Reservoir physical property parameter prediction method combined with deep learning

Abstract

The invention discloses a reservoir physical property parameter prediction method combined with deep learning, and the method comprises the steps: introducing the nonlinear correlation between an MICquantitative measurement physical property parameter and a logging curve, and selecting the logging curve which is obvious in response to the physical property parameter; introducing CEEMDAN to decompose the physical property parameter data sequence to obtain an IMF component and a residual RES component of an intrinsic mode function, and subjecting the physical property parameter data sequence tostationary processing; introducing SE to evaluate the complexity of each IMF component and RES margin, and recombining component sequences with similar entropy values to obtain a new intrinsic mode component; carrying out normalization processing on the new intrinsic mode component data and then dividing the new intrinsic mode component data into a training set and a test set; introducing an LSTMrecurrent neural network to establish a prediction model for the reconstructed new component, and obtaining a prediction value of each new intrinsic mode component; and carrying out inverse normalization on the prediction value of each new intrinsic mode component, and carrying out superposition reconstruction to obtain a physical parameter prediction result. According to the method, the modelingnumber of redundant information and prediction components is reduced, and the prediction precision and the prediction speed are improved.

Description

technical field [0001] The invention relates to a method for predicting reservoir physical property parameters combined with deep learning, and belongs to the technical fields of deep learning methods and petroleum geophysical prospecting. Background technique [0002] Porosity and permeability are important parameters reflecting the oil and gas storage capacity of reservoirs, and characterize the depositional characteristics of different geological periods. The heterogeneity of reservoir rock permeability and porosity distribution directly affects oil and gas distribution, migration and production. In oil and gas exploration, reservoir lithology parameters are the main basis for geologists to estimate the oil and gas content of the reservoir and determine the location of the well. The determination of the production well location directly affects the production layer of oil and gas. Therefore, predicting the distribution of rock permeability and porosity is an important c...

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Problems solved by technology

[0009] However, due to the randomness and volatility of the actual well curve sequence data, it is difficult to obtain the best prediction results only by using the above-mentioned single prediction model to directly predict the physical parameter sequence

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