Method, device and equipment for identifying shale facies rich in organic matters and medium

Abstract

The invention discloses an organic matter-rich shale lithofacies identification method and device, equipment and a medium. The method comprises the following steps: according to test data of at least one single well in a target interval in a target area, determining paleosalinity property data of each sub-area in the target area and at least one lithofacies type developed in the target interval; determining a target logging model corresponding to each lithofacies type according to each lithofacies type; according to the paleo-salinity property data of each sub-region, each lithofacies type and each target logging model, constructing a paleo-salinity-lithofacies-logging model; and according to the paleosalinity-lithofacies-logging model and a preset index parameter threshold value, determining an identification result of the organic matter-rich shale lithofacies of each single well in the target interval. According to the technical scheme, the lithofacies models under different water medium conditions are established, so that the organic matter-rich shale lithofacies are identified, and the identification precision of the organic matter-rich shale lithofacies is improved.

Description

Technical Field

[0001] The present application relates to the field of oil and gas exploration technology, and in particular to a method, device, equipment and medium for identifying organic-rich shale lithofacies. Background Art

[0002] With the decline of conventional oil and gas production and the continuous breakthroughs in shale oil and gas exploration and development technology at home and abroad, unconventional oil and gas has become an important successor to the exploration of global oil and gas resources. Different from the technical idea of ​​finding traps in conventional oil and gas exploration, the focus of unconventional oil and gas exploration and development is to evaluate and optimize the "sweet spots" of unconventional oil and gas geology and engineering based on the comprehensive use of geological, seismic and logging data.

[0003] Studies have shown that my country's continental shale oil resources are very rich, with a wide geological age span and a large number of distribution basins. According to current research, compared with North American marine shales, my country's continental shales have the characteristics of diverse sedimentary environments, complex mineral compositions, diverse vertical superposition relationships, large cumulative thickness, strong heterogeneity, and low degree of thermal evolution. The overall geological conditions are relatively complex, but they also have broad exploration prospects. Among them, controlled by factors such as the complex mineral composition of the shale strata, the development of laminae, and the diverse vertical superposition relationships, the shale lithofacies vary greatly in time and space, which brings certain difficulties to the evaluation and optimization of geological "sweet spots".

[0004] At present, many methods have been proposed for the identification of terrestrial shale lithofacies in my country, mainly focusing on the following aspects: first, to analyze the single characteristics of shale hydrocarbon generation, reservoir generation, oil content and engineering geological conditions, so as to realize the identification and selection of shale lithofacies; second, to divide the lithofacies types through multi-factor analysis such as shale mineral composition, hydrocarbon generation conditions, and reservoir generation conditions; third, to quantitatively characterize shale lithofacies through well logging, so as to realize the identification and division of shale lithofacies.

[0005] However, my country's continental lake basins are affected by factors such as paleo-tectonic, paleoclimate and paleo-water medium conditions, and large-scale shale deposits can be developed in freshwater, brackish water, salt water and salt lakes. In general, with the increase of lake water salinity, the chemical origin of carbonate and sulfate mineral components will increase; secondly, lake water salinity not only has an important control effect on the types and number of organisms in the lake, but also changes in lake water salinity will affect the preservation of organic matter; finally, in brackish water and salt water lakes, shale is subject to frequent seasonal changes and is more likely to form a laminated structure. In response to the above problems, the existing lithofacies division method is difficult to meet the identification and division of lithofacies or lithofacies combinations in shale formations under different water medium conditions. Summary of the invention

[0006] The present application provides a method, device, equipment and medium for identifying organic-rich shale lithofacies. By establishing lithofacies models under different water medium conditions, the identification of organic-rich shale lithofacies can be achieved, thereby improving the identification accuracy of organic-rich shale lithofacies.

[0007] According to one aspect of the present application, a method for identifying an organic-rich shale facies is provided, the method comprising:

[0008] According to the test data of at least one single well in the target area in the target layer section, determine the paleo-salinity property data of each sub-area in the target area and at least one lithofacies type developed in the target layer section; wherein the test data includes core and well logging data;

[0009] According to each of the lithofacies types, determining a target logging model corresponding to each of the lithofacies types;

[0010] Constructing a paleosalinity-lithofacies-logging model according to the paleosalinity property data of each sub-area, the lithofacies types and the target logging models;

[0011] According to the paleo-salinity-lithofacies-logging model and the preset index parameter threshold, the identification result of the organic-rich shale lithofacies in the target layer section of each single well is determined; wherein the preset index parameter threshold is used to represent the characteristics of the organic-rich shale lithofacies.

