Prediction method and device for oil-containing reservoir between coal, medium and computing equipment

Abstract

The invention relates to the technical field of oil exploration, and discloses an inter-coal oil-bearing reservoir prediction method and device, a medium and computing equipment. The method comprises the steps that single well curve characteristic analysis is carried out, qualitative analysis is carried out on curves of sound waves, resistivity, porosity and natural potential, and the characteristics of the oil-bearing reservoir are determined; carrying out quantitative analysis on the density and the gamma curve, and carrying out curve fitting; the fitted curve is optimized, and the influence of the coal seam is eliminated; establishing a favorable reservoir indication curve and a sound wave time difference cross plot, carrying out quantitative analysis on the oil-bearing reservoir, and dividing the interval of the oil-bearing reservoir to distinguish the oil-bearing reservoir; well-seismic calibration is carried out, and a coal seam oil-bearing reservoir removal initial model is established based on the favorable reservoir indication curve; and based on the coal seam-removed oil-bearing reservoir initial model, carrying out oil-bearing reservoir prediction, and drawing an oil-bearing reservoir planar graph and an oil-bearing reservoir profile graph. According to the method, the influence of the coal seam is eliminated, the applicability to the oil-containing reservoir between the coal is improved, and the distribution condition of the oil-containing reservoir is conveniently and accurately predicted.

Description

Technical Field

[0001] This invention relates to the field of petroleum exploration technology, and in particular to a method, apparatus, medium, and computing equipment for predicting oil-bearing reservoirs between coal seams. Background Technology

[0002] Domestic continental clastic rock oil and gas exploration has gradually shifted from structural oil and gas exploration to lithological oil and gas exploration, and from conventional oil and gas exploration to concealed or unconventional oil and gas exploration. Oil-bearing reservoir prediction is extremely important in the oil and gas exploration process; accurate prediction of oil-bearing reservoir distribution is crucial for improving exploration success rates. Coal-bearing strata mainly consist of various rock types such as coal seams and sandstone layers. Among them, the sandstone between coal seams is a good reservoir and an important area for oil exploration. However, coal-bearing strata have complex sedimentary sequences, variable lithology, and significant differences in physical properties between coal seams and surrounding rocks. Carbonaceous bands are commonly found in oil-bearing reservoirs, and carbonaceous mudstone is found in mudstone. Both have low densities, which significantly affect density curves, making conventional density-sonic cross-plots unable to distinguish oil-bearing reservoirs. Furthermore, the low velocity and low density characteristics of coal seams lead to energy attenuation and velocity reduction in seismic waves. The two factors mentioned above reduce the reliability and accuracy of inter-coal oil-bearing reservoir prediction, making it impossible to meet the needs of oil and gas exploration under the current complex geological conditions, and restricting the characterization of favorable targets and the progress of oil and gas exploration.

[0003] Currently, oil and gas exploration is mainly carried out through the following methods: First, well logging-seismic multi-attribute density curve reconstruction and inversion technology and application. This method uses conventional well logging data and seismic attribute information that is not affected by non-stratum factors, and uses the convolution factor method to fuse attribute information with higher weights to reconstruct density curves and carry out oil-bearing reservoir inversion. Second, seismic prediction method for tight gas-thin oil-bearing reservoirs in coal-bearing strata. This method uses a one-dimensional model to forward model the changes in the seismic response of sandstone oil-bearing reservoirs, and a two-dimensional model to implement the sandstone seismic response identification pattern, and carries out "phase control inversion" to improve prediction accuracy. Third, a step-by-step prediction method for tight oil-bearing reservoirs in coal-bearing strata based on neural network feature attributes. This method mainly uses wavelet decomposition and reconstruction to remove coal seams, and uses neutron logging attributes to distinguish sandstone (oil-bearing reservoirs) from coal seams and mudstone (non-oil-bearing reservoirs), thereby improving the identification accuracy of tight sandstone oil-bearing reservoirs.

