Oil reservoir quantification evaluation method, device and equipment of mud level reservoir and storage medium

Abstract

This application provides a method, apparatus, equipment, and storage medium for quantitative evaluation of oil layers in mud-class reservoirs, belonging to the field of oil and gas exploration and development technology. In this method, based on the parameter values ​​of multiple logging parameters of a first reference block in the target block, the fine-grained sedimentary rock reservoirs in the first reference block, the effective reservoirs within the fine-grained sedimentary rock reservoirs, and the distinction between industrial and non-industrial oil flow layers within the effective reservoirs can be made, thereby determining a set of quantitative evaluation thresholds. These multiple logging parameters include natural gamma, resistivity, spontaneous potential, total organic carbon content, compensated density, NMR porosity, and NMR saturation. This set of quantitative evaluation thresholds is used to identify industrial oil flow layers in the target block. The quantitative evaluation thresholds determined by the above method can efficiently and accurately identify industrial oil flow layers in the target block, thus providing a reliable basis for subsequent oil and gas exploration and development, and improving the efficiency of oil and gas exploration and development.

Description

Technical Field

[0001] This application relates to the field of oil and gas exploration and development technology, and in particular to a method, apparatus, equipment and storage medium for quantitative evaluation of oil layers in mud-level reservoirs. Background Technology

[0002] In the field of oil and gas exploration and development, identifying oil-bearing layers within a block provides a reliable basis for its exploration and development. When a block contains fine-grained sedimentary sandstone reservoirs within mud-class reservoirs, the high natural gamma and resistivity of these reservoirs, their wide distribution range, complex lithology, diverse mineral composition, and lack of a dominant mineral, coupled with the fact that different types of fine-grained sedimentary rock reservoirs all possess oil-producing capabilities, make accurate and efficient oil layer identification difficult. Therefore, there is an urgent need for an efficient and accurate quantitative evaluation method for oil layers, capable of quantitatively identifying industrial oil flow layers within mud-class reservoirs. Summary of the Invention

[0003] This application provides a method, apparatus, equipment, and storage medium for quantitative evaluation of oil layers in mud-level reservoirs. It can efficiently and accurately identify industrial oil flow layers in target blocks, thereby providing a reliable basis for subsequent oil and gas exploration and development, and improving the efficiency of oil and gas exploration and development. The technical solution is as follows:

[0004] On the one hand, a method for quantitative evaluation of oil reservoirs in mud-class reservoirs is provided, the method comprising:

[0005] For any target block, based on the parameter values ​​of multiple logging parameters of the first reference block in the target block, the fine-grained sedimentary rock reservoir in the first reference block is determined. The multiple logging parameters include natural gamma, resistivity, spontaneous potential, total organic carbon content, compensated density, nuclear magnetic porosity, and nuclear magnetic saturation.

[0006] Based on the parameters of spontaneous potential and total organic carbon content in the fine-grained sedimentary rock reservoir, the effective reservoirs in the fine-grained sedimentary rock reservoir are determined, and the effective reservoirs are fine-grained sedimentary rock reservoirs that can produce oil and gas.

[0007] Based on the resistivity, natural gamma, total organic carbon content and compensation density parameters of the effective reservoir, industrial oil flow layers and non-industrial oil flow layers are determined in the effective reservoir. The industrial oil flow layer is an effective reservoir in which industrial oil flow can be obtained, and the non-industrial oil flow layer is an effective reservoir in which industrial oil flow cannot be obtained according to the oil test results.

[0008] Based on the parameter values ​​of multiple logging parameters corresponding to the industrial oil flow layer and the non-industrial oil flow layer, a quantitative evaluation threshold group for the target block is determined. The quantitative evaluation threshold group is used to identify the industrial oil flow layer in the target block. The quantitative evaluation threshold group includes a target parameter threshold, a total organic carbon content threshold, a nuclear magnetic porosity threshold, and a nuclear magnetic saturation threshold. The target parameter threshold is used to indicate the threshold corresponding to the product of the ratio between resistivity and natural gamma and an adjustment coefficient.

[0009] On the other hand, a device for quantitative evaluation of oil reservoirs in mud-level reservoirs is provided, the device comprising:

[0010] The first determining module is used to determine, for any target block, a fine-grained sedimentary rock reservoir in the first reference block based on the parameter values ​​of multiple logging parameters of the first reference block in the target block. The multiple logging parameters include natural gamma, resistivity, spontaneous potential, total organic carbon content, compensated density, nuclear magnetic porosity, and nuclear magnetic saturation.

[0011] The second determining module is used to determine the effective reservoir in the fine-grained sedimentary rock reservoir based on the parameter values ​​of the spontaneous potential and the parameter values ​​of the total organic carbon content in the fine-grained sedimentary rock reservoir. The effective reservoir is a fine-grained sedimentary rock reservoir that can produce oil and gas.

[0012] The third determining module is used to determine the industrial oil flow layer and the non-industrial oil flow layer in the effective reservoir based on the parameter values ​​of resistivity, natural gamma, total organic carbon content and compensation density in the effective reservoir. The industrial oil flow layer is an effective reservoir in which industrial oil flow can be obtained, and the non-industrial oil flow layer is an effective reservoir in which industrial oil flow cannot be obtained by oil testing.

[0013] The fourth determining module is used to determine a quantitative evaluation threshold group for the target block based on the parameter values ​​of multiple logging parameters corresponding to the industrial oil flow layer and the non-industrial oil flow layer, respectively. The quantitative evaluation threshold group is used to identify the industrial oil flow layer in the target block. The quantitative evaluation threshold group includes a target parameter threshold, a total organic carbon content threshold, a nuclear magnetic porosity threshold, and a nuclear magnetic saturation threshold. The target parameter threshold is used to indicate the threshold corresponding to the product of the ratio between resistivity and natural gamma and the adjustment coefficient.

[0014] In some embodiments, the first determining module is used to determine, for any target block, the fine-grained sedimentary rock reservoir in the first reference block based on the parameter values ​​of natural gamma, resistivity, and total organic carbon content in the first reference block, as well as the lithology identification parameter set. The lithology identification parameter set is used to identify the reservoir type in the target block, and the lithology identification parameter set includes the parameter ranges of natural gamma, resistivity, and total organic carbon content corresponding to each type of reservoir in the target block.

[0015] In some embodiments, the first determining module is further configured to, for the target block, determine a first cross plot and a second cross plot based on the parameter values ​​of multiple logging parameters of a second reference block in the target block and the core analysis data of the second reference block. The core analysis data is used to indicate the reservoir type at each sampling point in the second reference block. The first cross plot is used to indicate the relationship between natural gamma and resistivity at each sampling point, and the second cross plot is used to indicate the relationship between total organic carbon content and resistivity at each sampling point. Based on the first cross plot and the second cross plot, determine the parameter range of natural gamma, the parameter range of resistivity, and the parameter range of total organic carbon content corresponding to each type of reservoir to obtain the lithology identification parameter set.

[0016] In some embodiments, the first determining module is configured to, for any target block, determine the fine-grained sedimentary rock reservoir in the first reference block based on the parameter values ​​of multiple logging parameters and core analysis data of the first reference block in the target block, wherein the core analysis data is used to indicate the reservoir type at different depths in the first reference block.

[0017] In some embodiments, the second determining module is configured to determine candidate reservoirs from the fine-grained sedimentary rock reservoirs based on the amplitude difference of the spontaneous potential in the fine-grained sedimentary rock reservoirs, wherein the amplitude difference of the spontaneous potential is the difference between a parameter value of the spontaneous potential and a baseline value of the spontaneous potential, and the amplitude difference of the spontaneous potential in the candidate reservoirs is greater than a preset amplitude difference threshold; and to determine the effective reservoirs from the candidate reservoirs based on the parameter value of the total organic carbon content in the candidate reservoirs, wherein the parameter value of the total organic carbon content in the effective reservoirs is greater than a preset carbon content threshold.

[0018] In some embodiments, the third determining module is used to determine a first curve group and a second curve group based on the resistivity parameter value, the natural gamma parameter value, the total organic carbon content parameter value, and the compensation density parameter value in the effective reservoir. The first curve group includes a resistivity curve and a natural gamma curve, and the second curve group includes a total organic carbon content curve and a compensation density curve. Based on the first curve group and the second curve group, the industrial oil flow layer and the non-industrial oil flow layer are determined from the effective reservoir. The difference between the curves in the first curve group corresponding to the industrial oil flow layer is greater than a first preset difference threshold, and the difference between the curves in the second curve group corresponding to the industrial oil flow layer is greater than a second preset difference threshold.

