Logging lithology identification method based on deep belief network

A technology of deep belief network and lithology recognition, which is applied in character and pattern recognition, earthwork drilling and production, wellbore/well parts, etc. It can solve problems such as slow convergence, many parameters to be adjusted, and poor results

Active Publication Date: 2019-05-10
XINJIANG INST OF ENG
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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

Method used

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  • Logging lithology identification method based on deep belief network
  • Logging lithology identification method based on deep belief network
  • Logging lithology identification method based on deep belief network

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Embodiment 1

[0065] refer to figure 1 , figure 2 , image 3 , is a schematic structural diagram of an embodiment of the present invention, and the method is applied to igneous rock reservoirs in the Songliao Basin. It is mainly completed by computer, and the equipment required to realize the method includes logging equipment, data communication interface and computer;

[0066] The well logging instrument is used for collecting well logging data;

[0067] The data communication interface is used to transmit the above-mentioned well logging data collected by the field logging instrument to the computer;

[0068] The computer is used to run the formation lithology identification algorithm of the deep belief network, and identify the formation lithology around the wellhead according to the logging data;

[0069] The method comprises the steps of:

[0070] Step 1. Use logging data to identify lithology around the wellbore. The main logging data are deep lateral resistivity, shallow latera...

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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...

Claims

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Application Information

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IPC IPC(8): E21B49/00G06K9/62
Inventor 向旻张峰玮帕尔哈提·祖努齐兴华安然
Owner XINJIANG INST OF ENG
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