Carbonate rock high-frequency sequence automatic identification method based on BP neural network

A BP neural network and carbonate rock technology, applied in the field of oil and gas exploration, can solve the problems of many human interference factors and a large number of manual operations, and achieve the effect of automatic division and difficult solutions

Active Publication Date: 2021-11-19
CHENGDU UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

[0012] (2) There are many human interference factors
[0018] Although the sequence division schemes of Vail and Cross are different, the operation process requires a lot of manual operations. Therefore, the Cross school scheme has the same disadvantages as the Vail school scheme.

Method used

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  • Carbonate rock high-frequency sequence automatic identification method based on BP neural network
  • Carbonate rock high-frequency sequence automatic identification method based on BP neural network
  • Carbonate rock high-frequency sequence automatic identification method based on BP neural network

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Embodiment Construction

[0034] The technical solution of the present invention will be further described below with reference to the drawings.

[0035] like figure 2 As shown, a carbonate high-frequency layout automation identification method based on a BP neural network, comprising the steps of:

[0036] S1, establish a high-frequency layer identification division standard database: Standard database according to the production data of a small amount of well Artificial partitioning scheme; on the basis of manual division The well curve establishes a high frequency layer identification division standard database;

[0037] This embodiment layer identification division standard database establishes high-frequency layer sequencing results provided by the China Petroleum Group Exploration and Development Research Institute, and some data is shown in Table 1.

[0038] Table 1

[0039] Top M Deep M Rotate DT GR Rhob 1542.796 1562.3 Reverse rotation 98 20.411 2.302 1562.3 1586.84 Ro...

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Abstract

The invention discloses a carbonate rock high-frequency sequence automatic identification method based on a BP neural network. The carbonate rock high-frequency sequence automatic identification method comprises the following steps of S1, establishing a high-frequency sequence identification and division standard database; and S2, compiling and debugging a BP neural network model based on python. By learning the sequence division standard of geologists with rich sequence division experience, the BP neural network with strong migration ability is established, problems of high sequence division difficulty and long period are effectively solved, automatic division of high-frequency sequences is realized, and the method can be used as an important means for understanding marine facies stratum sedimentary environment evolution and assisting in oil-gas exploration.

Description

Technical field [0001] The present invention belongs to the field of oil and gas exploration, and in particular, the present invention relates to a method of automated a carbonate high-frequency layer sequence automation recognition based on BP neural network. Background technique [0002] Sequence: "Sequence" This concept is of great guiding significance for oil and gas exploration. Since the 1980s, the four sequential land layers have emerged. The "Sequence" concept was earlier by Sloss and other scholars. In 1977, VAIL et al. Further established a classic sequential land layer with the sea plane lifting. Since then, the cause of the genera-sequencing part of Galloway. And the TR sequential layer layers represented by CROSS have been raised. Typically, high frequency sequence is generally considered to be a four-stage and subsequent higher levels of rotation. [0003] The conventional sequence of sequence is identified by the unconception surface or the corresponding integrated...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01V11/00G06N3/08
CPCG01V11/00G06N3/084Y02A90/10
Inventor 杨迪李柯然宋金民夏舜叶玥豪金鑫赵玲丽任佳鑫范建平冯宇翔王佳蕊陈伟
Owner CHENGDU UNIVERSITY OF TECHNOLOGY
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