[0019] In some embodiments, the fourth determining module is used to determine a third cross plot, a fourth cross plot, and a fifth cross plot based on the parameter values ​​of multiple logging parameters corresponding to the industrial oil flow layer and the non-industrial oil flow layer, respectively, and the oil testing and production data of the effective reservoir. The oil testing and production data is used to indicate the oil layer type at each sampling point in the effective reservoir. The oil layer type includes the industrial oil flow layer and the non-industrial oil flow layer. The third cross plot is used to indicate the relationship between the total organic carbon content and the target parameter at each sampling point. The fourth cross plot is used to indicate the relationship between the nuclear magnetic porosity and the target parameter at each sampling point. The fifth cross plot is used to indicate the relationship between the nuclear magnetic saturation and the target parameter at each sampling point. Based on the third cross plot, the fourth cross plot, and the fifth cross plot, the module determines the target parameter threshold, the total organic carbon content threshold, the nuclear magnetic porosity threshold, and the nuclear magnetic saturation threshold to obtain the quantitative evaluation threshold set.

[0020] On the other hand, a computer device is provided, the computer device including a processor and a memory, the memory being used to store at least one computer program, the at least one computer program being loaded and executed by the processor to implement the oil layer quantitative evaluation method for mud-level reservoirs in the embodiments of this application.

[0021] On the other hand, a computer-readable storage medium is provided, wherein at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by a processor to implement the oil layer quantitative evaluation method for mud-level reservoirs in the embodiments of this application.

[0022] On the other hand, a computer program product is provided, including a computer program that is executed by a processor to implement the oil layer quantitative evaluation method for mud-level reservoirs in the embodiments of this application.

[0023] This application provides a method for quantitative evaluation of oil-bearing layers in mud-class reservoirs. Based on the parameter values ​​of multiple logging parameters from a first reference block in a target block, it can progressively determine the fine-grained sedimentary rock reservoirs in the first reference block, the effective reservoirs within the fine-grained sedimentary rock reservoirs, and distinguish between industrial and non-industrial oil-bearing layers within the effective reservoirs, thereby determining a set of quantitative evaluation thresholds. These multiple logging parameters include natural gamma, resistivity, spontaneous potential, total organic carbon content, compensated density, NMR porosity, and NMR saturation. This set of quantitative evaluation thresholds is used to identify industrial oil-bearing layers in the target block. The quantitative evaluation thresholds determined through this method can efficiently and accurately identify industrial oil-bearing layers in the target block, thus providing a reliable basis for subsequent oil and gas exploration and development, and improving the efficiency of oil and gas exploration and development. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of an implementation environment provided according to an embodiment of this application;

[0026] Figure 2 This is a flowchart of a method for quantitative evaluation of oil reservoirs in mud-level reservoirs according to an embodiment of this application;

[0027] Figure 3 This is a flowchart of another method for quantitative evaluation of oil reservoirs in mud-level reservoirs provided in the embodiments of this application;

[0028] Figure 4 This is a schematic diagram of a cross-section diagram provided according to an embodiment of this application;

[0029] Figure 5 This is a schematic diagram illustrating a comprehensive evaluation of a dessert according to an embodiment of this application;

[0030] Figure 6 This is a schematic diagram illustrating a parameter correlation relationship according to an embodiment of this application;

[0031] Figure 7 This is a schematic diagram of a set of curves provided according to an embodiment of this application;

[0032] Figure 8 This is a schematic diagram of another intersection diagram provided according to an embodiment of this application;

[0033] Figure 9This is a schematic diagram illustrating another comprehensive evaluation of desserts provided according to an embodiment of this application;

[0034] Figure 10 This is a schematic diagram illustrating the oil production ratio of different layers according to an embodiment of this application;

[0035] Figure 11 This is a graph showing the relationship between oil production ratio and parameter ratio according to an embodiment of this application;

[0036] Figure 12 This is a block diagram of an oil layer quantitative evaluation device for mud-level reservoirs provided in an embodiment of this application;

[0037] Figure 13 This is a schematic diagram of the structure of a terminal according to an embodiment of this application;

[0038] Figure 14 This is a schematic diagram of the structure of a server according to an embodiment of this application. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0040] In this application, the terms "first," "second," etc., are used to distinguish identical or similar items with essentially the same function. It should be understood that there is no logical or temporal dependency between "first," "second," and "nth," nor are there any restrictions on quantity or execution order.

[0041] In this application, the term "at least one" means one or more, and "multiple" means two or more.

[0042] The following is a brief introduction to the terminology used in this application.

[0043] Terrestrial rift basins are lake basins formed on land due to crustal fracturing and subsidence. These basins have complex geological structures and sedimentary systems, and are important locations for oil and gas resources.

[0044] Mud-class reservoirs are reservoirs primarily composed of fine-grained sediments such as mudstone or shale. These reservoirs are characterized by their fine grain size, low porosity, and poor permeability. Mud-class reservoirs include fine-grained sedimentary rock reservoirs.

[0045] Fine-grained sedimentary rocks are sedimentary rocks composed of fine-grained clastic material with a grain size of less than 62.5 micrometers, and the content of fine-grained clastic material is more than 50%. Fine-grained sedimentary rocks have a slow deposition rate, small grain size, and exhibit heterogeneity and variations in laminar structure. Fine-grained sedimentary rock reservoirs belong to the mud-class reservoir category.

[0046] Total Organic Carbon (TOC): refers to the total amount of organic carbon in a rock, expressed as a percentage (%).

[0047] Free Hydrocarbon Content (S1): refers to the content of free hydrocarbons in rocks, used to characterize recoverable free crude oil, and is measured in milligrams per gram (mg / g).

[0048] Resistivity (RT): Used to reflect the properties of pore fluids (such as oil, gas, and water) and the electrical conductivity of the rock skeleton in a formation. The unit is ohm-meter (Ω·m).

[0049] Compensated density (DEN, Density): refers to the physical quantity used in well logging to measure formation density, with the unit being grams per cubic centimeter (g / cm3).

[0050] Natural gamma (GR, Gamma Ray): refers to the intensity of gamma rays produced by the natural decay of radioactive elements in the earth's crust, measured in API.

[0051] Spontaneous potential (SP) refers to the potential difference between the formation and the mud caused by the difference between diffusion potential and filtration potential, and is measured in millivolts (mV).

[0052] Core analysis data: used to describe the physical properties, mineral composition, and pore structure of rocks. Physical properties include porosity, permeability, and density. Mineral composition includes the types, content, and distribution characteristics of various minerals in the rock. Pore structure includes pore size, shape, and connectivity.

[0053] Production profile test data refers to the data on parameters such as oil production, water production, and production ratio of each production layer or production section of an oil well, obtained through specific testing methods under normal production conditions.

[0054] Oil well testing and production testing data: This includes data from both the oil well testing and production testing phases. Oil well testing data includes characteristic parameters of the oil and gas reservoir, such as pressure, production rate, and fluid properties. Production testing data reflects the production dynamics of the oil well and the decline in production and pressure.

[0055] Well logging data: Well logging is a method of measuring geophysical parameters by utilizing the electrochemical, electrical, acoustic, and radioactive properties of rock formations. Well logging data records the values ​​of multiple logging parameters, such as resistivity, spontaneous potential, spontaneous gamma, sonic transit time, compensated neutrons, and compensated density (lithological density), among other geological information.

[0056] It should be noted that all information (including but not limited to user equipment information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the parameter values ​​of many well logging parameters, core analysis data, and oil testing and production data involved in this application were all obtained with full authorization.

[0057] Figure 1 This is a schematic diagram of an implementation environment provided according to an embodiment of this application. See also... Figure 1 The implementation environment includes terminal 101 and server 102. Terminal 101 and server 102 can be connected directly or indirectly via wired or wireless communication, which is not limited herein.

[0058] Among them, terminal 101 can be various types of terminals such as mobile phones, desktop computers, laptops, and tablets. Server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0059] Optionally, the method for quantitative evaluation of oil layers in mud-level reservoirs provided in this application embodiment can be executed by terminal 101 alone, by server 102 alone, or by terminal 101 and server 102 interacting.

