Improved method for calculating toc in lacustrine sedimentary background

Abstract

The application provides an improved TOC calculation method in a lacustrine sediment background, comprising the following steps: step 1, establishing a relationship between acoustic waves and resistivity in a research well which is not affected by lithology; step 2, screening resistivity values and acoustic time difference values of a section affected by ash content in the well, making an intersection of RT and AC affected by ash content, and determining a centroid; step 3, screening resistivity values and acoustic time difference values of a section affected by argillaceous matter in the well, making an intersection of RT and AC affected by argillaceous matter, and determining a centroid; step 4, excluding the influence of resistivity on two logging curves; step 5, excluding the influence of acoustic time difference on the two logging curves; and step 6, calculating TOC values after lithology correction. The improved TOC calculation method in the lacustrine sediment background eliminates the change of lithology on acoustic time difference and resistivity curve form, realizes accurate prediction of the organic carbon content of a shale oil reservoir, and provides guidance for the development of a shale oil sweet spot.

Description

Improved TOC Calculation Method in Lacustrine Sedimentary Background Technical Field

[0001] This invention relates to the field of oilfield development technology, and in particular to an improved method for calculating TOC in a lacustrine sedimentary background. Background Technology

[0002] Total organic carbon (TOC) content is a key index for evaluating shale oil reservoirs. Accurately obtaining the organic matter content (TOC) is the basis for comprehensive geological evaluation of shale oil reservoirs and is one of the key parameters for solving shale oil reservoir problems.

[0003] Currently, for shale oil reservoirs in marine sedimentary settings, we propose three common methods for calculating TOC: ① Single-factor method: logging curves such as natural gamma, sonic transit time, density, or resistivity have response characteristics to organic matter content. Correlation analysis is performed between sensitive logging parameters and TOC, such as the Jinqiang model (2002), a single-factor calculation model for density, sonic, and resistivity; ② Multiple regression analysis method: there are multiple logging sensitive parameters related to TOC, and empirical formulas are calculated using the dependent variables of multiple logging parameters; ③ ΔlogR method: Passey et al. (1990) proposed a method to identify formations with high organic matter content (or density logging curves, neutron porosity logging curves) by using the amplitude difference between sonic and resistivity curves, i.e., the ΔlogR method.

[0004] Previous studies on the sedimentary environment of source rocks in the Jiyang Depression have revealed that the sedimentary environment is terrestrial deep-lake or semi-deep-lake sedimentary, with the main stratigraphic units located in the lower Sha-3 and upper Sha-4, characterized by complex structures, large vertical span, and multiple sweet spots. The two sets of source rocks in the upper Sha-4 and lower Sha-3 sub-members are characterized by six features: deep burial (2800-4300m), low maturity, high oil content, thick layers, high pressure coefficient, and well-developed fractures. These findings have laid the resource foundation for shale oil exploration in the Jiyang Depression.

[0005] Numerous methods exist for calculating organic carbon (OC) content using well logging curves, but the commonly used ΔlogR method is currently only applicable to marine strata or the typical Huxiang strata with high TOC in the lower sub-member of the Sha-3 shale oil reservoir in the Jiyang Depression. For the deep-water Huxiang strata with low TOC in the upper sub-member of the Sha-4 shale oil reservoir in the Jiyang Depression, the lithology is complex and variable due to various geological factors. The ΔlogR curve is not solely due to the increased sonic transit time and resistivity in mature source rocks, leading to a larger gap between the two curves. The ΔlogR curve is controlled by changes in lithological curve characteristics, thus limiting the application of the traditional ΔlogR method.

[0006] Chinese patent application CN201410101705.2 discloses a quantitative method for calculating paleowater depth in lacustrine sediments. This method includes: Step 1, selecting lakes similar to the study area; Step 2, establishing a quantitative relationship between TOC (Total Organic Carbon) of lakebed sediments and water depth for the selected lakes; Step 3, conducting TOC tests on mudstone in the study area and statistically analyzing the thickness of the target layer; Step 4, establishing a quantitative relationship between paleowater depth data and stratigraphic thickness; and Step 5, drawing a planar distribution map of paleowater depth. This quantitative method for calculating paleowater depth in lacustrine sediments further improves paleowater depth reconstruction methods and provides new research ideas and technical means for studying the evolution of paleolar sedimentary processes and predicting various types of lacustrine sand bodies.

