocessed before being input into the K-means clustering model. The specific steps are: first, data cleaning is used to remove missing values ​​and outliers in the data table, then correlation analysis and dimensionality reduction are performed on the data table, and then the data table is standardized in a normalized manner.

[0016] A further improvement is that in step three, the importance of the fractal attribute values ​​is analyzed by the random forest method to obtain the influence ranking of the three fractal attribute values ​​on the clustering process, the influence values ​​of the three fractal attributes and a histogram.

[0017] Further improvements are: in the step four, the two-way maximization method is used to calculate the proportion of each type in each cluster label and the proportion of each type in all cluster labels. The specific steps are: based on the analysis of the differences and connections between cluster labels and original classification labels, the two-way maximization method is used to obtain the proportion of each original label sample in each cluster label, as well as the proportion of each original label type in all cluster labels. A comprehensive analysis is performed to obtain the mapping relationship between cluster labels and original classification labels. According to the mapping relationship, the clustered data is reclassified to obtain the final classification label.

[0018] A further improvement is that: the mapping relationship is: {cluster label 0: original label 1, cluster label 1: original label 2, cluster label 2: original label 4, cluster label 3: original label 3}.

[0019] The beneficial effects of the present invention are as follows: the present invention performs multifractal calculation on the original T2 spectrum data obtained by nuclear magnetic resonance logging to obtain indicators such as the singular spectrum function and spectrum offset of the T2 spectrum data. Based on the effective porosity data obtained from the closed coring data of the corresponding well section, the Kmeans clustering method and machine learning methods such as random forest are used to classify different degrees of effective porosity. The fractal attributes corresponding to each category are systematically trained by machine learning, and finally a set of classification intervals of fractal attribute values ​​based mainly on the fractal attribute with the greatest influence is obtained. The classification intervals are applied to all fractal attribute values ​​of the target layer section to obtain a new set of classification results, which can accurately classify and evaluate shale reservoirs, and are more detailed and more accurate than traditional classification results. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 Schematic diagram of the process of the shale reservoir classification and evaluation method based on machine learning and optimal fractal attributes of the present invention;

[0022] Figure 2 This is a schematic diagram of clustering results of three fractal attribute values ​​in Example 3 of the present invention;

[0023] Figure 3 Schematic diagram of the correspondence between cluster labels and original classification labels in the third embodiment of the present invention;

[0024] Figure 4 This is a schematic diagram of the importance ranking results in Example 3 of the present invention;

[0025] Figure 5 This is a schematic diagram of the final classification labels obtained by the mapping relationship in the third embodiment of the present invention;

[0026] Figure 6 3 is a schematic diagram comparing the original classification results in Example 3 of the present invention and the classification results of the method of the present invention. DETAILED DESCRIPTION

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0028] Example 1

[0029] See also Figure 1 This embodiment provides a shale reservoir classification and evaluation method based on machine learning and optimal fractal attributes, comprising the following steps:

[0030] Step 1: Parallel analysis and processing of the original data of the target well depth T2 spectrum, and calculation of the fractal attribute index of the T2 spectrum

[0031] First, the original T2 spectrum data of the target well depth is obtained using nuclear magnetic resonance logging technology and analyzed and processed in parallel. The raw T2 spectrum data is dimensionlessly processed using a dimensionless formula so that all data converges within the interval [0, 1]. This eliminates the influence of dimension on the data and improves the performance of subsequent model processing. Then, the relevant fractal attribute values ​​of the analyzed and processed data are calculated using the fractal dimension calculation method. The fractal attribute indicators of each group of T2 spectra are obtained, specifically including the singular intensity, offset, and maximum and minimum offset values ​​of the T2 spectrum.

