Method, apparatus, storage medium and program product for calculating organic carbon content of mudstone

Abstract

The present disclosure relates to the technical field of oil and gas resource evaluation and development, and particularly relates to a method, device, storage medium and program product for calculating organic carbon content of mudstone, the method comprising: obtaining logging response characteristics of different types of mudstone, wherein the different types of mudstone include silty mudstone, pure mudstone, carbonaceous mudstone and coal seam; classifying mudstone in a target region based on the logging response characteristics of the different types of mudstone; for the different types of mudstone, respectively establishing an organic carbon content calculation model to calculate the organic carbon content of the different types of mudstone, and finally combining the results calculated by the different models to obtain the organic carbon content of the whole well section, thereby improving the accuracy and calculation precision of the measured organic carbon content of the well section including different types of mudstone.

Description

Technical Field

[0001] This disclosure relates to the field of oil and gas resource evaluation and development technology, and in particular to a method, equipment, storage medium and program product for calculating the organic carbon content of mudstone. Background Technology

[0002] In continental exploration, total organic carbon (TOC) content is one of the important parameters for evaluating the quality and hydrocarbon generation potential of source rock reservoirs. Therefore, accurate evaluation of TOC content is crucial. Identifying and evaluating the TOC content in mudstone based on well logging data is an important aspect of oil and gas exploration. However, the TOC content varies greatly among different types of mudstone. Accurately and quantitatively evaluating the TOC content in continental tight clastic rocks using well logging data is essential for understanding the overall quality of source rocks, studying hydrocarbon accumulation models, and selecting favorable areas. Existing quantitative evaluation studies on the TOC content of mudstone are limited, and existing models have poor accuracy, failing to meet the geological requirements for efficient gas reservoir exploration and evaluation. Summary of the Invention

[0003] To address the aforementioned issues, this disclosure provides a method, apparatus, storage medium, and program product for calculating the organic carbon content of mudstone.

[0004] In a first aspect, this disclosure provides a method for calculating the organic carbon content of mudstone, including:

[0005] The logging response characteristics of different types of mudstone are obtained, wherein the different types of mudstone include silty mudstone, pure mudstone, carbonaceous mudstone and coal seam;

[0006] Based on the logging response characteristics of the different types of mudstone, the mudstone in the target area is classified.

[0007] For different types of mudstone, separate models for calculating organic carbon content were established to calculate the organic carbon content of the different types of mudstone.

[0008] Specifically, for different types of mudstone, separate models for calculating organic carbon content are established, including:

[0009] For the silty mudstone, a regression fitting was performed between the natural gamma logging curve from the well logging data and the measured organic carbon content from the core to establish a calculation model for the organic carbon content of the silty mudstone:

[0010] TOC1 = 0.0204GR - 1.483

[0011] Wherein, TOC1 is the total organic carbon content of the silty mudstone, expressed as a percentage by mass, and GR is the natural gamma logging value, expressed as API.

[0012] In some embodiments, obtaining the logging response characteristics of different types of mudstone includes:

[0013] Determine the development characteristics of mudstone in the core samples, the organic carbon content values, and the response characteristics on the logging curves of multiple wells in the target area;

[0014] The response characteristics of the silty mudstone, the pure mudstone, the carbonaceous mudstone, and the coal seam were obtained on the natural gamma logging curve, the sonic transit time logging curve, the neutron logging curve, and the dual lateral deep and shallow resistivity logging curve, respectively.

[0015] In some embodiments, the response characteristics of the silty mudstone, the pure mudstone, the carbonaceous mudstone, and the coal seam on the natural gamma ray logging curve, the sonic transit time logging curve, the neutron logging curve, and the dual lateral resistivity logging curves respectively include:

[0016] The silty mudstone exhibits a medium-high natural gamma response on the natural gamma logging curve, a medium-high sonic transit time response on the sonic transit time logging curve, a medium-high neutron response on the neutron logging curve, and a medium resistivity value on the dual lateral deep and shallow resistivity logging curve.

[0017] The pure mudstone exhibits a high natural gamma response on the natural gamma logging curve, a high sonic transit time on the sonic transit time logging curve, a high neutron response on the neutron logging curve, and a low resistivity value on the dual lateral deep and shallow resistivity logging curve.

