CN117784247A

CN117784247A - Thin reservoir prediction method and device based on pre-stack wavelet decomposition

Info

Publication number: CN117784247A
Application number: CN202211192614.5A
Authority: CN
Inventors: 张克非; 李京南; 孙振涛; 马灵伟; 林正良
Original assignee: Sinopec Petroleum Geophysical Exploration Technology Research Institute Co ltd; China Petroleum and Chemical Corp
Current assignee: Sinopec Petroleum Geophysical Exploration Technology Research Institute Co ltd; China Petroleum and Chemical Corp
Priority date: 2022-09-28
Filing date: 2022-09-28
Publication date: 2024-03-29

Abstract

The invention provides a thin reservoir prediction method and a device based on pre-stack wavelet decomposition, wherein the method is based on post-stack calibration reflection coefficient contribution analysis, the reflection coefficient position corresponding to a thin reservoir is defined, the method is matched with a pre-stack gather after wavelet decomposition, the pre-stack gather corresponding to the reflection coefficient of the thin reservoir is subjected to dominant incidence angle domain superposition, post-stack data representing the characteristics of a thin reservoir geologic body is obtained, and thin reservoir prediction is completed.

Description

Thin reservoir prediction method and device based on pre-stack wavelet decomposition

Technical Field

The invention relates to the technical field of oil-gas seismic exploration, in particular to a thin reservoir prediction method and device based on pre-stack wavelet decomposition, a computer readable storage medium and electronic equipment.

Background

Current thin reservoir prediction methods fall into two main categories: 1 pre-stack and post-stack geostatistical inversion, based on the algorithm such as kriging interpolation, establishing a high-resolution geologic model through logging data and combining with seismic data to obtain probability distribution of different prediction results in space; 2 pre-stack and post-stack high resolution processing, the main frequency of pre-stack and post-stack seismic data is improved by a signal processing method, the purpose of improving resolution is further achieved, and thin reservoir prediction is completed.

Disclosure of Invention

In view of the foregoing, embodiments of the present invention provide a thin reservoir prediction method, apparatus, computer-readable storage medium, and electronic device based on pre-stack wavelet decomposition.

In a first aspect, an embodiment of the present invention provides a thin reservoir prediction method based on pre-stack wavelet decomposition, including:

s100, completing post-stack data synthesis record calibration of a research area, calculating a reflection coefficient sequence based on logging data, and determining the development position of a reservoir layer of the research area by analyzing contributions of different reflection coefficients to the post-stack data synthesis record;

s200, carrying out wavelet decomposition on the pre-stack channel set of the research area to obtain a plurality of pre-stack channel set subsequences corresponding to different main frequencies and energy sub-waves;

s300, selecting a prestack gather capable of reflecting the development characteristics of the reservoir from the plurality of prestack gather subsequences according to the waveform characteristics of the development positions of the reservoir, and forming a new prestack gather;

s400, performing dominant incidence angle range superposition on the new prestack gather to obtain partial superposition data capable of reflecting reservoir development characteristics;

s500, thin reservoir prediction is completed according to the partial superposition data.

According to an embodiment of the present invention, the step S100 includes:

s110, completing post-stack data synthesis record calibration of a research area, and calculating a reflection coefficient sequence based on logging data;

s120, convolving the reflection coefficient sequence and the wavelet to form a section capable of representing contributions of different reflection coefficients to post-stack data synthesis records;

s130, determining the development position of the reservoir, the reflection coefficient corresponding to the reservoir and the time range of the waveform after wavelet convolution from the profile.

According to an embodiment of the present invention, after the step S110 and before the step S120, the method further includes: and filtering the reflection coefficient sequence to enable the reflection coefficient sequence to have sparsity.

In the step S200, the pre-stack track set of the investigation region is a side-well pre-stack track set.

According to an embodiment of the present invention, in the step S300 described above: the pre-stack gathers that reflect the developmental characteristics of the reservoir are most similar to the waveform characteristics at the developmental location of the reservoir.

According to an embodiment of the present invention, the waveform features include peak features.