[0032] Step 2: Use machine learning methods to cluster fractal attribute values ​​and obtain new cluster labels

[0033] The movable porosity data is obtained from the sealed coring data of the target well, and the movable porosity data of the target well is obtained and divided into four categories from large to small as the original classification labels. The fractal attribute indicators calculated in step 1 and the original classification labels are then summarized into a data table. The summarized data table is then used as input data for the K-means clustering model. The fractal attribute values ​​are clustered using a machine learning method to obtain corresponding new cluster labels. Similar to the classification of movable porosity from large to small into four categories in the sealed coring data, this embodiment also divides the cluster labels into four categories to facilitate the next step of finding corresponding relationships. After the clustering is completed, four distinct clustering results are obtained.

[0034] In this embodiment, the summarized data table is first preprocessed before being input into the K-means clustering model. The specific steps are: first, data cleaning is used to remove missing values ​​in the data table (check the missing values ​​in the data table and choose to fill or delete them according to the specific situation) and outliers (identify and process outliers to prevent them from having a negative impact on the clustering results), then the data table is subjected to correlation analysis (analyze the correlation between each feature and the fractal attribute value and select the most relevant feature) and dimensionality reduction (if the feature dimension is high, principal component analysis is used for dimensionality reduction to reduce the amount of calculation and improve model performance), and then the data table is standardized by normalization (normalizing the data so that each feature is in the same dimension to avoid some features having too much impact on the clustering results due to dimensional differences) to ensure the quality of the data and the effectiveness of the model;

[0035] Step 3: Analyze the importance of the fractal attribute values ​​and take the fractal attribute with the highest importance as the optimal fractal attribute value.

[0036] Use the random forest method to analyze the importance of the fractal attribute values ​​obtained in step 1, and obtain the ranking of the influence of the three fractal attribute values ​​on the clustering process, the influence values ​​of the three fractal attributes, and the histogram. Then, the fractal attribute with the highest importance in the analysis results is used as the final classification standard, that is, the optimal fractal attribute value, which serves as the main basis for the subsequent final classification;

[0037] Step 4: Get the final classification label based on the mapping relationship between the cluster label and the original classification label

[0038] According to the correspondence between the original classification labels and the new cluster labels obtained in step 2, and based on the analysis of the differences and connections between the cluster labels and the original classification labels, a two-way maximization method is used to obtain the proportion of each original label sample in each cluster label, as well as the proportion of each original label type in all cluster labels. A comprehensive analysis is performed to obtain the mapping relationship between the cluster labels and the original classification labels. Based on this mapping relationship, the clustered data is reclassified to obtain the final classification label, i.e., the final label;

[0039] Step 5: Apply the final classification basis to all the optimal fractal attribute values ​​of the target well to obtain a new classification result

[0040] Based on the final labels obtained by mapping in step 4 and the optimal fractal attribute values ​​obtained in step 3, the intervals of the optimal fractal attribute values ​​of each of the four types of labels are obtained. This is used as the final classification basis and applied to all the optimal fractal attribute values ​​of the target well. Finally, a new shale reservoir classification and evaluation result based on the optimal fractal attribute is obtained, realizing shale reservoir classification and evaluation based on machine learning and optimal fractal attributes.

[0041] Example 2

[0042] The data used in this example comes from the Jimusar shale reservoir. The specific steps for classifying and evaluating the shale reservoir are as follows:

[0043] S1. Organize and select the target well's nuclear magnetic resonance logging data and sealed coring data, calculate the fractal dimension of the nuclear magnetic resonance T2 spectrum data, and obtain the singular intensity, offset, and maximum and minimum offset values ​​of the T2 spectrum;

[0044] S2. Combine the movable porosity data obtained from sealed coring and perform a joint analysis. The movable porosity obtained from sealed coring is divided into four categories from large to small according to the preset standard, which serve as the original classification labels;

[0045] For the fractal attribute values ​​obtained from the T2 spectrum, they are first dimensionlessly processed so that all data converge within the interval [0,1], eliminating the impact of dimension on the data and improving the performance of subsequent model processing. Then, the three main fractal attribute values ​​of the T2 spectrum are calculated using the multifractal calculation method. The three fractal attribute values ​​are clustered using the Kmeans method. Analogously, the movable porosity in the sealed coring data is divided into four categories from large to small according to the preset standard. Here, the cluster labels are also divided into four categories to facilitate the next step of finding corresponding relationships. After the clustering is completed, four distinct clustering results are obtained.

