Logging curve reconstruction method based on nonlinear autoregressive neural network model

A neural network model and non-linear autoregressive technology, applied in biological neural network models, neural learning methods, neural architectures, etc., to achieve the effects of optimizing weights and thresholds, improving reconstruction accuracy, and high iteration efficiency

Inactive Publication Date: 2022-01-18
SOUTHWEST PETROLEUM UNIV
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Problems solved by technology

[0007] The object of the present invention is to provide a kind of logging curve reconstruction method based on

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  • Logging curve reconstruction method based on nonlinear autoregressive neural network model
  • Logging curve reconstruction method based on nonlinear autoregressive neural network model
  • Logging curve reconstruction method based on nonlinear autoregressive neural network model

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Embodiment Construction

[0053] Such as figure 1 As shown, a logging curve reconstruction method based on the nonlinear autoregressive neural network model obtains the existing logging curve data and divides it into training curve data and testing curve data according to the acquisition depth of the first existing logging curve data Data; In the embodiment, six types of well logging data with a depth of 2750.375m to 3645.125m are selected as the first existing well logging data, which are respectively acoustic time difference, natural gamma ray, resistivity, density, natural Potential, compensated neutrons. To demonstrate the robustness of the invention, three different input / output combinations were chosen. The first combination takes natural gamma ray, resistivity, density, spontaneous potential, compensated neutron as input, and acoustic transit time logging curve as output; the second combination takes acoustic transit time, natural gamma ray, resistivity, spontaneous potential, Compensated neut...

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Abstract

The invention discloses a logging curve reconstruction method based on a nonlinear autoregressive neural network model. The logging curve reconstruction method comprises the following steps: dividing an existing logging curve data acquisition depth into training curve data and test curve data; establishing an NARX neural network model according to the training curve data; performing initial optimization on the NARX neural network model through a particle swarm algorithm; substituting training curve data into the NARX neural network model by using a Levenberg-Marquardt algorithm so that training can be completed; substituting the test curve data into the NARX neural network model for testing; and using the tested NARX neural network model to obtain reconstructed logging curve data. According to the method, the problem of falling into local optimum is effectively avoided, a nonlinear logging curve reconstruction system can be approximated with high precision, the nonlinear and sequential characteristics of logging data are fully utilized, the corresponding relation between curves can be accurately reflected, and the method has a good logging curve reconstruction capability.

Description

technical field [0001] The application belongs to the technical field of geophysical data processing, and in particular relates to a logging curve reconstruction method based on a nonlinear autoregressive neural network model. Background technique [0002] Logging curves can describe formation lithology, physical properties and oil and gas properties. Geologists can establish more accurate geological models by studying and analyzing log curves. For example, acoustic logging curves can be used for reservoir inversion and seismic horizon calibration. [0003] However, log curves may be lost due to borehole expansion, borehole collapse, and tool failure, but re-logging is usually not economically feasible. Therefore, it is of great significance to find an efficient, simple and low-cost logging curve generation method. The researchers proposed that a variety of methods can be used to artificially generate well logging curves using existing logging data, such as petrophysical ...

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Application Information

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IPC IPC(8): G06N3/04G06N3/08G01V1/40G06F30/27G06N3/00
CPCG06N3/006G06N3/08G06F30/27G01V1/40G06N3/044
Inventor 张剑郝翱枭杨云李坤盛行李梓涵
Owner SOUTHWEST PETROLEUM UNIV
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