According to an embodiment of the present invention, the step S500 includes: and extracting plane seismic attributes from the partial overlapped data, and carrying out thin reservoir plane characterization according to the plane seismic attributes so as to complete thin reservoir prediction.

In a second aspect, an embodiment of the present invention provides a thin-reservoir prediction apparatus based on pre-stack wavelet decomposition, which is characterized by comprising:

the position analysis module is used for completing post-stack data synthesis record calibration of the research area, calculating a reflection coefficient sequence based on logging data, and determining the development position of a reservoir layer of the research area by analyzing contributions of different reflection coefficients to the post-stack data synthesis record;

the gather optimization module is used for carrying out wavelet decomposition on the pre-stack gathers in the research area so as to obtain a plurality of pre-stack gather subsequences corresponding to different main frequency and energy sub-waves;

the gather reconstruction module is used for selecting a prestack gather capable of reflecting the development characteristics of the reservoir from the plurality of prestack gather subsequences according to the waveform characteristics of the development positions of the reservoir to form a new prestack gather;

the incidence angle superposition module is used for superposing the dominant incidence angle range of the new prestack gather so as to obtain partial superposition data capable of reflecting the development characteristics of the reservoir;

and the reservoir prediction module is used for completing thin reservoir prediction according to the partial superposition data.

In a third aspect, an embodiment of the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a thin reservoir prediction method based on pre-stack wavelet decomposition as described in the first aspect.

In a fourth aspect, an embodiment of the present invention provides an electronic device, including: a processor; a memory for storing the processor-executable instructions; wherein the processor is configured to execute the instructions to implement a thin reservoir prediction method based on pre-stack wavelet decomposition as described in the previous first aspect.

Compared with the prior art, the technical scheme of the invention has the following beneficial effects:

according to the thin reservoir prediction method based on pre-stack wavelet decomposition, the reflection coefficient position corresponding to the thin reservoir is defined based on post-stack calibration reflection coefficient contribution analysis, the reflection coefficient position is matched with a pre-stack gather after wavelet decomposition, the pre-stack gather corresponding to the reflection coefficient of the thin reservoir is subjected to dominant incidence angle domain superposition, post-stack data representing the characteristics of a thin reservoir geologic body are obtained, thin reservoir prediction is completed, calculation is completely performed based on result seismic data, frequency expansion processing is not performed, new error information is not introduced, the prediction result is objective, and the multi-resolution is low; in addition, the logging data in the scheme is only used for screening the prestack gather and does not participate in operation, so that the requirement on the logging data is low.

Drawings

In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of steps of a thin reservoir prediction method based on pre-stack wavelet decomposition according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a cross-section of post-stack composite recorded nominal reflectance contribution in accordance with an embodiment of the present invention;

FIG. 3 is a schematic diagram of a reconstructed pre-stack gather according to an embodiment of the present invention;

FIG. 4 is a schematic representation of thin reservoir plane predictions of an embodiment of the present invention;

fig. 5 is a schematic diagram of an electronic device according to an embodiment of the invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Example 1

In this embodiment, the thin reservoir prediction method based on pre-stack wavelet decomposition provided by the present invention mainly includes the following steps.

Step 1: after the post-stack synthesis record calibration is completed, a reflection coefficient series R is calculated based on logging data, filtering is carried out, sparsity of the filtered reflection coefficient series R_filter is ensured, the reflection coefficient series R_filter and the wavelet convolution are formed into a section capable of representing contribution of different reflection coefficients to the synthesis record, and the development position of a reservoir, the reflection coefficient corresponding to the reservoir and the time range of the waveform after the wavelet convolution can be defined;

step 2: carrying out wavelet decomposition on the well side channel pre-stack channel set to obtain a plurality of pre-stack channel set subsequences corresponding to different main frequencies and energy wavelets;

step 3: comparing the plurality of new prestack gathers with waveform characteristics at the development position of the reservoir, selecting the prestack gather which is most similar to the waveform at the development position of the reservoir and can reflect the development characteristics of the reservoir, and reconstructing to obtain the new prestack gather;

step 4: superposing the dominant incidence angle range of the new prestack gather to obtain partial superposition data which can embody the development characteristics of the reservoir;

step 5: and extracting plane seismic attributes from part of the superimposed data, and carrying out thin reservoir plane characterization to complete thin reservoir prediction.

