Method for identifying stratum lithofacies

Abstract

The method comprises the following steps: (1) taking a plurality of samples from a target stratum, carrying out element analysis, mineral content analysis and total organic carbon content analysis on each sample, and respectively obtaining correlation coefficients R of the mineral content and the total organic carbon content with each element content; (2) respectively selecting sensitive elements of the mineral content and the total organic carbon content according to the correlation coefficient R, and respectively establishing a change relation model (I), P = a + sigma (bi * Ei), (I), between the mineral content and the total organic carbon content and the corresponding sensitive element content; and (3) sampling while drilling from the target stratum, obtaining the content of each sensitive element, determining the mineral content and the total organic carbon content through the formula (I), and determining the type of the lithofacies of the target stratum. The method can accurately and rapidly identify the shale oil stratum lithofacies, has the advantages of low cost, simple operation and high timeliness, and is wide in application range.

Description

Technical Field

[0001] This invention relates to the field of lithofacies classification, and in particular to a method for identifying stratigraphic lithofacies. Background Technology

[0002] China has a wide development of continental shale, such as the Cretaceous in the Songliao Basin, the Paleogene in the Bohai Bay Basin, the Jurassic in the Sichuan Basin, and the Triassic in the Ordos Basin. Continental shale oil resources have considerable potential and have become an important replacement area for exploration and production growth in recent years. Unlike North American marine shale, my country's continental shale mainly formed in Mesozoic and Cenozoic rift basins and lacustrine basins. The sedimentary environments are small-scale, sensitive to climate and provenance, and exhibit diverse vertical superposition relationships and varied lateral sedimentary types, resulting in complex and variable reservoir properties, oil-bearing capacity, compressibility, and mobility. Therefore, shale lithofacies classification is the foundation for identifying continental shale oilfields and a crucial step in shale exploration and development. Currently, the technology for classifying the lithofacies of continental shale oilfields is relatively mature, and a "three-element, three-end-member" classification method based on "sedimentary structure, mineral composition, and organic matter abundance" has been basically established. However, the above methods have two main problems: first, the evaluation heavily relies on core samples and laboratory analysis. Its disadvantages are obvious: (1) The cost of drilling coring and laboratory testing is high; (2) Coring is usually not done during the development stage, and coring is not done in the horizontal section either. Data from adjacent wells or pilot wells cannot accurately represent the sedimentary facies type of the horizontal section. Secondly, it has poor timeliness and cannot provide support for formation evaluation while drilling during the horizontal section drilling process. Summary of the Invention

[0003] To address at least one technical problem in the existing technology, the present invention provides a method for identifying the lithofacies of a formation. The method of the present invention can obtain a lithofacies identification model by sampling from the target formation or even adjacent wells or pilot wells of the target formation. After inputting the elemental analysis obtained from the target formation into the model, the lithofacies of the target formation can be identified quickly and accurately. Moreover, the method of the present invention no longer relies solely on core analysis, thus improving timeliness.

[0004] This invention provides a method for identifying stratigraphic lithofacies, the method comprising:

[0005] (1) Take multiple samples from the target stratum and perform elemental analysis, mineral content analysis and total organic carbon content analysis on each sample. Analyze the correlation between the measured mineral content and the measured total organic carbon content and the content of each element in the elemental analysis to obtain the correlation coefficient between the mineral content and the content of each element and the correlation coefficient R between the total organic carbon content and the content of each element.

[0006] (2) Based on the correlation coefficient R, select the sensitive elements for the mineral content and total organic carbon content respectively, and establish the variation relationship model (I) between the mineral content and total organic carbon content and the corresponding sensitive element content, P=a+∑(b i ×E i ), (I); where P is the mineral content or total organic carbon content in the target stratum, E i Let b be the content of the i-th sensitive element in the target stratum. i For E i The coefficient of , where a is a constant.

[0007] (3) Take samples from the target formation while drilling to obtain the content of each sensitive element, and determine the mineral content and total organic carbon content by formula (I) to determine the type of lithofacies of the target formation.

[0008] Preferably, the number of samples in step (1) is ≥30, and more preferably, the number of samples is ≥50.

[0009] Preferably, the mineral content includes clay mineral content, carbonate mineral content, and felsic mineral content.

