Shale gas reservoir biogenic silica content logging calculation method based on unit volume effective photoelectric absorption cross section index

Abstract

The application discloses a shale gas reservoir biological silicon content logging calculation method based on unit volume effective photoelectric absorption cross section index, and solves the problems of complex cost, inability to meet field large-scale, regional, low-cost evaluation and large calculation error of existing biological silicon content logging calculation methods. The method calculates unit volume effective photoelectric absorption cross section index from logging photoelectric absorption cross section index Pe and logging lithology density DEN data, and then calculates shale gas reservoir biological silicon content. The method can be used in coring wells, and has universal application value for all shale gas wells, and has smaller error and lower cost.

Description

Technical Field

[0002] This invention relates to a method for well logging interpretation, characteristic analysis, and potential for modification of shale gas reservoirs in the field of petroleum engineering. Specifically, it is a well logging calculation method for the biogenic silica content of shale gas reservoirs based on the effective photoelectric absorption cross section index per unit volume. Background Technology

[0004] Biogenic silica in shale gas reservoirs, also known as organosilicon, is beneficial for the formation and preservation of various pores in shale, and is one of the important evaluation indicators for high-quality shale gas reservoirs. Furthermore, shale rich in biogenic silica is also conducive to reservoir fracturing and stimulation, leading to high oil and gas production and achieving commercially viable development. In the field of oil and gas exploration and development, the biogenic silica content of shale gas reservoirs is also referred to as the biogenic silica mass fraction, or simply biogenic silica content.

[0005] The most widely used method for quantitative evaluation of biogenic silica content in shale gas reservoirs, both domestically and internationally, is calculation based on silicon and aluminum content, commonly referred to as the silicon-aluminum elemental method. There are generally two types of methods for obtaining the content of silicon, aluminum, and other elements: One method involves elemental analysis of shale gas reservoir core samples. However, due to the difficulty and high cost of drilling and coring, this method has a high overall cost, resulting in fewer cored wells in actual production. This also makes it difficult to comprehensively reflect the distribution of biogenic silica in shale gas reservoirs within a region based solely on core sample analysis, affecting the effectiveness of regional evaluation. The second method involves obtaining the content of silicon, aluminum, and other elements in the formation through elemental logging, thereby calculating the biogenic silica content of the shale gas reservoir. While the second method can obtain the content of silicon, aluminum, and other elements in continuous formation profiles within the wellbore, it is not widely used in shale gas evaluation due to the high cost of logging instruments, high operating costs, and significant safety risks associated with logging operations in horizontal wells. This method cannot achieve the goal of calculating and comprehensively evaluating the biogenic silica content of all shale gas wells within a block.

[0006] Invention CN111982887A discloses a method for determining biogenic silicon in sedimentary rocks. The method involves washing, grinding, and drying the sedimentary rock to remove hydrochlorides and organic matter, followed by extraction to obtain an extract, sampling, and finally determining the biogenic silicon content in the sample. Other methods for obtaining biogenic silicon content from rock cores, including X-ray diffraction and infrared spectroscopy, first determine the elemental content and then the biogenic silicon content. This invention directly measures the biogenic silicon content in rocks; therefore, it also relies on core analysis from core wells, which cannot meet the needs of large-scale, regional, and low-cost on-site evaluation.

[0007] Invention CN108508182A discloses a logging method for rapidly determining the biogenic silica content in graptolite-facies thermal shale. The method determines the biogenic silica content based on the relationship between TOC and SiO2 content in graptolite-facies thermal shale from the Silurian Longmaxi Formation to the Ordovician Wufeng Formation. This method has proven effective in shale formations from the Silurian Longmaxi Formation to the Ordovician Wufeng Formation. However, accurate calculation relies on a strong correlation between TOC and SiO2, i.e., a correlation coefficient R greater than 0.7. Furthermore, in formations with poor correlation between TOC and SiO2, this method suffers from limitations, resulting in larger calculation errors. Summary of the Invention

[0009] The purpose of this invention is to address the aforementioned technical limitations by providing a method for calculating the biogenic silica content of shale gas reservoirs that is applicable not only to core wells but also to all shale gas wells, with smaller errors and lower costs.