[0004] However, none of the above methods eliminate the influence of coal seams, have poor applicability to inter-coal reservoirs, and cannot further distinguish between gas layers, gas-water co-layers, and water layers, thus failing to accurately predict oil-bearing reservoirs in inter-coal areas. Summary of the Invention

[0005] The main objective of this invention is to provide a method, apparatus, medium, and computing device for predicting oil-bearing reservoirs in coal seams, aiming to solve the technical problems of poor applicability of existing technologies to coal seam reservoirs and the inability to accurately predict oil-bearing reservoirs in coal seams.

[0006] To achieve the above objectives, this invention provides a method for predicting oil-bearing reservoirs in coal seams. The method includes: step S10, conducting single-well curve characteristic analysis, performing qualitative analysis on the curves of acoustic wave, resistivity, porosity, and spontaneous potential to determine the characteristics of oil-bearing reservoirs; step S20, performing quantitative analysis on density and gamma curves, and performing curve fitting; step S30, optimizing the fitted curves to eliminate the influence of coal seams; step S40, establishing a favorable reservoir indicator curve and an acoustic time difference cross-plot to perform quantitative analysis on oil-bearing reservoirs, dividing oil-bearing reservoir intervals to distinguish oil-bearing reservoirs; step S50, performing well-seismic calibration, and establishing an initial model of oil-bearing reservoirs without coal seams based on the favorable reservoir indicator curve; step S60, based on the initial model of oil-bearing reservoirs without coal seams, performing oil-bearing reservoir prediction, and drawing oil-bearing reservoir plan and profile maps.

[0007] In some embodiments, step S10 includes: step S110, acquiring data and conducting single-well curve characteristic analysis; step S120, conducting qualitative analysis on acoustic waves, resistivity, porosity and spontaneous potential; and step S130, determining the characteristics of the oil-bearing reservoir.

[0008] In some embodiments, step S20 includes: step S210, addressing the problem of density distortion in inter-coal oil-bearing reservoirs, obtaining sensitive curves for density and gamma that conform to the characteristics of oil-bearing reservoirs based on the response of favorable reservoir indicator curves; step S220, conducting quantitative analysis on the density and gamma curves; and step S230, performing curve fitting.

[0009] In some embodiments, step S230 includes: step S2301, normalizing and fitting the logging curve to convert the gamma curve into density-dimensional gamma; step S2302, fitting the curve based on the following formula: Oil content curve value n = a * density curve + b * density dimension gamma In this context, a and b are both weighted values, mainly determined through statistical data analysis. By analyzing the porosity of the core samples from the study area and establishing graphs with the gamma values ​​of density and density dimensions respectively, the slope k1 is determined by fitting the relationship between porosity and density, and the slope k2 is determined by fitting the relationship between porosity and density dimensions. a and b are then calculated using the slopes, where a = k1 / (k1+k2) and b = k2 / (k1+k2), and a+b = 1.

[0010] In some embodiments, step S40 includes: step S410, establishing a favorable reservoir indicator curve and an acoustic transit time cross plot based on drilling and testing data; step S420, performing quantitative analysis on the oil-bearing reservoir based on the favorable reservoir indicator curve and the acoustic transit time cross plot; and step S430, dividing the oil-bearing reservoir intervals based on the quantitative analysis results to distinguish the oil-bearing reservoirs.

[0011] In some embodiments, step S50 includes: step S510, performing well vibration calibration; and step S520, establishing an initial model of the oil-bearing reservoir without coal seams based on the favorable reservoir indicator curve.

[0012] Furthermore, to achieve the above objectives, this application embodiment also provides a coal-bearing oil reservoir prediction device, comprising: an analysis module for conducting single-well curve characteristic analysis, performing qualitative analysis on the curves of acoustic wave, resistivity, porosity, and spontaneous potential to determine the characteristics of the oil-bearing reservoir; a curve fitting module for conducting quantitative analysis on density and gamma curves and performing curve fitting; an optimization module for optimizing the fitted curves to eliminate the influence of the coal seam; an oil-bearing reservoir differentiation module for establishing a favorable reservoir indicator curve and an acoustic time difference cross-plot to perform quantitative analysis of the oil-bearing reservoir and divide the oil-bearing reservoir intervals to differentiate the oil-bearing reservoirs; a model establishment module for performing well-seismic calibration and establishing an initial model of the coal-bearing oil reservoir based on the favorable reservoir indicator curve; and a drawing module for predicting the oil-bearing reservoir based on the initial model of the coal-bearing oil reservoir and drawing a plan view and a profile view of the oil-bearing reservoir.