[0007] Chinese patent application CN202010010048.6 discloses a method and system for calculating and characterizing the TOC and oil saturation of shale reservoirs. The method includes: obtaining organic carbon ΔlogR from the parameter curve of the shale reservoir and calculating the TOC of the shale reservoir based on organic carbon ΔlogR; fitting the TOC to the measured TOC core data of the shale reservoir to obtain a model TOCmn1; superimposing lithological parameter attributes onto the model TOCmn1 to obtain an improved model TOCmn2; and calculating and characterizing the oil saturation of the shale reservoir based on the improved model TOCmn2 and / or organic carbon ΔlogR. This invention overcomes the negative impact of lithological correction factors by incorporating them; for traditional oil saturation models that are only applicable to pure sandstone formations dominated by intergranular porosity, the organic matter component is introduced for neutralization and adjustment, making it suitable for calculating the oil saturation of shale reservoirs.

[0008] Chinese patent application CN201910984171.5 relates to a method and system for calculating formation organic carbon content. The method may include: determining the relationship between formation lithology and electrical properties; selecting two lithology-sensitive logging data points; normalizing the lithology-sensitive logging data and then calculating a lithology correction factor; calculating the superposition coefficients corresponding to different formations; calculating the correction ΔLogR based on the superposition coefficients and the lithology correction factor; establishing an organic carbon content calculation model based on the correction ΔLogR; and calculating the formation organic carbon content based on the organic carbon content calculation model. This invention calculates the organic carbon content in formations by establishing a lithology correction factor to correct ΔLogR. The calculation results are highly accurate, highly operable, and widely applicable, providing reliable technical support for the comprehensive evaluation of formation source rocks.

[0009] The existing technologies described above are quite different from the present invention and have failed to solve the technical problem we want to solve. Therefore, we have invented a new and improved TOC calculation method in the lacustrine sedimentary background. Summary of the Invention

[0010] The purpose of this invention is to provide an improved TOC calculation method in a lacustrine sedimentary background that eliminates the influence of lithology on the changes in acoustic transit time and resistivity curve morphology, thereby enabling accurate prediction of organic carbon content in shale oil reservoirs.

[0011] The objective of this invention can be achieved through the following technical measures: an improved TOC calculation method in a lacustrine sedimentary background, comprising:

[0012] Step 1: Establish the relationship between acoustic waves and resistivity in the well, which is unaffected by lithology.

[0013] Step 2: Screen the resistivity and sonic transit time values ​​of the well's strata affected by ash, perform the intersection of deep resistivity (RT) and sonic transit time (AC) affected by ash, and determine the centroid.

[0014] Step 3: Screen the resistivity and sonic transit time values ​​of the well's mud-affected sections, perform the intersection of RT and AC values ​​affected by mud, and determine the centroid.

[0015] Step 4: Eliminate the influence of resistivity on the two logging curves;

[0016] Step 5: Eliminate the influence of acoustic transit time on the two logging curves;

[0017] Step 6: Calculate the TOC value after lithology correction.

[0018] The objective of this invention can also be achieved through the following technical measures:

[0019] In step 1, based on core and logging data, select the lithology-independent strata of the shale oil well, statistically analyze the resistivity and sonic transit time of the strata, and establish the relationship between sonic waves and resistivity in the well that are not affected by lithology.

[0020] In step 2, based on core and logging data, a grayish layer is selected, and the resistivity and sonic transit time values ​​of the layer affected by the gray matter in the well are screened. The intersection of RT and AC affected by the gray matter is calculated, and the centroid is determined.

[0021] In step 2, the sonic transit time curve and resistivity curve of the limestone section are superimposed using the traditional ΔlogR method. At this time, the sonic transit time increment is small, basically zero, while the resistivity increases significantly, resulting in an overestimation of the calculated TOC. The increased TOC is greatly affected by the limestone. Therefore, it is necessary to reduce the resistivity centroid of the increased part to the relationship between RT and AC, which is not affected by lithology.

[0022] In step 3, based on core and logging data, select the muddy sections, screen the resistivity and sonic transit time values ​​of the muddy sections of the well, perform the intersection of RT and AC values ​​affected by muddy materials, and determine the centroid.

