Method and system for predicting fracture porosity of carbonate reservoir

Abstract

The invention relates to a method and system for predicting carbonate reservoir fracture porosity, and the method comprises the steps: carrying out the intersection analysis of the seismic shear wave speed and the fracture porosity, building the relation between the shear wave speed and the fracture porosity, and fitting a relation curve; performing pre-stack inversion on the seismic shear wave velocity to obtain a shear wave velocity data volume; converting the shear wave velocity data volume into a porosity data volume by using a relation curve between the shear wave velocity and the fracture porosity; predicting a crack data volume based on the maximum likelihood attribute of the post-stack seismic data; and obtaining an intersection of the porosity data volume and the crack data volume to obtain the porosity of the crack. According to the method, the logging data is subjected to standardized processing, so that system errors caused by different instruments, different measurement environments, different acquisition time, different well conditions and the like among multiple wells are eliminated, and the consistency of multi-well logging information is improved. And obtaining an intersection of the obtained porosity data volume and the maximum likelihood attribute data volume, eliminating data of a non-fractured reservoir development part, and obtaining a fractured porosity data volume.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas development, and in particular to a method and system for predicting carbonate reservoir fracture porosity. Background Art

[0002] Modeling and numerical simulation of fracture-cavity carbonate reservoirs have always been a difficult problem that has troubled the industry. Holes are the main storage space of carbonate oil and gas reservoirs, and fractures are important connecting and dredging channels. In the current modeling and numerical simulation of fracture-cavity carbonate reservoirs, fractures are only used as seepage channels, not as storage spaces. With the continuous deepening of the discovery and development of fault-controlled carbonate reservoirs, the proportion of pore-cavity reservoirs has gradually decreased, and fracture-cavity reservoirs have become more and more important. Their role as storage space has become more prominent, but the prediction of fracture porosity is relatively difficult. How to relatively accurately predict the fracture porosity of fracture-cavity carbonate reservoirs is very urgent. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for predicting the fracture porosity of carbonate reservoirs, so as to solve the problem of using fractures as reservoir spaces and inaccurate fracture porosity prediction in fracture-cavity carbonate reservoir modeling.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] In a first aspect, the present invention provides a method for predicting fracture porosity of a carbonate reservoir, comprising:

[0006] Through the intersection analysis of seismic shear wave velocity and fracture porosity, the relationship between shear wave velocity and fracture porosity is established and the relationship curve is fitted;

[0007] Perform pre-stack inversion on the seismic shear wave velocity to obtain a shear wave velocity data volume;

[0008] The shear wave velocity data volume is converted into a porosity data volume using the relationship curve between the shear wave velocity and the fracture porosity;

[0009] Predict fracture data volume based on maximum likelihood attributes of post-stack seismic data;

[0010] The porosity data volume and the fracture data volume are intersected, that is, the porosity is assigned to the fractures at the same position in space to obtain the porosity of the fractures.

[0011] Optionally, the drilling and logging data in the area are standardized before fitting the relationship curve.

[0012] Optionally, shear wave logging data and wide-azimuth three-dimensional seismic data are collected and processed to preserve azimuth and offset, and pre-stack inversion is performed using commercial geophysical software to obtain a shear wave velocity data volume.

[0013] Optionally, fit the linear relationship between shear wave velocity and fracture porosity:

[0014] Y = 1.4115 - 0.000439779 × X, where Y is the fracture porosity and X is the shear wave velocity.

[0015] Optionally, the porosity data volume contains information about the vuggy reservoir, and the development location of the fracture reservoir is determined based on the maximum likelihood attribute prediction of the post-stack seismic data.

[0016] Optionally, the porosity data volume and the maximum likelihood attribute data volume are intersected, that is, corresponding porosity values ​​are assigned to fracture development locations, and data of non-fracture reservoir development locations are eliminated to obtain a fracture porosity data volume.

[0017] Optionally, the porosity data volume and the maximum likelihood attribute data volume are intersected, and corresponding porosity values ​​are assigned to fracture development locations to obtain a fracture porosity profile.

[0018] In a second aspect, the present invention provides a system for predicting the fracture porosity of a carbonate reservoir, comprising:

[0019] A relationship curve building module is used to conduct intersection analysis between seismic shear wave velocity and fracture porosity, establish the relationship between shear wave velocity and fracture porosity, and fit the relationship curve;

[0020] The data volume conversion module is used to perform pre-stack inversion on the seismic shear wave velocity to obtain the shear wave velocity data volume; the shear wave velocity data volume is converted into the porosity data volume using the relationship curve between the shear wave velocity and the fracture porosity;

[0021] The fracture porosity output module is used to predict the fracture data volume based on the maximum likelihood attribute of the post-stack seismic data; the porosity data volume and the fracture data volume are intersected, that is, the porosity is assigned to the fractures at the same position in space to obtain the porosity of the fracture.

