A method and device for identifying interlayer of development section of organic matter-rich shale

Abstract

The application discloses a method and device for identifying interlayer in rich organic matter shale development section, comprising: inputting a target well sensitive curve into a lithology identification model to obtain binary lithology data, and identifying non-rich organic matter shale in the binary lithology data as an interlayer; model establishment: determining a GR value lower limit of the rich organic matter shale according to binary lithology data of a coring section of a sample well; taking the binary lithology data and the sensitive curve section of the coring section as a sample, establishing a sample set, training a neural network model, establishing a lithology identification model, and inputting the GR value lower limit; sensitive curve screening: obtaining a plurality of sample points comprising binary lithology types and values of each to-be-screened curve; projecting the sample points on a coordinate system comprising a plurality of parallel number axes, connecting the projection points of the same sample points on different number axes to obtain a vein line; and screening the sensitive curve according to a distribution feature of the vein line. The method can quickly and accurately identify the interlayer in the rich organic matter shale development section through the sensitive curve screening and based on the binary lithology interpretation.

Description

Technical Field

[0001] The present invention relates to the technical field of geophysical well logging, and in particular to a method and device for identifying interlayers in organic-rich shale development sections. Background Art

[0002] In recent years, my country has seen a period of rapid development in the exploration and development of unconventional oil and gas resources. Continental shale oil resources, with their enormous potential, have become a key focus of exploration and development in the unconventional sector. Because continental lake basins are significantly influenced by terrigenous sediments, frequent interlayers are a common lithofacies characteristic of continental organic-rich shale intervals. The national standard GB / T31483-2015 stipulates the thickness and proportion of interlayers in organic-rich shale intervals: a single interlayer must be no thicker than 1 meter, and the cumulative interlayer thickness must account for less than 20% of the total thickness of the shale interval. The presence of interlayers significantly impacts the generation, displacement, migration, and accumulation of shale oil during its accumulation phase, as well as fracturing and seepage during its development phase. Therefore, detailed characterization of the frequent interlayers in organic-rich shale intervals is crucial.

[0003] Due to the high frequency and thin individual interlayers in organic-rich shale, current technologies often make it impossible to accurately identify interlayers solely through conventional logging and interpretation, unless systematic coring is performed. However, due to limited field exploration costs, large-scale drilling and coring wells are not practical. Shale oil production and research still rely primarily on logging and mud logging data, and even many older wells only have conventional log curves available. Summary of the Invention

[0004] The inventors discovered that existing lithology identification techniques based on conventional well logging curves have problems such as general applicability but insufficient precision, significant human interference, and low manual identification efficiency. Therefore, there is an urgent need to develop an efficient and high-precision interlayer identification technology suitable for organic-rich shale formations.

[0005] In order to at least partially solve the technical problems existing in the prior art, the inventors have made the present invention. Through specific implementation methods, they provide a method and device for identifying interlayers in organic-rich shale development sections. The method and device can quickly and accurately identify interlayers in organic-rich shale development sections through sensitivity curve screening and based on binary interpretation of lithology.

[0006] In a first aspect, an embodiment of the present invention provides a method for identifying interlayers in organic-rich shale development intervals, comprising:

[0007] Inputting the sensitivity curve of the target well into the lithologic identification model, obtaining the depth range of the organic-rich shale development section and the binary lithologic data within the range based on the model output, and identifying the non-organic-rich shale in the lithologic data as an interlayer;

[0008] The lithologic identification model is pre-established in the following manner: determining the GR value lower limit of organic-rich shale based on the binary lithologic data of the core section of the target layer of the sample well; using the binary lithologic data of the core section and the curve section of the selected sensitivity curve as a sample to establish a sample set; using the sample set to train a selected neural network model to establish a lithologic identification model, inputting the GR value lower limit into the lithologic identification model, and using the GR value lower limit to locate the organic-rich shale development section;

[0009] The type of sensitivity curve is pre-screened in the following manner: multiple sample points of the target layer are obtained, each sample point including a binary lithologic type and the value of each curve to be screened; each sample point is projected into a coordinate system containing multiple parallel axes, each axis corresponding to a different curve to be screened, and the projection points of the same sample point on different axes are connected to obtain a vein line; and at least one type of lithologic sensitivity curve is screened based on the distribution characteristics of the vein lines.

