A method for determining total organic carbon content of continental shale oil and gas reservoirs, applications

Abstract

The present application provides a method for determining total organic carbon content of continental shale oil and gas reservoir, comprising the following steps: step S1: according to the logging information of continental shale oil and gas reservoir, the first shale oil and gas reservoir total organic carbon content TOC logging evaluation is carried out, and the evaluation result of the total organic carbon content of continental shale oil and gas reservoir is obtained; step S2: the evaluation result obtained in step S1 is taken as the initial assumed total organic carbon content value TOC origin ; step S3: according to the logging response of continental shale oil and gas reservoir, the lithofacies is identified, the lithofacies in the reservoir is extracted, and the lithofacies probability curve P1+P2+…+P i +…=1 is obtained; step S4: using the shale lithofacies probability curve, the organic matter enrichment probability curve P enrich of the interlayer type shale series is constructed P enrich =1-(a*P1+b*P2+c*P3+…+i*P i +…), wherein a, b, c, …, i are selected from 0-1; step S5: using TOC origin of step S2 and P enrich of step S4, the final TOC of continental interlayer type shale oil and gas reservoir is obtained TOC origin =TOC enrich ×P origin 0> . The present application provides a quantitative evaluation method for continental shale oil and gas reservoir.

Description

Technical Field

[0001] This invention relates to the field of unconventional oil and gas exploration and development, specifically to a method and application for determining the total organic carbon content of continental shale oil and gas reservoirs. Background Technology

[0002] Currently, relatively mature TOC logging evaluation methods have been established both domestically and internationally for marine shale reservoirs. Schnoker (1979), Fertl, and Chilinger et al. (1988) used regional geostatistics to establish empirical relationships between core organic carbon content and single (or multiple) logging responses, such as rock bulk density and natural gamma ray, for TOC logging evaluation. However, organic matter enrichment is often influenced by multiple geological factors, and there are complex nonlinear relationships between these factors. Conventional regression methods are insufficient to express these intrinsic connections, resulting in unsatisfactory predictions and significant regional limitations.

[0003] The Passey (ΔLogR) method (1990) and its improved versions (Sondergeld et al. (2010), Zhu Guangyou et al. (2003)) utilize porosity curves and resistivity curves, such as sonic transit time, to determine a "baseline" by overlapping them in non-source rock sections. The total organic carbon content is evaluated by the degree of curve separation in organic carbon-rich sections. However, the above methods have poor applicability in shale reservoirs with rapid lithofacies changes and rich in conductive minerals.

[0004] Lu et al. (2016) considered the natural sources of radioactivity in shale and established the GR-KTH overlay method based on nuclear logging theory. This method is not affected by reservoir clay minerals, conductive minerals, formation water salinity, organic matter maturity, or reservoir hydrocarbon content, and is widely applicable to TOC logging evaluation of marine shale reservoirs. However, this method is difficult to provide accurate TOC evaluation results for interbedded shale formations, especially those rich in calcareous / shell-like interlayers and silt / fine sandy interlayers, in continental shale, particularly in continental lacustrine sediments.

[0005] Therefore, quantitatively calculating the total organic carbon (TOC) content of continental shale oil and gas reservoirs based on well logging data is a new challenge in the field of unconventional oil and gas exploration and development. Summary of the Invention

[0006] To address the aforementioned problems in the prior art, this invention proposes a method and its application for determining the total organic carbon content of continental shale oil and gas reservoirs.

[0007] In a first aspect, the present invention provides a method for determining the total organic carbon content of continental shale oil and gas reservoirs, comprising the following steps:

[0008] Step S1: Based on the logging information of continental shale oil and gas reservoirs, conduct the first logging evaluation of total organic carbon content in shale oil and gas reservoirs and obtain the evaluation results of total organic carbon content;

[0009] Step S2: Use the organic carbon content evaluation results obtained in Step S1 as the initial assumption for the total organic carbon content (TOC). origin ;

[0010] Step S3: Based on the logging response of continental shale oil and gas reservoirs, conduct lithofacies identification, extract the lithofacies from the reservoir, and obtain the lithofacies probability curve.