[0012] According to another aspect of the present application, a device for identifying organic-rich shale facies is provided, the device comprising:

[0013] A basic parameter determination module is used to determine the paleo-salinity property data of each sub-area in the target area and at least one lithofacies type developed in the target layer section according to the test data of at least one single well in the target area; wherein the test data includes core and well logging data;

[0014] A well logging model determination module, used to determine a target well logging model corresponding to each of the lithofacies types according to the lithofacies types;

[0015] A composite model building module, for building a paleo-salinity-lithofacies-well logging model according to the paleo-salinity property data of each sub-region, the lithofacies types and the target well logging models;

[0016] The lithofacies identification module is used to determine the identification result of the organic-rich shale lithofacies in the target layer of each single well according to the paleo-salinity-lithofacies-well logging model and the preset index parameter threshold; wherein the preset index parameter threshold is used to represent the characteristics of the organic-rich shale lithofacies.

[0017] According to another aspect of the present application, a device for identifying organic-rich shale facies is provided, the device comprising:

[0018] at least one processor; and

[0019] a memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for identifying the organic-rich shale facies described in any embodiment of the present application.

[0021] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the method for identifying the organic-rich shale facies described in any embodiment of the present application when executed by a processor.

[0022] The technical solution provided by the present application determines the paleo-salinity property data of each sub-area in the target area and at least one lithofacies type developed in the target layer according to the test data of at least one single well in the target area in the target layer; wherein the test data includes core and well logging data; according to each lithofacies type, a target well logging model corresponding to each lithofacies type is determined; according to the paleo-salinity property data of each sub-area, each lithofacies type and each target well logging model, a paleo-salinity-lithofacies-well logging model is constructed; according to the paleo-salinity-lithofacies-well logging model and the preset index parameter threshold, the identification result of the organic-rich shale lithofacies of each single well in the target layer is determined; wherein the preset index parameter threshold is used to represent the characteristics of the organic-rich shale lithofacies. The present technical solution realizes the identification of the organic-rich shale lithofacies by establishing the lithofacies model under different water medium conditions, thereby improving the identification accuracy of the organic-rich shale lithofacies.

[0023] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1A flow chart of a method for identifying organic-rich shale lithofacies provided in Example 1 of the present application;

[0026] Figure 2 A flow chart of a method for identifying organic-rich shale lithofacies provided in Example 2 of the present application;

[0027] Figure 3 A schematic diagram of the structure of a device for identifying organic-rich shale facies provided in Example 3 of the present application;

[0028] Figure 4 It is a structural schematic diagram of an apparatus for implementing a method for identifying an organic-rich shale lithofacies according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "target", "preset", "sub" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] Embodiment 1

[0032] Figure 1 This is a flow chart of a method for identifying an organic-rich shale facies provided in Example 1 of the present application. This embodiment is applicable to the case of identifying an organic-rich shale facies. The method can be executed by an organic-rich shale facies identification device. The organic-rich shale facies identification device can be implemented in the form of hardware and / or software. The organic-rich shale facies identification device can be configured in a device with data processing capabilities. Figure 1 As shown, the method includes:

[0033] S110, based on the test data of at least one single well in the target area in the target layer section, determine the paleo-salinity property data of each sub-area in the target area and at least one lithofacies type developed in the target layer section, wherein the test data includes core and well logging data.

[0034] The target area may be an area where shale is located, such as a continental lake basin. The sub-area may be an area obtained by dividing the target area according to actual needs. Well logging data includes cores and well logging data. Cores can be obtained by drilling and can be made into core slices for subsequent research. Well logging data can be obtained by electrical logging or non-electrical logging, such as natural gamma, density, resistivity, natural potential, etc.

[0035] Salinity refers to the mass fraction of all soluble salts in a medium and is one of the main markers for distinguishing marine and terrestrial environments. Paleosalinity refers to the salinity preserved in ancient sediments.

