Applied Single Correlation Analysis and Multiple Regression Analysis for Rapid Prediction of Reservoir Sensitivity

Abstract

Sensitivity study, the premise of the analysis of reservoir damage mechanism, is significant to optimize all the links of drilling and development process, as well as to develop the systematic oil and gas reservoir protection technology scheme. To study a variety of methods developed in recent years for the reservoir sensitivity prediction, it is found that using the combination of single correlation analysis and multiple regression analysis is a new, ideal, and rapid method for the prediction of reservoir sensitivity. Extract the relevant information of sensitivity on the basis of conventional core analysis and sensitivity analysis of minerals, and apply the single correlation analysis and multiple regression analysis to predict the reservoir sensitivity, the accuracy rate can reach 85%, which basically meets the needs of reservoir sensitivity prediction. Compared with the single prediction method, the prediction results obtaining from the combination of the two methods are obviously improved. It is simple, highly applicable, and has a clear physical meaning. The most common method of reservoir sensitivity prediction is sensitivity experiment. Conventional reservoir sensitivity experiment method requires a large number of cores, and the test is a long term procedure, while, during the process of drilling and cutting cores as well as pretreatment for the experiment, different degrees of damage may have been caused to cores. Therefore, conventional evaluation method needs plenty of manpower and material resources, it cannot make sure that the predicting outcome of each time is exactly correct however. Various methods for the fast prediction of reservoir sensitivity have been developed in recent years, such as Elman neural network, BP neural network, grey theory appraising method, multiple discriminate analysis, fuzzy mathematical method and so on, while, there exists defect in each method. Elman network can be regarded as forward neural network which possesses partial memory unit and feedback connection. Its major structure is feedback connection, which is used to memorize the output value of the previous moment, and its convergence speed is slow due to the fixed connection weight. It usually adopts BP neural network model in reservoir sensitivity prediction. However, there exists disadvantages like low convergence speed and failure in global optimization in the conventional BT algorithm towards BP algorithm. Neural network method changes the characteristics of all problems into figures, inference into numerical calculation, while neural network fails to carry out work when the data is insufficient. Besides, it requires corresponding complicated computer program when taking use of neural network method, thus the widespread use of this method has been limited. Grey theory is a new approach aims at studying the uncertainty of less data and poor information, which owns the advantage of being required with less data and small amount of calculation, thus it is extensively used in some certain industries. Within the grey model, the plane nipped between the upper and lower bounds of the forecasted future value is called gray plane, which is unfold in a shape of trumpet, that is to say, the farther in the future time, the larger grey interval of prediction value, in other words, the bigger the grey scale, the smaller the practical significance of the prediction value. Multiple discriminate analysis first divides sensitivity and the known samples of fabric parameter into several groups based on the size of sensitivity, respectively establishing the discriminant functional equation of each group of sample. It is impossible to grouping in detail, thus the predicting outcome is only an interval, but not a specific numerical value. 2nd Workshop on Advanced Research and Technology in Industry Applications (WARTIA 2016) © 2016. The authors Published by Atlantis Press 556 Based on the current method of fast prediction for reservoir sensitivity, this article combines with the previous research results, synthesizes the applied single correlation analysis and multiple regressions analysis to forecast reservoir sensitivity, getting over the disadvantages of other methods in forecasting reservoir sensitivity, supplementing the existed defects. 1. SINGLE CORRECTION ANALYSIS When one or several interrelated variables are taken certain values, although the precise value of its another corresponding variable cannot be confirmed, it still changes within a certain rage in accordance with certain rules. The dependency relation objectively exists among undemanding and uncertain quantities of such phenomenon is known as correlativity. Back in 1980, British statistician Pearson had put forward a calculation formula of measuring the linear relationship between two variables, see formula (1), r usually acts as product moment correlation coefficient. The value range of correlation coefficient r is from -1 to 1; the size of |r| reveals the strong and weak point of the linear relationship between variable x and y. The correlation coefficient value significance and correlative degree evaluation standard see table 1 and table 2.