[0010] Preferably, in step (1), curves showing the relationship between the measured clay mineral content, carbonate mineral content, felsic mineral content, and total organic carbon content and the content of each element in elemental analysis are plotted to obtain the correlation coefficients R between the clay mineral content, carbonate mineral content, felsic mineral content, and total organic carbon content and the content of each element in elemental analysis.

[0011] Preferably, the correlation coefficient R is obtained through correlation analysis.

[0012] Preferably, a positive correlation coefficient R is considered a positive correlation, and a negative correlation is considered a negative correlation.

[0013] Preferably, in step (2), the absolute value of the correlation coefficient R of the sensitive element is ≥0.1, and more preferably, the absolute value of the correlation coefficient R is ≥0.2.

[0014] Preferably, elements with large absolute values ​​of correlation coefficient R are selected from the elements as sensitive elements.

[0015] Preferably, the number of sensitive elements is 4-16, and more preferably 6-12.

[0016] Preferably, the sensitive elements include positively correlated sensitive elements and negatively correlated sensitive elements.

[0017] Preferably, the number of positively correlated sensitive elements and negatively correlated sensitive elements are each 3-6.

[0018] Preferably, the sensitive element includes elements with an atomic weight greater than 23.

[0019] Preferably, the sensitive elements include at least four elements with an atomic weight ≥ 23.

[0020] Preferably, the relationship model (I) between the mineral content and the total organic carbon content and the content of sensitive elements is established by multiple linear regression fitting.

[0021] Preferably, the target stratigraphic facies is a shale oil stratigraphic facies.

[0022] Preferably, in step (1), the sample is taken from a horizontal well pilot well or an adjacent well in the target formation.

[0023] The method for identifying stratigraphic lithofacies according to the present invention has the following advantages:

[0024] 1) The method of the present invention calculates the mineral content and total organic carbon content in the formation through the relational model with high accuracy. Based on the calculation results, it can accurately identify the lithofacies of shale oil formations, and the lithofacies identification accuracy rate can reach more than 85%, overcoming the technical difficulty of relying on core laboratory analysis for lithofacies identification of current shale oil wells.

[0025] This invention employs low-cost and widely applicable elemental logging technology, which is simple to operate, inexpensive, accurate in identifying petrographic features, and has a wide range of applications. Attached Figure Description

[0026] Figure 1 This is a lithofacies classification diagram of the present invention;

[0027] Figure 2 This is a multiple linear regression curve obtained by comparing and fitting the carbonate mineral content calculated by the variation relationship model of 28 core samples in Example 1 of the present invention with the laboratory measurement results;

[0028] Figure 3 The multiple linear regression curve obtained by comparing and fitting the felsite mineral content calculated by the variation relationship model of 28 core samples with the laboratory measurement results in Example 1 of the present invention;

[0029] Figure 4 The multiple linear regression curve obtained by comparing and fitting the clay mineral content calculated by the variation relationship model of 28 core samples with the laboratory measurement results in Example 1 of the present invention is shown.

[0030] Figure 5 The multiple linear regression curve obtained by comparing and fitting the organic carbon content calculated by the variation relationship model of 28 core samples in Example 1 of the present invention with the laboratory measurement results is shown.

[0031] Figure 6 This is a shale oil facies classification map obtained by classifying 28 core samples drilled from a pilot well in the target formation based on a variation relationship model, according to Embodiment 1 of the present invention.

[0032] Figure 7 This is a shale oil facies map obtained by classifying 50 rock cuttings from the horizontal section of the pilot well in the target formation based on a variation relationship model, according to Embodiment 1 of the present invention. Detailed Implementation

[0033] This invention provides a method for identifying stratigraphic lithofacies, the method comprising:

[0034] (1) Take multiple samples from the target stratum and perform elemental analysis, mineral content analysis and total organic carbon (TOC) content analysis on each sample. Analyze the correlation between the measured mineral content and the measured total organic carbon content and the content of each element in the elemental analysis to obtain the correlation coefficient between the mineral content and the content of each element and the correlation coefficient R between the total organic carbon content and the content of each element.

