Yield prediction method, device and equipment based on convolutional coding dynamic sequence network

A technology of convolution coding and production prediction, applied in the field of shale gas exploration and development, can solve the problems of deviation, memory neural network cannot consider changes with time, etc., to improve the accuracy and guide oilfield production and development.

Active Publication Date: 2022-01-21
CHINA UNIV OF PETROLEUM (BEIJING)
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Problems solved by technology

However, because the long-short-term memory neural network cannot consider some parameters that have a great impact on production but do not change with time, there is a large deviation between the production capacity prediction results obtained by using it and the actual situation

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  • Yield prediction method, device and equipment based on convolutional coding dynamic sequence network
  • Yield prediction method, device and equipment based on convolutional coding dynamic sequence network
  • Yield prediction method, device and equipment based on convolutional coding dynamic sequence network

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[0018] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described The embodiments are only some of the embodiments in this specification, not all of them. Based on one or more embodiments in this specification, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the scope of protection of the embodiments of this specification.

[0019] Hereinafter, a specific application scenario is taken as an example to describe the implementation of this specification. specific, figure 1 A schematic flowchart of a yield prediction method based on a convolutional coded dynamic sequence network provided by an embodiment of this specification. Although this description p...

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Abstract

The embodiment of the invention provides a yield prediction method, device and equipment based on a convolutional coding dynamic sequence network. The yield prediction method comprises the steps of obtaining static data and dynamic data corresponding to each target horizontal well, wherein the dynamic data comprises yield data corresponding to each time point in the preset time; dividing the dynamic data of each target horizontal well into first dynamic data and second dynamic data based on the prediction time point to obtain a sample data set, wherein the prediction time point is within the preset time; and training the convolutional coding dynamic sequence neural network by using the sample data set to obtain a target horizontal well yield prediction model. According to the embodiment of the invention, the accuracy of productivity prediction of the fractured horizontal well can be improved.

Description

technical field [0001] The present application relates to the technical field of shale gas exploration and development, and in particular to a production prediction method, device and equipment based on a convolutional coding dynamic sequence network. Background technique [0002] In recent years, the development of shale gas has gradually become a new hotspot of energy development in the world. Since shale gas mainly exists in organic-rich shale and interlayers in the form of adsorbed gas and free gas, accurate production prediction of fractured horizontal wells is very important. The exploration and development of shale gas has great guiding significance. [0003] In the prior art, due to the unique structure of the long-short-term memory neural network, which can "remember" the previous information, the long-short-term memory neural network is usually used for forecasting the time series production. However, because the long-short-term memory neural network cannot consid...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06F119/22
CPCG06F30/27G06F2119/22Y02P90/30
Inventor 薛亮覃吉刘月田韩江峡
Owner CHINA UNIV OF PETROLEUM (BEIJING)
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