Logging lithology identification method based on deep belief network

Abstract

The invention relates to a logging interpretation method related to lithology identification, in particular to a formation lithology identification method based on a deep belief network. The logging lithology identification method based on the deep belief network is mainly completed through a computer, and equipment needed for achieving the method comprises a logging instrument, a data communication interface and a computer. The method comprises the following steps: identifying lithology around a wellhead by utilizing logging data; preprocessing the logging data: performing normalization processing; digitalizing the lithology classification; calculating the correlation degree between the logging curve and the lithology; presetting the structure of the deep belief network; determining the number of restricted Boltzmann machines; determining a lithology classification boundary; training the deep belief network used for identifying formation lithology; and inputting the well logging dataof the well to be interpreted into the network, and carrying out lithology identification work. The identification method provided by the invention is simple in prediction, high in identification accuracy, good in effect, practical and reliable for regions lacking stratum element logging and imaging logging data.

Description

technical field [0001] The invention relates to a logging interpretation method related to lithology identification, in particular to a formation lithology identification method based on a deep belief network. Background technique [0002] At present, the most effective methods for lithology identification of igneous reservoirs are formation element logging and imaging logging. However, both are expensive and cannot be carried out on a large scale. In the case of only conventional well logging curves, the commonly used interpretation methods include crossplot method, multivariate statistical analysis method and BP neural network method. The crossplot method is simple and easy to use, but its effect on complex reservoirs is poor; the multivariate statistical analysis method has a small workload and fast speed, but it needs to adjust many parameters and is prone to large errors; the BP neural network method itself is a This kind of "shallow layer" neural network has local mi...

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Problems solved by technology

The crossplot method is simple and easy to use, but its effect on complex reservoirs is poor; the multivariate statistical analysis method has a small workload and fast speed, but it needs to adjust many parameters and is prone to large errors; the BP neural network method itself is a This kind of "shallow" neural network has local minimization problems, often cannot obtain the global optimal solution, and also has the disadvantage of slow convergence

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