[0013] In addition, to achieve the above objectives, embodiments of this application also provide a computer-readable storage medium including instructions that, when run on a computer, cause the computer to execute the coal-oil reservoir prediction method of any embodiment of this application.

[0014] In addition, to achieve the above objectives, this application also provides a computing device, which includes at least one processor, a memory, and an input / output unit; wherein the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute the coal-oil-bearing reservoir prediction method of any embodiment of this application.

[0015] This application provides a method for predicting oil-bearing reservoirs in coal seams. This method involves quantitative analysis of density and gamma curves, followed by curve fitting. By optimizing the fitted curves, the influence of coal seams is eliminated, improving the applicability to oil-bearing reservoirs in coal seams. By establishing favorable reservoir indicator curves and sonic transit time cross-plots, it can not only distinguish between oil-bearing and non-oil-bearing reservoir zones, but also further differentiate between gas layers, gas-water co-layers, and water layers, helping to differentiate oil-bearing reservoir zones. Using an initial oil-bearing reservoir model, oil-bearing reservoir prediction is conducted, and oil-bearing reservoir plan and profile maps are drawn, facilitating accurate prediction of oil-bearing reservoir distribution and providing strong technical support for well location deployment in the area. Attached Figure Description

[0016] Figure 1 A flowchart illustrating a method for predicting oil-bearing reservoirs between coal seams provided in an embodiment of this application; Figure 2A structural block diagram of a coal-bearing oil reservoir prediction device provided in an embodiment of this application; Figure 3 A fitting curve diagram provided for one embodiment of this application; Figure 4 A cross-plot of favorable reservoir indicator curves and acoustic transit time provided for an embodiment of this application; Figure 5 This is a schematic diagram of an oil-bearing reservoir inversion model after eliminating the influence of coal seams, provided in an embodiment of this application. Figure 6 A schematic diagram of a favorable reservoir indicator curve model and inversion results provided for an embodiment of this application; Figure 7 This is a schematic diagram of the structure of a medium provided in an embodiment of this application; Figure 8 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application.

[0017] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] To make the objectives, implementation methods and advantages of this application clearer, the exemplary implementation methods of this application will be clearly and completely described below with reference to the accompanying drawings of the exemplary embodiments of this application. Obviously, the described exemplary embodiments are only some embodiments of this application, and not all embodiments.

[0019] Based on the exemplary embodiments described in this application, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the appended claims. Furthermore, although the disclosures in this application are presented by way of one or more exemplary examples, it should be understood that each aspect of these disclosures can constitute a complete implementation on its own. It should be noted that the brief descriptions of terminology in this application are merely for the convenience of understanding the embodiments described below, and are not intended to limit the implementation of this application. Unless otherwise stated, these terms should be understood in their ordinary and common meaning.

[0020] Figure 1 This is a schematic diagram of a method for predicting oil-bearing reservoirs in coal seams according to one or more embodiments of this application, as shown below. Figure 1 As shown, the method may include the following steps: Step S10: Conduct single-well curve characteristic analysis, perform qualitative analysis on the curves of sonic wave, resistivity, porosity and spontaneous potential, and determine the characteristics of oil-bearing reservoirs.

[0021] In an exemplary embodiment, step S10 may specifically include the following steps: Step S110: Obtain data and conduct single-well curve characteristic analysis; Step S120: Perform qualitative analysis on acoustic waves, resistivity, porosity, and spontaneous potential; Step S130: Determine the characteristics of the oil-bearing reservoir.

[0022] Specifically, the data includes well logging data, coring data, and oil testing data for the study area. Spontaneous potential is the electrode potential of a metal in a corrosive system without the influence of external current. Based on the test results, the corrosivity of the soil through which the metal pipeline passes can be determined, and the general situation of corrosion under different soil environments can be understood. The characteristics of the oil-bearing reservoir are as follows: sonic velocity, density, gamma, resistivity, and porosity are all within the preset range. The preset range is determined according to the actual situation; this is only a qualitative description and does not limit specific values.