[0023] In step 3, the acoustic transit time curve and resistivity of the mudstone section are superimposed using the traditional ΔlogR method. At this time, the resistivity increment is small and basically zero, while the acoustic transit time increases significantly. The increased TOC part is greatly affected by the mudstone. Therefore, it is necessary to reduce the centroid of the increased acoustic transit time to the relationship between RT and AC, which is not affected by the lithology.

[0024] In step 4, organic-containing intervals that are significantly affected by ash are screened, and the affected acoustic transit time AC and resistivity RT values ​​are statistically analyzed. The relationship between acoustic waves and resistivity in the well that are not affected by lithology is established, and the increased resistivity is corrected to return to the value that is not affected by lithology. The influence of resistivity on the two logging curves is eliminated, thereby achieving an accurate calculation of the total organic carbon content of the shale interval.

[0025] In step 4, the centroid affected by ash is restored to the relationship unaffected by lithology, thus eliminating the influence of resistivity on the two logging curves, where:

[0026]

[0027] RT 校正 =RT-ΔRT 增量

[0028] ΔlogR=lg(ΔRT 校正 / RT 基线 )+K(Δt-Δt 基线 )

[0029] Where: ΔRT 增量 The resistivity increases due to the influence of ash, in Ω·m; RT 校正 RT is the gray mass correction factor, Ω·m; 标准 RT represents the resistivity unaffected by lithology, in Ω·m; RT represents the resistivity calculated with errors using the traditional ΔlogR method, in Ω·m; m represents the number of organic-containing intervals affected by ash. 基线 The resistivity of the baseline in non-oil-generating claystone corresponds to the baseline value; Δt is the measured acoustic transit time; Δt 基线 The acoustic transit time value corresponding to the baseline value in non-oil-generating claystone.

[0030] In step 5, organic-containing intervals that are significantly affected by silt are screened, and the affected acoustic transit time AC and resistivity RT values ​​are statistically analyzed. The relationship between acoustic transit time and resistivity in the well that is not affected by lithology is established, and the increased acoustic transit time is corrected to return to the value that is not affected by lithology. The influence of acoustic transit time on the two logging curves is eliminated, thereby achieving an accurate calculation of the total organic carbon content of the shale interval.

[0031] In step 5, the centroid affected by the clay content is restored to the relationship unaffected by lithology, thus eliminating the influence of sonic transit time on the two logging curves, where:

[0032]

[0033] AC 校正 =AC-ΔAC 增量

[0034] ΔlogR=lg(ΔRT / RT 基线 )+K(ΔAC 校正 -ΔAC 基线 )

[0035] Where: ΔAC 增量 The increased acoustic transit time due to the influence of mud quality, in μs / ft; AC 校正 The clay quality correction factor is in μs / ft; AC 标准 RT is the sonic transit time unaffected by lithology, in μs / ft; AC is the sonic transit time calculated with errors using the traditional ΔlogR method, in μs / ft; n is the number of organic-containing intervals affected by argillaceous material; 基线 The resistivity of the baseline in non-oil-generating claystone corresponds to the baseline value; ΔRT is the measured resistivity value; ΔAC 基线 The acoustic transit time value corresponding to the baseline value in non-oil-generating claystone.

[0036] In step 6, the lithology-corrected TOC value is calculated according to the ΔlogR formula described in steps 4 and 5.

[0037] This invention presents an improved TOC calculation method in lacustrine sedimentary settings, suitable for sedimentary environments of terrestrial deep or semi-deep lacustrine deposits with complex lithology. Traditional ΔlogR methods are limited, therefore this improved TOC calculation method in lacustrine sedimentary settings is proposed. It is highly applicable and has certain generalizability, providing important technical support for shale oil exploration and development. This invention improves the evaluation results of high TOC content in high-muddy or calcareous source rocks in lacustrine sedimentary settings using the ΔlogR method. It ensures that the calculated organic carbon content during hydrocarbon generation is not controlled by lithology, eliminating the influence of lithology on changes in acoustic transit time and resistivity curve morphology. Based on the principles and geological significance of the ΔlogR model, it achieves accurate prediction of organic carbon content in shale oil reservoirs, providing guidance for the development of shale oil's "sweet spots."