[0010] The objective of this invention is achieved by a well logging calculation method for biogenic silica content in shale gas reservoirs based on the effective photoelectric absorption cross section index per unit volume. The effective photoelectric absorption cross section index per unit volume is calculated from the well logging photoelectric absorption cross section index Pe and the well logging lithology density DEN data, and then the biogenic silica content of the shale gas reservoir in the well to be interpreted is calculated.

[0011] Specifically, the following methods are included:

[0012] 1) Obtain the biogenic silica content (Si_toc1) of shale gas reservoirs from core samples taken from core wells within the work area through elemental analysis.

[0013] 2) Obtain the logging lithological density DEN1 and logging photoelectric absorption cross section index Pe1 at the corresponding depth points of the core samples from the core well;

[0014] 3) Use the least squares method to determine the model coefficients of model 1 Si_toc1=a1•(Pe1•DEN1)+b1 and model 2 Si_toc1=a2•ln(Pe1• DEN1)+b2, and determine the optimal calculation model based on the correlation coefficient R.

[0015] In the formula: the dimension of well logging lithology density DEN1 is g / cm³. 3 The dimension of the photoelectric absorption cross section index Pe1 from well logging is b / e, and the dimension of the biogenic silica content Si_toc1 of the shale gas reservoir obtained from elemental analysis of core samples is %.

[0016] 4) Obtain the logging lithological density (DEN) and logging photoelectric absorption cross section index (Pe) of the shale gas reservoir section of the well to be interpreted in the work area through lithological density logging data;

[0017] 5) Substitute the logging lithology density DEN and logging photoelectric absorption cross section index Pe of the well to be interpreted in step 4) into the optimal model determined in step 3) to calculate the biogenic silica content Si_toc of the shale gas reservoir of the well to be interpreted;

[0018] 6) Output the calculation results.

[0019] In step 2), based on the lithological density logging data of the core wells in the work area, the logging lithological density DEN1 and logging photoelectric absorption cross section index Pe1 at the corresponding depth points of the core samples are obtained, and the data corresponding to abnormal layers with logging photoelectric absorption cross section index Pe1 greater than the upper limit of the area are removed.

[0020] Preferably, in step 2), the upper limit of the photoelectric absorption cross section index Pe1 in the Jiaoshiba, Pingqiao and Hongxing blocks of the Sichuan Basin is 8.0b / e, and the core samples exceeding this upper limit are data corresponding to the abnormal layer.

[0021] Preferably, in step 3), the method for determining the optimal calculation model based on the correlation coefficient R is as follows: the biogenic silica content of the shale gas reservoir obtained by back-calculation using the model is regressed with the biogenic silica content of the core sample obtained by elemental analysis of the core sample, and the model with the higher correlation coefficient R is the optimal calculation model.

[0022] Preferably, in step 3), the biogenic silica content of the shale gas reservoir calculated by the model is used as the Y-axis, and the biogenic silica content of the core sample obtained by elemental analysis of the core sample is used as the X-axis. The least squares method is used for linear fitting to obtain the correlation coefficient R.

[0023] Preferably, in step 4), the logging lithological density (DEN) and logging photoelectric absorption cross section index (Pe) of the shale gas reservoir section to be interpreted in the work area are obtained through lithological density logging data, and the product of the two is the effective photoelectric absorption cross section index per unit volume.

[0024] Studies have found a good correlation between the effective photoelectric absorption cross section index (EABS) per unit volume obtained from shale gas reservoir lithology density logging and the biogenic silica content of the shale gas reservoir. This correlation can be used to achieve the aforementioned objectives. The effective photoelectric absorption cross section index per unit volume of rock is the product of the rock's logging EABS index (Pe) and the logging lithology density (DEN). In the field of petroleum engineering, the biogenic silica content of shale gas reservoirs is also referred to as the biogenic silica mass fraction, or simply biogenic silica content.

