Oil field output prediction method based on dynamic radial basis function neural network

Abstract

The invention provides an oil field output prediction method based on a dynamic radial basis function neural network. The method comprises the steps that 1, factors which affect the output are determined according to oil field situations, and historical data are obtained and divided into a training data set and a test data set; 2, unitization processing is conducted on the data sets through a deviation standardization method; 3, an RBF neural network structure is adjusted in a dynamic mode through a sensitivity method, and a temporary RBF neural network prediction model is established; 4, a model error is corrected through a state transition probability matrix, and a stable RBF neural network oil output prediction model is obtained; 5, verification is conducted on the model through the test data sets obtained in the first step to judge whether the model meets expectations or not; 6 oil field output prediction is conducted through the output prediction model which meets the expectations and obtained in the fifth step. According to the oil field output prediction method based on the dynamic radial basis function neural network, the problem that the hidden layer neurons are too many or too small is avoided. and the obtained model has an adaptive adjustment function; second correction is conducted on a prediction error, and the prediction result is more accurate and reasonable.

Description

technical field [0001] The present invention relates to a method for predicting oilfield production based on a dynamic radial basis function neural network, in particular to a method for dynamically optimizing the structure of a radial basis function neural network through a sensitivity method, combined with a state transition probability method to correct residuals, and realizing Oilfield Production Prediction Methods. Background technique [0002] As the lifeblood of the national economy, oil production directly affects the country's economic development. For oil field production, to ensure a good economic benefit, there must be a high and stable oil production. Ensuring high and stable oilfield production is the central task of oilfield development and production. Therefore, the accurate prediction of oil production in oilfields has always been one of the important research tasks of oilfield development workers. [0003] Factors affecting oil production in oil fields c...

AI Technical Summary

Problems solved by technology

However, the statistical formula method (empirical method), the water drive characteristic curve method and the material balance equation method have certain defects: first, the influence of reservoir heterogeneity on oil production cannot be directly considered; second, it is impossible to consider various artificial Effects of Factor Changes on Oilfield Production

However, its dependence on geological data is too large, often resulting in errors in the understanding of reservoir geological conditions, making the prediction results of oil and gas reservoir numerical simulation useless

Although the understanding of reservoir geology can be corrected by finely fitting the oilfield development history, the fitting results are ambiguous. Accurate fitting of the oilfield development history often requires researchers to have solid geological knowledge, oil reservoir knowledge, oil recovery technology knowledge, mathematical calculation knowledge and computer knowledge, and the workload is heavy

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