[0018] The carbonaceous mudstone exhibits a high natural gamma response on the natural gamma logging curve, an extremely high sonic transit time on the sonic transit time logging curve, an extremely high neutron response on the neutron logging curve, and a medium to low resistivity value on the dual lateral deep and shallow resistivity logging curve.

[0019] The coal seam exhibits a medium-high natural gamma response on the natural gamma logging curve, an extremely high sonic transit time response on the sonic transit time logging curve, an extremely high neutron response on the neutron logging curve, and a medium resistivity value on the dual lateral deep and shallow resistivity logging curve.

[0020] In some embodiments, classifying the mudstone in the target area based on the logging response characteristics of the different types of mudstone includes:

[0021] Based on core calibration logging, mudstone types are classified using the combined differences in the logging data.

[0022] In some embodiments, classifying mudstone types based on core calibration logging and combined differences in the logging data includes:

[0023] The silty mudstone is preferentially identified based on the differences in natural gamma logging data, sonic transit time logging data, and resistivity logging data. The carbonaceous mudstone and the coal seam are identified based on the differences in sonic transit time logging data and neutron logging data compared to other mudstones. The carbonaceous mudstone and the coal seam are distinguished based on the sonic transit time logging data, and the remaining segments are pure mudstone segments.

[0024] In some embodiments, for different types of mudstone, separate models for calculating organic carbon content are established, including:

[0025] For the carbonaceous mudstone and the coal seam, a regression fitting was performed between the sonic transit time logging curves from the well logging data and the measured organic carbon content from the core samples to establish a calculation model for the organic carbon content of the carbonaceous mudstone and the coal seam:

[0026] TOC2 = 0.2012AC - 41.046

[0027] Wherein, TOC2 is the total organic carbon content of the carbonaceous mudstone and the coal seam, in mass percentage, and AC is the logging sonic transit time value, in microseconds per meter.

[0028] In some embodiments, for different types of mudstone, separate models for calculating organic carbon content are established, including:

[0029] For the pure mudstone, an organic carbon content calculation model for the pure mudstone is established based on the superposition area of ​​resistivity and sonic transit time logging curves in the well logging data:

[0030] ΔlgR=lg(R t / R 基线 )+0.02′(Δt-Dt 基线 )

[0031] TOC3=DlgR′10 (2.297-0.1688*LOM)

[0032] Where ΔlgR is the area of ​​overlap between the resistivity and sonic transit time logging curves, which is dimensionless. t R is the deep resistivity logging value. 基线 The resistivity baseline of mudstone when TOC3 is close to zero, and Δt is the sonic transit time logging value. 基线 The sonic transit time baseline value of mudstone when TOC3 is close to zero, where TOC3 is the total organic carbon content of the pure mudstone, and LOM is an index reflecting the maturity of organic matter.

[0033] In a second aspect, this disclosure provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the first aspect.

[0034] Thirdly, this disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect above.

[0035] Fourthly, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in the first aspect.

[0036] This disclosure provides a method, equipment, storage medium, and program product for calculating the organic carbon content of mudstone. By acquiring the logging response characteristics of different types of mudstone, and classifying the mudstone in the target area based on the logging response characteristics of different types of mudstone, an organic carbon content calculation model is established for each type of mudstone to calculate the organic carbon content of each type of mudstone. Finally, the results calculated by different models are combined to obtain the organic carbon content of the entire well section, which solves the problems of low accuracy and poor consistency of calculation by a single model.

[0037] This disclosure classifies mudstone and establishes mathematical calculation models for the organic carbon content of different types of mudstone. By calculating the organic carbon content of different types of mudstone separately, accurate organic carbon content data of each mudstone type can be obtained in a targeted manner. This improves the accuracy and calculation precision of the organic carbon content measured in well sections including different types of mudstone, and provides a calculation model and data basis for the evaluation of mudstone source rock quality. Attached Figure Description

[0038] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0039] Figure 1 This is a flowchart illustrating a method for calculating the organic carbon content of mudstone, provided as an embodiment of this disclosure.

[0040] Figure 2 This is a schematic diagram illustrating the response characteristics of silty mudstone on core samples and well logging curves provided in this embodiment of the disclosure.

[0041] Figure 3 A schematic diagram illustrating the response characteristics of pure mudstone on core samples and logging curves provided in this embodiment of the disclosure.