[0038] Compared with the prior art, the present invention has the following advantages:

[0039] This patent proposes an improved ΔlogR method for calculating TOC in a lacustrine sedimentary background. This method improves the evaluation results of high TOC content in high-muddy or calcareous source rocks in a lacustrine sedimentary background. It makes the calculated organic carbon content during hydrocarbon generation independent of lithology, eliminating the influence of lithology on the changes in acoustic transit time and resistivity curve shape. Based on the principle and geological significance of the ΔlogR model, it enables accurate prediction of organic carbon content in shale oil reservoirs, providing guidance for the development of the "sweet spot" of shale oil. Attached Figure Description

[0040] Figure 1 is a flowchart of a specific embodiment of the improved TOC calculation method in a lacustrine sedimentary background of the present invention;

[0041] Figure 2 is a schematic diagram illustrating the various features on the ΔlogR overlay diagram according to a specific embodiment of the present invention;

[0042] Figure 3 is a schematic diagram of the increase in TOC at the limestone part in the ΔlogR superimposed diagram of a specific embodiment of the present invention.

[0043] Figure 4 is a schematic diagram of the increase in TOC at the mudstone part in the ΔlogR superimposed diagram of a specific embodiment of the present invention.

[0044] Figure 5 is a schematic diagram of the logging curve and measured TOC content of well C113-X2, which is heavily affected by ash, according to a specific embodiment of the present invention.

[0045] Figure 6 is a schematic diagram of the logging curve and measured TOC content of well LY1, which is heavily affected by mud quality, according to a specific embodiment of the present invention.

[0046] Figure 7 is a schematic diagram of the TOC calculation results after lithology correction of the stratum affected by ash in a specific embodiment of the present invention;

[0047] Figure 8 is a schematic diagram of the TOC calculation results after lithology correction of the muddy layer according to a specific embodiment of the present invention.

[0048] Figure 9 is a TOC comparison chart of well LY1 after excluding the influence of clay in a specific embodiment of the present invention;

[0049] Figure 10 is a TOC comparison chart of well C113-2 after excluding the influence of ash matter, according to a specific embodiment of the present invention. Detailed Implementation

[0050] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0051] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, and / or combinations thereof.

[0052] The improved TOC calculation method in the lacustrine sedimentary background of this invention includes the following steps:

[0053] Step 1: Based on core and logging data, select the lithology-independent strata of the shale oil well, statistically analyze the resistivity and sonic transit time of the strata, and establish the relationship between sonic waves and resistivity in the well that are not affected by lithology.

[0054] Step 2: Based on core and logging data, select grayish intervals, screen the resistivity and sonic transit time values ​​of the intervals affected by gray matter in the well, perform the intersection of RT and AC affected by gray matter, and determine the centroid.

[0055] Step 3: Based on core and logging data, select the muddy sections, screen the resistivity and sonic transit time values ​​of the muddy sections of the well, perform the intersection of RT and AC affected by muddy materials, and determine the centroid.

[0056] Step 4: Return the centroid affected by the ash mass to the relationship that is not affected by lithology, thus eliminating the influence of resistivity on the two logging curves.

[0057] Step 5: Return the centroid affected by the mud to the relationship that is not affected by lithology, thus eliminating the influence of sonic transit time on the two logging curves.

[0058] Step 6: Calculate the lithology-corrected TOC value according to the ΔlogR formula described in Steps 4 and 5.

[0059] The following are several specific embodiments of the application of the present invention.

[0060] Example 1

[0061] In a specific embodiment 1 of the present invention, the improved TOC calculation method in a lacustrine sedimentary background includes the following steps:

[0062] In step 1, based on core and logging data, the lithology-independent strata of the shale oil well are selected, and the resistivity and sonic transit time of the strata are statistically analyzed to establish the relationship between sonic waves and resistivity in the well that are not affected by lithology.

[0063] In step 2, based on core and logging data, a grayish section is selected, and the resistivity and sonic transit time values ​​of the section affected by the gray matter in the well are screened. The intersection of deep resistivity (RT) and sonic transit time (AC) affected by the gray matter is calculated, and the centroid is determined.

