Lacustrine facies shale bed sequence stratigraphic division method based on principal component analysis

Abstract

The invention belongs to the field of unconventional oil-gas exploration, and particularly relates to a lacustrine facies shale bed sequence stratigraphic division method based on principal component analysis. According to the method, rapid elemental analysis is carried out on a rock core sample by combining a portable energy dispersion X-ray fluorescence spectrophotometer, standardized processing is carried out on collected data, and key stratum change characteristics are extracted from the processed data by utilizing principal component analysis (PCA); by comprehensively considering multi-dimensional geological features such as lithology, mineral composition and oxidation-reduction conditions and combining existing geological background information and logging data, sequence units of lacustrine facies shale are divided. Compared with an existing method, the method has the advantages that a large amount of data can be efficiently processed in the complex sedimentary environment, man-made interference is reduced, data processing efficiency is improved, and more accurate and comprehensive technical support is provided for lacustrine facies shale bed sequence division.

Description

Technical Field

[0001] The present technical solution relates to a method for lacustrine shale sequence stratigraphic division based on principal component analysis, which belongs to the field of unconventional oil and gas exploration, and is particularly suitable for the exploration and development of lacustrine shale oil and gas resources and related data analysis fields. Background Art

[0002] As shale oil is a strategic unconventional oil and gas resource, with the advancement of exploration and development around the world, the fine stratigraphic division of lacustrine shales has become a key step in identifying shale oil and gas "sweet spots". However, due to the complex sedimentary environment of lacustrine shales, the lithology, mineral composition, sedimentary structure and organic matter content are highly heterogeneous, making accurate stratigraphic division a difficult problem for geologists.

[0003] There are many limitations in the existing methods of shale sequence stratigraphic division. Although the analysis method based on element ratios can reveal some stratigraphic characteristics, it is often affected by the differences in the study area and the sample testing conditions, and often shows significant ambiguity; the paleontological relic method is only applicable to specific geological backgrounds, with a limited scope of application and difficult to promote. Although the stratigraphic division method based on lithofacies can provide detailed rock characteristics, due to the limitations of sample collection, XRD mineral composition and organic carbon content data are difficult to fully cover the entire stratum, and the experimental cost of this type of method is high, which limits large-scale application. In addition, the division method based on artificial core description is time-consuming and labor-intensive, highly subjective, and prone to ambiguity, affecting the accuracy and consistency of the division. Although the division method based on seismic stratigraphy and logging data provides a higher resolution, it is difficult to accurately identify complex sequence interfaces due to the small differences in lithology of lacustrine shales.

[0004] The existing technologies face the following major problems in the division of lacustrine shale sequences: lacustrine shale has significant heterogeneity and frequent lithology changes, and the existing logging and seismic methods cannot provide sufficient resolution to identify complex strata; the division method based on element analysis is difficult to provide reliable results due to regional differences and multi-solution problems; the applicability of existing technologies in complex sedimentary environments is limited, and it is impossible to fully consider multi-dimensional geological characteristics such as lithology, mineral composition, and organic matter content. Therefore, there is an urgent need for a comprehensive division method that can not only improve the accuracy of lacustrine shale sequence division, but also reduce the interference of multi-solution and human factors, while improving data processing efficiency. Summary of the invention

[0005] In order to solve the problems of multiple solutions, human subjective interference, and limited applicability of existing technologies in complex sedimentary environments in shale sequence stratigraphic division, this paper proposes a lacustrine shale sequence stratigraphic division method based on principal component analysis, aiming to improve the accuracy and consistency of sequence division through efficient collection and analysis of multi-dimensional geochemical data. The specific technical steps are as follows:

[0006] Step 1: Sample collection and preparation

[0007] Core samples are collected from the target lacustrine shale layer system to ensure that the samples cover different depths and multiple geological layers. After the samples are collected, they are cleaned and pre-processed to ensure the accuracy of subsequent analysis, and the geological background information of each sample is recorded in detail, including the collection depth, stratigraphic layer, etc., to provide reliable data support for subsequent stratigraphic division.