[0011] P1 + P2 + ... + P i +…=1(I),

[0012] In equation (Ⅰ), P1, P2…P i …represent the extracted lithofacies probabilities; lithofacies probability is the probability that a certain lithofacies will develop in a rock.

[0013] Step S4: Construct the organic matter enrichment probability curve P of interbedded shale strata using shale lithofacies probability curves. enrich :P enrich = 1 - (a×P1 + b×P2 + c×P3 + ... + i×P) i +…) (Ⅱ)

[0014] In the formula, a, b, c, ..., i are selected from 0 to 1; a, b, c, ..., i are the lithofacies sedimentary environment coefficients for each lithofacies, used to characterize the difficulty of enriching organic matter in the sedimentary environment of that lithofacies. The greater the difficulty, the closer the coefficient is to 1. Therefore, the above formula can be used to characterize the probability of enriching organic matter in interlayered shale formations in continental lacustrine basins: the probability is 0 to 1, 0 is completely impossible for organic matter enrichment, and 1 is completely favorable for organic matter enrichment.

[0015] Step S5: Utilize the TOC from step S2 origin P in step S4 enrich The TOC of the final continental interbedded shale oil and gas reservoir was obtained as follows:

[0016] TOC = TOC origin ×P enrich .

[0017] As a specific embodiment of the present invention, the terrestrial shale is a terrestrial lacustrine basin shale.

[0018] As a specific embodiment of the present invention, the continental lacustrine shale includes interbedded shale strata composed of clayey, calcareous, crustal, and sandy components. The continental lacustrine shale is an interbedded shale strata with clayey, calcareous, crustal, and sandy components as its main components, and the interbedded strata exhibit diverse interlayer types and frequent interlayer development.

[0019] As a specific embodiment of the present invention, step S1 includes the following sub-steps:

[0020] Step S1.1: Use one or more methods to conduct TOC logging evaluation of the rock oil and gas reservoir and obtain one or more total organic carbon content evaluation results;

[0021] Optionally, step S1.2: Select the most applicable result from several total organic content evaluation results as the initial assumption for the total organic carbon (TOC) content of continental shale oil and gas reservoirs. origin In this step, "best applicability" refers to the result that best matches the TOC of the core analysis among several evaluation results.

[0022] As a specific embodiment of the present invention, the method for evaluating the TOC of rock and gas reservoirs in step S1 includes, but is not limited to, at least one of the following: radioactive gamma method, radioactive uranium content method, density method, Passey (ΔLogR) method, and sonic transit time empirical formula method. The evaluation results obtained from the above methods are compared with the core analysis standard TOC, and the result with the best matching degree is taken as the evaluation result of total organic carbon content.

[0023] As a specific embodiment of the present invention, the logging data for continental shale oil and gas reservoirs in step S1 includes, but is not limited to: bulk density, uranium content in the natural gamma spectrum, sonic transit time, deep lateral resistivity, compensated neutrons, natural gamma, shallow lateral resistivity, microsphere focused resistivity, and uranium-free gamma. In practical applications, other logging data can also be selected as needed.

[0024] As a specific embodiment of the present invention, the method for lithofacies identification in step S3 includes, but is not limited to, machine learning methods or intersection plot methods.

[0025] As a specific embodiment of the present invention, the machine learning method utilizes conventional well logging response and employs methods including but not limited to clustering or decision tree methods to carry out lithofacies classification and identification.

[0026] As a specific embodiment of the present invention, the clustering method includes fuzzy clustering method or hierarchical clustering method.