[0036] The paleo-salinity property data may be the salinity property of the sedimentary water body, such as fresh water, brackish water, salt water, etc. The lithofacies type may be the lithofacies type of all lithofacies developed in the target interval, such as limestone, mudstone, felsic shale, etc.

[0037] In this scheme, the paleo-salinity restoration of the test data of the target layer section can be carried out through the predetermined paleo-salinity restoration method to determine the paleo-salinity property data; the physical property parameters of the target layer section can be determined through the test data, and then the lithofacies types can be divided according to the physical property parameters.

[0038] Optionally, based on the test data of at least one single well in the target area in the target layer segment, the paleo-salinity property data of each sub-area in the target area and at least one lithofacies type developed in the target layer segment are determined, including: based on the cores of at least one single well in the target area in the target layer segment, paleo-salinity restoration is performed on the cores of multiple single wells in the target area in the target layer segment to determine the paleo-salinity property data of each sub-area in the target area; based on the test data of at least one single well in the target area in the target layer segment, at least one lithofacies type developed in the target layer segment is determined.

[0039] In this scheme, paleo-salinity restoration can be performed on a large number of core samples obtained from each single well in the target layer, and the paleo-salinity property data of each single well can be determined according to the predetermined paleo-salinity range standard.

[0040] Optionally, based on the cores of at least one single well in the target area in the target interval, the cores of multiple single wells in the target area in the target interval are subjected to paleosalinity restoration to determine the paleosalinity property data of each sub-area in the target area, including: based on the cores of at least one single well in the target area in the target interval, the cores of multiple single wells in the target area in the target interval are subjected to paleosalinity restoration according to at least one paleosalinity restoration method to determine the paleosalinity values corresponding to each sub-area in the target area and each paleosalinity restoration method; based on the respective paleosalinity values, the paleosalinity property data of each sub-area in the target area is determined.

[0041] Among them, the paleosalinity restoration methods include sedimentary phosphate method, oxygen isotope method, carbon isotope method, trace element method, etc. In order to accurately restore the paleosalinity environment, one or more paleosalinity restoration methods can be used to restore the paleosalinity of the cores of at least one single well in the target area in the target interval.

[0042] Exemplarily, if the Sr / Ba ratio of the core sample is used as the paleosalinity value of the single well in the target interval, then when Sr / Ba < 0.5, it indicates that the paleosalinity property data of the sub-area where the single well is located is fresh water; when 0.5 < Sr / Ba < 1, it indicates that the paleosalinity property data of the sub-area where the single well is located is brackish water; when Sr / Ba > 1, it indicates that the paleosalinity property data of the sub-area where the single well is located is saline water.

[0043] Another exemplarily, if the Sr / Ca ratio of the core sample is used as the paleosalinity value of the single well in the target interval, then when Sr / Ca < 3, it indicates that the paleosalinity property data of the sub-area where the single well is located is fresh water; when 3 < Sr / Ca < 5, it indicates that the paleosalinity property data of the sub-area where the single well is located is brackish water; when Sr / Ca > 5, it indicates that the paleosalinity property data of the sub-area where the single well is located is saline water.

[0044] Optionally, before restoring the paleosalinity of the cores of multiple single wells in the target area in the target interval based on the cores of at least one single well in the target area in the target interval and determining the paleosalinity property data of each sub-area in the target area, the method further includes: determining at least one paleosalinity restoration method according to the sedimentary facies property of the target area.

[0045] Since the paleosalinity property data of the target area is also affected by various factors such as the external environment, the evolution of the product itself, biological activities, and sampling errors, and each paleosalinity restoration method has different applicable conditions and certain limitations, it is necessary to determine a more suitable paleosalinity restoration method for this area according to the sedimentary facies property of the target area.

[0046] Sedimentary facies may include continental facies, marine facies, marine-continental transitional facies, etc. Sediments in regions with different sedimentary facies have different formation environments, formation conditions, and other characteristics.

[0047] For example, a large number of ostracods, belonging to the class Crustacea of ​​the phylum Arthropoda, grow in the surface sediments of lakes. Ostracods shed their shells about eight times during their growth, leaving a large number of shell fossils in the sediments. The mineral composition of ostracod shells is mainly low-magnesium calcite, and they need to continuously absorb Ca in the water medium during their growth. 2+ To maintain the growth of the shell, it also absorbs a small amount of Mg 2+ and Sr 2+ Entering the shell, the contents of Mg and Sr in the shell vary with the concentrations of these ions in the water medium, so the Sr / Ca method is often used as a paleosalinity restoration method for lacustrine sediments.

