A method for predicting total organic carbon content of shale based on deep encoding and decoding network

Abstract

The invention belongs to the technical field of oil and gas exploration, and specifically relates to a method, system, and equipment for predicting the total organic carbon content of shale based on a deep encoding and decoding network, aiming to solve the problem of low prediction accuracy and low prediction accuracy of the existing shale total organic carbon content prediction method. The problem of poor robustness. The method includes obtaining a well logging curve based on the well logging data of the target shale layer to be predicted well location; preprocessing the well logging curve to obtain the preprocessed well logging curve; and performing windowing processing on the preprocessed well logging curve , after windowing, input the pre-trained TOC prediction model to obtain the TOC at the center of the window, as the prediction result of the total organic carbon content of the target shale formation to be predicted; the TOC prediction model is based on the improved 1D U-Net network Construct. The invention improves the accuracy and robustness of the prediction of the total organic carbon content of the shale formation.

Description

technical field [0001] The invention belongs to the technical field of oil and gas exploration, and in particular relates to a method, system and equipment for predicting the total organic carbon content of shale based on a deep encoding and decoding network. Background technique [0002] In recent years, with the development of shale gas and oil, the evaluation of source rocks is of great significance. Total organic carbon (TOC) plays an important role in source rock evaluation and is also an important parameter for reservoir prediction. The total organic carbon content is generally determined in the laboratory by the rock evaluation pyrolysis method, and it is the main index for evaluating the hydrocarbon generation potential of source rocks in shale gas reservoirs. These data are often scarce due to the limited number of core samples and expensive experiments. [0003] In the prior art, it has been confirmed that conventional wireline logging parameters (ie, acoustic lo...

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Problems solved by technology

[0005] In order to solve the above-mentioned problems in the prior art, that is, in order to solve the problems of low prediction accuracy and poor robustness of the existing shale total organic carbon content prediction method, the first aspect of the present invention proposes a deep encoding and decoding network based A method for predicting the total organic carbon content of shale, the method comprising:

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