[0046] S3. Use the random forest method to rank the influence of the three fractal attribute values ​​on the clustering process, obtain the influence degree values ​​and histograms of the three fractal attributes, record the fractal attribute with the greatest influence as the optimal fractal attribute, and use it as the main basis for the subsequent final classification. After the entire processing is completed, the number of original classification samples corresponding to each cluster label is obtained. For example, the number of samples with the original label of 1 under the cluster label of 0 is how many, the number of samples with the original label of 2 is how many, and so on;

[0047] S4. Based on the analysis of the differences and connections between cluster labels and original classification labels, a two-way maximization method is used to obtain the proportion of each original label sample in each cluster label, as well as the proportion of each original label type in all cluster labels. Comprehensive analysis results show that the mapping relationship between cluster labels and original classification labels is: {cluster label 0: original label 1, cluster label 1: original label 2, cluster label 2: original label 4, cluster label 3: original label 3};

[0048] According to the above mapping relationship, the clustered data is reclassified to obtain the final classification label;

[0049] S5. Based on the optimal fractal attribute value corresponding to the final classification label, obtain the interval of the optimal fractal attribute value under each final label, and use this as the final classification standard of the entire classification model;

[0050] Since the data used for model training is only the part corresponding to the movable porosity of sealed coring, rather than all T2 data, this embodiment applies the final classification criteria obtained above to the optimal fractal attribute values ​​obtained from all T2 data, and divides all layers with measured T2 data into four new categories. Compared with traditional reservoir classification and evaluation methods, the interpretation based on machine learning and optimal multifractal attributes is more detailed and the results obtained are more reasonable.

[0051] Example 3

[0052] See also Figure 2 、 Figure 3 、 Figure 4 、 Figure 5 、 Figure 6 The specific steps for classifying and evaluating shale reservoirs in this embodiment are as follows:

[0053] A1. First, the obtained T2 raw data is organized into a standard data file, which is then dimensionally processed using a Python script with a dimensionless formula. The obtained results are then organized into a standard data format for fractal calculations.

[0054] A2. Import the organized data into a Matlab script to calculate the fractal properties and obtain the corresponding fractal attribute values ​​of Δα, B, and Δf. Simultaneously, prepare the movable porosity data obtained from the sealed coring data and classify it into four categories from large to small according to certain criteria, which serve as the original category labels.

[0055] A3. Summarize the T2 fractal attribute values ​​and original classification labels of the movable porosity corresponding to the depth as the raw data for the subsequent machine learning method. Import the summarized data into the Python script for Kmeans clustering operation. While obtaining the clustering results, use the random forest method to rank the importance of the fractal attribute values. The ranking results are as follows: Figure 4 As shown, after running the script, we can finally get the distribution of the original label samples corresponding to each cluster label and the importance ranking of the three fractal attributes. The clustering results of the three fractal attribute values ​​are as follows: Figure 2 As shown;

[0056] A4. The fractal attribute value with the greatest degree of importance is taken as the optimal fractal attribute and the main indicator for subsequent classification. A two-way maximization analysis is performed on the distribution of the original label samples corresponding to the cluster labels. That is, the proportion of samples with different original labels under each cluster label and the proportion of different cluster labels corresponding to each original sample label are calculated respectively. After comprehensively considering all the proportion distributions, the most reasonable one-to-one correspondence is selected as the mapping relationship between the cluster label and the final classification label. Based on this mapping relationship, the clustering results are reclassified, and the reclassified results are used as the final classification results. The correspondence between the cluster label and the original classification label is as follows: Figure 3 As shown, the final classification label obtained according to the mapping relationship is as follows Figure 5 As shown;

[0057] A5. Based on the distribution interval of the optimal fractal attribute corresponding to the final classification label, it is applied to all T2 main fractal attributes of the target well to obtain the final classification result of the target well. The final classification result is imported into the well logging curve. Compared with the original classification result, it is found that the classification result obtained by the classification model is more sufficient and reasonable for explaining the oil test results. The comparison diagram of the original classification result and the classification result of the present invention is shown in the figure below. Figure 6 As shown, Figure 6 Track 1 is the original classification result, track 2 is the classification result of the present invention, and track 3 is the oil test interpretation result.