[0027] Secondly, the present invention provides an application of the method for determining the total organic carbon content of continental shale oil and gas reservoirs provided in the first aspect of the present invention in the quantitative calculation of the total organic carbon content in continental shale, especially in the quantitative calculation of the total organic carbon content in continental lacustrine basin shale.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] (1) This invention provides a quantitative evaluation method for the total organic carbon content of continental shale oil and gas reservoirs, especially continental lacustrine interlayer shale oil and gas reservoirs, and enriches the evaluation method for parameters of unconventional shale oil and gas reservoirs.

[0030] (2) This method has good application effect on continental shale oil and gas reservoirs, especially continental lacustrine interlayer shale oil and gas reservoirs. It is practical, universal and operable, and has good evaluation accuracy. Attached Figure Description

[0031] Figure 1 Example 1 of this invention: TOC logging interpretation results of Jurassic Dongyuemiao Formation continental shale oil in well FY10 in the eastern Sichuan Basin;

[0032] Figure 2 Example 2 of this invention: TOC logging interpretation results of Jurassic Dongyuemiao Formation continental shale oil in well XY3 in southeastern Sichuan Basin;

[0033] Figure 3 TOC logging interpretation results of Jurassic Qianfoya Formation continental shale gas in well LY1, northern Sichuan Basin;

[0034] Figure 4 The flowchart of a method for determining the total organic carbon content of continental shale oil and gas reservoirs according to the present invention. Detailed Implementation

[0035] The present invention will be further described below with reference to specific embodiments, but this does not constitute any limitation on the present invention.

[0036] To verify the effectiveness of the method of this invention in evaluating the total organic carbon content of interlayered shale oil and gas reservoirs in continental lacustrine basins, this method was applied to typical continental shale oil and gas wells in different areas of the Sichuan Basin, and the TOC content was verified using core analysis.

[0037] Example 1:

[0038] S1. For the Jurassic continental shale oil reservoir of well FY10 in the Fuling area of ​​eastern Sichuan Basin, collect well logging data and, based on the logging data (… Figure 1 (2-4), using volume density (DEN), natural gamma spectrum uranium content (URAN), acoustic transit time (AC), and deep lateral resistivity (LLD), the total organic carbon content was calculated using the density method, the modified density method, the empirical formula method for uranium content, and the Passey (ΔLogR) method, respectively, and four sets of calculation results were obtained. Figure 1 (5-8 courses);

[0039] Specifically, the density method is: TOC 密度法 =A / DEN-B(1)

[0040] TOC 密度法TOC is calculated using the density method, in %; DEN is the measured rock bulk density using logging instruments, in g / cm³. 3 A and B are model coefficients, which are 154.8 and 57.8 respectively in this example.

[0041] Specifically, the improved density method is: TOC 改进密度法 =KA / DEN-KB (2)

[0042] KA=1 / (1-1 / RHOG) (3)

[0043] KB = KA-1 (4)

[0044] TOC 改进密度法 TOC is calculated using the improved density method (Equation (2)), in %; DEN is the measured rock bulk density by the logging instrument, in g / cm³. 3 KA and KB are model coefficients, calculated according to the above formulas (3-4), and RHOG is the particle density, which is taken as 2.8 in this example.

[0045] Specifically, the empirical formula for uranium content is: TOC 铀含量法 =Uran*GF(5)

[0046] TOC 铀含量法 TOC is calculated using the empirical formula method for uranium content (Equation (5)), in %; Uran is the measured rock uranium content curve of the logging instrument, in ppm; G and F are model coefficients, which are 0.5 and 0 respectively in this example.

[0047] Specifically, the Passey(ΔLogR) method has a TOC. Passey法 =DeltaLogR*10 (2.297–0.1688*LOM) (6)

[0048] DeltaLogR = log 10 (LLD / LLD base )+0.02*(AC-AC base (7)

[0049] TOC Passey法 TOC is calculated using the Passey (ΔLogR) method (Equation (6)), in %; DeltaLogR is the model parameter, calculated according to the above equation (7); LLD is the measured depth lateral resistivity of the logging instrument, in ohm·m. base The resistivity baseline for non-shale well sections is expressed in ohms.m; in this example, it is taken as 5 ohms.m. AC represents the measured sonic transit time of the logging instrument, expressed in µs / ft. baseThe sonic transit time baseline for non-shale well sections is expressed in µs / ft, and in this example, it is taken as 90 µs / ft; LOM is the maturity index, and in this example, it is taken as 10.4.