[0035] (2) Based on the correlation coefficient R, select the sensitive elements for the mineral content and total organic carbon content respectively, and establish the variation relationship model (I) between the mineral content and total organic carbon content and the corresponding sensitive element content, P=a+∑(b i ×E i ), (I); where P is the mineral content or total organic carbon content in the target stratum, E i Let b be the content of the i-th sensitive element in the target stratum. i For E i The coefficient, where a is a constant;

[0036] (3) Take samples from the target formation while drilling to obtain the content of each sensitive element, and determine the mineral content and total organic carbon content by formula (I) to determine the type of lithofacies of the target formation.

[0037] In this invention, it is understood that the formal oil and gas development process in the art is carried out in the horizontal section of the target formation. As the drilling equipment operates in the horizontal section, samples can be taken while drilling to obtain the lithofacies type of the current location in real time.

[0038] In this invention, the lithofacies naming method can be: organic matter + mineral components + shale, such as organic-rich carbonate shale, organic-containing felsic mixed shale.

[0039] In a preferred embodiment of the present invention, the number of samples in step (1) is ≥30, preferably ≥50, and more preferably 50-300.

[0040] In a preferred embodiment of the present invention, the mineral content includes clay mineral content, carbonate mineral content, and felsic mineral content.

[0041] In a preferred embodiment of the present invention, in step (1), the correlation between the measured clay mineral content, carbonate mineral content, fels mineral content and the measured total organic carbon content and the content of each element in the elemental analysis is analyzed to obtain the correlation coefficient R between the clay mineral content, carbonate mineral content, fels mineral content and total organic carbon content and the content of each element in the elemental analysis.

[0042] In this invention, the correlation coefficient R is obtained by methods commonly used in the art, such as correlation analysis; or by using Origin software commonly used in the art for correlation analysis.

[0043] In a preferred embodiment of the present invention, a positive correlation coefficient R is considered a positive correlation, and a negative correlation is considered a negative correlation.

[0044] In a preferred embodiment of the present invention, the absolute value of the correlation coefficient R of the sensitive element is ≥0.1, and more preferably the absolute value of the correlation coefficient R is ≥0.2.

[0045] In a preferred embodiment of the present invention, elements with large absolute values ​​of correlation coefficient R are selected from the elements as sensitive elements.

[0046] In a preferred embodiment of the present invention, in step (2), the number of sensitive elements is 4-16, preferably 6-12.

[0047] In a preferred embodiment of the present invention, the sensitive elements include positively correlated sensitive elements and negatively correlated sensitive elements. The positively correlated and negatively correlated sensitive elements are selected based on the magnitude of the correlation coefficient R. Specifically, elements with a large correlation coefficient R are selected from those with positive correlation coefficients; and elements with a large absolute value of the correlation coefficient R are selected from those with negative correlation coefficients.

[0048] In a preferred embodiment of the present invention, the number of positively correlated sensitive elements and negatively correlated sensitive elements are each 2-8, preferably 3-6.

[0049] In a preferred embodiment of the present invention, the sensitive element includes elements with an atomic weight ≥ 23. Preferably, the sensitive element includes at least four elements with an atomic weight ≥ 23; for example, the sensitive element includes at least four of S, Zn, Fe, P, Ga, Mn, Na, Ca, Ba, K, Si, Al, Ti, V, U, Mo, Cu, In, Cd, Ag, Sn, Nb, Mg and Ni.

[0050] In a preferred embodiment of the present invention, the relationship model (I) between the mineral content and total organic carbon content and the content of sensitive elements is established by multiple regression fitting. The method of establishing the relationship model (I) by multiple linear regression fitting is a commonly used method in the art, such as using Origin software to perform multiple linear regression fitting.

[0051] In a preferred embodiment of the present invention, the target stratigraphic facies is a shale oil stratigraphic facies.

[0052] In a preferred embodiment of the present invention, in step (1), the sample is taken from a horizontal well pilot well or an adjacent well of the target formation.

[0053] In this invention, such as Figure 1 As shown, the types of lithofacies include carbonate shale, felsic shale, clay shale, mixed shale, organic-rich shale, organic-containing shale, and organic-poor shale.

[0054] In this invention, the types of lithofacies can also be classified according to Table 2.

[0055] The following detailed description of preferred embodiments of the present invention illustrates the principles of the invention and is not intended to limit the scope of the invention.