[0064] In shale oil reservoirs, immature source rocks in the limestone section typically exhibit a reverse superposition of acoustic and resistivity curves using the traditional ΔlogR method, albeit with a small amplitude. However, immature source rocks in the limestone section actually possess low natural gamma, low acoustic transit time, and high resistivity response characteristics. When the acoustic transit time and resistivity curves of the limestone section are superimposed using the traditional ΔlogR method, the acoustic transit time increment is small, essentially zero, while the resistivity increases significantly, resulting in an overestimation of the calculated TOC. It can be demonstrated that the increased TOC is significantly influenced by the limestone content. Therefore, it is necessary to reduce the resistivity centroid of the increased portion to a relationship between RT and AC that is unaffected by lithology.

[0065] In step 3, based on core and logging data, the muddy sections are selected, the resistivity and sonic transit time values ​​of the muddy sections of the well are screened, the RT and AC values ​​affected by the muddy sections are intersected, and the centroid is determined.

[0066] The TOC logging response characteristics of shale oil reservoirs affected by silt content are the opposite of those unaffected by lithology. In mudstone sections of the source rock, there are high natural gamma rays, high sonic transit time, and low resistivity response characteristics. Using the traditional ΔlogR method to overlay the sonic transit time curve and resistivity curve of the mudstone section, the resistivity increment is small, essentially zero, while the sonic transit time increases significantly. This demonstrates that the increased TOC portion is largely influenced by silt content. Therefore, it is necessary to reduce the centroid of the increased sonic transit time to the relationship between RT and AC, which is unaffected by lithology.

[0067] In step 4, the centroid affected by the ash is brought back to the relationship that is not affected by lithology, thus eliminating the influence of resistivity on the two logging curves.

[0068] Shale oil reservoirs are significantly affected by ash deposits, and improvements should be made to reservoir sections affected by ash. In heavily ash-affected sections, the resistivity in the superimposed portion of acoustic and resistivity data is increased due to ash, thus requiring correction of the increased resistivity portion. Organically contained sections significantly affected by ash are screened, and the affected acoustic transit time (AC) and resistivity (RT) values ​​are statistically analyzed. A relationship between acoustic and resistivity unaffected by lithology is established in the study well. The increased resistivity is corrected to return to its lithology-independent value, eliminating the influence of resistivity on the two logging curves, thereby achieving accurate calculation of the total organic carbon content of shale sections.

[0069] The formula for predicting TOC using the ΔlogR method, which is unaffected by gray matter, is as follows:

[0070]

[0071] RT 校正 =RT-ΔRT 增量

[0072] ΔlogR=lg(ΔRT 校正 / RT 基线 )+K(Δt-Δt 基线 )

[0073] Where: ΔRT 增量 The resistivity increases due to the influence of ash, in Ω·m; RT 校正 RT is the gray mass correction factor, Ω·m; 标准 RT represents the resistivity unaffected by lithology, in Ω·m; RT represents the resistivity calculated with errors using the traditional ΔlogR method, in Ω·m; m represents the number of organic-containing intervals affected by ash. 基线 The resistivity of the baseline in non-oil-generating claystone corresponds to the baseline value; Δt is the measured acoustic transit time; Δt 基线 The acoustic transit time value corresponding to the baseline value in non-oil-generating claystone.

[0074] In step 5, the centroid affected by the mud is brought back to the relationship that is not affected by lithology, thus eliminating the influence of sonic transit time on the two logging curves.

[0075] Shale oil reservoirs are heavily affected by clay content, and improvements should be made to reservoir sections affected by clay. In reservoir sections heavily affected by clay, the resistivity in the superimposed portion of acoustic and resistivity data is increased due to the influence of ash, thus requiring correction of the increased acoustic transit time. Similarly, organic-containing sections significantly affected by clay are screened, and the affected acoustic transit time AC and resistivity RT values ​​are statistically analyzed. A relationship between acoustic and resistivity unaffected by lithology is established in the study well. The increased acoustic transit time is corrected to return to values ​​unaffected by lithology, eliminating the influence of acoustic transit time on the two logging curves, thereby achieving accurate calculation of the total organic carbon content of shale sections.