[0048] For example, the Koch formula is applicable to a wider salinity range and is more suitable as a method for paleo-salinity restoration in terrestrial sedimentary areas.

[0049] Of course, the embodiments of the present invention can also select a suitable paleo-salinity restoration method according to actual conditions based on reference to sedimentary facies properties.

[0050] Optionally, at least one lithofacies type developed in the target layer segment is determined based on test data of at least one single well in the target area in the target layer segment, including: determining at least one lithofacies feature developed in the target layer segment based on test data of at least one single well in the target area in the target layer segment; determining at least one lithofacies type developed in the target layer segment based on each of the lithofacies features.

[0051] Among them, lithofacies characteristics may include mineral composition, laminar structure, TOC (Total Organic Carbon) content, free hydrocarbon S1, physical properties and oil saturation. In addition, it may also include hydrocarbon generation indices such as vitrinite reflectance Ro, organic matter type, reservoir space and characteristics, fracture development and other reservoir generation indices, compressibility indices such as Poisson's ratio and Young's modulus, and oil content indices such as crude oil density and movable oil saturation.

[0052] Specifically, at least one lithofacies characteristic developed in the target layer section can be determined by comprehensively analyzing the core observation results and logging data.

[0053] Furthermore, the classification of lithofacies types according to lithofacies characteristics can be based on the classification principle of "TOC content-sedimentary structure-mineral composition". The naming of lithofacies types can at least reflect the mineral composition and TOC content of different lithofacies. According to the actual situation of the target area, the TOC content can be further roughly divided into three categories: high TOC, medium TOC, and low TOC. The high, medium, and low grade classification standards should be further distinguished according to the TOC evaluation standards of source rocks in each basin.

[0054] S120: According to each of the lithofacies types, determine a target logging model corresponding to each of the lithofacies types.

[0055] Specifically, according to the identified lithofacies types, the logging curve types that best reflect different lithofacies types and their lithofacies characteristics can be found in various logging data to establish target logging models corresponding to different lithofacies types.

[0056] S130, constructing a paleosalinity-lithofacies-well logging model according to the paleosalinity property data of each sub-region, the lithofacies types and the target well logging models.

[0057] Specifically, we can conduct an analysis on the matching relationship between paleo-salinity property data and lithofacies types in the target area, clarify the lithofacies types developed in different paleo-salinity sub-areas, construct a paleo-salinity-lithofacies-well logging identification map, and use the paleo-salinity-lithofacies-well logging model to provide data reference for subsequent research.

[0058] S140. Determine the identification result of the organic-rich shale lithofacies in the target layer of each single well according to the paleo-salinity-lithofacies-well logging model and the preset indicator parameter threshold; wherein the preset indicator parameter threshold is used to represent the characteristics of the organic-rich shale lithofacies.

[0059] The preset index parameter threshold can be determined based on the knowledge and achievements of exploration and development in the study area. Optionally, the preset index parameter may include at least one of mineral composition, total organic carbon content, free hydrocarbon S1 content, porosity, physical properties and oil content.

[0060] The identification results may be parameters such as the development depth and thickness of the organic-rich shale in the target layer of each single well.

[0061] In this scheme, the preset indicator parameter thresholds can be used to judge and identify each paleo-salinity-lithofacies-logging model, identify and select the organic-rich shale lithofacies in the target area, and determine the identification results of the organic-rich shale lithofacies in the target layer of each single well.

[0062] The embodiment of the present invention provides a method for identifying organic-rich shale lithofacies, which determines paleo-salinity property data of each sub-area in the target area and at least one lithofacies type developed in the target layer according to the test data of at least one single well in the target area; determines the target logging model corresponding to each lithofacies type according to each lithofacies type; constructs a paleo-salinity-lithofacies-logging model according to the paleo-salinity property data of each sub-area, each lithofacies type and each target logging model; determines the identification result of the organic-rich shale lithofacies of each single well in the target layer according to the paleo-salinity-lithofacies-logging model and the preset index parameter threshold. This technical solution realizes the identification of organic-rich shale lithofacies by establishing lithofacies models under different water medium conditions, thereby improving the identification accuracy of organic-rich shale lithofacies.