[0023] Step S20: Perform quantitative analysis on density and gamma curves, and then perform curve fitting.

[0024] In an exemplary embodiment, step S20 may specifically include the following steps: Step S210: To address the issue of density distortion in inter-coal oil-bearing reservoirs, based on the response of favorable reservoir indicator curves, obtain sensitive curves for density and gamma that conform to the characteristics of oil-bearing reservoirs. Step S220: Perform quantitative analysis on density and gamma curve; Step S230: Perform curve fitting.

[0025] The gamma curve is a special type of tone curve. When the gamma value equals 1, the curve is a straight line at 45° to the coordinate axis, indicating that the input and output densities are the same. A gamma value higher than 1 will cause the output to darken, while a gamma value lower than 1 will cause the output to brighten.

[0026] In an exemplary embodiment, step S230 may specifically include the following steps: Step S2301: Normalize and fit the logging curve to convert the gamma curve into density-dimensional gamma. Step S2302, curve fitting is performed based on the following formula: Oil content curve value n = a * density curve + b * density dimension gamma (1) In this context, a and b are both weighted values, mainly determined through statistical data analysis. By analyzing the porosity of the core samples from the study area and establishing graphs with the gamma values ​​of density and density dimensions respectively, the slope k1 is determined by fitting the relationship between porosity and density, and the slope k2 is determined by fitting the relationship between porosity and density dimensions. a and b are then calculated using the slopes, where a = k1 / (k1+k2) and b = k2 / (k1+k2), and a+b = 1.

[0027] In addition, the dimension, also called the dimension, refers to the inherent and measurable physical property of a physical quantity, used to characterize the properties (categories) of a physical quantity, such as time, length, mass, density, etc., and it is objective.

[0028] Step S30: Optimize the fitted curve to eliminate the influence of the coal seam.

[0029] For example, we can set GR (gamma) >= 40, DEN (density) <= 1.5, and AC (acoustic transit time) >= 128, and set the curve value to 2.55. Curve fitting refers to selecting an appropriate curve type to fit the observed data and using the fitted curve equation to analyze the relationship between the two variables. A specific fitted curve is shown below. Figure 3 As shown.

[0030] Step S40: Establish favorable reservoir indicator curves and acoustic transit time cross plots to perform quantitative analysis of oil-bearing reservoirs, divide oil-bearing reservoir intervals, and distinguish oil-bearing reservoirs.

[0031] Specifically, Figure 4 To facilitate the intersection of reservoir indicator curves and acoustic transit time diagrams, acoustic transit time refers to the time difference between the received acoustic waves. This difference can be used for correlation calculations to solve for various quantities. Quantitative analysis determines the content of various components in a substance and includes three categories: gravimetric analysis, volumetric analysis, and instrumental analysis. In broader research, qualitative analysis is extended to the study of things from a qualitative perspective, that is, grasping the qualitative characteristics of things from their constituent elements and their interrelationships.

[0032] In an exemplary embodiment, step S40 may include the following steps: Step S410: Based on drilling and well testing data, establish favorable reservoir indicator curves and sonic transit time cross plots; Step S420: Quantitative analysis of the oil-bearing reservoir is performed based on the favorable reservoir indicator curve and the acoustic transit time cross plot; Step S430: Based on the quantitative analysis results, divide the oil-bearing reservoir intervals to distinguish the oil-bearing reservoirs.

[0033] In step S410, based on drilling and oil testing data, readings are taken for well sections with different lithologies and oil testing results to establish a cross-plot of favorable reservoir indicator curves and sonic transit time. The cross-plot has a good distinguishing effect. The boundary of oil-bearing reservoirs is when the favorable reservoir indicator curve value is less than 2.42, and the boundary of oil-bearing reservoirs is when the favorable reservoir indicator curve value is less than 2.28.

[0034] Step S50: Perform well-vibration calibration and establish an initial model of the oil-bearing reservoir without coal seams based on the favorable reservoir indicator curve.

[0035] In an exemplary embodiment, step S50 may include the following steps: Step S510: Perform well vibration calibration; Step S520: Based on the favorable reservoir indicator curve, establish an initial model of the oil-bearing reservoir without coal seam.

