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A Reservoir Representation Method Based on Convolutional Neural Network

A convolutional neural network and oil and gas reservoir technology, applied in the field of devices, oil and gas reservoir characterization methods, equipment and readable storage media, can solve the problems of low efficiency, time-consuming and labor-intensive development of oil and gas reservoirs, and heavy workload

Active Publication Date: 2020-07-07
CHINA UNIV OF PETROLEUM (BEIJING)
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  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

[0008] The purpose of this application is to provide a convolutional neural network-based oil and gas reservoir characterization method, device, equipment, and readable storage medium to solve the problem that traditional high-frequency processing schemes need to Manual corrections and revisions required huge workload, time-consuming and labor-intensive, resulting in low efficiency of oil and gas reservoir development

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  • A Reservoir Representation Method Based on Convolutional Neural Network
  • A Reservoir Representation Method Based on Convolutional Neural Network
  • A Reservoir Representation Method Based on Convolutional Neural Network

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Embodiment 1

[0053] The following is an introduction to Embodiment 1 of a convolutional neural network-based oil and gas reservoir characterization method provided by this application, see figure 1 , embodiment one includes:

[0054] S101. Obtain seismic trace data;

[0055] S102. Intercepting the seismic trace data corresponding to the preset order of sedimentary cycles;

[0056] Specifically, the interception operation on the seismic trace data can be realized through the interception unit, which is located outside the convolutional neural network and is used to limit the input data to ensure that the input data is the seismic trace data corresponding to the depositional cycle of the preset order.

[0057] Among them, the sedimentary cycle refers to the regular and periodic repetition of several rocks with similar lithology and lithofacies on the vertical bottom section. According to the different scales, the sedimentary cycle is divided into levels, such as figure 2 As shown, the sc...

Embodiment 2

[0066] see Figure 4 , embodiment two specifically includes:

[0067] S401. Obtain seismic trace data;

[0068] S402. Intercepting the seismic trace data corresponding to the lowest order sedimentary cycle identifiable by seismic;

[0069] S403. Input the intercepted seismic trace data into the pre-trained convolutional neural network to obtain high-frequency synthetic records;

[0070] S404. According to the high-frequency synthetic records, determine the development position, geometric shape, scale and superposition relationship of small-scale geological bodies, so as to realize reservoir characterization.

[0071] This embodiment provides a convolutional neural network-based reservoir characterization method for oil and gas reservoirs, using convolutional neural networks to learn the relationship between relatively low-frequency seismic trace data and high-resolution synthetic records, corresponding to specific levels of depositional cycles The seismic trace data is the ...

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Abstract

The present application discloses a convolutional neural network-based oil and gas reservoir characterization method, device, equipment, and readable storage medium. The scheme includes: acquiring seismic trace data; intercepting seismic trace data corresponding to preset levels of depositional cycles; The intercepted seismic trace data is input into the pre-trained convolutional neural network to obtain high-frequency synthetic records; according to the high-frequency synthetic records, the distribution of small-scale geological bodies is determined to achieve accurate reservoir characterization. Since the convolutional neural network can increase the frequency of seismic data greatly, and the convolutional neural network has the ability of automatic learning, the scheme has a large frequency improvement, high processing accuracy, and high processing efficiency. In addition, for the problem of large differences in different sedimentary cycles, the program intercepts seismic trace data of preset levels of sedimentary cycles as input, which is more pertinent, further improves the accuracy of high-frequency processing, and ultimately improves the development of oil and gas reservoirs. efficiency.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a convolutional neural network-based oil and gas reservoir characterization method, device, equipment and readable storage medium. Background technique [0002] Most of my country's onshore oil and gas reservoirs have entered the late stage of development with high water cut and high recovery degree. Therefore, accurate characterization of small-scale geological bodies is the key to improving oil and gas reservoir recovery. Seismic data are the only direct and full-coverage observation data of cross-well geological bodies, but the vertical resolution of existing seismic data is almost all greater than 10m. The horizontal resolution is about 20m, and it is difficult to support fine and accurate characterization of the seismic response characteristics of interwell geological bodies. [0003] In recent years, many methods and theories have been developed for high-resolu...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01V1/30G01V1/36G06N3/04G06N3/08
CPCG01V1/306G01V1/36G06N3/08G01V2210/52G01V2210/624G06N3/045
Inventor 徐朝晖方惠京孙盼科徐怀民
Owner CHINA UNIV OF PETROLEUM (BEIJING)