[0063] Embodiment 2

[0064] Figure 2 This is a flow chart of a method for identifying organic-rich shale facies provided in Example 2 of this application. This example is optimized based on the above example. Figure 2 As shown, the method of this embodiment specifically includes the following steps:

[0065] S210, based on the test data of at least one single well in the target area in the target layer section, determine the paleo-salinity property data of each sub-area in the target area and at least one lithofacies type developed in the target layer section, wherein the test data includes core and well logging data.

[0066] S220: According to each of the lithofacies types, determine a target logging model corresponding to each of the lithofacies types.

[0067] S230, constructing a paleosalinity-lithofacies-logging model according to the paleosalinity property data of each sub-region, the lithofacies types and the target logging models.

[0068] S240, determining the identification result of the organic-rich shale lithofacies in the target layer of each single well according to the paleo-salinity-lithofacies-well logging model and the preset index parameter threshold, wherein the preset index parameter threshold is used to represent the characteristics of the organic-rich shale lithofacies.

[0069] S250, determining a paleo-salinity-lithofacies-well logging model corresponding to the organic-rich shale rock according to the paleo-salinity-lithofacies-well logging model and preset index parameter thresholds.

[0070] Specifically, the comparisons can be performed in sequence according to preset indicator parameter thresholds, and the paleo-salinity-lithofacies-well logging model that meets the lithofacies characteristics of organic-rich shale can be determined according to the comparison results.

[0071] S260. Determine the identification result of the organic-rich shale lithofacies in the target layer of each single well according to the paleo-salinity-lithofacies-well logging model corresponding to the organic-rich shale rock.

[0072] Specifically, according to the logging curve characteristics of the single well in the target layer section, the organic-rich shale lithofacies in the target area are identified and selected, and the identification results of the organic-rich shale lithofacies in the target layer section of each single well are determined.

[0073] An embodiment of the present invention provides a method for identifying organic-rich shale lithofacies, which method determines paleo-salinity property data of each sub-area in the target area and at least one lithofacies type developed in the target layer segment based on test data of at least one single well in the target area in the target layer segment; determines a target logging model corresponding to each lithofacies type according to each lithofacies type; constructs a paleo-salinity-lithofacies-logging model according to the paleo-salinity property data of each sub-area, each lithofacies type and each target logging model; determines a paleo-salinity-lithofacies-logging model corresponding to organic-rich shale rock according to the paleo-salinity-lithofacies-logging model and preset indicator parameter thresholds; and determines the identification result of the organic-rich shale lithofacies of each single well in the target layer segment according to the paleo-salinity-lithofacies-logging model corresponding to the organic-rich shale rock. This technical solution, based on the paleo-salinity conditions of lake water during the sedimentary period, systematically evaluates and optimizes the lithofacies types or combination types of organic-rich shale in shale formations, further improves the accuracy and reliability of shale oil "sweet spot" target optimization, and provides solid and favorable decision-making support for shale oil exploration and deployment targets.

[0074] Embodiment 3

[0075] Figure 3 This is a schematic diagram of the structure of a device for identifying organic-rich shale facies provided in Example 3 of the present application. Figure 3 As shown, the device comprises:

[0076] The basic parameter determination module 310 is used to determine the paleo-salinity property data of each sub-area in the target area and at least one lithofacies type developed in the target layer section according to the test data of at least one single well in the target area; wherein the test data includes core and well logging data;

[0077] The well logging model determination module 320 is used to determine the target well logging model corresponding to each of the lithofacies types according to the lithofacies types;

[0078] A composite model building module 330 is used to build a paleo-salinity-lithofacies-well logging model according to the paleo-salinity property data of each sub-region, the lithofacies types and the target well logging models;

[0079] The lithofacies identification module 340 is used to determine the identification result of the organic-rich shale lithofacies in the target layer of each single well based on the paleo-salinity-lithofacies-well logging model and the preset indicator parameter threshold; wherein the preset indicator parameter threshold is used to represent the characteristics of the organic-rich shale lithofacies.