[0036] Specifically, refer to Figures 5-6 By establishing an initial model of the oil-bearing reservoir without coal seams, inversion was carried out to obtain, for example... Figure 5 The oil-bearing reservoir inversion model shown is after eliminating the influence of coal seams. Figure 6 To facilitate the identification of oil-bearing reservoirs from the perspective of the reservoir indicator curve model and the schematic diagram of the inversion results, the planar profile shows a better identification effect and a clear distribution pattern of oil-bearing reservoirs, which is convenient for oil-bearing reservoir prediction and can be used to draw planar and profile maps of oil-bearing reservoirs.

[0037] Step S60: Based on the initial model of the oil-bearing reservoir after coal seam removal, carry out oil-bearing reservoir prediction and draw oil-bearing reservoir plan and profile.

[0038] A sectional view, also known as a section view, is a diagram showing the internal structure of an object by cutting it along a certain direction. A sectional view is an orthographic projection of the remaining part onto a projection plane, which is made by imagining that an object is cut open by a cutting plane and removing the part between the observer and the cutting plane.

[0039] A plan view is a type of map that uses a horizontal plane instead of a level surface. Under this premise, the ground features within the survey area can be projected onto a plane along the vertical direction, and the resulting similar figure, reduced to a specified symbol and scale, is called a plan view.

[0040] This application provides a method for predicting oil-bearing reservoirs in coal seams through one or more embodiments. This method involves quantitative analysis of density and gamma curves, followed by curve fitting. By optimizing the fitted curves, the influence of coal seams is eliminated, improving the applicability to oil-bearing reservoirs in coal seams. By establishing favorable reservoir indicator curves and sonic transit time cross-plots, it can not only distinguish between oil-bearing and non-oil-bearing reservoir zones, but also further differentiate between gas layers, gas-water co-layers, and water layers, helping to differentiate oil-bearing reservoir zones. Using an initial oil-bearing reservoir model, oil-bearing reservoir prediction is conducted, and oil-bearing reservoir plan and profile maps are drawn, facilitating accurate prediction of oil-bearing reservoir distribution and providing strong technical support for well location deployment in the area.

[0041] Based on the above description of the method and related figures for predicting oil-bearing reservoirs between coal seams, this application also provides a device for predicting oil-bearing reservoirs between coal seams, such as... Figure 2 As shown, the coal-bearing oil reservoir prediction device 200 may include the following modules: Analysis module 210 is used to conduct single-well curve characteristic analysis, perform qualitative analysis on the curves of sonic waves, resistivity, porosity and spontaneous potential, and determine the characteristics of oil-bearing reservoirs. The curve fitting module 220 is used to perform quantitative analysis of density and gamma curves and to perform curve fitting. Optimization module 230 is used to optimize the fitted curve and eliminate the influence of coal seam. The oil-bearing reservoir differentiation module 240 is used to establish favorable reservoir indicator curves and acoustic transit time cross plots, perform quantitative analysis of oil-bearing reservoirs, divide oil-bearing reservoir intervals, and differentiate oil-bearing reservoirs. The model building module 250 is used for well-vibration calibration and establishes an initial model of the oil-bearing reservoir without coal seams based on the favorable reservoir indicator curve. The drawing module 260 is used to predict oil-bearing reservoirs based on the initial model of the oil-bearing reservoirs after coal seam removal, and to draw plan and profile maps of the oil-bearing reservoirs.

[0042] Based on the above embodiments, this application also provides a computer-readable storage medium, see reference. Figure 7 The computer-readable storage medium shown is an optical disc 50, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it will implement the steps described in the above-described method implementation, such as: conducting single-well curve characteristic analysis; performing qualitative analysis on the curves of acoustic wave, resistivity, porosity, and spontaneous potential to determine the characteristics of oil-bearing reservoirs; performing quantitative analysis on density and gamma curves and performing curve fitting; optimizing the fitted curves to eliminate the influence of coal seams; establishing a favorable reservoir indicator curve and an acoustic time difference cross-plot to perform quantitative analysis of oil-bearing reservoirs, dividing oil-bearing reservoir intervals to distinguish oil-bearing reservoirs; performing well-seismic calibration; establishing an initial model of oil-bearing reservoirs without coal seams based on the favorable reservoir indicator curves; and performing oil-bearing reservoir prediction based on the initial model of oil-bearing reservoirs without coal seams, drawing oil-bearing reservoir plan and profile diagrams. The specific implementation methods of each step will not be repeated here.