[0008] Step 2: Geochemical data collection

[0009] A portable energy dispersive X-ray fluorescence spectrometer (pXRF) was used to analyze the elements of the processed samples, and the concentration data of the main elements (such as Si, Ca, Mg, K) and trace elements (such as Ti, Zr, Mo, U, etc.) in the samples were collected. In order to better deal with the high heterogeneity of shale and to characterize the formation characteristics more finely, a sample point was collected every 10 cm. The high-resolution elemental data generated in this step provides a reliable basis for the subsequent principal component analysis (PCA).

[0010] Step 3: Data processing and standardization

[0011] To ensure the integrity and comparability of the data, the collected geochemical data were first cleaned to remove outliers and fill in missing data to ensure the integrity of the sample data. The data were then standardized to eliminate the magnitude differences between different elements and make them comparable, thereby improving the accuracy of data analysis.

[0012] Step 4: Principal Component Analysis (PCA)

[0013] Before formally conducting principal component analysis (PCA), use the Bartlett sphericity test or KMO test to evaluate the suitability of the data. The Bartlett sphericity test is used to test the correlation between variables. A P value less than 0.05 indicates a significant correlation and is suitable for principal component analysis. The KMO test is used to evaluate the suitability of factor analysis. A KMO value greater than 0.6 can be used for principal component analysis. These tests ensure that the data is suitable for PCA dimensionality reduction analysis.

[0014] Step 5: Use the load diagram generated by PCA to clarify the geological significance of each principal component

[0015] After executing PCA, multiple principal components are extracted according to the characteristics of the specific data set, and each principal component reflects different characteristics of the strata, such as mineral composition, terrigenous debris input intensity, redox conditions, etc. Since the geological background of different work areas or well sections may be different, the principal components extracted by PCA need to be adjusted according to the actual situation, and the geological significance represented by each principal component must also be specifically determined to ensure its accuracy and wide applicability.

[0016] Step 6: Sequence division and verification

[0017] Based on the change trend of the main components extracted by PCA, combined with the existing geological background information and logging data, the sequence units of lacustrine shale are divided. The sequence interfaces and units are identified by the changes in the main component scores in different layers. The division results are then compared and verified with the geological and logging data to ensure the accuracy and consistency of the results.

[0018] Beneficial effects of the present invention:

[0019] (1) Through the comprehensive analysis of multi-dimensional geological characteristics, the present invention effectively overcomes the multi-solution problem in existing methods, reduces human subjective interference, and significantly improves the accuracy and efficiency of data processing;

[0020] (2) This technology solves the problem of inconsistent results caused by different sample testing conditions and element polysolutions, ensures the accuracy of data analysis, simplifies the data analysis steps in the exploration process, and can quickly process a large amount of geochemical data. It is suitable for the division of lacustrine shale sequences under different geological conditions, especially in complex sedimentary environments, and shows strong applicability and flexibility.

[0021] (3) This technology not only performs well in unconventional oil and gas exploration, but can also be applied to geological research and paleoenvironmental restoration. Through precise stratigraphic division and geological feature analysis, it helps geologists gain a deeper understanding of the evolution of lacustrine sedimentary environments and provides reliable data support for regional geological surveys and scientific research.

[0022] (4) In resource assessment and oil and gas development, the present invention can effectively improve data processing efficiency, reduce exploration costs, and provide a scientific basis for the efficient development of oil and gas resources. The present invention has the characteristics of simple operation, wide applicability, and high accuracy. It is suitable for the exploration and development of lacustrine shale oil and gas resources, and also has broad application prospects in the research of other complex sedimentary environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The present invention will be further described below in conjunction with the accompanying drawings.

[0024] Figure 1 The present invention is a flow chart of the method for lacustrine shale sequence stratigraphic division based on principal component analysis (PCA).