[0080] The embodiment of the present invention provides an organic-rich shale lithofacies identification device, which determines paleo-salinity property data of each sub-area in the target area and at least one lithofacies type developed in the target layer according to the test data of at least one single well in the target area in the target layer; wherein the test data includes core and well logging data; according to each lithofacies type, a target well logging model corresponding to each lithofacies type is determined; according to the paleo-salinity property data of each sub-area, each lithofacies type and each target well logging model, a paleo-salinity-lithofacies-well logging model is constructed; according to the paleo-salinity-lithofacies-well logging model and a preset index parameter threshold, the identification result of the organic-rich shale lithofacies of each single well in the target layer is determined; wherein the preset index parameter threshold is used to represent the characteristics of the organic-rich shale lithofacies. This technical solution realizes the identification of the organic-rich shale lithofacies by establishing lithofacies models under different water medium conditions, thereby improving the identification accuracy of the organic-rich shale lithofacies.

[0081] Furthermore, the basic parameter determination module 310 includes:

[0082] A paleo-salinity property data determination unit is used to perform paleo-salinity restoration on the cores of multiple single wells in the target layer section in the target area according to the cores of at least one single well in the target area, and determine the paleo-salinity property data of each sub-area in the target area;

[0083] The lithofacies type determination unit is used to determine at least one lithofacies type developed in the target layer interval based on the test data of at least one single well in the target area in the target layer interval.

[0084] Furthermore, the paleo-salinity property data determination unit includes:

[0085] A paleo-salinity value determination subunit is used to perform paleo-salinity restoration on the cores of multiple single wells in the target layer section according to at least one paleo-salinity restoration method based on the cores of at least one single well in the target area, and determine the paleo-salinity values ​​corresponding to each sub-area in the target area and each paleo-salinity restoration method;

[0086] The paleo-salinity property data determination subunit is used to determine the paleo-salinity property data of each sub-area in the target area according to the paleo-salinity values.

[0087] Furthermore, the basic parameter determination module 310 also includes:

[0088] A paleo-salinity restoration method determination unit is used to perform paleo-salinity restoration on the cores of multiple single wells in the target area in the target layer segment based on the cores of at least one single well in the target area, and to determine at least one paleo-salinity restoration method according to the sedimentary facies properties of the target area before determining the paleo-salinity property data of each sub-area in the target area.

[0089] Furthermore, the lithofacies type determination unit includes:

[0090] A lithofacies characteristic determination subunit, for determining at least one lithofacies characteristic developed in the target layer interval according to test data of at least one single well in the target area in the target layer interval;

[0091] The lithofacies type determination subunit is used to determine at least one lithofacies type developed in the target layer segment according to each of the lithofacies characteristics.

[0092] Furthermore, the lithofacies identification module 340 includes:

[0093] A composite model screening unit, for determining a paleo-salinity-lithofacies-well logging model corresponding to the organic-rich shale rock according to the paleo-salinity-lithofacies-well logging model and a preset index parameter threshold;

[0094] The lithofacies identification unit is used to determine the identification result of the organic-rich shale lithofacies in the target layer of each single well according to the paleo-salinity-lithofacies-well logging model corresponding to the organic-rich shale rock.

[0095] Furthermore, the preset index parameters include at least one of mineral composition, total organic carbon content, free hydrocarbon S1 content, porosity, physical properties and oil content.

[0096] An organic-rich shale facies identification device provided in an embodiment of the present application can execute an organic-rich shale facies identification method provided in any embodiment of the present application, and has functional modules and beneficial effects corresponding to the execution method.

[0097] Embodiment 4

[0098] Figure 4A schematic diagram of a device 10 that can be used to implement an embodiment of the present application is shown. The device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0099] like Figure 4 As shown, the device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the device 10 can also be stored. The processor 11, ROM 12 and RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0100] A number of components in the device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0101] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for identifying organic-rich shale lithofacies.

[0102] In some embodiments, the method for identifying an organic-rich shale facies may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for identifying an organic-rich shale facies described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the method for identifying an organic-rich shale facies in any other suitable manner (e.g., by means of firmware).

[0103] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0104] The computer programs for implementing the methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer programs are executed by the processor, the functions / operations specified in the flow charts and / or block diagrams are implemented. The computer programs may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0105] In the context of the present application, a computer readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device or equipment. A computer readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer readable storage medium may be a machine readable signal medium. A more specific example of a machine readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0106] To provide interaction with a user, the systems and techniques described herein can be implemented on a device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0107] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0108] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0109] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution of this application can be achieved, and this document is not limited here.