[0022] In a third aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of a method for predicting fracture porosity of carbonate reservoirs when executing the computer program.

[0023] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a method for predicting fracture porosity of a carbonate reservoir.

[0024] Compared with the prior art, the present invention has the following technical effects:

[0025] The present invention standardizes the logging data to eliminate the systematic errors caused by different instruments, different measurement environments, different acquisition times and different borehole conditions among multiple wells, and improve the consistency of multi-well logging data. The obtained porosity data body is intersected with the maximum likelihood attribute data body to eliminate the data of non-fracture reservoir development areas. The fracture porosity data body is obtained. Basic data is prepared for subsequent fracture-cavity carbonate reservoir modeling and numerical simulation. It is used in the modeling of fracture-cavity carbonate reservoirs, and has achieved good results, improved the prediction accuracy of fracture porosity, and has promoted the advancement of fracture-cavity carbonate reservoir modeling technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Fracture porosity prediction flow chart.

[0027] Figure 2 Intersection diagram of shear wave velocity and fracture porosity and fitting relationship curve.

[0028] Figure 3 Fracture profile predicted by maximum likelihood attributes.

[0029] Figure 4 Fracture porosity profile obtained by intersection. DETAILED DESCRIPTION

[0030] The present invention is further described below in conjunction with the accompanying drawings:

[0031] See also Figures 1 to 4 , a method for predicting fracture porosity in carbonate reservoirs:

[0032] Step 1: Carry out rock physical analysis based on the standardization of logging data, conduct intersection analysis between seismic shear wave velocity and fracture porosity, establish the relationship between shear wave velocity and fracture porosity, and fit the relationship curve.

[0033] Step 2: Perform pre-stack inversion on the seismic shear wave velocity to obtain a shear wave velocity data volume.

[0034] Step 3: Convert the shear wave velocity data volume into a porosity data volume using the relationship curve between shear wave velocity and fracture porosity.

[0035] Step 4: Predict fractures based on the maximum likelihood attributes of post-stack seismic data.

[0036] Step 5: Take the intersection of the porosity data volume and the fracture data volume, that is, assign the porosity to the fractures at the same position in space to obtain the porosity of the fractures.

[0037] In step 1, the logging data is standardized to eliminate the systematic errors caused by different instruments, different measurement environments, different acquisition times and different borehole conditions among multiple wells, and improve the consistency of multi-well logging data. On this basis, intersection analysis is carried out to establish the relationship between shear wave velocity and fracture porosity and fit the relationship curve.

[0038] In step 2, pre-stack inversion can be implemented in a variety of commercial geophysical software to obtain a shear wave velocity data volume.

[0039] The commercial geophysical software currently in use mainly include JASON, GeoEast, EPOS and other software or application platforms. Among them, JASON is the most widely used inversion software in the geophysical industry. It can help geoscientists study, process and interpret data and geological models more accurately, and can solve relevant scientific research and production problems in geophysical inversion and result interpretation.

[0040] GeoEast is a comprehensive research platform in the field of geophysics developed by Oriental Geophysical Company. It covers the entire process of work modules from data acquisition, data processing, and comprehensive interpretation. It can solve relevant scientific research and production problems in the field of oil and gas geophysical exploration and development. The software can be used to complete pre-stack / post-stack inversion work.

[0041] EPOS is a geophysical data processing and geological comprehensive interpretation software launched by Paradigm. It is particularly advantageous in seismic data processing and comprehensive interpretation. This software can be used to complete pre-stack / post-stack inversion work.

[0042] In step 3, the shear wave velocity data volume obtained in step 2 is converted into a porosity data volume by using the shear wave velocity data volume obtained in step 2 and the wave impedance and fracture porosity relationship curve established in step 1.

[0043] In step 4, the maximum likelihood attribute of the post-stack data is used to predict fractures. Since the porosity data volume obtained in step 3 contains information about the vug-type reservoir, it is necessary to determine the spatial location of fracture development. This step determines the location of fracture reservoir development based on the maximum likelihood attribute prediction of the post-stack seismic data.

[0044] In step 5, the porosity data volume obtained in step 3 and the maximum likelihood attribute data volume in step 4 are intersected to eliminate the data of the non-fracture reservoir development area. The fracture porosity data volume is obtained, and the basic data is prepared for the subsequent fracture-cavity carbonate reservoir modeling and numerical simulation.