[0042] Figure 4 This is a schematic diagram illustrating the response characteristics of carbonaceous mudstone on core samples and well logging curves provided in this embodiment of the disclosure.

[0043] Figure 5 This is a schematic diagram of an organic carbon content calculation model for silty mudstone provided in an embodiment of this disclosure.

[0044] Figure 6 This is a schematic diagram of an organic carbon content calculation model for carbonaceous mudstone and coal seams provided in an embodiment of this disclosure.

[0045] Figure 7 This is a schematic diagram comparing mudstone types classified according to well logging curves and TOC calculations provided in an embodiment of this disclosure.

[0046] Figure 8 This is a schematic diagram comparing mudstone types classified according to well logging curves and TOC calculations provided in an embodiment of this disclosure.

[0047] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0048] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0049] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0050] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0051] Continental tight clastic rocks are widely distributed in major oil and gas basins worldwide, possessing enormous exploration and development potential and serving as important oil and gas producing formations. Typically, the scale of oil and gas resources in continental tight clastic rocks largely depends on the quality and thickness of adjacent source rocks (usually mudstone). The quality of each mudstone layer within a formation plays a fundamental controlling role in the enrichment and accumulation of adjacent sandstone oil and gas reservoirs. However, actual drilling data shows that mudstone layers contain not only pure mudstone with high clay content but also fine-silty mudstone of a certain thickness. Furthermore, carbonaceous mudstone and coal seams are commonly observed, exhibiting characteristics of coal-bearing source rocks. This demonstrates the complex and varied geological characteristics of mudstone lithology. Further experimental analysis shows that there are large differences in organic carbon content among different types of mudstone. How to accurately and quantitatively evaluate the organic carbon content in continental tight clastic rocks using well logging data is of great importance for the comprehensive quality of source rocks, hydrocarbon accumulation model research, and selection of favorable areas. There are few existing quantitative evaluation studies on the organic carbon content of continental mudstone, and the existing models have poor accuracy, which makes it difficult to meet the geological requirements for efficient gas reservoir exploration and evaluation.

[0052] Due to the complexity of mudstone types, most wells did not collect gamma-ray spectra, only conventional logging curves. Current gamma-ray regression models for calculating TOC content are significantly affected by clay, resulting in low accuracy. The ΔlgR model's evaluation results in silty mudstone sections are significantly overestimated, inconsistent with core analysis. Furthermore, existing models fail to consider the abnormally high TOC content in carbonaceous mudstone and coal seams, making their evaluation accuracy clearly insufficient for practical requirements.

[0053] Therefore, a new method for calculating the organic carbon content of mudstone is needed to address the problems existing in the current technology.

[0054] Example 1

[0055] Figure 1 This is a flowchart illustrating a method for calculating the organic carbon content of mudstone, provided as an embodiment of this disclosure.

[0056] like Figure 1 As shown, a method for calculating the organic carbon content of mudstone includes:

[0057] S100. Obtain the logging response characteristics of different types of mudstone, including silty mudstone, pure mudstone, carbonaceous mudstone, and coal seam.

[0058] Due to the large thickness of strata in some areas and the differences in sedimentary environments at different times and in different regions, there are various types of mudstone. In addition to pure mudstone, there are also silty mudstone and carbonaceous mudstone. In some areas, relatively thin coal seams (coal lines) are also developed inside the mudstone.

[0059] To differentiate between different types of mudstone, including silty mudstone, pure mudstone, carbonaceous mudstone, and coal seams, this study, based on core observation, well logging lithology, and TOC analysis data, and after lithological nomenclature and depth localization, determined the development characteristics, TOC values, and response characteristics of mudstone in multiple wells within the target area on core samples. The study obtained the response characteristics of silty mudstone, pure mudstone, carbonaceous mudstone, and coal seams on natural gamma ray logging, sonic transit time logging, neutron logging, and dual lateral resistivity logging (both shallow and deep). Figure 2 , Figure 3 and Figure 4 . Figure 2 The core of silty mudstone and its response characteristics on the above logging curves are shown. Silty mudstone is developed in the core at a depth of 4460-4464m. The mudstone cross-section shows wrinkling. The measured TOC content of the core is low, with an average value of less than 1.0%. Figure 3 The image shows pure mudstone (logging) and its response characteristics on the above logging curves. The core contains relatively pure mudstone at a depth of 4340-4346m. The measured TOC content of the cuttings is moderate, with most sample points having measured values ​​between 1% and 2%. Figure 4 The results show the carbonaceous mudstone (or thin coal seam) and its response characteristics on the above logging curves. The core shows that the carbonaceous mudstone is relatively typical at a depth of 4974-4976m. The measured TOC content of the rock cuttings is very high, with the measured TOC value generally greater than 6%, and usually as high as about 30%.

