On a new method of estimating shear wave velocity from conventional well logs

Abstract

Shear wave velocity is a critical parameter for the characterization of hydrocarbon reservoirs. Compared with compressional wave velocity which almost exist in every well, shear wave velocity hasn't been recorded in those days for the older wells due to the lack of logging equipment or limited funds. Furthermore, measuring shear wave velocity is fairly time- and money-consuming as it can only be gained by the analysis of core samples conducted in the lab or from the dipole sonic imager (DSI). To cope with the above puzzles, a new methodology, by integrating extreme learning machines (ELM) and technique of mean impact value (MIV), is proposed in this paper with the support of log data collected from two wells located in unconventional shale gas reservoir in Ordos Basin, China. Based on mean impact value, a well-trained ELM model is taken to identify optimal well logs and five well logs are identified to provide the significant and valid information for the estimation of shear wave velocity. 3201 data points collected from well L4 are used in constructing the model. Compared with artificial neural network used in the former studies Levenberg-Marquardt algorithm (ANN-LM), the ELM model's prediction accuracy has been evaluated, the results indicates that the ELM model outperforms ANN-LM model with faster calculating speed and better performance. Additionally, the efficiency of the model proposed here is well investigated by using another well with 3201 data points. Through the comparison among ELM, support vector regression (SVR), convolutional neural network (CNN) and four widely known empirical formulas, what can be concluded is that ELM model is more efficient in fast calculation and high precision. From the result, it can be demonstrated that the use of ELM model, combined with the analysis of mean impact value (MIV), is a more efficient and promising method for shear wave velocity estimation process from conventional well log data, which can be recognized as a promising tool with an extended application. And this research can be applied into a software system for rapid acquisition of shear wave velocity logs.