[0025] Figure 2 Figure 2 shows the proportion of variance explained by principal components for the example.

[0026] Figure 3 This is the loading diagram of the principal components from PC1 to PC4 in the Niuye 1 well of the example.

[0027] Figure 4This is a sequence cross-section diagram of the upper sub-member of Sha 4 in the Niuye 1 well of Example. DETAILED DESCRIPTION

[0028] In order to make the technical solution of the present invention clearer and easier to understand, the sequence stratigraphic division method of the present invention is described in detail below in combination with the specific embodiment of the shale of the upper 4th sub-section of Shahejie 1 Well. The described embodiment is a part of the embodiment of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Figure 1 As shown, the lacustrine shale sequence stratigraphic division method based on principal component analysis of the present invention has the following specific implementation steps:

[0030] Step S1: Sample collection and preparation

[0031] All core sections of the upper Sha 4 section of Well Niuye 1 were cleaned to ensure that there was no impurity interference on the surface. Then core samples were systematically collected, and the collection depth of each sample was recorded in detail to ensure that the samples covered the entire well section.

[0032] Step S2: Geochemical data collection

[0033] A portable energy dispersive X-ray fluorescence spectrometer (pXRF) was used to analyze the processed core samples and collect concentration data of major elements (such as Si, Ca, Mg, K) and trace elements (such as Ti, Zr, Mo, U, etc.). In order to more finely characterize the heterogeneity of lacustrine shale, a sample point was collected every 10 cm, and the collected high-resolution element data provided the basis for subsequent principal component analysis (PCA).

[0034] Step S3: Data processing and standardization

[0035] The geochemical data measured by pXRF are preprocessed to ensure the accuracy and availability of the data. First, outliers are filtered to eliminate abnormal data that may be generated during the measurement process, and missing data samples are supplemented according to actual conditions. Subsequently, the data are standardized to eliminate the magnitude differences between different elements, ensuring that the data of different elements are well comparable during the analysis process, and providing a reliable data basis for the subsequent principal component analysis (PCA).

[0036] Step S4: Principal component analysis

[0037] The Bartlett sphericity test and KMO test were performed on the core sample data of Niuye 1 well. The results showed that the P value was less than 0.05 and the KMO value was greater than 0.6, indicating that the data were suitable for PCA analysis. About 5,000 data points were analyzed by PCA and four principal components were extracted.

[0038] Figure 2 The contribution of each component to the total variance is analyzed by PCA. The first principal component (PC1) explains 43.0% of the variance, mainly reflecting redox conditions; the second principal component (PC2) explains 18.6% of the variance, reflecting lithological changes; the third principal component (PC3) explains 7.8% of the variance, representing mineral composition; the fourth principal component (PC4) explains 5% of the variance, reflecting other geological characteristics. These four principal components cumulatively explain 74.5% of the variance, and the subsequent principal components contribute less, so the analysis should focus on these four principal components.

[0039] Step S5: Clarify the geological significance of each principal component through the load diagram generated by PCA

[0040] Figure 3 This is the loading diagram of the principal components from PC1 to PC4 in Well Niuye 1, showing the projection positions of different elements on each principal component axis. This diagram intuitively reveals the relationship between each principal component and the formation characteristics, and clarifies the geological significance of the principal components.

[0041] PC1 mainly reflects redox conditions, and the projections of redox-sensitive elements (such as Cr, Cu, V, U, Mn, Pb, Ni, Zn, and Fe) are significantly concentrated in the positive area of ​​PC1. PC2 indicates both lithological changes and the intensity of terrigenous debris input. Siliceous debris elements (such as Al, Si, and K) are projected in the negative area of ​​the PC2 axis, while carbonate elements (such as Ca) are projected in the positive area of ​​PC2. Elements such as Zr and Ti that indicate the intensity of terrigenous debris input are close to siliceous debris elements such as Al, Si, and K, indicating that PC2 can not only be used for lithological change analysis, but also as an indicator of the intensity of terrigenous debris input, further indicating that the siliceous debris minerals in the study area mainly come from terrigenous debris input. PC3 also reflects redox characteristics, and Mo and S elements are concentrated in the positive quadrant of PC3. PC4 reflects the enrichment of dolomite minerals, and Mg is projected in the positive quadrant of PC4. Overall, these principal components reveal the geological characteristics of redox conditions, lithology changes, mineral composition and intensity of terrigenous debris input in the Niuye 1 well.