[0010] In a second aspect, an embodiment of the present invention provides a device for identifying interlayers in organic-rich shale development sections, the device comprising an interlayer identification module, a lithology identification model establishment module, and a sensitivity curve screening module;

[0011] The interlayer identification module is used to input the sensitivity curve of the target well into the lithologic identification model, obtain the depth range of the organic-rich shale development section and the binary lithologic data within the range based on the model output, and identify the non-organic-rich shale in the lithologic data as an interlayer;

[0012] The lithologic identification model is pre-established by the lithologic identification model establishment module in the following manner: determining a GR value lower limit of organic-rich shale based on binary lithologic data of a core section of a target layer in a sample well; establishing a sample set by taking the binary lithologic data of the core section and a curve section of a selected sensitivity curve as a sample; training a selected neural network model using the sample set to establish a lithologic identification model, inputting the GR value lower limit into the lithologic identification model, and using the GR value lower limit to locate the organic-rich shale development section;

[0013] The sensitivity curve screening module pre-screens the type of the sensitivity curve in the following manner: obtaining multiple sample points of the target layer, each sample point including a binary lithologic type and the value of each curve to be screened; projecting each sample point into a coordinate system containing multiple parallel axes, each axis corresponding to a different curve to be screened; connecting the projection points of the same sample point on different axes to obtain a vein line; and screening at least one type of lithologic sensitivity curve based on the distribution characteristics of the vein line.

[0014] In a third aspect, an embodiment of the present invention provides a computer storage medium having computer executable instructions stored therein. When the computer executable instructions are executed by a processor, the above-mentioned method for identifying interlayers in organic-rich shale development sections is implemented.

[0015] In a fourth aspect, an embodiment of the present disclosure provides a server comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for identifying interlayers in organic-rich shale development sections when executing the program.

[0016] The beneficial effects of the above technical solutions provided by the embodiments of the present invention include at least:

[0017] (1) The method for identifying interlayers in organic-rich shale development sections provided by the embodiment of the present invention is aimed at the specific scenario of identifying thin interlayers in organic-rich shale development sections. It abandons the universal pursuit of conventional well logging lithologic interpretation to interpret and characterize various lithologies developed in the study area separately, and limits the lithologic interpretation objects to two types: organic-rich shale and interlayers (non-organic-rich shale). This reduces the multi-solution nature of well logging lithologic interpretation and improves the interpretation accuracy. At the same time, it reduces the amount of calculation and improves the recognition speed of batch interpretation of multiple wells.

[0018] (2) The method for identifying intercalations in organic-rich shale development intervals provided by the present invention requires only core data from sample wells during the lithologic identification model establishment phase. Once the modeling is complete, only conventional well logging curves are required during the application phase to achieve high-precision characterization of organic-rich shale and intercalations. While ensuring interpretation accuracy, this significantly reduces the requirements for data quality.

[0019] (3) The method for identifying interlayers in organic-rich shale development sections provided by the embodiment of the present invention uses artificial intelligence methods such as neural networks to eliminate human influence as much as possible and further improve the reliability of lithologic interpretation.

[0020] (4) The method for identifying interlayers in organic-rich shale development sections provided by the embodiment of the present invention establishes a coordinate system containing multiple parallel axes, each axis corresponding to a different curve to be screened, and draws vein lines corresponding to different sample points in the coordinate system, thereby achieving synchronous comparison of curves of different types in the same coordinate system, improving the accuracy of sensitive curve selection, making it less likely that sensitive curves will be missed, and greatly improving screening efficiency.

[0021] (5) The method for identifying interlayers in organic-rich shale development sections provided by the embodiment of the present invention connects the projection points of the same sample point on different number axes to obtain a vein line. One sample point corresponds to one vein line, and the possibility of two vein lines completely overlapping is very small, thereby avoiding the situation where a large number of points overlap due to a simple point-projection method, and making the final lithologic distribution vein line map more complete and representative.

[0022] (6) The method for identifying intercalations in organic-rich shale development sections provided by the embodiment of the present invention reduces the influence of human subjective factors in the process of selecting sensitive curves and improves the objectivity of the selection results by converting the correlation between logging curves and lithology into more intuitive line convergence and differentiation degrees.