According to the scheme, wavelet decomposition is carried out on the prestack gather, the contribution section of the reflection coefficient of the post-stack synthetic record and the prestack gathers are compared, and the prestack gather of the thin reservoir information can be preferably embodied, so that thin reservoir prediction is completed. The method aims at carrying out multidimensional information mining on the obtained seismic data, does not improve the frequency band of the seismic data and does not introduce high-frequency logging information, but directly picks up response characteristics of a thin reservoir in a pre-stack channel set after wavelet decomposition based on the pre-stack channel set of wavelet decomposition, and completes thin reservoir prediction.

Example two

FIG. 1 shows a specific flow of a thin reservoir prediction technique based on pre-stack wavelet decomposition in accordance with the present invention. The technical scheme of the first embodiment is further described below with reference to the accompanying drawings and specific examples.

According to step 1, post-stack synthetic record calibration is completed, a reflection coefficient sequence R is calculated based on logging data, filtering is performed, sparsity of the filtered reflection coefficient sequence R_filter is ensured, the reflection coefficient sequence R_filter and wavelet convolution are formed into a section (figure 2) capable of representing contribution of different reflection coefficients to the synthetic record, and the development position of a reservoir, the reflection coefficient corresponding to the reservoir and the time range of the waveform after wavelet convolution are defined. The reflection coefficient energy of the development part of the reservoir in the reflection coefficient contribution section in fig. 2 has obvious strong wave crest characteristics, but becomes a weak wave crest in the corresponding post-stack section, so that the reflection coefficient energy is difficult to identify, and the reflection coefficient contribution of other stratum counteracts the reflection coefficient contribution of the development part of the reservoir, so that the method is a method for hopefully reflecting the reflection coefficient energy of the development part of the reservoir in the pre-stack channel set by a pre-stack wavelet decomposition method, and reservoir prediction is carried out;

according to the step 2, carrying out wavelet decomposition on the well bypass pre-stack channel set to obtain N pre-stack channel set subsequences corresponding to different main frequencies and energy wavelets;

according to the step 3, comparing N new prestack gathers with waveform characteristics at a development position of a reservoir, selecting the prestack gathers which are most similar to the waveform at the development position of the reservoir and can reflect the development characteristics of the reservoir, obtaining the new prestack gathers by a wavelet decomposition reconstruction method, wherein the reconstructed prestack gathers mainly reflect the response characteristics of earthquakes at the development position of the reservoir (figure 3), the newly reconstructed gathers in the angle of incidence range 3-17 show the response characteristics which can represent the development peaks of the reservoir, the reservoir is a sandstone reservoir, the thickness is 6m, the porosity is 7%, the impedance characteristics of the reservoir are high-wave impedance, the characteristics of the reservoir are difficult to identify in a superposition section due to poor physical property and thin thickness, and the response characteristics of the peaks of the reservoir through the reconstructed gathers are more prominent;

step 4: superposing the new prestack gather in a dominant incidence angle range (3-17 degrees) to obtain partial superposition data which can embody the development characteristics of the reservoir;

step 5: and (3) extracting plane seismic attributes from part of the superimposed data, carrying out thin reservoir plane characterization (figure 4), wherein the left half part of the figure can be used for seeing the shadow of a very blurred river channel in the early stage, and the right half part of the figure is used for describing the river channel development morphology through the method clearly, so that thin reservoir prediction can be completed based on the shadow.

Example III

Based on the thin reservoir prediction method based on pre-stack wavelet decomposition provided in the first embodiment, the present embodiment further provides a thin reservoir prediction device based on pre-stack wavelet decomposition, which includes:

Example IV

The present embodiment provides a computer readable medium having stored thereon a computer program which, when executed by a processor, implements the steps of a thin reservoir prediction method based on pre-stack wavelet decomposition as described in the above embodiments.

It should be noted that, all or part of the flow of the method of the above embodiment may be implemented by a computer program, which may be stored in a computer readable storage medium and which, when executed by a processor, implements the steps of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. Of course, there are other ways of readable storage medium, such as quantum memory, graphene memory, etc. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

Example five

As shown in fig. 5, an embodiment of the present invention further provides an electronic device. At the hardware level, the electronic device comprises a processor, optionally an internal bus, a network interface, a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.