[0050] S2. Comparing the TOC calculated using core analysis, the analysis of the total organic carbon content calculated by the four methods revealed that, due to the frequent overlapping of complex lithofacies in interbedded shale, the influence of lithology on the logging response is far greater than that of organic matter. At depths above 2776m, the reservoir generally contains argillaceous / shell-like and silty components, frequently overlapping clayey shale in thin layers of argillaceous limestone, argillaceous / shell-like limestone, argillaceous / shell-like shale, and silty shale. This causes an increase in reservoir bulk density (DEN), resistivity (LLD), and a decrease in the enrichment of radioactive uranium (URAN). The accuracy of the four TOC calculation results is low, especially around 2780-2785m, where the higher argillaceous / shell-like components (40-60% by mass) cause a sharp increase in DEN and LLD, and a sharp decrease in URAN, resulting in serious deviations in the TOC calculation results of all four groups. In contrast, the Passey method, by overlaying the acoustic transit time with the resistivity curve, offset some of the influence of lithofacies background, resulting in a curve that best matched the TOC from the core analysis. It demonstrated the highest accuracy among the four sets of results. Therefore, the Passey method's calculation results were chosen as the initial assumption for the total organic carbon (TOC) content. origin .

[0051] S3. To improve the accuracy of TOC calculation, it is necessary to perform lithofacies identification based on the reservoir logging response and extract shale lithofacies probability curves. In this embodiment, a machine identification method is selected, specifically Techlog, using the Ipsom software module. Figure 1 Six conventional logging responses from channels 2 to 4 were used to classify and identify the shale oil reservoir in the well using a 10*10 (100 neuron nodes) topological network and hierarchical clustering (HC) method, with minimization of intra-class variance as the criterion.

[0052] In this embodiment, four lithofacies were identified: massive clayey shale, laminated clayey shale with limestone / shell-bearing interlayers, silty shale, and argillaceous shell-bearing limestone. The probabilities of these four lithofacies are P1, P2, P3, and P4, respectively, satisfying the geological constraint P1+P2+P3+P4=1( Figure 1 (Course 9).

[0053] S4. Considering the different sedimentary environments of the above four lithofacies and the varying degrees of difficulty in enriching organic matter, construct an organic matter enrichment probability curve P for terrestrial interbedded shale formations using the above lithofacies probability curves. enrich ( Figure 1 (Question 10)

[0054] P enrich=1-(0×P1+0×P2+0.32×P3+0.35×P4)

[0055] In the formula, the coefficients for the four types of lithofacies sedimentary environments are taken as 0, 0, 0.32, and 0.35, respectively, obtained from core-calibrated well logging. This indicates that the sedimentary environments of the two types of non-claytic shale, silty shale and argillaceous shell limestone, present significant challenges in enriching organic matter (but organic matter enrichment is still possible; the greater the difficulty, the closer the coefficient is to 1). The P calculated by the above formula... enrich It can be used to characterize the probability of organic matter enrichment in the interlayered shale formations of the continental lacustrine basin in well FY10.

[0056] S5. Initial assumptions for the total organic carbon content (TOC) calculated in step S2. origin The probability curve of organic matter enrichment in the interbedded shale formation calculated in step S4 is P. enrich Calculate the total organic carbon (TOC) content of continental interbedded shale oil reservoirs. Figure 1 (Chapter 11):

[0057] TOC = TOC origin ×P enrich .

[0058] The TOC results calculated in this example show that the TOC calculation results for shale reservoirs with a depth of 2776m and containing ash / shell and silty components have been significantly improved. In particular, the TOC calculation results for the mudstone shell limestone interval around 2780-2785m, with a high ash / shell content (40-60% by mass), show a significant improvement in evaluation accuracy compared with core analysis. This demonstrates that the present invention has a significant advantage in solving the problem of evaluating the total organic carbon content of continental interlayered shale oil reservoirs.