[0023] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0024] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0026] Figure 1 This is a flow chart of a method for establishing a lithologic identification model for an organic-rich shale development section in Example 1 of the present invention;

[0027] Figure 2 This is a flow chart of the method for simultaneous screening of multiple wells, multiple curves, and multiple lithologic sensitivity curves in Example 2 of the present invention;

[0028] Figure 3 This is an example diagram of a sample well in the fourth embodiment of the present invention;

[0029] Figure 4 This is an example diagram of the simultaneous comparison and screening of sensitivity curves of all sample points and the lithologic distribution vein lines in the fourth embodiment of the present invention;

[0030] Figure 5 This is a process diagram of establishing a lithology identification model through a neural network in the fourth embodiment of the present invention;

[0031] Figure 6 A comparison diagram of the lithologic profile and core description of the sample well automatically established using the lithologic identification model in the fourth embodiment of the present invention;

[0032] Figure 7 This is a comparison chart of the calibration results of the calibration well in Example 4 of the present invention;

[0033] Figure 8 Schematic diagram of the structure of the device for identifying interlayers in organic-rich shale development sections according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0035] It should be understood that the terms described herein are intended only to describe particular embodiments and are not intended to limit the present invention. In addition, for numerical ranges herein, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Each smaller range between any intermediate value within a stated value or stated range and any other stated value or intermediate value within the stated range is also encompassed by the present invention. The upper and lower limits of these smaller ranges may be independently included or excluded within the scope.

[0036] Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the invention belongs. Although the present invention describes only preferred methods and materials, any methods and materials similar or equivalent to those described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials related to the documents. In the event of any conflict with any incorporated document, the content of this specification shall prevail.

[0037] In the embodiment of the present application, the TOC lower limit of organic-rich shale is used as a standard to classify organic-rich shale and non-organic-rich shale. Specifically, the lithology of rocks with TOC greater than the lower limit is defined as organic-rich shale; the lithology of rocks with TOC not greater than the lower limit is defined as non-organic-rich shale.

[0038] The lower limit of TOC of organic-rich shale is flexibly set according to the actual geological conditions of the study area, and can usually be set to 2%, 3% or 5%. Optionally, it can also be set to other values.

[0039] Example 1

[0040] The first embodiment of the present invention provides a method for establishing a lithologic identification model for an organic-rich shale development section, and the process thereof is as follows: Figure 1 As shown, the following steps are included:

[0041] Step S11: Determine the lower limit of the GR value of the organic-rich shale based on the binary lithologic data of the core section of the target layer of the sample well.

[0042] Wells cored in the target layer of the study area are selected as sample wells. Optionally, the selected wells can be divided into sample wells and verification wells. The relevant data of the verification wells do not participate in the training of the model and are only used for the later verification of the model.

[0043] Specifically, the binary lithologic data of the coring section, including organic-rich shale and non-organic-rich shale, is obtained from the core description data, thin section identification data, and TOC test data of the coring section of the target layer of the sample well. The acquisition process includes the following steps:

[0044] (1) Based on the thin section identification data of the core section of the target layer of the sample well, the lithologic description data in the core description data is corrected to obtain preliminary lithologic data, which includes mud shale.

[0045] The lithologic descriptions in core data are merely empirical descriptions of the cores made by geologists based on visual observation. Due to the limitations of visual observation and subjective factors, the accuracy of these lithologic descriptions requires further refinement. While thin section identification data can accurately assign lithologic names, thin section identification data is limited in sampling. Therefore, preliminary lithologic data is obtained based on the lithologic descriptions in the core data and verified with thin section identification data.

[0046] The preliminary description data obtained may include clastic rocks such as shale, coarse sandstone, medium sandstone, fine sandstone and siltstone, and may also include carbonate rocks or other rock types.

[0047] (2) Based on the TOC detection data, the mud shale with a TOC value greater than the preset TOC lower limit of organic-rich shale in the preliminary lithologic data is determined as organic-rich shale, and the binary lithologic data of the coring section are obtained.

[0048] Mud shales with TOC values ​​not greater than the lower limit of TOC of organic-rich shales, as well as other lithologies other than mud shales, are identified as non-organic-rich shales.

[0049] The lower limit of TOC of organic-rich shale is flexibly set according to the actual geological conditions of the study area, and can usually be set to 2%, 3% or 5%. Optionally, it can also be set to other values.

[0050] Determining the lower limit of the GR value of organic-rich shale can include extracting the lithology and GR values ​​of sampling points with thin section identification data and TOC detection data based on the binary lithology data and GR curves of the sample wells; and statistically obtaining the lower limit of the GR value of the organic-rich shale based on the lithology and GR values ​​of multiple sampling points.

[0051] Since the lithology identification in this embodiment is based on the well logging curve, the well logging curve of the sample well must first be standardized; after obtaining the preliminary lithology data or binary lithology data of the coring section, the standardized well logging curve is depth-reset.

[0052] The GR values ​​of the above sampling points are extracted from the GR curve after standardization and depth resetting.