[0060] The target area is a specific area for geological exploration and investigation. For example, the target area can be an area with mudstone layers, such as the Sichuan Basin or the central Sichuan region. This disclosure does not make any specific limitations.

[0061] For example, the response characteristics of silty mudstone, pure mudstone, carbonaceous mudstone, and coal seams on natural gamma ray logging, sonic transit time logging, neutron logging, and dual lateral resistivity logging include:

[0062] The response characteristics of silty mudstone on natural gamma logging curves are medium to high natural gamma, on sonic transit logging curves are medium to high sonic transit, on neutron logging curves are medium to high neutron, and on dual lateral deep and shallow resistivity logging curves are medium resistivity values.

[0063] Pure mudstone exhibits a high natural gamma response on natural gamma logging curves, a high sonic transit time response on sonic transit time logging curves, a high neutron response on neutron logging curves, and a low resistivity value on dual lateral deep and shallow resistivity logging curves.

[0064] The response characteristics of carbonaceous mudstone on natural gamma logging curves are high natural gamma, on sonic transit logging curves are extremely high sonic transit, on neutron logging curves are extremely high neutron, and on dual lateral deep and shallow resistivity logging curves are medium to low resistivity values.

[0065] The coal seam's response characteristics on the natural gamma logging curve are medium to high natural gamma, on the sonic transit logging curve are very high sonic transit, on the neutron logging curve are very high neutron, and on the dual lateral deep and shallow resistivity logging curves are medium resistivity values.

[0066] Specifically, among the above logging response characteristics, high natural gamma ranges approximately greater than 120 API, relatively high natural gamma ranges approximately 100-120 API, and medium-high natural gamma ranges approximately 75-100 API; ultra-high acoustic transit time ranges approximately greater than 300 μs / m, high acoustic transit time ranges approximately greater than 250 μs / m, and medium-high acoustic transit time ranges approximately 200-250 μs / m; ultra-high neutron ranges approximately greater than 30%, high neutron ranges approximately 20%-30%, and medium-high neutron ranges approximately 10%-20%; medium resistivity ranges approximately 100 ohm·m, medium-low resistivity ranges approximately 40-100 ohm·m, and low resistivity ranges approximately less than 40 ohm·m.

[0067] Among different types of mudstone, silty mudstone has a low TOC content, pure mudstone has a medium TOC content, carbonaceous mudstone has a high TOC content, and coal seams have an extremely high TOC content.

[0068] S200. Based on the logging response characteristics of different types of mudstone, mudstone in the target area is classified.

[0069] Well logging response characteristics analysis of different types of mudstone shows that there are significant differences in well logging response characteristics among silty mudstone, pure mudstone, carbonaceous mudstone and coal seam. Therefore, the combined differences in well logging response characteristics of different types of mudstone can be used to effectively distinguish different types of mudstone within the mudstone strata of the target area.

[0070] Specifically, mudstone types can be classified based on the differences in combinations of logging data using core calibration logging.

[0071] In the well logging interpretation section of the target area, core calibration logging technology can quickly and intuitively establish the relationship between well logging data and core analysis data. It can establish an interpretation model based on the statistical relationship between data, taking into account the essential relationship between geological parameters and well logging physical quantities. Then, based on the well logging response characteristics of different types of mudstone, the mudstone type can be identified.

[0072] The steps for classifying mudstone types may include: prioritizing the identification of silty mudstone based on differences in natural gamma logging, sonic transit time logging, and resistivity logging; identifying carbonaceous mudstone and coal seams based on differences in sonic transit time logging and neutron logging data compared to other mudstones; distinguishing carbonaceous mudstone and coal seams based on sonic transit time logging data; and classifying the remaining segments as pure mudstone segments.

[0073] Among them, the distinction between carbonaceous mudstone and coal seam based on acoustic transit time data includes: mudstone types with acoustic data greater than 350 μs / m are coal seams.