[0076] The formula for predicting TOC using the ΔlogR method, which is unaffected by mud quality, is as follows:

[0077]

[0078] AC 校正 =AC-ΔAC 增量

[0079] ΔlogR=lg(ΔRT / RT 基线 )+K(ΔAC 校正 -ΔAC 基线 )

[0080] Where: ΔAC 增量 The increased acoustic transit time due to the influence of mud quality, in μs / ft; AC 校正 The clay quality correction factor is in μs / ft; AC 标准 RT is the sonic transit time unaffected by lithology, in μs / ft; AC is the sonic transit time calculated with errors using the traditional ΔlogR method, in μs / ft; n is the number of organic-containing intervals affected by argillaceous material; 基线 The resistivity of the baseline in non-oil-generating claystone corresponds to the baseline value; ΔRT is the measured resistivity value; ΔAC 基线 The acoustic transit time value corresponding to the baseline value in non-oil-generating claystone.

[0081] In step 6, the lithology-corrected TOC value is calculated according to the ΔlogR formula described in steps 4 and 5.

[0082] Example 2

[0083] In a specific embodiment 2 of the present invention, this section uses the target formation (Shale Oil Stratification 4, Upper Shale Oil Stratification 1) of the C113-X2 shale oil well in the Boxing Depression of the Jiyang Depression and the LY1 shale oil well in the Lijin Depression as examples to illustrate the specific implementation of the method.

[0084] As shown in Figure 1, the improved TOC calculation method in a lacustrine sedimentary background includes the following steps:

[0085] In step 1, based on core and logging data, select the lithology-independent strata of the shale oil well, statistically analyze the resistivity and sonic transit time of the strata, and establish the relationship between sonic waves and resistivity in the well that are not affected by lithology.

[0086] In step 2, based on core and logging data, a grayish layer is selected, and the resistivity and sonic transit time values ​​of the layer affected by the gray matter in the well are screened. The intersection of RT and AC affected by the gray matter is calculated, and the centroid is determined.

[0087] In shale oil reservoirs, immature source rocks in the limestone section typically exhibit a reverse superposition of acoustic and resistivity curves using the traditional ΔlogR method, albeit with a small amplitude. However, immature source rocks in the limestone section actually possess low natural gamma, low acoustic transit time, and high resistivity response characteristics. When the acoustic transit time and resistivity curves of the limestone section are superimposed using the traditional ΔlogR method, the acoustic transit time increment is small, essentially zero, while the resistivity increases significantly, resulting in an overestimation of the calculated TOC. It can be demonstrated that the increased TOC is significantly influenced by the limestone content. Therefore, it is necessary to reduce the resistivity centroid of the increased portion to a relationship between RT and AC that is unaffected by lithology.

[0088] In step 3, based on core and logging data, select the muddy sections, screen the resistivity and sonic transit time values ​​of the muddy sections of the well, perform the intersection of RT and AC values ​​affected by muddy materials, and determine the centroid.

[0089] The TOC logging response characteristics of shale oil reservoirs affected by silt content are the opposite of those unaffected by lithology. In mudstone sections of the source rock, there are high natural gamma rays, high sonic transit time, and low resistivity response characteristics. Using the traditional ΔlogR method to overlay the sonic transit time curve and resistivity curve of the mudstone section, the resistivity increment is small, essentially zero, while the sonic transit time increases significantly. This demonstrates that the increased TOC portion is largely influenced by silt content. Therefore, it is necessary to reduce the centroid of the increased sonic transit time to the relationship between RT and AC, which is unaffected by lithology.

[0090] In step 4, the centroid affected by the ash is brought back to the relationship that is not affected by lithology, thus eliminating the influence of resistivity on the two logging curves.

[0091] Well C113-X2 in the Boxing Depression of the study area is heavily affected by ash deposits, and improvements should be made to the reservoir sections affected by ash. In the heavily ash-affected reservoir sections, the resistivity in the superimposed portion of sonic logging and resistivity is increased due to ash, thus requiring correction of the increased resistivity portion. Organic-bearing sections significantly affected by ash deposits should be screened, and the affected sonic transit time (AC) and resistivity (RT) values ​​should be statistically analyzed. A relationship between sonic logging and resistivity unaffected by lithology should be established in the study well. The increased resistivity should be corrected to return to the lithology-independent value, eliminating the influence of resistivity on the two logging curves, thereby achieving accurate calculation of the total organic carbon content of the shale sections.