[0042] Step S6: Layer division and verification

[0043] According to the changing trends of the four principal component score curves and combined with the logging data, the sequence interfaces and sequence units of the upper Sha 4th subsection of Well Niuye 1 were divided. Subsequently, the division results were compared and verified with the existing geological and logging data to ensure the accuracy and consistency of the division results.

[0044] Figure 4 This is a sequence profile of the upper subsection of Sha 4 in Niuye 1 well. Sequence division is performed based on the changing trends of the four principal component score curves. The figure compares the results of the previous division based on the GR curve, and finds that the present invention has a higher degree of precision in sequence division. The profile also shows that the division results of this patent are in good consistency with the changing trends of paleoenvironmental indicators, further verifying the high efficiency of the present invention.

[0045] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0046] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

Claims

1. A method for lacustrine shale sequence stratigraphic division based on principal component analysis, characterized in that: The method comprises the following steps: S1. Sample collection and preparation Collect core samples from the target lacustrine shale system to ensure that the samples cover different depths and multiple geological horizons; S2. Geochemical data collection Use energy dispersive X-ray fluorescence spectrometer to perform elemental analysis on the processed samples, collect a sample point at a certain distance, and collect the concentration data of the main elements and trace elements in the samples; S3. Data processing and standardization The geochemical data collected in step S2 is subjected to outliers removal and missing data filling to ensure the integrity of the sample data, and then the data is subjected to standardization to eliminate the magnitude differences between different elements, making them comparable and improving the accuracy of data analysis; S4. Principal Component Analysis (PCA) Use Bartlett's sphericity test or KMO test to evaluate the suitability of the data after the standardization process in step S3, analyze the data points through PCA, extract multiple principal components according to the characteristics of the specific data set, and adjust the principal components extracted by PCA according to the geological background of different work areas or well sections; S5. Clarify the geological significance of each principal component Through the loading diagram generated by PCA, the geological significance represented by each principal component is clarified; S6. Sequence division and verification Based on the changing trend of the main components extracted by PCA, combined with the existing geological background information and logging data, the stratigraphic units of the lacustrine shale are divided. The sequence interfaces and units are identified through the changes in the principal component scores in different strata. The division results are then compared and verified with the geological and logging data to ensure the accuracy and consistency of the results.

2. The method for lacustrine shale sequence stratigraphic division based on principal component analysis according to claim 1, characterized in that: In step S1, after the samples are collected, they need to be cleaned and pre-processed to ensure the accuracy of subsequent analysis, and the geological background information of each sample, including the collection depth, stratigraphic position, etc., is recorded in detail to provide reliable data support for subsequent stratigraphic division.

3. The method for lacustrine shale sequence stratigraphic division based on principal component analysis according to claim 1, characterized in that: In step S2, the main elements include Si, Ca, Mg, and K, and the trace elements include Ti, Zr, Mo, and U, and the interval is 10 cm.

4. The method for lacustrine shale sequence stratigraphic division based on principal component analysis according to claim 1, characterized in that: In step S4, the Bartlett sphericity test is used to test the correlation between variables. A P value less than 0.05 indicates that the correlation is significant and suitable for PCA. The KMO test is used to evaluate the applicability of factor analysis. PCA can be performed when the KMO value is greater than 0.

6. These tests ensure that the data is suitable for PCA dimensionality reduction analysis.

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