[0056] Example 1

[0057] (1) A pilot well in the Sha-4 section of the Jiyang Depression was continuously drilled and cored. 28 core samples were taken and laboratory analysis was conducted to obtain data on clay mineral content, carbonate mineral content, felsic mineral content, total organic carbon content and the content of each element.

[0058] Correlation analyses were conducted on the content of clay minerals, carbonate minerals, felsic minerals, and total organic carbon with the content of each element, and the correlation coefficients R between the content of clay minerals, carbonate minerals, felsic minerals, and total organic carbon and the content of each element were obtained.

[0059] (2) Five elements with a positive correlation to clay mineral content and a correlation coefficient R ≥ 0.1 were selected as sensitive elements (correlation coefficient R from high to low is S, Zn, Fe, Ga, Mn). Then, five elements with a negative correlation to clay mineral content and an absolute value of the correlation coefficient R ≥ 0.1 were selected as sensitive elements (correlation coefficient R from high to low is P, Na, Ca, Ba, K). A model was established to show the relationship between clay mineral content and the content of the ten sensitive elements.

[0060] W 黏土矿物 =22.43 + 1.98 × W Fe -3.48×W Na -2.40×WP +4.31×W S -0.28×W Ca

[0061] +120.02×W Mn +0.02×W Zn +1.44×W Ga +0.015×W Ba +3.75×W K

[0062] The correlation and correlation coefficient R of each sensitive element are shown in Table 1, arranged in descending order of the absolute value of the correlation coefficient R.

[0063] Using the same method, models were established to show the relationship between the content of carbonate minerals, felsic minerals, and total organic carbon and their respective sensitive elements:

[0064] W 碳酸盐矿物 =84.95 - 345.02 × W Mn +15.81×W P -0.076×W Ca -0.53×W Si +14.10×W K

[0065] -3.70×W Al +1.60×W Ti +1.82×W S -3.26×W Fe +3.91×W Na

[0066] W 长英矿物 =46.10 - 105.91 × W Mn -1.82×W Ti +2.14×W Al +0.95×W Si +0.007×W V

[0067] -0.27×W Ca -0.003×W Ba -13.77×W K -4.37×W U +101.88×W P

[0068] W 总有机碳 =69.31 + 2.65 × W Mn +98.82×W Mo +1.06×W Cu -6.48×WIn -614.87×W Cd

[0069] -1467.74×W Ag +38.85×W Sn -0.072×W Nb +2.65×W Mg +0.012×W Ni

[0070] In the formula: W 黏土矿物 —Percentage of clay mineral content;

[0071] W 碳酸盐矿物 —Percentage of carbonate mineral content;

[0072] W 长英矿物 —Percentage of longan mineral content;

[0073] W 总有机碳 —Total organic carbon mass percentage;

[0074] W X —The mass percentage of each sensitive element.

[0075] Model validation:

[0076] The mineral content and total organic carbon content of 28 core samples were calculated using a variation relationship model and compared with laboratory measurements. Multiple linear regression was then performed, and the results are as follows: Figures 2-5 It can be seen that the correlation coefficient R between the results calculated by the variation relationship model and the laboratory measurement results is greater than 0.7, indicating that the calculation model is reliable. Based on the model calculation results, the lithofacies of 28 core samples were classified, and the classification results are as follows: Figure 6 As shown, compared with the experimental results, only 3 out of 28 sample data did not match, with a petrographic identification accuracy rate of 89.3%.

[0077] (3) Take 50 rock cuttings from the shale oil formation in the horizontal section of the pilot well and carry out elemental analysis and whole-rock analysis. Then, input the measured element content into the variation relationship model of clay minerals, carbonate minerals, fels minerals and total organic carbon to calculate the clay mineral content, carbonate mineral content, fels mineral content and total organic carbon content of each rock cutting.

[0078] Based on the calculation results and reference Figure 1 The rock fragments extracted from the target strata were subjected to lithofacies classification, and the lithofacies classification results are as follows: Figure 7 As shown, the lithofacies classification results based on laboratory whole-rock analysis (such as...) Figure 7 Compared to the data shown, only 6 out of 50 data points did not match, resulting in a lithofacies identification accuracy rate of 88%.