[0092] The formula for predicting TOC using the ΔlogR method, which is unaffected by gray matter, is as follows:

[0093]

[0094] RT 校正 =RT-ΔRT 增量

[0095] ΔlogR=lg(ΔRT 校正 / RT 基线 )+K(Δt-Δt 基线 )

[0096] Where: ΔRT 增量 The resistivity increases due to the influence of ash, in Ω·m; RT 校正 RT is the gray mass correction factor, Ω·m; 标准 RT represents the resistivity unaffected by lithology, in Ω·m; RT represents the resistivity calculated with errors using the traditional ΔlogR method, in Ω·m; m represents the number of organic-containing intervals affected by ash. 基线 The resistivity of the baseline in non-oil-generating claystone corresponds to the baseline value; Δt is the measured acoustic transit time; Δt 基线 The acoustic transit time value corresponding to the baseline value in non-oil-generating claystone.

[0097] In step 5, the centroid affected by the mud is brought back to the relational formula that is not affected by lithology, thus eliminating the influence of sonic transit time on the two logging curves.

[0098] Well LY1 in the Lijin Depression of the study area is heavily affected by silt deposits, and improvements should be made to the reservoir sections affected by silt. In the heavily silted reservoir sections, the resistivity in the superimposed portion of sonic logging and resistivity is increased due to the presence of silt, thus requiring correction of the increased sonic transit time. Similarly, organic-containing sections significantly affected by silt were screened, and the affected sonic transit time AC and resistivity RT values ​​were statistically analyzed. A relationship between sonic logging and resistivity unaffected by lithology was established in the study well. The increased sonic transit time was corrected to return to values ​​unaffected by lithology, eliminating the influence of sonic transit time on the two logging curves, thereby achieving accurate calculation of the total organic carbon content of the shale sections.

[0099] The formula for predicting TOC using the ΔlogR method, which is unaffected by mud quality, is as follows:

[0100]

[0101] AC 校正 =AC-ΔAC 增量

[0102] ΔlogR=lg(ΔRT / RT 基线 )+K(ΔAC 校正 -ΔAC 基线 )

[0103] Where: ΔAC 增量 The increased acoustic transit time due to the influence of mud quality, in μs / ft; AC 校正 The clay quality correction factor is in μs / ft; AC 标准 RT is the sonic transit time unaffected by lithology, in μs / ft; AC is the sonic transit time calculated with errors using the traditional ΔlogR method, in μs / ft; n is the number of organic-containing intervals affected by argillaceous material; 基线 The resistivity of the baseline in non-oil-generating claystone corresponds to the baseline value; ΔRT is the measured resistivity value; ΔAC 基线 The acoustic transit time value corresponding to the baseline value in non-oil-generating claystone.

[0104] In step 6, the lithology-corrected TOC value is calculated according to the ΔlogR formula described in steps 4 and 5.

[0105] This patent proposes two improved ΔlogR methods that eliminate the influence of lithology, which were applied in two study wells along with the traditional ΔlogR method. A comparison of 328 sampling points yielded good results. The study shows that the original ΔlogR method had a correlation coefficient of 52.3% and 58.6% between predicted and measured TOC values ​​in study wells LY1 and C113-X2, respectively. The correlation coefficient of the ΔlogR method eliminating the influence of clay increased to 88.1%, and the correlation coefficient of the ΔlogR method eliminating the influence of ash was 91.8%. Experiments demonstrate that the predicted TOC values, which are significantly affected by ash and clay, are relatively reliable. The TOC curve predicted by the improved ΔlogR method accurately reflects the vertical variation of measured TOC. This method ensures that the calculated organic carbon content during hydrocarbon generation is not controlled by lithology, eliminating the influence of lithology on sonic transit time and resistivity curve morphology. Based on the principles and geological significance of the ΔlogR model, it achieves accurate prediction of organic carbon content in shale oil reservoirs, providing guidance for the development of the "sweet spot" of shale oil.

[0106] Example 3

[0107] In a specific embodiment 3 of the present invention, as shown in Figure 2, mature source rocks are characterized by large acoustic transit time and high resistivity. However, shale oil wells often contain immature source rocks that are either grayish or muddy, as shown in Figures 3 and 4. When grayish, immature source rocks exhibit low natural gamma, low acoustic transit time, and high resistivity response; when muddy, immature source rocks exhibit high natural gamma, high acoustic transit time, and low resistivity response. In the shale oil well shown in Figure 5, the resistivity increases significantly at depths of 3465-3470m, indicating a strong influence from gray matter. Similarly, as shown in Figure 6, the acoustic transit time increases significantly at depths of 3780-3785m, indicating a strong influence from mud matter. The data that are heavily affected by mud and lime were statistically analyzed and made into a scatter plot, as shown in Figures 7 and 8. It can be clearly seen that the part affected by lithology increases. Therefore, it is necessary to correct the increased part to the standard line of resistivity and sonic transit time that is not affected by lithology. The corrected part is shown in Figures 9 and 10.