[0059] Example 2:

[0060] S1. For the Jurassic continental shale oil reservoir of well XY3 in the Fuxing area of ​​eastern Sichuan Basin, collect well logging data and, based on the logging data ( Figure 2 (2-4), selecting bulk density (DEN), acoustic transit time (AC), and deep lateral resistivity (RD), the total organic carbon content was calculated using the density method, the modified density method, and the Passey (ΔLogR) method, respectively, and three sets of calculation results were obtained. Figure 2 (5-7 dishes), please note that Figure 2 The curves of RD and RS in the middle basically overlap;

[0061] Specifically, the density method is: TOC 密度法 =A / DEN-B(1)

[0062] TOC 密度法TOC is calculated using the density method, in %; DEN is the measured rock bulk density using logging instruments, in g / cm³. 3 A and B are model coefficients, which are 154.8 and 57.8 respectively in this example.

[0063] Specifically, the improved density method is: TOC 改进密度法 =KA / DEN-KB (2)

[0064] KA=1 / (1-1 / RHOG) (3)

[0065] KB = KA-1 (4)

[0066] TOC 改进密度法 TOC is calculated using the improved density method (Equation (2)), in %; DEN is the measured rock bulk density by the logging instrument, in g / cm³. 3 KA and KB are model coefficients, calculated according to the above formulas (3-4), and RHOG is the particle density, which is taken as 2.8 in this example.

[0067] Specifically, the empirical formula for uranium content is: TOC 铀含量法 =Uran*GF(5)

[0068] Specifically, the Passey(ΔLogR) method has a TOC. Passey法 =DeltaLogR*10 (2.297–0.1688*LOM) (6)

[0069] DeltaLogR = log 10 (RD / RD base )+0.02*(AC-AC base (7)

[0070] TOC Passey法 TOC is calculated using the Passey (ΔLogR) method (Equation (6)), in %; DeltaLogR is the model parameter, calculated according to the above equation (7); RD is the measured depth lateral resistivity of the logging instrument, in ohm·m. base The resistivity baseline for non-shale well sections is expressed in ohms.m; in this example, it is taken as 5 ohms.m. AC represents the measured sonic transit time of the logging instrument, expressed in µs / ft. base The sonic transit time baseline for non-shale well sections is expressed in µs / ft, and in this example, it is taken as 90 µs / ft; LOM is the maturity index, and in this example, it is taken as 10.4.

[0071] S2. Comparing the TOC calculated using core analysis, the analysis of the total organic carbon content calculated by the three methods revealed that, due to the frequent overlapping of complex lithofacies in interbedded shale, the influence of lithology on the logging response is far greater than that of organic matter. At depths above 2930m, the reservoir generally contains calcareous / shell-like and silty components, frequently overlapping clayey shale in the form of thin interlayers of argillaceous limestone, argillaceous shell-like limestone, calcareous / shell-like shale, and silty shale. This causes an increase in reservoir bulk density (DEN) and resistivity (LLD), resulting in low accuracy in all three TOC calculations. Particularly around 2940m, the higher calcareous / shell-like components (40-60% by mass) cause a sharp increase in both DEN and LLD, leading to significant deviations in all four TOC calculations. In contrast, the Passey method, by overlaying the acoustic transit time with the resistivity curve, offset some of the influence of lithofacies background, resulting in a curve that best matches the TOC obtained from core analysis. Among the three sets of results, its accuracy was relatively good. Therefore, the calculation results from the Passey method were chosen as the initial assumption for the total organic carbon (TOC) content. origin .

