Application of BP neural network method to predict the stress sensitivity of ultra deep carbonate reservoir in Shunbei Oilfield

Shunbei Oilfield is a typical stress-sensitivity deep oilfield. Coring in deep wells is difficult or there are not sufficient cores to support the experiments while the rock samples are of strong heterogeneity, so the stress sensitivity cannot be tested, so as to impact the evaluation accuracy of reservoir physical properties and productivity. In this paper, 6 kinds of testing data (e.g. logging and well testing) and 7 parameters (e.g. permeability and fracture width) related to stress sensitivity and rock composition and reservoir temperature and pressure data of 7 wells in the Yingshan Formation were collected. Then, 11 main control factors of stress-sensitivity damage were screened out by means of single correlation analysis and gray association analysis and loaded into the established BP neural network. Excitation function and network parameters were set for training until the expected error was reached. The input layer parameters with known stress-sensitivity damage result were loaded into the trained model to carry out calculation. The calculation results were compared with the known results. It is shown that the coincidence rate of predicted stress-sensitivity damage degree is 100% and the average prediction error is 7.15%. What’s more, the established network was used for the prediction of the Yijianfang Formation in Shunbei. Taking the laboratory test value as the reference, the calculated stress-sensitivity permeability damage rate and the error of predicted critical stress are both less than 10%, indicating that this model is also applicable to the stress-sensitivity damage prediction of other carbonate reservoirs. In conclusion, BP neural network method can be used to predict the stress sensitivity degree of carbonate reservoir and can solve the difficulty that the difficult coring in deep carbonate reservoirs fails to support experiments.