[0074] Based on the above method, silty mudstone, coal seam, carbonaceous mudstone and pure mudstone can be identified sequentially from different mudstone types.

[0075] Table 1 shows the logging response characteristics of different types of mudstone in the mudstone interval.

[0076] Table 1 Well logging response characteristics of different lithologies in mudstone sections

[0077]

[0078] S300. For different types of mudstone, establish separate models for calculating organic carbon content to calculate the organic carbon content of different types of mudstone.

[0079] Based on a comprehensive comparison of the geological understanding of mudstone development areas, the lithological composition characteristics of mudstone strata, and the well logging response characteristics of different lithologies within mudstone strata, and on the understanding of the relationship between organic carbon content and well logging response characteristics of different types of mudstone, well logging sensitivity curves are selected for different mudstone types, and TOC mathematical calculation models are established respectively.

[0080] By establishing TOC logging evaluation models sequentially based on mudstone type and conducting calculations, the accuracy of quantitative interpretation of TOC logging can be improved.

[0081] For example, core measurements of TOC show that, compared to pure mudstone, carbonaceous mudstone, and coal seams, silty mudstone was deposited in shallower water bodies, has a higher sand content, and a lower TOC content. Therefore, the amount of mudstone determines the TOC content of silty mudstone. Thus, a mathematical model for calculating TOC can be established by regression fitting between the natural gamma logging curve from well logging data and the measured TOC.

[0082] Specifically, in step S300, for different types of mudstone, separate calculation models for organic carbon content are established, including:

[0083] For silty mudstone, a regression fitting was performed using the natural gamma logging curves from well logging data and the measured organic carbon content data from the core samples to establish an organic carbon content calculation model applicable to silty mudstone, such as... Figure 5 As shown:

[0084] TOC1 = 0.0204GR - 1.483 (1)

[0085] R 2 =0.8051 (2)

[0086] In equation (1), TOC1 represents the total organic carbon content of silty mudstone, expressed as a percentage by mass (w / w, %), and GR represents the natural gamma ray value from well logging, expressed as API. In equation (2), R represents the correlation coefficient between the total organic carbon content and the natural gamma ray value from well logging, dimensionless. 2 It is used to measure the degree of model fit, which can be understood as the accuracy of the fit between the actual collected point values ​​and the function that can be fitted by the calculated model.

[0087] For example, in pure mudstone, clay minerals have the characteristics of low resistivity and medium-high sonic transit time. When organic carbon is developed in pure mudstone, resistivity and sonic transit time will increase simultaneously with the increase of organic carbon content. Therefore, the organic carbon content in pure mudstone can be calculated based on the superposition area of ​​resistivity and sonic transit time logging curves.

[0088] Specifically, in step S300, for different types of mudstone, separate calculation models for organic carbon content are established, including:

[0089] For pure mudstone, an organic carbon content calculation model is established based on the superposition area of ​​resistivity and sonic transit time logging curves from well logging data:

[0090] ΔlgR=lg(R t / R 基线 )+0.02′(Δt-Dt 基线 (3)

[0091] TOC3=DlgR′10 (2.297-0.1688*LOM) (4)

[0092] In equations (3) and (4), ΔlgR is the area of ​​overlap between the resistivity and sonic transit time logging curves, which is dimensionless. t This is a deep resistivity logging value, in ohm-meters (ohm·m), R 基线The resistivity baseline of mudstone when TOC3 is close to zero, and Δt is the sonic transit time logging value in microseconds per meter (µs / m). 基线 The sonic transit time baseline value of mudstone when TOC3 is close to zero is given. TOC3 is the total organic carbon content of pure mudstone, expressed as a percentage by mass (w / w, %). LOM is an index reflecting the maturity of organic matter.

[0093] LOM is closely related to regional empirical values ​​and can be determined through experimental values. This disclosure does not impose specific limitations. For example, LOM values ​​are usually distributed between 5 and 12. Generally, newer strata correspond to smaller values, while older strata correspond to larger values.

[0094] For example, carbonaceous mudstone and coal seams have high organic carbon content, but their corresponding natural gamma-ray logging values ​​are low. Therefore, the aforementioned fitting model between natural gamma-ray logging curves and organic carbon content cannot be used to evaluate the organic carbon content of carbonaceous mudstone and coal seams. Testing the aforementioned ΔlgR method to calculate the organic carbon content in carbonaceous mudstone and coal seams revealed a discrepancy between the calculated organic carbon content and its actual value. Therefore, the aforementioned two organic carbon content calculation models cannot be used to calculate the organic carbon content of carbonaceous mudstone and coal seams.

