Shear Wave Prediction in Carbonate Reservoirs: Can Artificial Neural Network Outperform Regression Analysis-

摘要

Formulating a prediction tool that can estimate the shear wave velocity (V_s) is of particular importance for many applications related to petrophysics, seismic, and geomechanics. Shear wave data can be measured from both in-situ field and laboratory tests. However, they are often not measured during well logging for cost and time-saving purposes. For this reason, various prediction methods including regression analysis and artificial neural network (ANN) can be used for predicting the shear wave velocity. This study was conducted on dataset taken from a producing section in SE Iraq in which simple systematic equations have been demonstrated to predict V_s from measurable well logs. The results reveal that the compressional wave velocity (V_p is more conservative in predicting V_s rather than bulk density. Considering those parameters together can increase the performance metrics of the predictive methods. Although the results of regression analysis and ANN resemble to be closely, the higher value of determination coefficient (0.96) and the lower value of mean square error (0.0011) of ANN demonstrated that the ANN is more precise than regression analysis. An empirical model with high performance using ANN has been also developed to estimate V_s from measurable well logs. Comparison of the developed models with the literature is then presented. The validity of the proposed models was successfully checked with data from another field study. This study presents efficient and cost-effective methods for predicting V_s by incorporating measurable well logs as long as the rock tests and shear log measurements are not available.