[0079] Table 1

[0080]

[0081] Table 2

[0082]

[0083]

[0084] from Figure 2-6 It can be seen that the correlation coefficient between the clay mineral content, carbonate mineral content, felsic mineral content and total organic carbon content in the target stratum rock fragments calculated by the method of the present invention and the clay mineral content, carbonate mineral content, felsic mineral content and total organic carbon content obtained by laboratory analysis is >0.75, indicating that the method of the present invention has high accuracy;

[0085] from Figure 7 As can be seen, compared with the lithofacies classification results based on laboratory analysis, only 6 out of 50 data points did not conform to the lithofacies classification method of this invention, with a conformity rate of 88%.

[0086] It should be noted that the embodiments described above are only for explaining the present invention and do not constitute any limitation on the present invention. The present invention has been described with reference to typical embodiments, but it should be understood that the words used therein are descriptive and explanatory terms, not limiting terms. Modifications can be made to the present invention within the scope of the claims, and revisions can be made to the present invention without departing from the scope and spirit of the present invention. Although the present invention described herein relates to specific methods, materials, and embodiments, it does not mean that the present invention is limited to the specific examples disclosed herein; on the contrary, the present invention can be extended to all other methods and applications with the same function.

Claims

1. A method for identifying stratigraphic lithofacies, characterized in that, The method includes: (1) Take multiple samples from the target stratum and perform elemental analysis, mineral content analysis and total organic carbon content analysis on each sample. Analyze the correlation between the measured mineral content and the measured total organic carbon content and the content of each element in the elemental analysis to obtain the correlation coefficient R between the mineral content and the content of each element and the correlation coefficient R between the total organic carbon content and the content of each element. (2) Based on the correlation coefficient R, select the sensitive elements for the mineral content and total organic carbon content respectively, and establish the variation relationship model (I) between the mineral content and total organic carbon content and the corresponding sensitive element content. P=a+∑(b i ×E i ),(I) Where P represents the mineral content or total organic carbon content in the target stratum, and E... i Let b be the content of the i-th sensitive element in the target stratum. i For E i The coefficient, where a is a constant; (3) Take samples from the target formation while drilling to obtain the content of each sensitive element, and determine the mineral content and total organic carbon content by formula (I) to determine the type of lithofacies of the target formation.

2. The method according to claim 1, characterized in that, The number of samples in step (1) is ≥30, preferably ≥50; And / or, the mineral content includes clay mineral content, carbonate mineral content, and felsic mineral content.

3. The method according to claim 2, characterized in that, In step (1), the curves showing the relationship between the measured clay mineral content, carbonate mineral content, fels mineral content, and total organic carbon content and the content of each element in the elemental analysis are plotted respectively, and the correlation coefficients R between the clay mineral content, carbonate mineral content, fels mineral content, and total organic carbon content and the content of each element in the elemental analysis are obtained.

4. The method according to any one of claims 1-3, characterized in that, The correlation coefficient R is obtained through correlation analysis.

5. The method according to claims 1-4, characterized in that, If the correlation coefficient R is positive, it is considered a positive correlation; otherwise, it is considered a negative correlation.

6. The method according to claims 1-5, characterized in that, In step (2), the absolute value of the correlation coefficient R of the sensitive element is ≥0.1, preferably ≥0.2; Preferably, elements with large absolute values ​​of correlation coefficient R are selected from the elements as sensitive elements; Preferably, in step (2), the number of sensitive elements is 4-16, and more preferably 6-12.

7. The method according to claim 5 or 6, characterized in that, The sensitive elements include positively correlated sensitive elements and negatively correlated sensitive elements; Preferably, the number of positively correlated sensitive elements and negatively correlated sensitive elements are each 3-6.

8. The method according to any one of claims 1-7, characterized in that, The sensitive elements include elements with an atomic weight ≥ 23; Preferably, the sensitive elements include at least four elements with an atomic weight ≥ 23.

9. The method according to any one of claims 1-8, characterized in that, The model (I) showing the relationship between the mineral content and total organic carbon content and the content of sensitive elements was established by fitting multiple linear regression.

10. The method according to any one of claims 1-9, characterized in that, The target formation facies is a shale oil formation facies; And / or, in step (1), the sample is taken from a horizontal well pilot well or an adjacent well in the target formation.

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