[0095] The summary and analysis of the logging response characteristics of carbonaceous mudstone and coal seams revealed that the sonic transit time logging data would increase abnormally in the carbonaceous mudstone and coal seam intervals. Therefore, it is possible to consider using the sonic transit time logging curve and the core measured organic carbon content data to perform regression fitting and establish an organic carbon content calculation model to quantitatively evaluate the organic carbon content of carbonaceous mudstone and coal seams.

[0096] Specifically, in step S300, for different types of mudstone, separate calculation models for organic carbon content are established, including:

[0097] For carbonaceous mudstone and coal seams, a regression fitting was performed between the sonic transit time logging curves from well logging data and the measured organic carbon content from core samples to establish a calculation model for organic carbon content in carbonaceous mudstone and coal seams, such as... Figure 6 As shown:

[0098] TOC2 = 0.2012AC - 41.046 (5)

[0099] R 2 =0.9741 (6)

[0100] In equation (5), TOC2 is the total organic carbon content of carbonaceous mudstone and coal seam, expressed as a percentage by mass (w / w, %), and AC is the logging sonic transit time value, expressed as microseconds per meter (µs / m). In equation (6), R is the correlation coefficient between the total organic carbon content and the logging sonic transit time value, dimensionless. 2 It is used to measure the degree of model fit, which can be understood as the accuracy of the fit between the actual collected point values ​​and the function that can be fitted by the calculated model.

[0101] This disclosure analyzes and summarizes the development characteristics, types, and response features of mudstone on well logging curves. By selecting appropriate and sensitive well logging curve combinations, a method for quantitatively evaluating the organic carbon content of mudstone is established according to mudstone type. By comparing the evaluation results with existing models and measured TOC results, the new model significantly improves the calculation accuracy of organic carbon content, and the actual evaluation effect is significant.

[0102] This disclosure provides a method for calculating the organic carbon content of mudstone. By calibrating the core and logging lithology, the response characteristics of different types of mudstone on the main logging curves are clarified. Well logging identification standards for different types of mudstone are established. For each type of mudstone, a preferred well logging sensitive curve is selected, and an organic carbon content calculation model is established. This provides a highly accurate method for calculating organic carbon content for terrestrial source rock evaluators.

[0103] This disclosure provides a method, equipment, storage medium, and program product for calculating the organic carbon content of mudstone. By acquiring the logging response characteristics of different types of mudstone, and classifying the mudstone in the target area based on the logging response characteristics of different types of mudstone, an organic carbon content calculation model is established for each type of mudstone to calculate the organic carbon content of each type of mudstone. Finally, the results calculated by different models are combined to obtain the organic carbon content of the entire well section, which solves the problems of low accuracy and poor consistency of calculation by a single model.

[0104] Example 2

[0105] Based on the above embodiments, this embodiment provides an application example.

[0106] The techniques described in the above embodiments of this disclosure have been applied to the evaluation of organic carbon content in the Xujiahe Formation of the Sichuan Basin, and have shown very good application results.

[0107] In practical applications, after identifying the mudstone interval, the logging data of the mudstone interval is first used to identify silty mudstone, coal seams, carbonaceous mudstone, and pure mudstone based on the logging response characteristics of the identified different types of mudstone. Then, a natural gamma regression model is used to calculate the organic carbon content of the silty mudstone; a ΔlgR model after core organic carbon content calibration is used to calculate the organic carbon content of the pure mudstone; and a sonic transit time model is used to calculate the organic carbon content of the carbonaceous mudstone and coal seams. By calculating the organic carbon content of each of these different types of mudstone, the organic carbon content of the entire well section is obtained. Figure 7 and Figure 8 ,exist Figure 7 and Figure 8 In this context, the new model represents a combination of different models in Embodiment 1 of this disclosure. Different types of calculation models are used to determine the organic carbon content of different types of mudstone, thereby improving the accuracy of organic carbon content calculation.

