Logging lithology identification method and system based on convolutional neural network

A convolutional neural network and lithology identification technology, applied in the field of logging lithology identification based on convolutional neural network, can solve the problems of low accuracy, large influence of human factors, low accuracy of logging curve identification, etc., to improve efficiency and the effect of precision

Pending Publication Date: 2020-08-28
BC P INC CHINA NAT PETROLEUM CORP +1
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Problems solved by technology

[0003] Conventional methods of using logging data to identify formation lithology mainly include crossplot method, statistical method and imaging logging, but the traditional identification methods are low in accuracy, slow in efficiency and greatly influenced by human factors, while tomography logging is expensive , is not conducive to wide practical application, so it is of great significance to develop a high-precision automatic lithology identification method for logging data interpretation
At present, it is possible to automatically identify lithology by establishing a logging interpretation model of rock type-logging parameters, but this method has low identification accuracy for logging curves with inconspicuous features

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  • Logging lithology identification method and system based on convolutional neural network
  • Logging lithology identification method and system based on convolutional neural network
  • Logging lithology identification method and system based on convolutional neural network

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[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0026] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, device, method or computer program product. Therefore, the present disclosure may be embodied in the form of complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0027] In view of the fact that the lithology of the prior art is low in accuracy, slow in e...

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Abstract

The invention provides a logging lithology identification method and system based on a convolutional neural network. The logging lithology identification method based on the convolutional neural network comprises the steps: obtaining current well logging data; inputting the current logging data into a pre-created convolutional neural network framework optimal model to obtain each lithology probability of the current logging data; and determining the lithology corresponding to the maximum value of the lithology probability as the lithology of the current logging data. According to the logging lithology identification method, lithology can be quickly divided, and the lithology identification efficiency and precision are improved.

Description

technical field [0001] The invention relates to the technical field of petroleum geophysical exploration, in particular to a logging lithology identification method and system based on a convolutional neural network. Background technique [0002] Geophysical logging lithology identification is an important content in the research of oil and gas bearing evaluation and reservoir description, and is the basis for solving various parameters of oil and gas reservoirs. Compared with other lithology identification methods (such as taking cores), the use of well logging data to identify formation lithology has the characteristics of high speed and low cost, so it is widely used. [0003] Conventional methods of using logging data to identify formation lithology mainly include crossplot method, statistical method and imaging logging, but the traditional identification methods are low in accuracy, slow in efficiency and greatly influenced by human factors, while tomography logging is ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/02G06F16/215G06N3/04E21B49/00
CPCG06Q10/0639G06Q50/02G06F16/215G06N3/08E21B49/00G06N3/045
Inventor 熊伟徐晨万忠宏李磊高英楠陈萍
Owner BC P INC CHINA NAT PETROLEUM CORP
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