[0025] This invention provides a solution for calculating the biogenic silica content of shale gas reservoirs using the effective photoelectric absorption cross section index per unit volume obtained from lithology density logging data. This solution is more convenient and has a wider range of applications than traditional methods for determining the biogenic silica content of shale gas reservoirs.

[0026] This invention has been applied to 60 wells in multiple shale gas blocks, including Jiaoshiba, Pingqiao, and Hongxing in the Sichuan Basin. The calculated biogenic silica content of the shale gas reservoir is close to that obtained from the elemental analysis of the core samples, with an average error of no more than 15%. It can meet the needs of field shale gas reservoir logging interpretation, characteristic analysis, and potential for modification evaluation. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating the workflow of the present invention.

[0029] Figure 2 This is a cross-plot of the biogenic silica content calculated by Model 1 of this invention and the biogenic silica content analyzed by core samples.

[0030] Figure 3 This is a cross-plot of the biogenic silica content calculated by Model 2 of this invention and the biogenic silica content analyzed by core samples.

[0031] Figure 4 This is a diagram illustrating an application example of the S shale gas field W well in this invention;

[0032] Figure 5 This is a diagram illustrating an application example of the R well in the S shale gas field according to the present invention. Detailed Implementation

[0033] Reference Figure 1 The specific steps of this invention are as follows:

[0034] 1) The biogenic silica content Si_toc1 of shale gas reservoirs obtained by elemental analysis of core samples from core wells in the work area;

[0035] 2) Based on the lithological density logging data of the core wells in the work area, obtain the logging lithological density DEN1 and logging photoelectric absorption cross section index Pe1 at the corresponding depth points of the core samples, and remove the data corresponding to the abnormal layers where the logging photoelectric absorption cross section index Pe1 is greater than the upper limit of the region. The upper limit of the logging photoelectric absorption cross section index in the Jiaoshiba, Pingqiao and Hongxing blocks of the Sichuan Basin is taken as 8.0b / e.

[0036] 3) The model coefficients of model 1 Si_toc1=a1•(Pe1•DEN1)+b1 and model 2 Si_toc1=a2•ln(Pe1•DEN1)+b2 are determined by the least squares method. Then, the biogenic silica content of shale gas reservoir obtained by back-calculation of the model is regressed with the biogenic silica content of core sample obtained by elemental analysis of core sample. The model with the higher correlation coefficient is the best calculation model.

[0037] 4) Obtain the logging lithological density (DEN) and logging photoelectric absorption cross section index (Pe) of the shale gas reservoir section of the well to be interpreted in the work area through lithological density logging data. The product of the two is the effective photoelectric absorption cross section index per unit volume.

[0038] The logging lithological density (DEN) and logging photoelectric absorption cross section index (Pe) data of the well to be interpreted are stored at depth intervals of 0.1m to 1.0m and saved in txt format.

[0039] 5) Substitute the logging lithology density DEN and logging photoelectric absorption cross section index Pe of the well to be interpreted in step 4) into the optimal calculation model determined in step 3) to calculate the biogenic silica content Si_toc of the shale gas reservoir of the well to be interpreted;

[0040] 6) Output the calculation results.

[0041] The present invention will now be described in detail with reference to specific embodiments.

[0042] Example 1: Well W in the S Shale Gas Field of Sichuan Basin

[0043] 1) Elemental analysis of 287 shale core samples from 4 core wells in the S shale gas field yielded the biogenic silica content Si_toc1 in the shale gas reservoir of the field.