[0053] Step S12: The binary lithologic data of the coring section and the curve segment of the selected sensitivity curve are taken as a sample to establish a sample set.

[0054] In some embodiments, after obtaining the binary lithologic data of the coring section, the method further includes screening the sensitivity curves of organic-rich shale and non-organic-rich shale from all types of well logging curves of the sample well.

[0055] Sensitivity analysis can be used to optimize lithologic sensitivity curves for organic-rich shale, identifying the curve most sensitive to the organic-rich shale and its interlayers. Lithologic sensitivity analysis automatically calculates the correlation between each curve and lithology, directly selecting the curve with the best correlation. Alternatively, the method described in Example 2 can be used to screen sensitivity curves.

[0056] Step S13: Use the sample set to train the selected neural network model, establish a lithology recognition model, and input the GR value lower limit into the lithology recognition model.

[0057] The lower limit of the GR value is used to locate the development interval of organic-rich shale.

[0058] Example 2

[0059] The second embodiment of the present invention provides a method for synchronous screening of multiple wells, multiple curves and multiple lithologic sensitivity curves. Figure 2 As shown, the following steps are included:

[0060] Step S21: Acquire multiple sample points of the target layer.

[0061] Each sample point includes the binary lithology type and the value of each curve to be screened.

[0062] The curve to be screened may be depth-calibrated based on the binary lithologic data of the sample well in the first embodiment, and a matching relationship between the lithologic data and the curve to be screened in depth may be established to obtain a well logging lithologic calibration chart.

[0063] The extraction of sample points can be based on the sampling points of the logging curve. For example, if the logging curve is sampled every 0.125m, the sample points are also sampled every 0.125m, or the samples are sampled after thinning.

[0064] The sampling method of the above-mentioned sample points is different from the traditional method of using rock sample collection points as calibration points. This improvement will greatly improve the richness and vertical continuity of sample data.

[0065] Multiple wells with continuous core samples in the target layer of the study area can be identified as sample wells. The sample wells are representative and comprehensive, thereby greatly reducing the impact of geological heterogeneity on the accuracy of lithologic sensitivity curve screening.

[0066] Step S22: Project each sample point into a coordinate system containing multiple parallel axes, where each axis corresponds to a different curve to be screened.

[0067] Based on the well logging lithology calibration chart, the numerical distribution range of each curve to be screened is statistically analyzed.

[0068] Establish a coordinate system consisting of multiple parallel axes, with the axes aligned at both ends and their values ​​matching the corresponding numerical distribution range. This can be achieved by aligning the values ​​at both ends with the corresponding numerical distribution range, or by ensuring that the minimum value is less than the minimum value in the numerical distribution range, and the maximum value is greater than the maximum value in the numerical distribution range.

[0069] Taking the well logging curve GR as an example, if the numerical distribution range of GR in the target layer is 200 API to 500 API, the two end values ​​of the number axis corresponding to the well logging curve GR can be set to 200 API and 500 API respectively.

[0070] In some embodiments, the numerical values ​​of the axes in the established coordinate system change in the same direction.

[0071] Taking the example of each number axis being distributed vertically, the values ​​of each number axis gradually increase from top to bottom, or gradually decrease from top to bottom.

[0072] Step S23: Connect the projection points of the same sample point on different number axes to obtain a vein line.

[0073] Use line type or color to distinguish rock types.

[0074] Step S24: screening at least one type of lithologic sensitivity curve according to the distribution characteristics of the vein lines.

[0075] In some embodiments, the method may include screening at least one type of lithologic sensitivity curve based on the following knowledge, according to the distribution characteristics of the vein lines on the number axis:

[0076] The more convergent the vein lines of organic-rich shale on a certain axis and the more distinct they are from the vein lines of non-organic-rich shale, the more sensitive the screening curve corresponding to that axis is to organic-rich shale, and vice versa.

[0077] The method for simultaneous screening of multi-well, multi-curve, and multi-lithologic sensitivity curves provided in the second embodiment of the present invention reduces the influence of human subjective factors in the sensitivity curve selection process and improves the objectivity of the selection results by converting the correlation between logging curves and lithology into a more intuitive line convergence and differentiation degree.

[0078] The multi-well multi-curve multi-lithology sensitive curve synchronous screening method provided by the second embodiment of the present application establishes a coordinate system comprising a plurality of parallel number axes, each number axis corresponding to a different curve to be screened, and plots the vein lines corresponding to different sample points in the coordinate system, thereby achieving the synchronous comparison of curves of different types in the same coordinate system, improving the accuracy of sensitive curve selection, reducing the likelihood of sensitive curve omission, and greatly improving the screening efficiency.