[0108] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0109] Except for the technical features described in the specification, all other technologies are known to those skilled in the art.

Claims

1. An improved method for calculating TOC in a lacustrine sedimentary background, characterized in that, The improved TOC calculation method in a lacustrine sedimentary background includes: Step 1, establishing the relationship between acoustic wave and resistivity in the well unaffected by lithology; Step 2, screening the resistivity and acoustic transit time values ​​of the well's strata affected by argillaceous material, performing the intersection of deep resistivity (RT) and acoustic transit time (AC) affected by argillaceous material, and determining the centroid; Step 3, screening the resistivity and acoustic transit time values ​​of the well's strata affected by argillaceous material, performing the intersection of RT and AC affected by argillaceous material, and determining the centroid; Step 4, eliminating the influence of resistivity on the two logging curves; Step 5, eliminating the influence of acoustic transit time on the two logging curves; Step 6, calculating the lithology-corrected TOC value.

2. The improved TOC calculation method in a lacustrine sedimentary background according to claim 1, characterized in that, In step 1, based on core and logging data, select the lithology-independent strata of the shale oil well, statistically analyze the resistivity and sonic transit time of the strata, and establish the relationship between sonic waves and resistivity in the well that are not affected by lithology.

3. The improved TOC calculation method in a lacustrine sedimentary background according to claim 1, characterized in that, In step 2, based on core and logging data, a grayish layer is selected, and the resistivity and sonic transit time values ​​of the layer affected by the gray matter in the well are screened. The intersection of RT and AC affected by the gray matter is calculated, and the centroid is determined.

4. The improved TOC calculation method in a lacustrine sedimentary background according to claim 3, characterized in that, In step 2, the sonic transit time curve and resistivity curve of the limestone section are superimposed using the traditional ΔlogR method. At this time, the sonic transit time increment is small, basically zero, while the resistivity increases significantly, resulting in an overestimation of the calculated TOC. The increased TOC is greatly affected by the limestone. Therefore, it is necessary to reduce the resistivity centroid of the increased part to the relationship between RT and AC, which is not affected by lithology.

5. The improved TOC calculation method in a lacustrine sedimentary background according to claim 1, characterized in that, In step 3, based on core and logging data, select the muddy sections, screen the resistivity and sonic transit time values ​​of the muddy sections of the well, perform the intersection of RT and AC values ​​affected by muddy materials, and determine the centroid.

6. The improved TOC calculation method in a lacustrine sedimentary background according to claim 5, characterized in that, In step 3, the acoustic transit time curve and resistivity of the mudstone section are superimposed using the traditional ΔlogR method. At this time, the resistivity increment is small and basically zero, while the acoustic transit time increases significantly. The increased TOC part is greatly affected by the mudstone. Therefore, it is necessary to reduce the centroid of the increased acoustic transit time to the relationship between RT and AC, which is not affected by the lithology.

7. The improved TOC calculation method in a lacustrine sedimentary background according to claim 1, characterized in that, In step 4, organic-containing intervals that are significantly affected by ash are screened, and the affected acoustic transit time AC and resistivity RT values ​​are statistically analyzed. The relationship between acoustic waves and resistivity in the well that are not affected by lithology is established, and the increased resistivity is corrected to return to the value that is not affected by lithology. The influence of resistivity on the two logging curves is eliminated, thereby achieving an accurate calculation of the total organic carbon content of the shale interval.

8. The improved TOC calculation method in a lacustrine sedimentary background according to claim 1, characterized in that, In step 5, organic-containing intervals that are significantly affected by silt are screened, and the affected acoustic transit time AC and resistivity RT values ​​are statistically analyzed. The relationship between acoustic transit time and resistivity in the well that is not affected by lithology is established, and the increased acoustic transit time is corrected to return to the value that is not affected by lithology. The influence of acoustic transit time on the two logging curves is eliminated, thereby achieving an accurate calculation of the total organic carbon content of the shale interval.

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