[0060] In some embodiments, when the terminal 101 executes the method alone, the terminal 101 can obtain the parameter values ​​of multiple logging parameters of the first reference block in the target block, and perform data processing based on these parameters to gradually identify the fine-grained sedimentary rock reservoir in the first reference block, the effective reservoir in the fine-grained sedimentary rock reservoir, and the industrial oil flow layer and non-industrial oil flow layer in the effective reservoir, and finally determine the quantitative evaluation threshold group of the target block.

[0061] In some embodiments, when the server 102 executes the method alone, the server 102 can perform data calculations independently. It processes the parameter values ​​of multiple logging parameters from the first reference block in the target block uploaded to the server by other devices, thereby progressively identifying fine-grained sedimentary reservoirs in the first reference block, effective reservoirs within those fine-grained sedimentary reservoirs, and industrial and non-industrial oil flow layers within those effective reservoirs, ultimately determining the quantitative evaluation threshold set for the target block. After obtaining the quantitative evaluation threshold set, the server 102 can store the quantitative evaluation threshold set on the server, send it to other devices for display to relevant personnel, or continue performing other data calculations based on the quantitative evaluation threshold set, without limitation.

[0062] In some embodiments, when the terminal 101 and server 102 interactively execute the method, the terminal 101 and server 102 are associated, and the server 102 provides background services to the terminal 101. Optionally, the server 102 undertakes the main computing work, and the terminal 101 undertakes the secondary computing work; or, the server 102 undertakes the secondary computing work, and the terminal 101 undertakes the main computing work; or, the server 102 and the terminal 101 use a distributed computing architecture for collaborative computing.

[0063] That is, some steps in this method are executed by terminal 101, while others are executed by server 102. For example, terminal 101 obtains the parameter values ​​of multiple logging parameters of the first reference block in the target block, and gradually identifies the fine-grained sedimentary rock reservoir, the effective reservoir in the fine-grained sedimentary rock reservoir, and the industrial oil flow layer and non-industrial oil flow layer in the effective reservoir in the first reference block. Terminal 101 sends the above parameter values ​​and identification results to server 102. Server 102 processes the received data to determine the quantitative evaluation threshold group of the target block, and sends the quantitative evaluation threshold group to terminal 101. Terminal 101 receives the quantitative evaluation threshold group and displays it.

[0064] It should be noted that in the following embodiments, the method for quantitative evaluation of oil reservoirs in mud-level reservoirs proposed in this application is described using a terminal alone. That is, the data acquisition and data processing processes in this method are all performed by the terminal.

[0065] Figure 2 This is a flowchart of a method for quantitative evaluation of oil reservoirs in mud-class reservoirs according to an embodiment of this application. This method is applied in a terminal environment. (See also...) Figure 2 The method includes the following steps:

[0066] 201. For any target block, the terminal determines the fine-grained sedimentary rock reservoir in the first reference block based on the parameter values ​​of multiple logging parameters of the first reference block in the target block. The multiple logging parameters include natural gamma, resistivity, spontaneous potential, total organic carbon content, compensated density, nuclear magnetic porosity, and nuclear magnetic saturation.

[0067] In this embodiment, the target block exhibits complex lithology, characterized by diverse mineral composition, the absence of a dominant mineral, and relatively low clay content. Fine-grained sedimentary rocks and source rocks in the target block are typically interbedded frequently, lacking natural oil-producing capacity. Conventional methods for identifying oil-bearing reservoirs in the target block often directly classify fine-grained sedimentary rocks as non-oil-producing reservoirs. However, many types of fine-grained sedimentary rocks, after fracturing, often possess oil-producing capacity and can be considered oil-bearing layers. Therefore, more accurate and efficient oil-bearing layer identification is needed for the target block.

[0068] The target block is a block containing mud-grade reservoirs, such as a continental rift basin-type block. The first reference block belongs to the target block. The parameter values ​​of multiple logging parameters of the first reference block are used to indicate the distribution and properties of reservoirs in the first reference block. Optionally, the parameter value of each logging parameter can be in the form of a logging curve or a logging data table; this embodiment of the application does not impose any limitations on this.

[0069] It should be noted that since lithology controls physical properties, and physical properties control oil-bearing properties, lithological identification is crucial for the quantitative evaluation of oil-bearing reservoirs. Processing the relevant data from the first reference block enables preliminary lithological identification, determining fine-grained sedimentary reservoirs within the first reference block. This facilitates further identification of oil-bearing reservoirs and the establishment of standards for identifying oil-bearing reservoirs in target blocks.

[0070] 202. Based on the parameters of spontaneous potential and total organic carbon content in fine-grained sedimentary rock reservoirs, the terminal determines the effective reservoirs in the fine-grained sedimentary rock reservoirs. The effective reservoirs are fine-grained sedimentary rock reservoirs that can produce oil and gas.

[0071] In this embodiment, spontaneous potential (SP) anomalies are commonly observed in fine-grained sedimentary rock reservoirs. The better the reservoir conditions, the greater the amplitude difference in SP, and consequently, the stronger the oil production capacity of the reservoir. Since the oil production capacity and SP of fine-grained sedimentary rock reservoirs exhibit these characteristics, and these reservoirs are essentially water-free after fracturing, the magnitude of the SP amplitude difference can be used to qualitatively identify effective reservoirs within them. In other words, SP is an important parameter for identifying effective reservoirs in fine-grained sedimentary rock reservoirs.

[0072] In fine-grained sedimentary rock reservoirs, total organic carbon (TOC) content reflects the abundance of organic matter in the rock. Organic matter is the primary source of hydrocarbons; a higher TOC content indicates that the reservoir may contain more hydrocarbons and potentially have a stronger oil-producing capacity. Therefore, the TOC content can be used to qualitatively identify effective reservoirs in fine-grained sedimentary rock reservoirs, making it another important parameter for identifying effective reservoirs in these reservoirs.

[0073] It should be noted that, based on the above lithological identification, by analyzing reservoir characteristics and combining the parameters of spontaneous potential and total organic carbon content, it is possible to qualitatively determine fine-grained sedimentary rock reservoirs and identify effective reservoirs from them.

[0074] 203. Based on the resistivity, natural gamma, total organic carbon content and compensation density parameters in the effective reservoir, the terminal determines the industrial oil flow layer and the non-industrial oil flow layer in the effective reservoir. The industrial oil flow layer is the effective reservoir in which industrial oil flow can be obtained, and the non-industrial oil flow layer is the effective reservoir in which industrial oil flow cannot be obtained according to the oil test results.

[0075] In this embodiment, the difference between the resistivity parameter and the natural gamma parameter reflects the oil production capacity. A larger difference indicates better oil production capacity. Similarly, the difference between the total organic carbon content parameter and the compensation density parameter also reflects oil production capacity. A larger difference indicates better oil production capacity. There is a difference in oil production capacity between industrial and non-industrial oil flow layers; industrial oil flow layers have better oil production capacity than non-industrial oil flow layers. Therefore, by analyzing the magnitude of these two differences, the oil production capacity of an effective reservoir can be further refined, thereby distinguishing between industrial and non-industrial oil flow layers from the effective reservoir. This process can be summarized as using the curve intersection method of total organic carbon content and compensation density, and the curve intersection method of resistivity and natural gamma, to further qualitatively identify the effective reservoir.

[0076] It should be noted that any of the differences described above can be reflected by the difference between the corresponding two curves. Each curve indicates the change of the corresponding logging parameter value with depth. The difference between the two curves corresponding to any depth value is used to determine the difference between the corresponding logging parameter values ​​of the two curves at that depth value.

[0077] 204. Based on the parameter values ​​of multiple logging parameters corresponding to industrial oil flow layers and non-industrial oil flow layers, the terminal determines the quantitative evaluation threshold group of the target block. The quantitative evaluation threshold group is used to identify industrial oil flow layers in the target block. The quantitative evaluation threshold group includes target parameter threshold, total organic carbon content threshold, nuclear magnetic porosity threshold, and nuclear magnetic saturation threshold. The target parameter threshold is used to indicate the threshold corresponding to the product of the ratio between resistivity and natural gamma and the adjustment coefficient.