[0079] The multi-well multi-curve multi-lithology sensitive curve synchronous screening method provided by the second embodiment of the present application connects the projection points of the same sample point on different number axes to obtain a vein line, and one sample point corresponds to one vein line, and the likelihood of two vein lines completely coinciding is very small, thereby avoiding the situation of a large number of projection point coincidences caused by the simple projection point method, and making the final obtained lithology distribution vein line graph more complete and representative.

[0080] Embodiment three

[0081] The method for identifying the interlayer of the organic matter-rich shale development section provided by the third embodiment of the present application comprises the following steps: inputting the sensitive curve of a target well into a lithology identification model, obtaining the depth range of the organic matter-rich shale development section and the binary lithology data in the range according to the model output result, and identifying the non-organic matter-rich shale in the lithology data as an interlayer.

[0082] The lithology identification model is pre-established by the method in the first embodiment.

[0083] The type of the sensitive curve is pre-screened by the method in the second embodiment.

[0084] The working principle of the lithology identification model is that the GR value lower limit of the organic matter-rich shale is used to preliminarily locate the organic matter-rich shale development section, the binary lithology data of the organic matter-rich shale development section is determined according to the neural network learning result of the sensitive curve of the organic matter-rich shale development section, and the result for output is obtained.

[0085] In some embodiments, before the sensitive curve of the target well is input into the lithology identification model, the method further comprises the following steps: using the cutting logging data to remove the non-organic matter-rich shale high GR value outliers of the sensitive curve of the target well. For example, the interference of special high GR rocks on the high GR response of the organic matter-rich shale is removed, such as high uranium content sandstone.

[0086] The working principle of the method for identifying interlayers in organic-rich shale development sections provided in Example 3 of the present application is as follows: Based on the fact that organic-rich shale is formed in a reducing environment that makes uranium more easily precipitated, resulting in organic-rich shale having high uranium and high natural gamma characteristics that are different from other lithologies, natural gamma is combined with conventional rock cuttings logging to eliminate interference from other non-shale high-GR rocks (such as sandstone-type uranium deposits) and identify the vertical distribution of high-GR organic-rich shale development sections. In the identified organic-rich shale development sections, the correlation between various conventional well logging curves and organic-rich shale is automatically calculated, and the curve with the best correlation is directly selected as the input curve. A neural network method is used to automatically establish a lithologic identification model for organic-rich shale and non-organic-rich shale. The lithologic identification model is continuously verified and iterated through calibration wells to ensure that the interpretation results are consistent with the actual results. Ultimately, various conventional well logging curves are input to the front end, and the terminal automatically generates a high-precision lithologic profile containing organic-rich shale and non-organic-rich shale.

[0087] The method for identifying interlayers in organic-rich shale development sections provided in Example 3 of the present invention is aimed at the specific scenario of identifying thin interlayers in organic-rich shale development sections. It abandons the universal pursuit of conventional well logging lithologic interpretation to interpret and characterize various lithologies developed in the study area separately, and limits the lithologic interpretation objects to two types: organic-rich shale and interlayers (non-organic-rich shale). This reduces the multi-solution nature of well logging lithologic interpretation and improves the interpretation accuracy; at the same time, it reduces the amount of calculation and improves the recognition speed of batch interpretation of multiple wells.

[0088] The method for identifying intercalations in organic-rich shale intervals, provided in Example 3 of the present invention, requires core data from sample wells only during the lithologic identification model development phase. Once the model is complete, conventional well logging curves are sufficient for high-precision characterization of organic-rich shale and intercalations. This significantly reduces the requirements for data quality while ensuring interpretation accuracy.

[0089] The method for identifying intercalations in organic-rich shale development intervals provided in the third embodiment of the present invention uses artificial intelligence methods such as neural networks to eliminate human influence as much as possible and further improve the reliability of lithologic interpretation.

[0090] Example 4

[0091] The fourth embodiment of the present invention provides a specific application of a method for identifying interlayers in organic-rich shale development intervals based on lithologic binarization, comprising the following steps:

[0092] S101: In a shale oil and gas exploration block, two wells with continuous coring in an organic-rich shale development interval were selected as sample wells and calibration wells, respectively, to provide test samples and lithologic description objects for lithologic classification and high-precision lithologic characterization of the organic-rich shale development interval.