[0058] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A shale reservoir classification and evaluation method based on machine learning and optimal fractal attributes, characterized in that: The following steps are involved: Step 1: First, use nuclear magnetic resonance logging technology to obtain the raw data of the T2 spectrum of the target well depth, and then analyze and process it in parallel. The raw T2 spectrum data is dimensionless processed using a dimensionless formula so that all data converges within the interval [0, 1] to eliminate the influence of dimension on the data and improve the performance of subsequent model processing. Then, the fractal dimension calculation method is used to calculate the relevant fractal attribute values ​​of the analyzed data to obtain the fractal attribute indicators of each group of T2 spectra, specifically including the singular intensity, offset, and maximum and minimum offset values ​​of the T2 spectrum; Step 2: Obtain movable porosity data from the sealed coring data of the target well. Obtain the movable porosity data of the target well and divide it into four categories from large to small as the original classification labels. Then summarize the fractal attribute indicators calculated in step 1 and the original classification labels into a data table. Then, use the summarized data table as the input data of the K-means clustering model. Use machine learning methods to cluster the fractal attribute values ​​to obtain corresponding new cluster labels. Analogously, the movable porosity in the sealed coring data is divided into four categories from large to small. The cluster labels are also divided into four categories. After clustering is completed, four distinct clustering results are obtained. Step 3: Use the random forest method to analyze the importance of fractal attribute indicators, and then use the fractal attribute with the highest importance in the analysis results as the final classification standard, that is, the optimal fractal attribute value; Step 4: Based on the correspondence between the original classification labels and the new cluster labels, calculate the proportion of each type in each cluster label and the proportion of each type in all cluster labels. Comprehensively analyze the mapping relationship between the cluster labels and the original classification labels, and obtain the final label for each group of data based on this mapping relationship; Step 5: Based on the final labels and the optimal fractal attribute values, the intervals of the optimal fractal attribute values ​​for each of the four types of labels are obtained. This interval is used as the final classification basis and applied to all the optimal fractal attribute values ​​of the target well to obtain a new shale reservoir classification and evaluation result based mainly on the optimal fractal attribute.

2. The shale reservoir classification and evaluation method based on machine learning and optimal fractal attributes according to claim 1, characterized in that: In the step 2, the summarized data table is preprocessed before being input into the K-means clustering model. The specific steps are: first, data cleaning is used to remove missing values ​​and outliers in the data table, then correlation analysis and dimensionality reduction are performed on the data table, and then the data table is standardized in a normalized manner.

3. The shale reservoir classification and evaluation method based on machine learning and optimal fractal attributes according to claim 1, characterized in that: In the step three, the importance of the fractal attribute values ​​is analyzed by the random forest method to obtain the influence ranking of the three fractal attribute values ​​on the clustering process, the influence values ​​of the three fractal attributes and a histogram.

4. The shale reservoir classification and evaluation method based on machine learning and optimal fractal attributes according to claim 1, characterized in that: In the step four, the two-way maximization method is used to calculate the proportion of each type in each cluster label and the proportion of each type in all cluster labels. The specific steps are: based on the analysis of the differences and connections between the cluster labels and the original classification labels, the two-way maximization method is used to obtain the proportion of each original label sample in each cluster label, as well as the proportion of each original label type in all cluster labels. A comprehensive analysis is performed to obtain the mapping relationship between the cluster labels and the original classification labels. According to the mapping relationship, the clustered data is reclassified to obtain the final classification label.

5. The shale reservoir classification and evaluation method based on machine learning and optimal fractal attributes according to claim 4, characterized in that: The mapping relationship is: {cluster label 0: original label 1, cluster label 1: original label 2, cluster label 2: original label 4, cluster label 3: original label 3}.

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