Mathematical modeling for total organic carbon content prediction with logging parameters by neural networks: A case study of shale gas well in South China

Abstract

The lower Cambrian Niutitang Formation in northwestern Hunan, South China, has already reached its high or over matured stage and is formed with hydrothermal activity and deposition. Thus, it is extremely difficult to predict the total organic carbon (TOC) content accurately by common methods with well-logging data. To solve this problem, we use artificial neural networks for predicting the TOC of the black shales in our study case. We got the input vectors through principal component analysis and based on the relationships and the logging response mechanism between TOC and logging data. In the back-propagation algorithm, some important parameters including the sample databases, the number of hidden layer nodes, transfer function, and weight value adjustment were all optimized correctly in the networks. Then, we built the mathematical model through a large number of learning sample datum and the error function between the actual and expected outputs, and we found that the results are good according to many performance indicators. In testing samples, mean absolute and relative errors are all reduced probably due to the datum ranges and features being focused, but the accuracy also drops when the numbers of participating samples are reduced. Through redefining the [Formula: see text] sample database, we gained more accurate values for the high-TOC source rock. Finally, we think that the results suggest that the method is suitable for shale gas resource exploration under similar geologic conditions and data characteristics.