[0045] The specific embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings. The illustrative examples and descriptions of the present invention are used to explain the present invention, but are not intended to limit the present invention.

[0046] like Figure 1 As shown, a method for predicting fracture porosity of fracture-cavity carbonate reservoirs of the present invention performs data processing according to the following steps.

[0047] Step 1: First, the logging data of drilling wells in the area need to be standardized to eliminate the systematic errors caused by different instruments, different measurement environments, different acquisition times and different wellbore conditions among multiple wells, improve the consistency of logging data among multiple wells, and provide reliable logging curves for single wells and stable logging curves among multiple wells for logging formation evaluation and rock physics modeling for reservoir prediction. Through comparative analysis, it is believed that the correlation between fracture porosity and shear wave impedance is good, and both show a linear trend (such as Figure 2 ).

[0048] Step 2: Use pre-stack inversion to obtain shear wave velocity data. This requires shear wave logging data, wide-azimuth 3D seismic data, and processing that preserves azimuth and offset. Pre-stack inversion and obtaining multiple elastic parameters. The present invention focuses on shear wave velocity because the shear wave velocity has a better fitting relationship with fracture porosity.

[0049] Step 3: Use the linear relationship between shear wave velocity and fracture porosity fitted in step 1 ( Figure 2 ), the shear wave velocity data volume obtained in step 2 is converted into a porosity data volume, but this porosity data volume contains information about the porous reservoir, and it is necessary to accurately guide which parts have developed fractures.

[0050] Step 4: Predicting fractures using the maximum likelihood attributes of post-stack data is a mature technical method. This step determines the development location of fracture reservoirs based on the maximum likelihood attribute prediction of post-stack seismic data ( Figure 3 ).

[0051] Step 5: Take the intersection of the porosity data volume obtained in step 3 and the maximum likelihood attribute data volume in step 4, that is, assign corresponding porosity values ​​to the fracture development positions, and eliminate the data of the non-fracture reservoir development positions. The fracture porosity data volume is obtained. The basic data is prepared for the subsequent fracture-cavity carbonate reservoir modeling and numerical simulation.

[0052] Figure 1 It is a technical flow chart of the present invention, which specifically includes:

[0053] Through the intersection analysis of shear wave velocity and fracture porosity, the relationship between shear wave velocity and fracture porosity is established and the relationship curve is fitted;

[0054] Perform pre-stack inversion to obtain shear wave velocity data volume;

[0055] The shear wave velocity data volume is converted into a porosity data volume using the relationship curve between the shear wave velocity and the fracture porosity;

[0056] Predict fracture data volume based on maximum likelihood attributes of post-stack seismic data;

[0057] The porosity data volume and the fracture data volume are intersected, that is, the porosity is assigned to the fractures at the same position in space to obtain the porosity of the fractures.

[0058] Several main technical steps and overall implementation process of the present invention are described. The present invention standardizes the logging data to eliminate the systematic errors caused by different instruments, different measurement environments, different acquisition times and different wellbore conditions among multiple wells, and improves the consistency of multi-well logging data. The obtained porosity data volume is intersected with the maximum likelihood attribute data volume to eliminate the data of the non-fracture reservoir development area. The fracture porosity data volume is obtained.

[0059] Figure 2 It is the intersection relationship and fitting relationship curve between shear wave velocity and fracture porosity. The shear wave velocity data is obtained by pre-stack inversion. It is necessary to have shear wave logging data, wide-azimuth three-dimensional seismic data in this area and to carry out processing to retain azimuth and offset. Pre-stack inversion and obtaining multiple elastic parameters, the present invention focuses on shear wave velocity, because the shear wave velocity and fracture porosity have a better fitting relationship.

[0060] The analysis shows that the fracture porosity and shear wave velocity in the study area have a good correlation, and both show a linear trend (the red line in the figure). Based on this, the calculation formulas for the two are obtained by fitting:

[0061] Y = 1.4115 - 0.000439779 × X, where Y is the fracture porosity and X is the shear wave velocity.

[0062] Figure 3 is the fracture prediction profile obtained in step 3. The darker ones in the figure are the predicted fractures. The linear relationship between the shear wave velocity and the fracture porosity fitted in step 1 ( Figure 2 ), the shear wave velocity data volume obtained in step 2 is converted into a porosity data volume, but this porosity data volume contains information about the porous reservoir, and it is necessary to accurately guide which parts have developed fractures.