[0078] In this embodiment, sensitive logging parameters are extracted from multiple logging parameters. These sensitive logging parameters are those that can directly reflect the differences between industrial oil flow layers and other reservoirs. Sensitive logging parameters include resistivity, natural gamma ray, total organic carbon content, nuclear magnetic resonance porosity, and nuclear magnetic resonance saturation.

[0079] Since the values ​​of sensitive logging parameters corresponding to industrial and non-industrial oil flow layers in the first reference block are known, the terminal can determine the threshold values ​​for each sensitive logging parameter based on these values, such as the total organic carbon content threshold, NMR porosity threshold, and NMR saturation threshold, or determine the threshold values ​​for combinations of multiple sensitive logging parameters, such as the target parameter threshold. By combining these parameter thresholds, industrial and non-industrial oil flow layers can be distinguished, thereby achieving a quantitative evaluation of the effective reservoir.

[0080] This application provides a method for quantitative evaluation of oil-bearing layers in mud-class reservoirs. Based on the parameter values ​​of multiple logging parameters from a first reference block in a target area, it can progressively determine the fine-grained sedimentary rock reservoirs in the first reference block, the effective reservoirs within the fine-grained sedimentary rock reservoirs, and distinguish between industrial and non-industrial oil-bearing layers within the effective reservoirs, thereby determining a set of quantitative evaluation thresholds. These multiple logging parameters include natural gamma, resistivity, spontaneous potential, total organic carbon content, compensated density, NMR porosity, and NMR saturation. This set of quantitative evaluation thresholds is used to identify industrial oil-bearing layers in the target area. The quantitative evaluation thresholds determined in this way can efficiently and accurately identify industrial oil-bearing layers in the target area, thus providing a reliable basis for subsequent oil and gas exploration and development, and improving the efficiency of oil and gas exploration and development.

[0081] The above Figure 2 This paper introduces a simplified procedure for quantitative evaluation of oil reservoirs in mud-class reservoirs. See below. Figure 3 As shown, a detailed introduction is given to the method for quantitative evaluation of oil reservoirs in mud-class reservoirs. Figure 3 This is a flowchart of another method for quantitative evaluation of oil reservoirs in mud-class reservoirs according to an embodiment of this application. The method is applied to a terminal and includes the following steps:

[0082] 301. For any target block, the terminal determines the fine-grained sedimentary rock reservoir in the first reference block based on the parameter values ​​of multiple logging parameters of the first reference block in the target block. The multiple logging parameters include natural gamma, resistivity, spontaneous potential, total organic carbon content, compensated density, nuclear magnetic porosity, and nuclear magnetic saturation.

[0083] In this embodiment of the application, the principle of the terminal determining the fine-grained sedimentary rock reservoir in the first reference block is the same as that in step 201, and will not be repeated here.

[0084] In some embodiments, the terminal identifies fine-grained sedimentary rock reservoirs from a first reference block based on lithological analysis data. Accordingly, for any target block, the terminal determines the fine-grained sedimentary rock reservoirs in the first reference block based on the parameter values ​​of multiple logging parameters and core analysis data from the first reference block within the target block.

[0085] Core analysis data is used to indicate reservoir types at different depths in the first reference block. Reservoir type is determined by the lithology of the rocks within the reservoir. Optionally, lithology includes high-gamma sandstone, low-permeability sandstone, and fine-grained sedimentary rocks. Reservoir types are categorized into high-gamma sandstone reservoirs, low-permeability sandstone reservoirs, and fine-grained sedimentary rock reservoirs. Fine-grained sedimentary rocks can be further classified into felsic, dolomitic, and mixed types. High-gamma sandstone and low-permeability sandstone are not oil-producing, while fine-grained sedimentary rocks can produce oil after fracturing. Based on core analysis data, for reservoirs indicated by logging parameters, the terminal can directly read the lithology of rocks in different reservoirs, thereby identifying fine-grained sedimentary rock reservoirs from the first reference block.

[0086] In some embodiments, the terminal identifies fine-grained sedimentary reservoirs from a first reference block based on a set of lithological identification parameters. Accordingly, for any target block, the terminal determines the fine-grained sedimentary reservoirs in the first reference block based on the parameters of natural gamma, resistivity, total organic carbon content, and the set of lithological identification parameters in the first reference block.

[0087] The lithology identification parameter set is used to identify the reservoir type in the target block. This parameter set serves as a standard for lithological identification of the target block, enabling the differentiation of different lithologies. The lithology identification parameter set includes the parameter ranges for natural gamma, resistivity, and total organic carbon content for each type of reservoir (different lithologies) in the target block. Therefore, when the natural gamma, resistivity, and total organic carbon content values ​​of a reservoir within a certain depth range all fall within the corresponding parameter ranges for fine-grained sedimentary rocks, the reservoir is ultimately determined to be a fine-grained sedimentary rock reservoir.

[0088] See Table 1 below, which is an exemplary set of lithological identification parameters.

[0089] Table 1 Lithological identification parameter group

[0090] Lithology Natural Gamma (API) Resistivity (Ω·m) Total organic carbon content (%) High Gamma Sandstone 80-120 3-20 none Low permeability sandstone 60-80 3-20 none Fine-grained sedimentary rocks 75-105 10-200 1-6

[0091] In some embodiments, the determination of the lithology identification parameter set includes: for a target block, the terminal determines a first cross plot and a second cross plot based on the parameter values ​​of multiple logging parameters of a second reference block in the target block and the core analysis data of the second reference block; based on the first cross plot and the second cross plot, the terminal determines the parameter range of natural gamma, the parameter range of resistivity, and the parameter range of total organic carbon content corresponding to each type of reservoir, thereby obtaining the lithology identification parameter set.

[0092] The second reference block and the first reference block may be the same, different, or overlap; this embodiment does not impose any restrictions on this. Core analysis data is used to indicate the reservoir type at each sampling point in the second reference block. The terminal projects the logging parameter values ​​of multiple sampling points for each reservoir type onto a chart, obtaining a first cross-plot and a second cross-plot. The horizontal axis of the first cross-plot represents natural gamma, and the vertical axis represents resistivity. The horizontal and vertical coordinate values ​​of any data point in the first cross-plot are determined by the resistivity parameter value and the natural gamma parameter value at the corresponding sampling point. The horizontal axis of the second cross-plot represents total organic carbon content, and the vertical axis represents resistivity. The horizontal and vertical coordinate values ​​of any data point in the second cross-plot are determined by the resistivity parameter value and the total organic carbon content parameter value at the corresponding sampling point. The sampling points corresponding to the data points in both cross-plots are located within the second reference block.

[0093] For a clearer description of the first and second intersection plots, please refer to [link / reference]. Figure 4 As shown, Figure 4 This is a schematic diagram of a cross-plot provided according to an embodiment of this application. Figure 4 (a) is the first intersection diagram. Figure 4 (b) shows the second cross plot. In the first and second cross plots, the reservoir types at the sampling points corresponding to the data points are divided into five categories: felsic fine-grained sedimentary rocks, dolomitic fine-grained sedimentary rocks, mixed fine-grained sedimentary rocks, high-gamma sandstone, and low-permeability sandstone.

[0094] In one possible implementation, when the terminal determines the first and second cross-plots, the terminal divides the second reference block's Kong 2 section formation vertically into four oil groups from top to bottom, and uses core analysis data from four wells in the second reference block to establish the first and second cross-plots.

[0095] 302. Based on the parameters of spontaneous potential and total organic carbon content in fine-grained sedimentary rock reservoirs, the terminal determines the effective reservoirs in the fine-grained sedimentary rock reservoirs. The effective reservoirs are fine-grained sedimentary rock reservoirs that can produce oil and gas.

[0096] In this embodiment of the application, the principle of the terminal determining the effective reservoir in the fine-grained sedimentary rock reservoir is the same as that in step 202, and will not be repeated here.

[0097] In some embodiments, the terminal identifies effective reservoirs from fine-grained sedimentary rock reservoirs based on the magnitude relationship between the amplitude difference of spontaneous potential and the parameter value of total organic carbon content and corresponding thresholds. Accordingly, the terminal determines candidate reservoirs from fine-grained sedimentary rock reservoirs based on the amplitude difference of spontaneous potential; and determines effective reservoirs from candidate reservoirs based on the parameter value of total organic carbon content in the candidate reservoirs.