The processor, network interface, and memory may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, a PCI (PeripheralComponent Interconnect, peripheral component interconnect standard) bus, or EISA (Extended Industry StandardArchitecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, the figures are shown with only line segments, but not with only one bus or one type of bus.

And the memory is used for storing programs. In particular, the program may include program code including computer-operating instructions. The memory may include memory and non-volatile storage and provide instructions and data to the processor. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs. The processor executes the program stored in the memory to perform all of the steps in a thin-reservoir prediction method based on pre-stack wavelet decomposition as described above.

The communication bus mentioned by the above devices may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, etc. The communication bus may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus. The communication interface is used for communication between the electronic device and other devices.

The bus includes hardware, software, or both for coupling the above components to each other. For example, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a micro channel architecture (MCa) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus, or a combination of two or more of the above. The bus may include one or more buses, where appropriate. Although embodiments of the invention have been described and illustrated with respect to a particular bus, the invention contemplates any suitable bus or interconnect.

The Memory may include random access Memory (Random Access Memory, RAM) or may include Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.

The memory may include mass storage for data or instructions. By way of example, and not limitation, the memory may comprise a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the foregoing. The memory may include removable or non-removable (or fixed) media, where appropriate. In a particular embodiment, the memory is a non-volatile solid state memory. In a particular embodiment, the memory includes Read Only Memory (ROM). The ROM may be mask programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these, where appropriate.

The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processing, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

It should be noted that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional allocation may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the functions described above. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present invention. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

The apparatus, device, system, module or unit described in the above embodiments may be implemented in particular by a computer chip or entity or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a car-mounted human-computer interaction device, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

Although the invention provides method operational steps as described in the examples or flowcharts, more or fewer operational steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one way of performing the order of steps and does not represent a unique order of execution. When implemented in an actual device or end product, the instructions may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even in a distributed data processing environment) as illustrated by the embodiments or by the figures.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It should be noted that in this document relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

In this specification, each embodiment is described in a related manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, electronic devices, and readable storage medium embodiments, since they are substantially similar to method embodiments, the description is relatively simple, and references to parts of the description of method embodiments are only required.

The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A thin reservoir prediction method based on pre-stack wavelet decomposition, comprising:

2. The thin-reservoir prediction method based on pre-stack wavelet decomposition according to claim 1, wherein said step S100 comprises:

3. The thin-reservoir prediction method based on pre-stack wavelet decomposition according to claim 2, wherein after said step S110 and before said step S120, said method further comprises:

and filtering the reflection coefficient sequence to enable the reflection coefficient sequence to have sparsity.

4. The thin-reservoir prediction method based on pre-stack wavelet decomposition according to claim 1, wherein in step S200, the pre-stack set of the investigation region is a side-of-well pre-stack set.

5. The thin-reservoir prediction method based on pre-stack wavelet decomposition according to claim 1, wherein in said step S300:

the pre-stack gathers that reflect the developmental characteristics of the reservoir are most similar to the waveform characteristics at the developmental location of the reservoir.

6. The thin-reservoir prediction method based on pre-stack wavelet decomposition according to claim 1, wherein said waveform features comprise peak features.

7. The thin-reservoir prediction method based on pre-stack wavelet decomposition according to claim 6, wherein said step S500 comprises:

and extracting plane seismic attributes from the partial overlapped data, and carrying out thin reservoir plane characterization according to the plane seismic attributes so as to complete thin reservoir prediction.

8. A thin reservoir prediction apparatus based on pre-stack wavelet decomposition, comprising:

9. A computer readable storage medium, having stored thereon a computer program which, when executed by a processor, implements a thin reservoir prediction method based on pre-stack wavelet decomposition according to any one of claims 1 to 7.

10. An electronic device, comprising:

a processor;

a memory for storing the processor-executable instructions;

wherein the processor is configured to execute the instructions to implement a thin reservoir prediction method based on pre-stack wavelet decomposition as claimed in any one of claims 1 to 7.