[0063] Figure 4 It is the fracture porosity profile obtained in step 5, that is, the porosity value of the corresponding position is assigned to the spatial position with fractures (the porosity value comes from the porosity data volume obtained in step 3). Dark color (represents the development of fractures and large porosity.

[0064] In yet another embodiment of the present invention, a system for predicting the fracture porosity of carbonate reservoirs is provided, which can be used to implement the above-mentioned method for predicting the fracture porosity of carbonate reservoirs. Specifically, the system includes:

[0065] A relationship curve building module is used to conduct intersection analysis between shear wave velocity and fracture porosity, establish the relationship between shear wave velocity and fracture porosity, and fit the relationship curve;

[0066] The data volume conversion module is used to perform pre-stack inversion to obtain the shear wave velocity data volume; the shear wave velocity data volume is converted into the porosity data volume using the relationship curve between the shear wave velocity and the fracture porosity;

[0067] The fracture porosity output module is used to predict the fracture data volume based on the maximum likelihood attribute of the post-stack seismic data; the porosity data volume and the fracture data volume are intersected, that is, the porosity is assigned to the fractures at the same position in space to obtain the porosity of the fracture.

[0068] The division of modules in the embodiments of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present invention may be integrated into one processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0069] In another embodiment of the present invention, a computer device is provided, the computer device comprising a processor and a memory, the memory is used to store a computer program, the computer program comprises program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a method for predicting the fracture porosity of carbonate reservoirs.

[0070] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for predicting the fracture porosity of carbonate reservoirs in the above embodiment.

[0071] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting fracture porosity of carbonate reservoirs, characterized in that: include: Through the intersection analysis of seismic shear wave velocity and fracture porosity, the relationship between shear wave velocity and fracture porosity is established and the relationship curve is fitted; Perform pre-stack inversion on the seismic shear wave velocity to obtain a shear wave velocity data volume; The shear wave velocity data volume is converted into a porosity data volume using the relationship curve between the shear wave velocity and the fracture porosity; Predict fracture data volume based on maximum likelihood attributes of post-stack seismic data; The porosity data volume and the fracture data volume are intersected, that is, the porosity is assigned to the fractures at the same position in space to obtain the porosity of the fractures.

2. A method for predicting fracture porosity of carbonate reservoirs according to claim 1, characterized in that: Before fitting the relationship curve, the drilling and logging data in the area were standardized.

3. The method for predicting fracture porosity of carbonate reservoirs according to claim 1, characterized in that: Shear wave logging data and wide-azimuth 3D seismic data were collected and processed to preserve azimuth and offset. Prestack inversion was performed using commercial geophysical software to obtain shear wave velocity data volumes.

4. The method for predicting fracture porosity of carbonate reservoirs according to claim 1, characterized in that: The fitted linear relationship between shear wave velocity and fracture porosity: Y = 1.4115 - 0.000439779 × X, where Y is the fracture porosity and X is the shear wave velocity.

5. The method for predicting fracture porosity of carbonate reservoirs according to claim 1, characterized in that: The porosity data volume contains information about vuggy reservoirs, and the location of fracture reservoir development is determined based on the maximum likelihood attribute prediction of post-stack seismic data.

6. A method for predicting fracture porosity of carbonate reservoirs according to claim 5, characterized in that: The porosity data volume and the maximum likelihood attribute data volume are intersected, that is, the corresponding porosity values ​​are assigned to the fracture development positions, and the data of the non-fracture reservoir development positions are eliminated to obtain the fracture porosity data volume.

7. A method for predicting carbonate reservoir fracture porosity according to claim 6, characterized in that: The porosity data volume and the maximum likelihood attribute data volume are intersected, and the corresponding porosity values ​​are assigned to the fracture development positions to obtain the fracture porosity profile.

8. A system for predicting fracture porosity of carbonate reservoirs, characterized in that: include: A relationship curve building module is used to conduct intersection analysis between seismic shear wave velocity and fracture porosity, establish the relationship between shear wave velocity and fracture porosity, and fit the relationship curve; The data volume conversion module is used to perform pre-stack inversion on the seismic shear wave velocity to obtain the shear wave velocity data volume; the shear wave velocity data volume is converted into the porosity data volume using the relationship curve between the shear wave velocity and the fracture porosity; The fracture porosity output module is used to predict the fracture data volume based on the maximum likelihood attribute of the post-stack seismic data; the porosity data volume and the fracture data volume are intersected, that is, the porosity is assigned to the fractures at the same position in space to obtain the porosity of the fracture.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for predicting the fracture porosity of a carbonate reservoir as claimed in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a method for predicting fracture porosity of a carbonate reservoir as claimed in any one of claims 1 to 7 are implemented.

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