CNN well-seismic joint inversion method and system, storage medium, equipment and application

Abstract

The invention belongs to the technical field of seismic and logging joint inversion, and discloses a CNN well-seismic joint inversion method and system, a storage medium, equipment and application. The method comprises the steps: searching an inversion mapping operator f1: y-> x from seismic data y to logging data x, i.e. X = f1 (y), with the seismic data y as the input and the logging data x as the output; reconstructing a logging curve in the forward direction; and reversely updating the weight and the bias. A four-layer network structure containing two hidden layers comprises an input layer, a first convolution layer, a second convolution layer and an output layer, and the two hidden layers are convolution layers. Some virtual logging curves are interpolated by using a Kriging interpolation technology, and virtual logging data and real logging data are used as training data for convolutional neural network learning. Under the condition that a real well is not additionally added, the number of learning samples can be increased through virtual well logging, an inversion mapping operator is searched for in a wider range, and over-fitting of local training data is prevented.

Description

technical field [0001] The invention belongs to the technical field of combined seismic and well logging inversion, and in particular relates to a CNN well and seismic combined inversion method, system, storage medium, equipment and application. Background technique [0002] At present, the closest existing technology: fine description of contemporary oil and gas reservoirs puts forward higher requirements for geophysics, and the interpenetration and organic integration of geology, well logging, seismic, reservoir engineering and other disciplines has become inevitable. Geophysical parameters (such as velocity and density) are important information for studying the internal structure of oil and gas reservoirs and the characteristics of reservoir fluids. These information can be obtained either directly through logging or indirectly through seismic inversion. Logging data is characterized by high vertical resolution and horizontal sparseness; seismic data is characterized by ...

AI Technical Summary

Problems solved by technology

These methods have the following disadvantages: ①The learning samples are all simulated data; ②A priori deterministic forward mapping operator is required to synthesize seismic records; ③It is difficult to obtain ideal inversion results in the case of less training data

[0006] Difficulty in solving the above technical problems: Excessive drilling costs make the input well logging data very limited, and the existing learning samples of well-seismic joint inversion are all simulated data, lacking sufficient actual data support, and a priori It is difficult to find the deterministic forward mapping operator required for synthetic seismic records, and it is difficult to obtain ideal inversion results in the case of less training data

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