[0044] 2) Based on the lithological density logging data from the above 4 core wells, the logging lithological density DEN1 and logging photoelectric absorption cross section index Pe1 at the corresponding depth points of 287 shale core samples were obtained, and the data corresponding to the abnormal intervals where the photoelectric absorption cross section index Pe1 was greater than the regional upper limit of 8.0b / e were removed; among the 287 shale core samples, 29 core samples had Pe1 values ​​in the abnormal intervals greater than 8.0b / e;

[0045] 3) After removing the biogenic silica content (Si_toc1), logging lithological density (DEN1), and logging photoelectric cross-section index (Pe1) from the 29 shale core samples, the Si_toc1, DEN1, and Pe1 of the 258 shale core samples were subjected to least squares regression analysis using Model 1 (Si_toc1=a1•(Pe1•DEN1)+b1) and Model 2 (Si_toc1=a2•ln(Pe1•DEN1)+b2). ​​The model coefficients were a1=-1.875, b1=36.447, a2=-24.722, and b2=74.883, i.e., Model 1 Si_toc1=-1.875•(Pe1•DEN1)+36.447 and Model 2 Si_toc1=-24.722•ln(Pe1•DEN1)+74.883.

[0046] The shale gas reservoir biogenic silica content calculated using Model 1 (referred to as the calculated biogenic silica content) was used as the Y-axis, and the core biogenic silica content obtained from elemental analysis of core samples (referred to as the core biogenic silica content) was used as the X-axis. A least-squares linear fit was performed, and the correlation coefficient R was 0.81 (see...). Figure 2The shale gas reservoir biogenic silica content calculated using Model 2 was used as the Y-axis, and the shale gas reservoir biogenic silica content obtained from elemental analysis of core samples was used as the X-axis. A least squares linear fit was performed, with a correlation coefficient R of 0.83 (see...). Figure 3 Comparing the correlation coefficients of Model 1 and Model 2, Model 2 has a higher correlation coefficient than Model 1, making Model 2 the optimal calculation model. Therefore, Model 2 is selected.

[0047] 4) Obtain the logging lithological density DEN and logging photoelectric absorption cross section index Pe from the lithological density logging data of the well to be interpreted, and multiply the two to obtain the effective photoelectric absorption cross section index per unit volume.

[0048] 5) Using the logging lithological density DEN and logging photoelectric absorption cross section index Pe of the well to be interpreted in step 4) into the optimal model determined in step 3), calculate the biogenic silica content Si_toc of the shale gas reservoir in well W;

[0049] 6) Output the calculation results: the biogenic silica content of the shale gas reservoirs in the 3291.4–3363.0 m interval of the Wujiaping and Maokou Formations of Well W (see...). Figure 4 (Abbreviated as biogenic silicon content). The calculated biogenic silicon content of the shale gas reservoir is compared with the core biogenic silicon content obtained from the elemental analysis of the lithological samples of the well. The average error is 12%, which is less than 15%, which can meet the needs of field shale gas reservoir logging interpretation, reservoir characteristic analysis, and compressibility evaluation.

[0050] Example 2: Well R in the S Shale Gas Field

[0051] 1) Well R and Well W in Example 1 both belong to the S shale gas field and use the same core well for modeling. Therefore, the optimal calculation model and model coefficients determined in Example 1 can be used. That is, the optimal calculation model is Model 2 Si_toc1=-24.722•ln(Pe1• DEN1)+74.883.

[0052] 2) Obtain the logging lithological density (DEN) and logging photoelectric absorption cross section index (Pe) from the lithological density logging data of the well to be interpreted, and multiply the two to obtain the effective photoelectric absorption cross section index per unit volume.

[0053] 3) Using the logging lithological density DEN and photoelectric absorption cross section index Pe of the well R to be interpreted in step 2), or the obtained effective photoelectric absorption cross section index per unit volume, we input the optimal calculation model determined in step 1) to calculate the biogenic silica content Si_toc of the shale gas reservoir in well R.