[0098] The amplitude difference of spontaneous potential is the difference between the parameter value of spontaneous potential and the baseline value of spontaneous potential. The amplitude difference of spontaneous potential in candidate reservoirs is greater than the preset amplitude difference threshold, and the parameter value of total organic carbon content in effective reservoirs is greater than the preset carbon content threshold.

[0099] It should be noted that the above-mentioned method of first determining candidate reservoirs based on spontaneous potential and then determining organic reservoirs based on total organic carbon content is merely an exemplary approach. This application does not restrict the screening order, as long as the amplitude difference of spontaneous potential in the identified effective reservoirs is greater than a preset amplitude difference threshold, and the parameter value of total organic carbon content is greater than a preset carbon content threshold.

[0100] For a clearer description of the process of identifying effective reservoirs from fine-grained sedimentary rock reservoirs, see [link to relevant documentation]. Figure 5 As shown, Figure 5 This is a schematic diagram of a comprehensive evaluation of a dessert according to an embodiment of this application. The vertical axis represents the depth value. 501 indicates the curve corresponding to the parameter value of the natural potential and the curve corresponding to the baseline value of the natural potential; 502 indicates the curve corresponding to the parameter value of the total organic carbon content.

[0101] It should be noted that using the parameters of spontaneous potential and total organic carbon content to identify effective reservoirs is only an exemplary method. The terminal can also use the parameters of natural gamma and resistivity as criteria for identifying effective reservoirs, simply by setting corresponding preset thresholds for each parameter. That is, based on lithological identification, by analyzing reservoir characteristics, the terminal combines the logging response characteristics of total organic carbon content with the curves corresponding to spontaneous potential, natural gamma, and resistivity to qualitatively determine fine-grained sedimentary rock reservoirs and identify effective reservoirs within them.

[0102] 303. Based on the parameter values ​​of resistivity, natural gamma, total organic carbon content and compensation density in the effective reservoir, the terminal determines the first curve group and the second curve group. The first curve group includes the resistivity curve and the natural gamma curve, and the second curve group includes the total organic carbon content curve and the compensation density curve.

[0103] In this embodiment, the resistivity curve is used to describe the change of resistivity parameter value with depth value, the natural gamma curve is used to describe the change of natural gamma parameter value with depth value, the total organic carbon content curve is used to describe the change of total organic carbon content parameter value with depth value, and the compensated density curve is used to describe the change of compensated density parameter value with depth value.

[0104] It should be noted that the quantitative evaluation of oil reservoirs primarily targets those capable of yielding industrial oil flows, which contain recoverable, free-state crude oil. Free hydrocarbon content is a crucial parameter characterizing free-state crude oil; higher free hydrocarbon content indicates more free-state crude oil. Free hydrocarbon content shows a positive correlation with total organic carbon (TOC) content, while TOC content shows an inverse correlation with compensation density. Therefore, the difference between TOC content and compensation density can characterize free-state crude oil; a larger difference indicates more free-state crude oil. Based on this characteristic, the cross-plotting method of TOC content versus compensation density, and the cross-plotting method of resistivity versus natural gamma, can further qualitatively identify effective reservoirs.

[0105] To better illustrate the correlations between free hydrocarbon content and total organic carbon content, and between total organic carbon content and compensation density, see [reference needed]. Figure 6 As shown, Figure 6 This is a schematic diagram of a parameter correlation provided according to an embodiment of this application. Figure 6 (a) is a cross-plot of free hydrocarbon content and total organic carbon content. In this cross-plot, the horizontal axis represents total organic carbon content, and the vertical axis represents free hydrocarbon content. As shown in the plot, when the total organic carbon content is no more than 1%, the free hydrocarbon content is generally low, ranging from 0.2 mg / g to 3.0 mg / g, and varies considerably. When the total organic carbon content is between 1% and 6%, the free hydrocarbon content is generally high, ranging from 3.0 mg / g to 20.0 mg / g, and the free hydrocarbon content and total organic carbon content show a certain positive correlation. Figure 6 (b) is a graph showing the relationship between total organic carbon content and compensation density. In this graph, the horizontal axis represents compensation density, and the vertical axis represents total organic carbon content. As the compensation density gradually increases, the total organic carbon content gradually decreases in this cross plot.

[0106] 304. Based on the first curve group and the second curve group, the terminal determines the industrial oil flow layer and the non-industrial oil flow layer from the effective reservoir. The difference between the curves in the first curve group corresponding to the industrial oil flow layer is greater than the first preset difference threshold, and the difference between the curves in the second curve group corresponding to the industrial oil flow layer is greater than the second preset difference threshold.

[0107] In this embodiment, an industrial oil flow layer is an effective reservoir from which industrial oil flow can be obtained, while a non-industrial oil flow layer with high oil production capacity is an effective reservoir from which industrial oil flow cannot be obtained based on oil testing results. The oil production capacity of an industrial oil flow layer is higher than that of a non-industrial oil flow layer.

[0108] For easier description of the first and second curve groups, see [link to relevant documentation]. Figure 7 As shown, Figure 7 This is a schematic diagram of a set of curves according to an embodiment of this application. The vertical axis represents depth values. 701 is the first curve set, and 702 is the second curve set. By comparing the difference between two curves in each curve set at any depth with a corresponding preset difference threshold, it is possible to determine whether the depth belongs to an industrial oil flow layer or a non-industrial oil flow layer.

[0109] It should be noted that steps 303 to 304 above are an exemplary method for the terminal to determine the industrial oil flow layer and non-industrial oil flow layer in the effective reservoir based on the parameter values ​​of resistivity, natural gamma, total organic carbon content and compensation density in the effective reservoir. The principle is the same as that of step 203.

[0110] 305. Based on the parameter values ​​of multiple logging parameters corresponding to industrial and non-industrial oil flow layers, as well as the oil testing and production data of effective reservoirs, the terminal determines the third, fourth, and fifth cross plots.

[0111] In this embodiment, the oil testing and production data are used to indicate the oil layer type at each sampling point in the effective reservoir. The oil layer type includes industrial flow oil layers and non-industrial flow oil layers. The terminal projects the logging parameter values ​​of multiple sampling points corresponding to each oil layer type onto the chart to obtain the third cross plot, the fourth cross plot, and the fifth cross plot.

[0112] In the third cross plot, the horizontal axis represents the total organic carbon content, and the vertical axis represents the target parameter. The horizontal and vertical coordinate values ​​of any data point in the third cross plot are determined by the total organic carbon content parameter value and the target parameter value at the corresponding sampling point. In the fourth cross plot, the horizontal axis represents NMR porosity, and the vertical axis represents the target parameter. The horizontal and vertical coordinate values ​​of any data point in the fourth cross plot are determined by the NMR porosity parameter value and the target parameter value at the corresponding sampling point. In the fifth cross plot, the horizontal axis represents the target parameter, and the vertical axis represents NMR saturation. The horizontal and vertical coordinate values ​​of any data point in the fifth cross plot are determined by the NMR saturation parameter value and the target parameter value at the corresponding sampling point. The target parameter is the product of the ratio between resistivity and natural gamma and an adjustment factor. The adjustment factor can be set to 100. The sampling points corresponding to the data points in all the above cross plots are located within the first reference block.

[0113] For a clearer description of the various intersection diagrams mentioned above, please refer to [link / reference]. Figure 8 As shown, Figure 8 This is a schematic diagram of another intersection diagram provided according to an embodiment of this application. Figure 8 (a) is the third intersection plot. Figure 8 (b) is the fourth intersection diagram. Figure 8 (c) is the fifth cross plot. In the above cross plots, the oil layer types at the sampling points corresponding to the data points include three categories: Class I oil layers, Class II oil layers, and Class III oil layers. Among them, Class I oil layers are those where industrial oil flow was obtained from oil testing, i.e., industrial oil flow layers; Class II and Class III oil layers are those where industrial oil flow was not obtained from oil testing, i.e., non-industrial oil flow layers.

[0114] It should be noted that the oil testing and production data can be used to verify whether the distinction between industrial and non-industrial oil flow layers in the organic reservoir is correct in step 304. When determining the cross-plot, it can be drawn based on the oil layer type at each sampling point indicated by the oil testing and production data, or based on the oil layer type at each sampling point in the organic reservoir determined in step 304, or based on a combination of the oil layer types indicated by the oil testing and production data and the oil layer types indicated in step 304. This application embodiment does not impose any limitations on this.