[0108] Figure 7 This diagram illustrates the well logging data analysis of the mudstone interval corresponding to the 3395m-3445m depth in Well XX1, and the comparisons between the organic carbon content data obtained through the GR regression model and the measured organic carbon content data from the core, the organic carbon content data obtained through the DeltaLogR model and the measured organic carbon content data from the core, and the organic carbon content data obtained through a comprehensive new model and the measured organic carbon content data from the core. The organic carbon content data obtained through the model is shown as curves, while the measured organic carbon content data from the core is shown as solid dots in the figure. In the mudstone types shown in the figure, 1 represents silty mudstone, 2 represents pure mudstone, 3 represents carbonaceous mudstone, and 4 represents coal seam.

[0109] For example, in Figure 7 The layer with a depth range of 3410m-3413m can be analyzed as a pure mudstone layer based on the matching of well logging response characteristics of different types of mudstone and well logging data of this layer. Figure 7 It is clear that the organic carbon content calculated by the GR regression model and the DeltaLogR model is significantly higher than the measured organic carbon content in the core. The organic carbon content values ​​measured by these two models do not match the measured organic carbon content values ​​in the core well. However, the calculation results of the new model match the measured organic carbon content in the core well.

[0110] Figure 8This diagram illustrates the well logging data analysis of the mudstone interval corresponding to depths of 4220m-4380m in Well XX2, and the comparisons between organic carbon content data obtained through the GR regression model and the measured organic carbon content data from the core, the organic carbon content data obtained through the DeltaLogR model and the measured organic carbon content data from the core, and the organic carbon content data obtained through a comprehensive new model and the measured organic carbon content data from the core. The organic carbon content data obtained through the model is shown as curves, while the measured organic carbon content data from the core is shown as solid dots in the diagram. In the mudstone types shown in the diagram, 1 represents silty mudstone, 2 represents pure mudstone, 3 represents carbonaceous mudstone, and 4 represents a coal seam.

[0111] For example, Figure 8 The depth range is approximately between 4320m and 4330m, around 4325m. Based on the well logging response characteristics of different mudstone types and the matching of well logging data for this section, it can be analyzed that this section is carbonaceous mudstone. Figure 8 It is clear that the organic carbon content calculated by the GR regression model and the DeltaLogR model is significantly lower than the organic carbon content measured in the core. The organic carbon content values ​​measured by these two models do not match the organic carbon content values ​​measured in the core well. However, the calculation results of the new model match the organic carbon content values ​​measured in the core well.

[0112] Compared to using a single model to calculate the organic carbon content (OCC) of mudstone sections, this disclosure employs different OCC models for different mudstone types to calculate their respective OCC content, and then combines the data from these different mudstone types to synthesize the OCC content data for the entire well section. This significantly improves the accuracy of OCC calculations and yields remarkable practical evaluation results. The calculation results from the combined OCC model in this disclosure agree better with the measured results, providing fundamental data support for source rock parameter calculation and exploration evaluation.

[0113] This disclosure discloses an embodiment that classifies mudstone and then establishes mathematical calculation models for the organic carbon content of different types of mudstone. By calculating the organic carbon content of different types of mudstone separately, accurate organic carbon content data for each mudstone type can be obtained in a targeted manner. This improves the accuracy of organic carbon content measured in well sections including different types of mudstone and provides a calculation model and data basis for the evaluation of mudstone source rock quality.

[0114] Example 3

[0115] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0116] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the above embodiments.

[0117] In some embodiments of this example, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described in the above embodiments.

[0118] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods in the above embodiments.

[0119] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0120] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0121] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0122] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0123] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0124] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0125] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0126] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.

Claims

1. A method for calculating the organic carbon content of mudstone, characterized in that, include: The logging response characteristics of different types of mudstone are obtained, wherein the different types of mudstone include silty mudstone, pure mudstone, carbonaceous mudstone and coal seam; Based on the logging response characteristics of the different types of mudstone, the mudstone in the target area is classified. For different types of mudstone, separate models for calculating organic carbon content were established to calculate the organic carbon content of the different types of mudstone. Specifically, for different types of mudstone, separate models for calculating organic carbon content are established, including: For the silty mudstone, a regression fitting was performed between the natural gamma logging curve from the well logging data and the measured organic carbon content from the core to establish a calculation model for the organic carbon content of the silty mudstone: TOC1 = 0.0204GR - 1.483 Wherein, TOC1 is the total organic carbon content of the silty mudstone, expressed as a percentage by mass, and GR is the natural gamma logging value, expressed as API.