[0054] 4) Output the calculation results. The calculated biogenic silica content of the shale gas reservoirs in the Wujiaping Formation and Maokou Formation of Well R in the 3598.0–3685.0 m interval is 0.7%–30.8% (see...). Figure 5 The biogenic silicon content (abbreviated as biogenic silicon content) has an arithmetic mean of 13.7%. Based on calculated data such as the biogenic silicon content of the Wujiaping Formation shale gas reservoir in Well R, the 3607.5–3625.0m section of the Wujiaping Formation was selected as the window for horizontal sidetracking. A 1500m long horizontal section was fractured and tested, achieving an open flow rate of 21.0 × 10⁻⁶ m. 4 m 3 / d, based on a yield of 6.0×10 4 m 3 / d production trial mining has been carried out continuously and stably for more than 160 days, with a cumulative output exceeding 1000×10 4 m 3 .

Claims

1. A well logging calculation method for biogenic silica content in shale gas reservoirs based on the effective photoelectric absorption cross-section index per unit volume, characterized in that, The effective photoelectric absorption cross section index per unit volume is calculated using the well logging photoelectric absorption cross section index Pe and the well logging lithology density DEN data. Then, the biogenic silica content of the shale gas reservoir in the well to be interpreted is calculated. The specific steps include: 1) Obtain the biogenic silica content (Si_toc1) of shale gas reservoirs from core samples taken from core wells within the work area through elemental analysis. 2) Obtain the logging lithological density DEN1 and logging photoelectric absorption cross section index Pe1 at the corresponding depth points of the core samples from the core well; 3) The model coefficients of model 1 Si_toc1=a1•(Pe1•DEN1)+b1 and model 2 Si_toc1=a2•ln(Pe1•DEN1)+b2 are determined by the least squares method. The optimal calculation model is determined by the correlation coefficient R obtained by regression between the biogenic silica content of shale gas reservoir obtained by back-calculation using the model and the biogenic silica content of core sample obtained by experimental analysis of core sample elements. In the formula: the dimension of well logging lithology density DEN1 is g / cm³. 3 The dimension of the photoelectric absorption cross section index Pe1 of the well logging is b / e, and the dimension of the biogenic silicon content Si_toc1 of the shale gas reservoir obtained by elemental analysis of the core sample is %, and a1, b1, a2, and b2 are model coefficients. 4) Obtain the logging lithological density (DEN) and logging photoelectric absorption cross section index (Pe) of the shale gas reservoir section of the well to be interpreted in the work area through lithological density logging data, and multiply the two to obtain the effective photoelectric absorption cross section index per unit volume. 5) Substitute the logging lithology density DEN and logging photoelectric absorption cross section index Pe of the well to be interpreted in step 4) into the optimal model determined in step 3) to calculate the biogenic silica content Si_toc of the shale gas reservoir of the well to be interpreted; 6) Output the calculation results.

2. The well logging calculation method for biogenic silica content in shale gas reservoirs based on the effective photoelectric absorption cross-section index per unit volume as described in claim 1, characterized in that, In step 2), based on the lithological density logging data of the core wells in the work area, the logging lithological density DEN1 and logging photoelectric absorption cross section index Pe1 at the corresponding depth points of the core samples are obtained, and the data corresponding to abnormal layers with logging photoelectric absorption cross section index Pe1 greater than the upper limit of the area are removed.

3. The well logging calculation method for biogenic silica content in shale gas reservoirs based on the effective photoelectric absorption cross-section index per unit volume as described in claim 1, characterized in that, In step 3), the method for determining the optimal calculation model based on the correlation coefficient R is as follows: the biogenic silica content of the shale gas reservoir obtained by back-calculation of the model is regressed with the biogenic silica content of the core sample obtained by elemental analysis of the core sample, and the model with the higher correlation coefficient R is the optimal calculation model.

4. The well logging calculation method for biogenic silica content in shale gas reservoirs based on the effective photoelectric absorption cross-section index per unit volume as described in claim 3, characterized in that, In step 3), the biogenic silica content of the shale gas reservoir calculated by the model is used as the Y-axis, and the biogenic silica content of the core sample obtained by elemental analysis is used as the X-axis. The least squares linear fitting method is used to obtain the correlation coefficient R.

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