[0115] 306. Based on the third, fourth, and fifth cross plots, the terminal determines the target parameter threshold, total organic carbon content threshold, NMR porosity threshold, and NMR saturation threshold to obtain a set of quantitative evaluation thresholds.

[0116] In this embodiment, a quantitative evaluation threshold set is used to identify industrial oil flow reservoirs in a target block. The quantitative evaluation threshold set includes a target parameter threshold, a total organic carbon content threshold, a nuclear magnetic resonance (NMR) porosity threshold, and a NMR saturation threshold. The target parameter threshold indicates the threshold corresponding to the product of the ratio between resistivity and natural gamma and an adjustment coefficient. Therefore, when a reservoir within a certain depth range in the target block meets the requirements of the quantitative evaluation threshold set, the terminal determines that the reservoir is an industrial oil flow reservoir.

[0117] See Table 2 below, which is an example set of quantitative evaluation thresholds.

[0118] Table 2 Quantitative Evaluation Threshold Groups

[0119] Lithology Nuclear magnetic porosity NMR saturation Target parameters Total organic carbon content Predominantly fine-grained sedimentary rocks ≥3.5％ ≥60％ ≥25 ≥2.5％

[0120] It should be noted that steps 305 to 306 above are an exemplary method for the terminal to determine the quantitative evaluation threshold group of the target block based on the parameter values ​​of multiple logging parameters corresponding to industrial oil flow layers and non-industrial oil flow layers, respectively. The principle is the same as that of step 204.

[0121] It should be noted that the target block contains complex lithology due to the frequent interbedding of fine-grained sedimentary rocks and source rocks, and the fine-grained sedimentary rock reservoirs exhibit high natural gamma and resistivity with wide distribution ranges. To achieve quantitative evaluation of the oil reservoirs, sensitive logging parameters need to be extracted and determined. These sensitive logging parameters are used in step 305 to determine multiple cross-plots. The process of extracting and determining these sensitive logging parameters is described below.

[0122] See Figure 9 As shown, Figure 9 This is a schematic diagram illustrating another comprehensive evaluation of a sweet spot according to an embodiment of this application. Since the lithology of fine-grained sedimentary rock reservoirs determines their physical properties and oil-bearing capacity, fine-grained sedimentary rock reservoirs are first classified into three types based on their natural gamma: The first type is fine-grained sedimentary rock reservoirs with a natural gamma of not less than 95 API. These reservoirs generally have mixed lithology, a single-layer thickness of less than 1 meter, a resistivity typically in the range of 10-20 Ω·m, a total organic carbon content typically less than 2%, and a relatively low free hydrocarbon content, as shown in Figure 901. This indicates that the oil-bearing capacity of this type of reservoir is relatively poor. The second type is fine-grained sedimentary rock reservoirs with a natural gamma of not more than 85 API. These reservoirs are generally felsic or dolomitic, with a single-layer thickness typically in the range of 1-2 meters. Their physical properties are generally good, with a resistivity in the range of 15-200 Ω·m, as shown in Figure 902. The level of resistivity can effectively reflect the oil-bearing capacity of the reservoir. The third type is fine-grained sedimentary rock reservoirs with natural gamma rays in the range of 85-95 API. These reservoirs have the most complex lithology, the largest cumulative thickness, and resistivity of not less than 20 Ω·m, and up to 1000 Ω·m, as shown in Figure 903. Based on this, see... Figure 10 As shown, Figure 10 This is a schematic diagram illustrating the oil production percentage of different formations according to an embodiment of this application. The horizontal axis represents different oil-producing formations, and the vertical axis represents the oil production percentage. It can be seen that different types of fine-grained sedimentary reservoirs all have oil-producing capacity, but the production percentage varies significantly across different formations. This diagram was determined using production profile test data from fracturing tracers in four wells within the reservoir enhancement area. See also... Figure 11 As shown, Figure 11 This is a graph showing the relationship between oil production percentage and parameter ratios according to an embodiment of this application. The horizontal axis represents the oil production percentage, and the vertical axis represents the ratio of free hydrocarbon content to total organic carbon content. It can be seen that there is a certain positive correlation between the ratio of free hydrocarbon content to total organic carbon content and the oil production percentage. Due to the limited data on free hydrocarbon content in previous vertical wells, a relevant logging interpretation model could not be established. Therefore, based on the above analysis, resistivity, natural gamma ray, total organic carbon content, NMR porosity, and NMR saturation were selected as sensitive logging parameters, and the target parameter and total organic carbon content were used as the main parameters for quantitative evaluation of the oil reservoir.

[0123] This application provides a method for quantitative evaluation of oil-bearing layers in mud-class reservoirs. Based on the parameter values ​​of multiple logging parameters from a first reference block in a target area, it can progressively determine the fine-grained sedimentary rock reservoirs in the first reference block, the effective reservoirs within the fine-grained sedimentary rock reservoirs, and distinguish between industrial and non-industrial oil-bearing flow layers within the effective reservoirs. This results in the determination of multiple cross-plots and ultimately, a set of quantitative evaluation thresholds. Through this method, based on lithology and reservoir identification, sensitive logging parameters are optimized to efficiently identify industrial oil-bearing flow layers in lithologically complex mud-class reservoirs and determine the set of quantitative evaluation thresholds. This enables the efficient and accurate identification of industrial oil-bearing flow layers in the target area, providing a reliable reference for efficient exploration and development, resource potential evaluation, sweet spot selection, reservoir stimulation, and well and layer selection for similar reservoirs, thereby improving the efficiency of oil and gas exploration and development.

[0124] Figure 12 This is a block diagram of an oil reservoir quantitative evaluation device for mud-class reservoirs according to an embodiment of this application. The device is used to perform the steps of the above-described oil reservoir quantitative evaluation method for mud-class reservoirs, see [link to relevant documentation]. Figure 12 The oil layer quantitative evaluation device for the mud-level reservoir includes: a first determination module 1201, a second determination module 1202, a third determination module 1203, and a fourth determination module 1204.

[0125] The first determining module 1201 is used to determine, for any target block, a fine-grained sedimentary rock reservoir in the first reference block based on the parameter values ​​of multiple logging parameters of the first reference block in the target block. The multiple logging parameters include natural gamma, resistivity, spontaneous potential, total organic carbon content, compensated density, nuclear magnetic porosity and nuclear magnetic saturation.

[0126] The second determining module 1202 is used to determine the effective reservoir in the fine-grained sedimentary rock reservoir based on the parameter values ​​of the spontaneous potential and the parameter values ​​of the total organic carbon content in the fine-grained sedimentary rock reservoir. The effective reservoir is a fine-grained sedimentary rock reservoir that can produce oil and gas.

[0127] The third determining module 1203 is used to determine the industrial oil flow layer and the non-industrial oil flow layer in the effective reservoir based on the parameter values ​​of resistivity, natural gamma, total organic carbon content and compensation density in the effective reservoir. The industrial oil flow layer is the effective reservoir in which industrial oil flow can be obtained, and the non-industrial oil flow layer is the effective reservoir in which industrial oil flow cannot be obtained by the oil test results.

[0128] The fourth determining module 1204 is used to determine the quantitative evaluation threshold group of the target block based on the parameter values ​​of multiple logging parameters corresponding to industrial oil flow layers and non-industrial oil flow layers respectively. The quantitative evaluation threshold group is used to identify industrial oil flow layers in the target block. The quantitative evaluation threshold group includes target parameter threshold, total organic carbon content threshold, nuclear magnetic porosity threshold, and nuclear magnetic saturation threshold. The target parameter threshold is used to indicate the threshold corresponding to the product of the ratio between resistivity and natural gamma and the adjustment coefficient.

[0129] In some embodiments, the first determining module 1201 is used to determine, for any target block, a fine-grained sedimentary rock reservoir in the first reference block based on the parameter values ​​of natural gamma, resistivity, and total organic carbon content in the first reference block, as well as a lithology identification parameter set. The lithology identification parameter set is used to identify the reservoir type in the target block, and includes the parameter ranges of natural gamma, resistivity, and total organic carbon content corresponding to each type of reservoir in the target block.