[0093] Figure 3, Figure 7 The selected sample well and the check well in the embodiment are respectively shown, and both of the wells are systematically cored in the organic matter rich shale development section and the overlying and underlying strata thereof, and the conventional logging curves are complete.

[0094] S102: In combination with core observation, thin section identification and TOC detection data, the lithology of the organic matter rich shale development section is binarized: the mud grade mineral composition ratio of the organic matter rich shale is not less than 50%, and TOC is greater than or equal to 2.0%; the mud grade mineral of the interlayer is less than 50% or TOC is less than 2.0%.

[0095] S103: The lower limit of the GR value of the organic matter rich shale is counted.

[0096] The GR value of the organic matter rich shale sample collection point determined in the step S102 is counted to determine the lower limit of the GR value of the organic matter rich shale. In the embodiment, according to the mathematical statistics result, the lower limit of the GR value of the organic matter rich shale is defined as 180 API, and the strata with the GR value above the lower limit are defined as the interlayer.

[0097] The lower limit value in the embodiment is only for a specific area, and the lower limit of the GR value of the organic matter rich shale in different areas is different, but can be determined through the combing and counting of the TOC value and the GR value of the rock sample.

[0098] When the selected high GR strata (the GR value is greater than the lower limit of the GR value of the organic matter rich shale) are applied in the wells other than the sample well, the high GR strata which are not shale, such as sandstone type uranium, should be removed in combination with the conventional cutting logging.

[0099] After the distribution of the high GR organic matter rich shale is determined, the longitudinal distribution range of the organic matter rich shale development section is determined by the index that the interlayer ratio is not more than 20% in the embodiment, such as the layer section with the depth between 1240.20m and 1255.26m shown in FIG. 5. Figure 3

[0100] S104: The lithology sensitive curve is selected by the sensitivity analysis method for the organic matter rich shale development section determined in the step S103.

[0101] In this step, only the sensitivity curve of the organic matter rich shale and the interlayer is selected, the lithology type is less, and the response of the determined sensitive curve to the corresponding lithology is more sensitive and more accurate.

[0102] In the embodiment, the lithology interpretation sensitive curve of the organic matter rich shale development section is determined by the lithology sensitivity analysis, which is GR, CNL and AC. Among the five curves, the GR, CNL and AC curves realize the clear distinction of the organic matter rich shale and the sandstone interlayer, as shown in FIG. 6. Figure 4

[0103] ​​S105: In the embodiment, the lithology interpretation sensitive curves GR, CNL and AC are taken as input curves, and a binary lithology identification model of the organic-rich shale and interlayer is established based on the three sensitive curves through a neural network algorithm. The model establishment process is as shown in Figure 5

[0104] S106: In the embodiment, the binary lithology identification model is used to convert the GR, CNL and AC curve data of the sample well into the data of two lithologies of the organic-rich shale and the interlayer, and automatically generate a lithology profile. The established lithology profile is as shown in Figure 6

[0105] S107: In the embodiment, the lithology identification model is applied to the check well. The lithology profile is automatically generated by using the conventional logging curve data of the GR, CNL and AC curves in the check well, and the established lithology profile is as shown in Figure 7

[0106] Based on the inventive concept of the present application, the embodiment of the present application further provides a device for identifying the interlayer of the organic-rich shale development section, and the structure thereof is as shown in Figure 8

[0107] The interlayer identification module 81 is configured to input the sensitive curves of a target well into a lithology identification model, obtain the depth range of the organic-rich shale development section and binary lithology data in the range according to the output result of the model, and identify the non-organic-rich shale in the lithology data as the interlayer.

[0108] The lithology identification model is pre-established by the lithology identification model establishment module 82 in the following manner: the lower limit of the GR value of the organic-rich shale is determined according to the binary lithology data of the cored section of the target layer of a sample well; the binary lithology data of the cored section and the curve section of the selected sensitive curve are taken as a sample to establish a sample set; the selected neural network model is trained by using the sample set to establish the lithology identification model; and the lower limit of the GR value is input into the lithology identification model, and the lower limit of the GR value is used to locate the organic-rich shale development section.

[0109] ​​​​The type of the sensitivity curve is pre-screened by the sensitivity curve screening module 83 in the following manner: a plurality of sample points of the target layer are obtained, each sample point comprising a binary lithology type and a value of each curve to be screened; each sample point is projected in a coordinate system comprising a plurality of parallel number axes, each number axis corresponding to a different curve to be screened, and a vein line is obtained by connecting the projection points of the same sample point on different number axes; and at least one type of lithology sensitive curve is screened according to the distribution characteristics of the vein line.