[0110] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for identifying organic-rich shale facies, characterized in that: The method comprises: According to the test data of at least one single well in the target area in the target layer section, determine the paleo-salinity property data of each sub-area in the target area and at least one lithofacies type developed in the target layer section; wherein the test data includes core and well logging data; According to each of the lithofacies types, determining a target logging model corresponding to each of the lithofacies types; Constructing a paleosalinity-lithofacies-logging model according to the paleosalinity property data of each sub-area, the lithofacies types and the target logging models; According to the paleo-salinity-lithofacies-well logging model and the preset index parameter threshold, the identification result of the organic-rich shale lithofacies in the target layer section of each single well is determined; wherein the preset index parameter threshold is used to represent the characteristics of the organic-rich shale lithofacies.

2. The method according to claim 1, characterized in that: According to the test data of at least one single well in the target area in the target layer section, the paleo-salinity property data of each sub-area in the target area and at least one lithofacies type developed in the target layer section are determined, including: Based on the core of at least one single well in the target area, paleo-salinity restoration is performed on the cores of multiple single wells in the target area to determine the paleo-salinity property data of each sub-area in the target area; At least one lithofacies type developed in the target layer interval is determined based on test data of at least one single well in the target area.

3. The method according to claim 2, characterized in that According to the core of at least one single well in the target area, paleo-salinity restoration is performed on the cores of multiple single wells in the target area, and paleo-salinity property data of each sub-area in the target area are determined, including: According to the core of at least one single well in the target area, paleo-salinity restoration is performed on the cores of multiple single wells in the target area according to at least one paleo-salinity restoration method, and the paleo-salinity value corresponding to each sub-area in the target area and each paleo-salinity restoration method is determined; According to the paleo-salinity values, paleo-salinity property data of each sub-area in the target area is determined.

4. The method according to claim 3, characterized in that Before performing paleo-salinity restoration on the cores of multiple single wells in the target layer section according to the cores of at least one single well in the target region and determining the paleo-salinity property data of each sub-region in the target region, the method further comprises: At least one paleo-salinity restoration method is determined based on the sedimentary facies properties of the target area.

5. The method according to claim 2, characterized in that: Determine at least one lithofacies type developed in the target layer interval based on test data of at least one single well in the target area, including: Determine at least one lithofacies characteristic developed in the target layer interval based on test data of at least one single well in the target area; At least one lithofacies type developed in the target layer section is determined based on each of the lithofacies characteristics.

6. The method according to claim 1, characterized in that According to the paleo-salinity-lithofacies-logging model and the preset index parameter threshold, the identification results of the organic-rich shale lithofacies in the target layer of each single well are determined, including: Determining a paleo-salinity-lithofacies-well logging model corresponding to the organic-rich shale rock according to the paleo-salinity-lithofacies-well logging model and preset index parameter thresholds; According to the paleo-salinity-lithofacies-well logging model corresponding to the organic-rich shale rock, the identification result of the organic-rich shale lithofacies in the target layer section of each single well is determined.

7. The method according to claim 1, characterized in that The preset index parameters include at least one of mineral composition, total organic carbon content, free hydrocarbon S1 content, porosity, physical properties and oil content.

8. A device for identifying organic-rich shale facies, characterized in that: The device comprises: A basic parameter determination module is used to determine the paleo-salinity property data of each sub-area in the target area and at least one lithofacies type developed in the target layer section according to the test data of at least one single well in the target area; wherein the test data includes core and well logging data; A well logging model determination module, used to determine a target well logging model corresponding to each of the lithofacies types according to the lithofacies types; A composite model building module, for building a paleo-salinity-lithofacies-well logging model according to the paleo-salinity property data of each sub-region, the lithofacies types and the target well logging models; The lithofacies identification module is used to determine the identification result of the organic-rich shale lithofacies in the target layer of each single well according to the paleo-salinity-lithofacies-well logging model and the preset index parameter threshold; wherein the preset index parameter threshold is used to represent the characteristics of the organic-rich shale lithofacies.

9. An electronic device, characterized in that: The device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for identifying the organic-rich shale facies according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for identifying organic-rich shale facies according to any one of claims 1 to 7 when executed.

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