[0130] In some embodiments, the first determining module 1201 is further configured to, for a target block, determine a first cross plot and a second cross plot based on the parameter values ​​of multiple logging parameters of a second reference block in the target block and the core analysis data of the second reference block. The core analysis data is used to indicate the reservoir type at each sampling point in the second reference block. The first cross plot is used to indicate the relationship between natural gamma and resistivity at each sampling point, and the second cross plot is used to indicate the relationship between total organic carbon content and resistivity at each sampling point. Based on the first cross plot and the second cross plot, determine the parameter range of natural gamma, the parameter range of resistivity, and the parameter range of total organic carbon content corresponding to each type of reservoir to obtain a lithology identification parameter set.

[0131] In some embodiments, the first determining module 1201 is used to determine, for any target block, a fine-grained sedimentary rock reservoir in the first reference block based on the parameter values ​​of multiple logging parameters of the first reference block in the target block and core analysis data, wherein the core analysis data is used to indicate the reservoir type at different depths in the first reference block.

[0132] In some embodiments, the second determining module 1202 is used to determine candidate reservoirs from fine-grained sedimentary rock reservoirs based on the amplitude difference of spontaneous potential in the fine-grained sedimentary rock reservoirs, wherein the amplitude difference of spontaneous potential is the difference between the parameter value of spontaneous potential and the baseline value of spontaneous potential, and the amplitude difference of spontaneous potential in the candidate reservoirs is greater than a preset amplitude difference threshold; and to determine effective reservoirs from the candidate reservoirs based on the parameter value of total organic carbon content in the candidate reservoirs, wherein the parameter value of total organic carbon content in the effective reservoirs is greater than a preset carbon content threshold.

[0133] In some embodiments, the third determining module 1203 is used to determine a first curve group and a second curve group based on the parameter values ​​of resistivity, natural gamma, total organic carbon content, and compensation density in the effective reservoir. The first curve group includes a resistivity curve and a natural gamma curve, and the second curve group includes a total organic carbon content curve and a compensation density curve. Based on the first curve group and the second curve group, an industrial oil flow layer and a non-industrial oil flow layer are determined from the effective reservoir. The difference between the curves in the first curve group corresponding to the industrial oil flow layer is greater than a first preset difference threshold, and the difference between the curves in the second curve group corresponding to the industrial oil flow layer is greater than a second preset difference threshold.

[0134] In some embodiments, the fourth determining module 1204 is used to determine a third cross plot, a fourth cross plot, and a fifth cross plot based on the parameter values ​​of multiple logging parameters corresponding to industrial and non-industrial oil flow layers and the oil testing and production data of the effective reservoir. The oil testing and production data are used to indicate the oil layer type at each sampling point in the effective reservoir. The oil layer type includes industrial and non-industrial oil flow layers. The third cross plot is used to indicate the relationship between total organic carbon content and target parameters at each sampling point. The fourth cross plot is used to indicate the relationship between nuclear magnetic porosity and target parameters at each sampling point. The fifth cross plot is used to indicate the relationship between nuclear magnetic saturation and target parameters at each sampling point. Based on the third, fourth, and fifth cross plots, the target parameter threshold, total organic carbon content threshold, nuclear magnetic porosity threshold, and nuclear magnetic saturation threshold are determined to obtain a set of quantitative evaluation thresholds.

[0135] This application provides a quantitative evaluation device for mud-scale reservoirs. Based on the parameter values ​​of multiple logging parameters from a first reference block in a target area, it can progressively determine the fine-grained sedimentary rock reservoirs in the first reference block, the effective reservoirs within the fine-grained sedimentary rock reservoirs, and distinguish between industrial and non-industrial oil flow layers within the effective reservoirs, thereby determining a quantitative evaluation threshold set. These multiple logging parameters include natural gamma, resistivity, spontaneous potential, total organic carbon content, compensated density, NMR porosity, and NMR saturation. This quantitative evaluation threshold set is used to identify industrial oil flow layers in the target block. The quantitative evaluation threshold set determined by the above device can efficiently and accurately identify industrial oil flow layers in the target block, thus providing a reliable basis for subsequent oil and gas exploration and development, and improving the efficiency of oil and gas exploration and development.

[0136] It should be noted that the above-described embodiment of the mud-level reservoir oil layer quantitative evaluation device is only illustrated by the division of the above functional modules when running the application. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the terminal can be divided into different functional modules to complete all or part of the functions described above. In addition, the above-described embodiment of the mud-level reservoir oil layer quantitative evaluation device and the embodiment of the mud-level reservoir oil layer quantitative evaluation method belong to the same concept, and the specific implementation process can be found in the method embodiment, which will not be repeated here.

[0137] Figure 13This is a schematic diagram of a terminal according to an embodiment of this application. The terminal 1300 can be a portable mobile terminal, such as a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The terminal 1300 may also be referred to as a user device, portable terminal, laptop terminal, desktop terminal, or other names.

[0138] Typically, terminal 1300 includes a processor 1301 and a memory 1302.

[0139] Processor 1301 may include one or more processing cores, such as a 4-core processor or a 13-core processor. Processor 1301 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 1301 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 1301 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 1301 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0140] The memory 1302 may include one or more computer-readable storage media, which may be non-transitory. The memory 1302 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 1302 are used to store at least one computer program, which is executed by the processor 1301 to implement the reservoir quantification evaluation method for mud-level reservoirs provided in the method embodiments of this application.

[0141] In some embodiments, the terminal 1300 may also optionally include a peripheral device interface 1303 and at least one peripheral device. The processor 1301, memory 1302, and peripheral device interface 1303 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 1303 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: a radio frequency circuit 1304, a display screen 1305, a camera assembly 1306, an audio circuit 1307, and a power supply 1308.

[0142] Peripheral device interface 1303 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 1301 and memory 1302. In some embodiments, processor 1301, memory 1302 and peripheral device interface 1303 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 1301, memory 1302 and peripheral device interface 1303 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0143] The radio frequency (RF) circuit 1304 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 1304 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 1304 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. In some embodiments, the RF circuit 1304 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 1304 can communicate with other terminals through at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 1304 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0144] Display screen 1305 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 1305 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 1301 for processing. In this case, display screen 1305 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 1305, disposed on the front panel of terminal 1300; in other embodiments, there may be at least two display screens, disposed on different surfaces of terminal 1300 or in a folded design; in still other embodiments, display screen 1305 may be a flexible display screen, disposed on a curved or folded surface of terminal 1300. Furthermore, display screen 1305 may be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. The display screen 1305 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0145] The camera assembly 1306 is used to acquire images or videos. In some embodiments, the camera assembly 1306 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the terminal, and the rear-facing camera is located on the back of the terminal. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 1306 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm-light flash and a cool-light flash, which can be used for light compensation at different color temperatures.

[0146] The audio circuit 1307 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to the processor 1301 for processing, or input to the radio frequency circuit 1304 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each located at a different part of the terminal 1300. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 1301 or the radio frequency circuit 1304 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 1307 may also include a headphone jack.

[0147] Power supply 1308 is used to power the various components in terminal 1300. Power supply 1308 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 1308 includes a rechargeable battery, the rechargeable battery can support wired charging or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0148] Those skilled in the art will understand that Figure 13 The structure shown does not constitute a limitation on terminal 1300 and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0149] Figure 14 This is a schematic diagram of a server structure according to an embodiment of this application. The server 1400 can vary considerably due to different configurations or performance. It may include one or more Central Processing Units (CPUs) 1401 and one or more memories 1402. The memory 1402 stores at least one computer program, which is loaded and executed by the processor 1401 to implement the oil layer quantitative evaluation method for mud-level reservoirs provided in the above-described method embodiments. Of course, the server may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The server may also include other components for implementing device functions, which will not be elaborated here.

[0150] This application also provides a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to implement the oil layer quantitative evaluation method for mud-level reservoirs in the above embodiments. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, or optical data storage device, etc.

[0151] This application also provides a computer program product, including a computer program that is executed by a processor to implement the oil layer quantitative evaluation method for mud-level reservoirs in this application.