[0043] It should be noted that examples of computer-readable storage media may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0044] Furthermore, based on the above embodiments, this application also provides a computing device. Figure 8A block diagram is shown of an exemplary computing device 60 suitable for implementing embodiments of the present application. The computing device 60 may be a computer system or a server. Figure 8 The computing device 60 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0045] like Figure 8 As shown, the components of computing device 60 may include, but are not limited to: one or more processors or processing units 601, system memory 602, and bus 603 connecting different system components (including system memory 602 and processing unit 601).

[0046] The computing device 60 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computing device 60, including volatile and non-volatile media, removable and non-removable media.

[0047] System memory 602 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 6021 and / or cache memory 6022. Computing device 60 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, ROM 6023 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 8 (Not shown in the image, usually referred to as "hard drive"). Although not shown in... Figure 8 The diagram illustrates that a disk drive for reading and writing to removable non-volatile disks (e.g., "floppy disks") and an optical disk drive for reading and writing to removable non-volatile optical disks (e.g., CD-ROMs, DVD-ROMs, or other optical media) can be provided. In these cases, each drive can be connected to a bus 603 connecting different system components via one or more data media interfaces. The system memory 602 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0048] A program / utility 6025 having a set (at least one) of program modules 6024 may be stored, for example, in system memory 602, and such program modules 6024 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment. Program modules 6024 typically perform the functions and / or methods described in the embodiments of this application.

[0049] The computing device 60 can also communicate with one or more external devices 604 (such as a keyboard, pointing device, display, etc.). This communication can be performed via input / output (I / O) interface 605. Furthermore, the computing device 60 can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapter 606. Figure 8 As shown, network adapter 606 communicates with other modules of computing device 60 (such as processing unit 601, etc.) via bus 603, which connects different system components. It should be understood that, although... Figure 8 Other hardware and / or software modules may be used in conjunction with computing device 60, as not shown in the diagram.

[0050] The processing unit 601 executes various functional applications and data processing by running programs stored in the system memory 602. For example, it performs single-well curve characteristic analysis, qualitative analysis of curves for acoustic waves, resistivity, porosity, and spontaneous potential to determine oil-bearing reservoir characteristics; quantitative analysis of density and gamma curves, and curve fitting; optimization of the fitted curves to eliminate coal seam influence; establishment of favorable reservoir indicator curves and acoustic time-of-flight cross-plots to quantitatively analyze oil-bearing reservoirs, delineate oil-bearing reservoir intervals, and distinguish oil-bearing reservoirs; well-seismic calibration; and based on favorable reservoir indicator curves, establish an initial model of oil-bearing reservoirs without coal seams; based on the initial model of oil-bearing reservoirs without coal seams, conduct oil-bearing reservoir prediction, and draw oil-bearing reservoir plan and profile diagrams. The specific implementation methods of each step will not be repeated here. It should be noted that although several units / modules or sub-units / sub-modules of this inter-coal seam oil-bearing reservoir prediction device are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules for embodiment.

[0051] In the description of this application, it should be noted that the terms "first", "second", and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0052] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0053] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0054] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0055] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0056] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0057] Finally, it should be noted that the above embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the technical scope disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

[0058] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

Claims

1. A method for predicting oil-bearing reservoirs between coal seams, characterized in that, The method includes: Step S10: Conduct single-well curve characteristic analysis, perform qualitative analysis on the curves of sonic wave, resistivity, porosity and spontaneous potential to determine the characteristics of oil-bearing reservoirs; Step S20: Perform quantitative analysis on density and gamma curves, and then perform curve fitting. Step S30: Optimize the fitted curve to eliminate the influence of the coal seam; Step S40: Establish favorable reservoir indicator curves and acoustic transit time cross plots to perform quantitative analysis of oil-bearing reservoirs, divide oil-bearing reservoir intervals, and distinguish oil-bearing reservoirs. Step S50: Perform well-vibration calibration and establish an initial model of the oil-bearing reservoir without coal seams based on the favorable reservoir indicator curve; Step S60: Based on the initial model of the oil-bearing reservoir after coal seam removal, carry out oil-bearing reservoir prediction and draw oil-bearing reservoir plan and profile.