[0110] In some embodiments, the lithology identification model establishing module 82 is configured to:

[0111] According to the thin section identification data of the coring section of the target layer of the sample well, the lithology description data in the core description data is corrected to obtain preliminary lithology data, the preliminary lithology data comprising shale; and according to the TOC detection data, the shale with a TOC value greater than a preset lower limit value of TOC of the organic-rich shale in the preliminary lithology data is determined as the organic-rich shale to obtain binary lithology data of the organic-rich shale of the coring section.

[0112] In some embodiments, the lithology identification model establishing module 82 is configured to:

[0113] Based on the binary lithology data and the GR curve of the sample well, the lithology and GR value of the sampling point with thin section identification data and TOC detection data are extracted; and according to the lithology and GR value of the plurality of sampling points, the lower limit of the GR value of the organic-rich shale is statistically obtained.

[0114] In some embodiments, the interlayer identification module 81 is further configured to:

[0115] The sensitivity curve of the target well is subjected to non-organic-rich shale high GR abnormal value elimination by using the cutting logging data.

[0116] In some embodiments, the sensitivity curve screening module 83 is configured to screen at least one type of lithology sensitive curve according to the distribution characteristics of the vein line on the number axis based on the following understanding:

[0117] The more the vein lines of the organic-rich shale converge on a number axis, and the more obvious the differentiation between the vein lines of the non-organic-rich shale, the stronger the sensitivity of the curve to be screened corresponding to the number axis to the organic-rich shale, and vice versa.

[0118] The specific execution manner of the above steps is described in Embodiment One, Embodiment Two and Embodiment Three.

[0119] Based on the inventive concept of the present application, the embodiment of the present application further provides a computer storage medium, wherein computer executable instructions are stored in the computer storage medium, and the computer executable instructions are executed by a processor to implement the method for identifying the interlayer of the organic matter rich shale development section.

[0120] Based on the inventive concept of the present application, the embodiment of the present application further provides a server, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the program to implement the method for identifying the interlayer of the organic matter rich shale development section.

[0121] Unless specifically stated otherwise, terms such as processing, computing, calculating, determining, displaying, and the like, can refer to an action and / or process of one or more processing or computing systems, or similar devices, that manipulate and / or transform data represented as physical (e.g., electronic) quantities within the processing system's registers and / or memories into other data similarly represented as physical quantities within the processing system's memories, registers or other such information storage, transmission or display devices. Information and signals can be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that can be referenced throughout the above description can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0122] It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example. Based upon design preferences, the specific order or hierarchy of steps in the processes can be re-arranged while remaining within the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and as such the order is not limited by the accompanying scheme.

[0123] In the above detailed description, various features are grouped together in single embodiments for the purpose of streamlining the disclosure. This disclosed approach is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are explicitly recited in each claim. Rather, as the following claims reflect, inventive subject matter can lie in fewer than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into this detailed description, where each claim by itself is a separate embodiment of the present application.

[0124] Those skilled in the art will also appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments herein may be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described around their functions. Whether such functions are implemented as hardware or software depends on the specific application and the design constraints imposed on the entire system. A skilled person may implement the described functions in an adaptable manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of this disclosure.

[0125] The steps of the methods or algorithms described in conjunction with the embodiments herein may be directly embodied as hardware, software modules executed by a processor, or a combination thereof. The software module may be located in a RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, register, hard disk, removable disk, CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and storage medium may also be present in a user terminal as discrete components.

[0126] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or external to the processor. In the latter case, it is communicatively coupled to the processor via various means, which are well known in the art.

[0127] The foregoing description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purposes of describing the above embodiments, but one of ordinary skill in the art will recognize that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to encompass all such changes, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent the term "comprising" is used in the specification or claims, the term is intended to be encompassed in a manner similar to the term "including," as explained in terms of "including," used as a transitional word in the claims. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or."