[0152] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0153] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for evaluating oil reservoirs in a shale formation, characterized by, The method includes: For any target block, based on the parameter values ​​of multiple logging parameters of the first reference block in the target block, the fine-grained sedimentary rock reservoir in the first reference block is determined. The multiple logging parameters include natural gamma, resistivity, spontaneous potential, total organic carbon content, compensated density, nuclear magnetic porosity, and nuclear magnetic saturation. Based on the parameters of spontaneous potential and total organic carbon content in the fine-grained sedimentary rock reservoir, the effective reservoirs in the fine-grained sedimentary rock reservoir are determined, and the effective reservoirs are fine-grained sedimentary rock reservoirs that can produce oil and gas. Based on the resistivity, natural gamma, total organic carbon content and compensation density parameters of the effective reservoir, industrial oil flow layers and non-industrial oil flow layers are determined in the effective reservoir. The industrial oil flow layer is an effective reservoir in which industrial oil flow can be obtained, and the non-industrial oil flow layer is an effective reservoir in which industrial oil flow cannot be obtained according to the oil test results. Based on the parameter values ​​of multiple logging parameters corresponding to the industrial oil flow layer and the non-industrial oil flow layer, a quantitative evaluation threshold group for the target block is determined. The quantitative evaluation threshold group is used to identify the industrial oil flow layer in the target block. The quantitative evaluation threshold group includes a target parameter threshold, a total organic carbon content threshold, a nuclear magnetic porosity threshold, and a nuclear magnetic saturation threshold. The target parameter threshold is used to indicate the threshold corresponding to the product of the ratio between resistivity and natural gamma and an adjustment coefficient.

2. The method for evaluating the oil reservoir of the shale reservoir according to claim 1, characterized in that, For any target block, determining the fine-grained sedimentary reservoir in the first reference block based on the parameter values ​​of multiple logging parameters from the first reference block within the target block includes: For any target block, based on the parameters of natural gamma, resistivity, and total organic carbon content in the first reference block, as well as the lithology identification parameter set, the fine-grained sedimentary reservoir in the first reference block is determined. The lithology identification parameter set is used to identify the reservoir type in the target block. The lithology identification parameter set includes the parameter ranges of natural gamma, resistivity, and total organic carbon content corresponding to each type of reservoir in the target block.

3. The method for evaluating the oil reservoir of the shale reservoir according to claim 2, characterized in that, The methods for determining the lithological identification parameter set include: For the target block, based on the parameter values ​​of multiple logging parameters of the second reference block in the target block and the core analysis data of the second reference block, a first cross plot and a second cross plot are determined. The core analysis data is used to indicate the reservoir type at each sampling point in the second reference block. The first cross plot is used to indicate the relationship between natural gamma and resistivity at each sampling point. The second cross plot is used to indicate the relationship between total organic carbon content and resistivity at each sampling point. Based on the first and second cross plots, the parameter ranges of natural gamma, resistivity, and total organic carbon content corresponding to each type of reservoir are determined to obtain the lithology identification parameter set.

4. The method for evaluating the oil reservoir of the shale reservoir according to claim 1, characterized in that, For any target block, determining the fine-grained sedimentary reservoir in the first reference block based on the parameter values ​​of multiple logging parameters from the first reference block within the target block includes: For any target block, based on the parameter values ​​of multiple logging parameters and core analysis data of the first reference block in the target block, the fine-grained sedimentary rock reservoir in the first reference block is determined, and the core analysis data is used to indicate the reservoir type at different depths in the first reference block.

5. The method for evaluating the oil reservoir of the shale reservoir according to claim 1, wherein, The determination of effective reservoirs in the fine-grained sedimentary rock reservoir based on the parameters of spontaneous potential and total organic carbon content in the fine-grained sedimentary rock reservoir includes: Based on the amplitude difference of spontaneous potential in the fine-grained sedimentary rock reservoir, candidate reservoirs are determined from the fine-grained sedimentary rock reservoir. The amplitude difference of spontaneous potential is the difference between the parameter value of spontaneous potential and the baseline value of spontaneous potential. The amplitude difference of spontaneous potential in the candidate reservoirs is greater than a preset amplitude difference threshold. The effective reservoir is determined from the candidate reservoirs based on the parameter value of the total organic carbon content in the candidate reservoirs, wherein the parameter value of the total organic carbon content in the effective reservoirs is greater than a preset carbon content threshold.

6. The method for evaluating the oil reservoir of the shale reservoir according to claim 1, wherein, The determination of industrial and non-industrial oil flow layers in the effective reservoir based on the resistivity, natural gamma, total organic carbon content, and compensated density parameters includes: Based on the resistivity parameter value, natural gamma parameter value, total organic carbon content parameter value and compensation density parameter value in the effective reservoir, a first curve group and a second curve group are determined. The first curve group includes the resistivity curve and the natural gamma curve, and the second curve group includes the total organic carbon content curve and the compensation density curve. Based on the first curve group and the second curve group, the industrial oil flow layer and the non-industrial oil flow layer are determined from the effective reservoir. The difference between the curves in the first curve group corresponding to the industrial oil flow layer is greater than a first preset difference threshold, and the difference between the curves in the second curve group corresponding to the industrial oil flow layer is greater than a second preset difference threshold.

7. The method for evaluating the oil reservoir of the shale reservoir according to claim 1, wherein, The determination of the quantitative evaluation threshold set for the target block based on the parameter values ​​of multiple logging parameters corresponding to the industrial oil flow layer and the non-industrial oil flow layer includes: Based on the parameter values ​​of multiple logging parameters corresponding to the industrial oil flow layer and the non-industrial oil flow layer, respectively, and the oil testing and production data of the effective reservoir, a third cross plot, a fourth cross plot, and a fifth cross plot are determined. The oil testing and production data are used to indicate the oil layer type at each sampling point in the effective reservoir. The oil layer type includes the industrial oil flow layer and the non-industrial oil flow layer. The third cross plot is used to indicate the relationship between the total organic carbon content and the target parameter at each sampling point. The fourth cross plot is used to indicate the relationship between the nuclear magnetic porosity and the target parameter at each sampling point. The fifth cross plot is used to indicate the relationship between the nuclear magnetic saturation and the target parameter at each sampling point. Based on the third, fourth, and fifth cross plots, the target parameter threshold, total organic carbon content threshold, NMR porosity threshold, and NMR saturation threshold are determined to obtain the quantitative evaluation threshold set.

8. An apparatus for quantitative evaluation of oil reserves of a clayey reservoir, characterized in that it comprises: The device includes: The first determining module is used to determine, for any target block, a fine-grained sedimentary rock reservoir in the first reference block based on the parameter values ​​of multiple logging parameters of the first reference block in the target block. The multiple logging parameters include natural gamma, resistivity, spontaneous potential, total organic carbon content, compensated density, nuclear magnetic porosity, and nuclear magnetic saturation. The second determining module is used to determine the effective reservoir in the fine-grained sedimentary rock reservoir based on the parameter values ​​of the spontaneous potential and the parameter values ​​of the total organic carbon content in the fine-grained sedimentary rock reservoir. The effective reservoir is a fine-grained sedimentary rock reservoir that can produce oil and gas. The third determining module is used to determine the industrial oil flow layer and the non-industrial oil flow layer in the effective reservoir based on the parameter values ​​of resistivity, natural gamma, total organic carbon content and compensation density in the effective reservoir. The industrial oil flow layer is an effective reservoir in which industrial oil flow can be obtained, and the non-industrial oil flow layer is an effective reservoir in which industrial oil flow cannot be obtained by oil testing. The fourth determining module is used to determine a quantitative evaluation threshold group for the target block based on the parameter values ​​of multiple logging parameters corresponding to the industrial oil flow layer and the non-industrial oil flow layer, respectively. The quantitative evaluation threshold group is used to identify the industrial oil flow layer in the target block. The quantitative evaluation threshold group includes a target parameter threshold, a total organic carbon content threshold, a nuclear magnetic porosity threshold, and a nuclear magnetic saturation threshold. The target parameter threshold is used to indicate the threshold corresponding to the product of the ratio between resistivity and natural gamma and the adjustment coefficient.

9. A computer device, comprising: The computer device includes a processor and a memory, the memory being used to store at least one computer program, the at least one computer program being loaded by the processor and executed as the oil layer quantitative evaluation method for mud-level reservoirs according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store at least one computer program for executing the oil layer quantitative evaluation method for mud-level reservoirs as described in any one of claims 1 to 7.

Images