[0072] S3. To improve the accuracy of TOC calculation, it is necessary to perform lithofacies identification based on the reservoir logging response and extract shale lithofacies probability curves. In this embodiment, a machine identification method is selected, specifically the Techlog software and the Ipsom module. Figure 2 Six conventional logging responses from channels 2 to 4 were used to classify and identify the shale oil reservoir in the well using a 10*10 (100 neuron nodes) topological network and hierarchical clustering (HC) method with minimization of intra-class variance as the criterion.

[0073] In this embodiment, four lithofacies were identified: massive clayey shale, laminated clayey shale with limestone / shell-like interlayers, silty shale, and argillaceous shell-like limestone. The probabilities of these four lithofacies are P1, P2, P3, and P4, respectively, satisfying the geological constraint P1 + P2 + P3 + P4 = 1 (…). Figure 2 (Course 8).

[0074] S4. Considering the different sedimentary environments of the above four lithofacies and the varying degrees of difficulty in enriching organic matter, construct an organic matter enrichment probability curve P for terrestrial interbedded shale formations using the above lithofacies probability curves. enrich ( Figure 2 (Course 9)

[0075] P enrich =1-(0×P1+0×P2+0.32×P3+0.35×P4)

[0076] In the formula, the four lithofacies sedimentary environments are represented by 0, 0, 0.32, and 0.35, respectively, indicating that the sedimentary environments of the two types of non-claytic shale, silty shale and argillaceous shell limestone, present significant challenges in enriching organic matter (although organic matter enrichment is still possible; the greater the difficulty, the closer the coefficient is to 1). The P calculated by the above formula... enrich It can be used to characterize the probability of organic matter enrichment in the interlayered shale formations of the continental lacustrine basin in well XY3.

[0077] S5. Initial assumptions for the total organic carbon content (TOC) calculated in step S2. origin The probability curve of organic matter enrichment in the interbedded shale formation calculated in step S4 is P. enrich Calculate the total organic carbon (TOC) content of continental interbedded shale oil reservoirs. Figure 2 (Course 10):

[0078] TOC = TOC origin ×P enrich .

[0079] The TOC results calculated in this example show that the TOC calculation results for reservoirs deeper than 2930m containing ash / shell and silty components have been significantly improved. In particular, the TOC calculation results for the mudstone shell limestone interval around 2940m, with a high ash / shell content (40-60% by mass), show a significant improvement in evaluation accuracy compared with core analysis. This demonstrates that the present invention has a significant advantage in solving the problem of evaluating the total organic carbon content of continental interlayered shale oil reservoirs.

[0080] Example 3:

[0081] S1. For the Jurassic continental shale reservoir of well LY1 in the Langzhong area of ​​northern Sichuan Basin, collect well logging data and, based on the logging data ( Figure 3 (2-4), selecting bulk density (DEN), acoustic transit time (AC), and deep lateral resistivity (RD), the total organic carbon content was calculated using the density method, the modified density method, the Passey (ΔLogR) method, and the empirical formula method for acoustic transit time, respectively, and four sets of calculation results were obtained. Figure 3 (5-8 courses);

[0082] Specifically, the density method is: TOC 密度法 =A / DEN-B(1)

[0083] TOC 密度法 TOC is calculated using the density method, in %; DEN is the measured rock bulk density using logging instruments, in g / cm³. 3 A and B are model coefficients, which are 154.8 and 57.8 respectively in this example.

[0084] Specifically, the improved density method is: TOC 改进密度法=KA / DEN-KB (2)

[0085] KA=1 / (1-1 / RHOG) (3)

[0086] KB = KA-1 (4)

[0087] TOC 改进密度法 TOC is calculated using the improved density method (Equation (2)), in %; DEN is the measured rock bulk density by the logging instrument, in g / cm³. 3 KA and KB are model coefficients, calculated according to the above formulas (3-4), and RHOG is the particle density, which is taken as 2.8 in this example.