2. The method for calculating the organic carbon content of mudstone according to claim 1, characterized in that, The acquisition of logging response characteristics for different types of mudstone includes: Determine the development characteristics of mudstone in the core samples, the organic carbon content values, and the response characteristics on the logging curves of multiple wells in the target area; The response characteristics of the silty mudstone, the pure mudstone, the carbonaceous mudstone, and the coal seam were obtained on the natural gamma logging curve, the sonic transit time logging curve, the neutron logging curve, and the dual lateral deep and shallow resistivity logging curve, respectively.

3. The method for calculating the organic carbon content of mudstone according to claim 2, characterized in that, The response characteristics of the silty mudstone, the pure mudstone, the carbonaceous mudstone, and the coal seam on the natural gamma ray logging curve, sonic transit time logging curve, neutron logging curve, and dual lateral resistivity logging curves, respectively, include: The silty mudstone exhibits a medium-high natural gamma response on the natural gamma logging curve, a medium-high sonic transit time response on the sonic transit time logging curve, a medium-high neutron response on the neutron logging curve, and a medium resistivity value on the dual lateral deep and shallow resistivity logging curve. The pure mudstone exhibits a high natural gamma response on the natural gamma logging curve, a high sonic transit time on the sonic transit time logging curve, a high neutron response on the neutron logging curve, and a low resistivity value on the dual lateral deep and shallow resistivity logging curve. The carbonaceous mudstone exhibits a high natural gamma response on the natural gamma logging curve, an extremely high sonic transit time on the sonic transit time logging curve, an extremely high neutron response on the neutron logging curve, and a medium to low resistivity value on the dual lateral deep and shallow resistivity logging curve. The coal seam exhibits a medium-high natural gamma response on the natural gamma logging curve, an extremely high sonic transit time on the sonic transit time logging curve, an extremely high neutron response on the neutron logging curve, and a medium resistivity value on the dual lateral deep and shallow resistivity logging curve.

4. The method for calculating the organic carbon content of mudstone according to claim 1, characterized in that, The classification of mudstone in the target area based on the logging response characteristics of the different types of mudstone includes: Based on core calibration logging, mudstone types are classified using the combined differences in the logging data.

5. The method for calculating the organic carbon content of mudstone according to claim 4, characterized in that, The method of classifying mudstone types based on core calibration logging and utilizing the combined differences in the logging data includes: The silty mudstone is preferentially identified based on the differences in natural gamma logging data, sonic transit time logging data, and resistivity logging data. The carbonaceous mudstone and the coal seam are identified based on the differences in sonic transit time logging data and neutron logging data compared to other mudstones. The carbonaceous mudstone and the coal seam are distinguished based on the sonic transit time logging data, and the remaining segments are pure mudstone segments.

6. The method for calculating the organic carbon content of mudstone according to claim 1, characterized in that, For different types of mudstone, separate models for calculating organic carbon content were established, including: For the carbonaceous mudstone and the coal seam, a regression fitting was performed between the sonic transit time logging curves from the well logging data and the measured organic carbon content from the core samples to establish a calculation model for the organic carbon content of the carbonaceous mudstone and the coal seam: TOC2 = 0.2012AC - 41.046 Wherein, TOC2 is the total organic carbon content of the carbonaceous mudstone and the coal seam, in mass percentage, and AC is the logging sonic transit time value, in microseconds per meter.

7. The method for calculating the organic carbon content of mudstone according to claim 1, characterized in that, For different types of mudstone, separate models for calculating organic carbon content were established, including: For the pure mudstone, an organic carbon content calculation model for the pure mudstone is established based on the superposition area of ​​resistivity and sonic transit time logging curves in the well logging data: ΔlgR=lg(R t / R 基线 )+0.02′(Δt-Dt 基线 ) TOC3=DlgR′10 (2.297-0.1688*LOM) Where ΔlgR is the area of ​​overlap between the resistivity and sonic transit time logging curves, which is dimensionless. t R is the deep resistivity logging value. 基线 The resistivity baseline of mudstone when TOC3 is close to zero, and Δt is the sonic transit time logging value. 基线 The sonic transit time baseline value of mudstone when TOC3 is close to zero, where TOC3 is the total organic carbon content of the pure mudstone, and LOM is an index reflecting the maturity of organic matter.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 7.

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