2. The method for predicting oil-bearing reservoirs between coal seams according to claim 1, characterized in that, Step S10 includes: Step S110: Obtain data and conduct single-well curve characteristic analysis; Step S120: Perform qualitative analysis on acoustic waves, resistivity, porosity, and spontaneous potential; Step S130: Determine the characteristics of the oil-bearing reservoir.

3. The method for predicting oil-bearing reservoirs between coal seams according to claim 1, characterized in that, Step S20 includes: Step S210: To address the issue of density distortion in inter-coal oil-bearing reservoirs, based on the response of favorable reservoir indicator curves, obtain sensitive curves for density and gamma that conform to the characteristics of oil-bearing reservoirs. Step S220: Perform quantitative analysis on density and gamma curve; Step S230: Perform curve fitting.

4. The method for predicting oil-bearing reservoirs between coal seams according to claim 3, characterized in that, Step S230 includes: Step S2301: Normalize and fit the logging curve to convert the gamma curve into density-dimensional gamma. Step S2302, curve fitting is performed based on the following formula: Oil content curve value n = a * density curve + b * density dimension gamma In this context, a and b are both weighted values, mainly determined through statistical data analysis. By analyzing the porosity of the core samples from the study area and establishing graphs with the gamma values ​​of density and density dimensions respectively, the slope k1 is determined by fitting the relationship between porosity and density, and the slope k2 is determined by fitting the relationship between porosity and density dimensions. a and b are then calculated using the slopes, where a = k1 / (k1+k2) and b = k2 / (k1+k2), and a+b = 1.

5. The method for predicting oil-bearing reservoirs between coal seams according to claim 1, characterized in that, Step S40 includes: Step S410: Based on drilling and well testing data, establish favorable reservoir indicator curves and sonic transit time cross plots; Step S420: Quantitative analysis of the oil-bearing reservoir is performed based on the favorable reservoir indicator curve and the acoustic transit time cross plot; Step S430: Based on the quantitative analysis results, divide the oil-bearing reservoir intervals to distinguish the oil-bearing reservoirs.

6. The method for predicting oil-bearing reservoirs between coal seams according to claim 1, characterized in that, Step S50 includes: Step S510: Perform well vibration calibration; Step S520: Based on the favorable reservoir indicator curve, establish an initial model of the oil-bearing reservoir without coal seam.

7. A device for predicting oil-bearing reservoirs between coal seams, characterized in that, include: The analysis module is used to conduct single-well curve characteristic analysis, perform qualitative analysis on the curves of sonic waves, resistivity, porosity and spontaneous potential, and determine the characteristics of oil-bearing reservoirs. The curve fitting module is used to perform quantitative analysis of density and gamma curves and to perform curve fitting. The optimization module is used to optimize the fitted curve and eliminate the influence of the coal seam. The oil-bearing reservoir differentiation module is used to establish favorable reservoir indicator curves and sonic transit time cross plots, perform quantitative analysis of oil-bearing reservoirs, and divide oil-bearing reservoir intervals to differentiate oil-bearing reservoirs. The model building module is used for well-vibration calibration and establishes an initial model of the oil-bearing reservoir without coal seams based on the favorable reservoir indicator curve. The drawing module is used to predict oil-bearing reservoirs and draw plan and profile maps of oil-bearing reservoirs based on the initial model of the oil-bearing reservoirs after coal seam removal.

8. A computer-readable storage medium, characterized in that, It includes instructions that, when run on a computer, cause the computer to perform the method for predicting oil-bearing reservoirs between coal seams as described in any one of claims 1 to 6.

9. A computing device, characterized in that, The computing device includes: At least one processor, memory, and input / output unit; The memory is used to store computer programs, and the processor is used to call the computer programs stored in the memory to execute the coal-oil-bearing reservoir prediction method according to any one of claims 1 to 6.

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