[0088] Specifically, the empirical formula for uranium content is: TOC 铀含量法 =Uran*GF(5)

[0089] Specifically, the Passey(ΔLogR) method has a TOC. Passey法 =DeltaLogR*10 (2.297–0.1688*LOM) (6)

[0090] DeltaLogR = log 10 (RD / RD base )+0.02*(AC-AC base (7)

[0091] TOC Passey法 TOC is calculated using the Passey (ΔLogR) method (Equation (6)), in %; DeltaLogR is the model parameter, calculated according to the above equation (7); RD is the measured depth lateral resistivity of the logging instrument, in ohm·m. base The resistivity baseline for non-shale well sections is expressed in ohms.m; in this example, it is taken as 5 ohms.m. AC represents the measured sonic transit time of the logging instrument, expressed in µs / ft. base The sonic transit time baseline for non-shale well sections is expressed in µs / ft, and in this example, it is taken as 90 µs / ft; LOM is the maturity index, and in this example, it is taken as 10.4.

[0092] Specifically, the empirical formula for acoustic time difference is TOC. 声波法 =AC / HI(8)

[0093] TOC 声波法 TOC is calculated using the empirical formula method of acoustic transit time (Equation (8)), in %; AC is the measured rock acoustic transit time of the logging instrument, in us / ft; H and I are model coefficients, which are 11 and 5 respectively in this example.

[0094] S2. Comparing the TOC calculated by core analysis with the total organic carbon content calculated by the four methods, it was found that due to the frequent overlapping of complex lithofacies in the interbedded shale, the influence of lithology on the logging response is far greater than that of organic matter. This shale reservoir generally contains silty sandstone, with silty (fine) sandstone frequently overlapping silty clayey shale in thin interlayers, causing an increase in reservoir bulk density (DEN) and resistivity (LLD). The accuracy of the four TOC calculation results was relatively low. In contrast, the curve obtained by the sonic transit time empirical formula method showed the best match with the core analysis TOC, and its accuracy was relatively good among the four results. Therefore, the calculation result of the sonic transit time empirical formula method was selected as the initial assumption for the total organic carbon content (TOC). origin .

[0095] S3. To improve the accuracy of TOC calculation, it is necessary to perform lithofacies identification based on the reservoir logging response and extract shale lithofacies probability curves. In this embodiment, a machine identification method is selected, specifically the Techlog software and the Ipsom module. Figure 3 Five conventional logging responses (GR, CNL, AC, DEN, and RD) from channels 2 to 4 were used. A 13*13 topological network (169 neurons) was employed, and fuzzy clustering (FC) was used to classify and identify the lithofacies of the shale gas reservoir in this well.

[0096] In this embodiment, two lithofacies types were identified: silty clayey shale and silty (fine) sandstone. The probabilities of these two lithofacies types are P1 and P2, respectively, satisfying the geological constraint P1 + P2 = 1. Figure 3 (Course 9).

[0097] S4. Considering the different sedimentary environments of the two types of lithofacies mentioned above, and the varying degrees of difficulty in enriching organic matter, an organic matter enrichment probability curve P for terrestrial interbedded shale strata is constructed using the above lithofacies probability curves. enrich ( Figure 3 (Question 10)

[0098] P enrich = 1 - (0.1 × P1 + 0.6 × P2)

[0099] In the formula, the coefficients for the two types of lithofacies sedimentary environments are 0.1 and 0.6, respectively, indicating that the sedimentary environment in which silt (fine) sandstone is located presents greater difficulty in enriching organic matter (but it is still possible to enrich organic matter; the greater the difficulty, the closer the coefficient is to 1). The P calculated by the above formula... enrich It can be used to characterize the probability of organic matter enrichment in the terrestrial lacustrine interlayered shale formations of well LY1.

[0100] S5. Initial assumptions for the total organic carbon content (TOC) calculated in step S2. origin The probability curve of organic matter enrichment in the interbedded shale formation calculated in step S4 is P.enrich Calculate the total organic carbon (TOC) content of terrestrial interlayer shale gas reservoirs. Figure 3 (Chapter 11):

[0101] TOC = TOC origin ×P enrich .

