A committee neural network for prediction of normalized oil content from well log data: An example from South Pars Gas Field, Persian Gulf

Abstract

Normalized oil content (NOC) is an important geochemical factor for identifying potential pay zones in hydrocarbon source rocks. The present study proposes an optimal and improved model to make a quantitative and qualitative correlation between NOC and well log responses by integration of neural network training algorithms and the committee machine concept. This committee machine with training algorithms (CMTA) combines Levenberg–Marquardt (LM), Bayesian regularization (BR), gradient descent (GD), one step secant (OSS), and resilient back-propagation (RP) algorithms. Each of these algorithms has a weight factor showing its contribution in overall prediction. The optimal combination of the weights is derived by a genetic algorithm. The method is illustrated using a case study. For this purpose, 231 data composed of well log data and measured NOC from three wells of South Pars Gas Field were clustered into 194 modeling dataset and 37 testing samples for evaluating reliability of the models. The results of this study show that the CMTA provides more reliable and acceptable results than each of the individual neural networks differing in training algorithms. Also CMTA can accurately identify production pay zones (PPZs) from well logs.