Claims

1. A method for identifying interlayers in organic-rich shale development intervals, characterized in that: include: Inputting the sensitivity curve of the target well into the lithologic identification model, obtaining the depth range of the organic-rich shale development section and the binary lithologic data within the range based on the model output, and identifying the non-organic-rich shale in the lithologic data as an interlayer; The lithologic identification model is pre-established in the following manner: determining the GR value lower limit of organic-rich shale based on the binary lithologic data of the core section of the target layer of the sample well; using the binary lithologic data of the core section and the curve section of the selected sensitivity curve as a sample to establish a sample set; using the sample set to train a selected neural network model to establish a lithologic identification model, inputting the GR value lower limit into the lithologic identification model, and using the GR value lower limit to locate the organic-rich shale development section; The type of sensitivity curve is pre-screened in the following manner: multiple sample points of the target layer are obtained, each sample point including a binary lithologic type and the value of each curve to be screened; each sample point is projected into a coordinate system containing multiple parallel axes, each axis corresponding to a different curve to be screened, and the projections of the same sample point on different axes are connected to obtain a vein line; based on the distribution characteristics of the vein lines on the axes, at least one type of lithologic sensitivity curve is screened based on the following understanding: the more convergent the vein lines of organic-rich shale on a certain axis and the more distinct the difference between the vein lines of organic-rich shale and non-organic-rich shale, the greater the sensitivity of the curve to be screened corresponding to that axis to the organic-rich shale, and vice versa.

2. The method according to claim 1, characterized in that The model output result is obtained from the lithology identification model in the following way: Locating the organic-rich shale development section according to the GR value lower limit and the GR curve of the target well; According to the neural network learning result of the sensitive curve segment corresponding to the organic-rich shale development segment, the binary lithologic data thereof is determined to obtain a result for output.

3. The method according to claim 1, characterized in that The binary lithologic data of the core sampling section of the target layer of the sample well is obtained based on the core description data, thin section identification data and TOC detection data of the core sampling section of the target layer of the sample well.

4. The method according to claim 3, characterized in that The binary lithologic data of the core section of the target layer of the sample well is obtained by the following method: According to the thin section identification data of the core section of the target layer of the sample well, the lithology description data in the core description data is corrected to obtain preliminary lithology data, wherein the preliminary lithology data includes mud shale; According to the TOC detection data, the mud shale with a TOC value greater than a preset lower limit of TOC of organic-rich shale in the preliminary lithological data is determined as organic-rich shale, and the binary lithological data of the organic-rich shale in the coring section is obtained.

5. The method according to claim 4, characterized in that Determining the lower limit of the GR value of organic-rich shale includes: Based on the binary lithologic data and GR curve of the sample well, the lithologic properties and GR values ​​of the sampling points with thin section identification data and TOC detection data are extracted; Based on the lithology and GR values ​​of multiple sampling points, the lower limit of the GR value of organic-rich shale was obtained statistically.

6. The method according to claim 1, characterized in that Before inputting the sensitivity curve of the target well into the lithologic identification model, the method further includes: Using cuttings logging data, the sensitivity curve of the target well is used to eliminate high GR outliers of non-organic-rich shale.

7. The method according to claim 1, characterized in that The two ends of the number axis in the coordinate system are aligned, and the values ​​at the two ends match the numerical distribution range of the corresponding to-be-screened curve determined according to the multiple sample points.

8. A device for identifying interlayers in organic-rich shale development sections, characterized by: The device includes an interlayer identification module, a lithology identification model establishment module and a sensitivity curve screening module; The interlayer identification module is used to input the sensitivity curve of the target well into the lithologic identification model, obtain the depth range of the organic-rich shale development section and the binary lithologic data within the range based on the model output, and identify the non-organic-rich shale in the lithologic data as an interlayer; The lithologic identification model is pre-established by the lithologic identification model establishment module in the following manner: determining a GR value lower limit of organic-rich shale based on binary lithologic data of a core section of a target layer in a sample well; establishing a sample set by taking the binary lithologic data of the core section and a curve section of a selected sensitivity curve as a sample; training a selected neural network model using the sample set to establish a lithologic identification model, inputting the GR value lower limit into the lithologic identification model, and using the GR value lower limit to locate the organic-rich shale development section; The sensitivity curve screening module pre-screens the type of the sensitivity curve in the following manner: obtaining multiple sample points of the target layer, each sample point including a binary lithologic type and the value of each curve to be screened; projecting each sample point into a coordinate system comprising multiple parallel axes, each axis corresponding to a different curve to be screened; connecting the projections of the same sample point on different axes to obtain a vein line; and screening at least one type of lithologic sensitivity curve based on the distribution characteristics of the vein lines on the axes and the following understanding: the more convergent the vein lines of organic-rich shale on a certain axis and the more distinct the difference between the vein lines of non-organic-rich shale and organic-rich shale, the greater the sensitivity of the curve to be screened corresponding to that axis to the organic-rich shale, and vice versa.

9. A computer storage medium, characterized in that The computer storage medium stores computer-executable instructions, which, when executed by a processor, implement the method according to any one of claims 1 to 7.

10. A server, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program.

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