[0102] The TOC results calculated in this example show that the accuracy of the total organic carbon content evaluation is improved in all well sections with silt (fine) sandstone development, effectively correcting the lithological and lithofacies background. This demonstrates that the present invention has significant advantages in solving the problem of evaluating the total organic carbon content of continental interlayered shale oil and gas reservoirs.

[0103] In summary, the method for determining the total organic carbon content of continental shale oil and gas reservoirs provided by this invention has significant advantages in solving the problem of evaluating the total organic carbon content of continental interlayered shale oil reservoirs.

[0104] 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 determining the total organic carbon content of continental shale oil and gas reservoirs, characterized in that, Includes the following steps: Step S1: Based on the logging information of continental shale oil and gas reservoirs, conduct the first logging evaluation of total organic carbon content in shale oil and gas reservoirs and obtain the evaluation results of total organic carbon content; Step S2: Use the organic carbon content evaluation results obtained in Step S1 as the initial assumption for the total organic carbon content (TOC). origin ; Step S3: Based on the logging response of continental shale oil and gas reservoirs, conduct lithofacies identification, extract the lithofacies from the reservoir, and obtain the lithofacies probability curve. P1+P2+…+P i +…=1(I), In equation (Ⅰ), P1, P2, ..., P i , ... represent the extracted lithofacies probabilities; Step S4: Construct the organic matter enrichment probability curve P of interbedded shale strata using shale lithofacies probability curves. enrich :P enrich =1-(a×P1+b×P2+c×P3+…+i×P i +…)(II) In the formula, a, b, c, ..., i are selected from 0 to 1 respectively; Step S5: Utilize the TOC from step S2 origin P in step S4 enrich The final total organic carbon (TOC) content of the shale oil and gas reservoir was obtained as follows: TOC=TOC origin ×P enrich 。 2. The method according to claim 1, characterized in that, The terrestrial shale is terrestrial lacustrine basin shale.

3. The method according to claim 2, characterized in that, The terrestrial lacustrine shale includes interbedded shale formations composed of clayey, calcareous, crustaceous, and sandy components.

4. The method according to any one of claims 1-3, characterized in that, Step S1 includes the following sub-steps: Step S1.1: Use one or more methods to conduct TOC logging evaluation of shale oil and gas reservoirs and obtain one or more total organic carbon content evaluation results; Step S1.2: Select the most applicable result from several total organic carbon content evaluation results as the initial assumption for the total organic carbon content (TOC) of continental shale oil and gas reservoirs. origin .

5. The method according to claim 4, characterized in that, The methods for conducting TOC logging evaluation of rock oil and gas reservoirs in step S1 include, but are not limited to, at least one of the following: radioactive gamma method, radioactive uranium content method, density method, Passey's ΔLogR method, and sonic transit time empirical formula method.

6. The method according to any one of claims 1-3, characterized in that, The logging data for continental shale oil and gas reservoirs in step S1 include, but are not limited to: bulk density, uranium content in natural gamma spectrum, sonic transit time, deep directional resistivity, compensated neutron, natural gamma, shallow lateral resistivity, microsphere focused resistivity, and uranium-free gamma.

7. The method according to any one of claims 1-3, characterized in that, The methods for lithofacies identification in step S3 include, but are not limited to, machine learning methods or intersection plot methods.

8. The method according to claim 7, characterized in that, The machine learning method described above utilizes conventional well logging responses and employs methods including, but not limited to, clustering or decision tree methods to perform lithofacies classification and identification.

9. The method according to claim 8, characterized in that, The clustering methods include fuzzy clustering methods or hierarchical clustering methods.

10. The application of the method for determining the total organic carbon content of continental shale oil and gas reservoirs according to any one of claims 1-9 in the quantitative calculation of the total organic carbon content in continental shale.

11. The application according to claim 10, characterized in that, Application of methods for determining the total organic carbon content of continental shale oil and gas reservoirs in the quantitative calculation of total organic carbon content in continental lacustrine basin shale.

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