Total organic carbon content prediction based on support-vector-regression machine with particle swarm optimization

Total organic carbon (TOC) is a significant factor to evaluate the hydrocarbon potential of the unconventional reservoir, while it is difficult to obtain the continuous and accurate prediction result of TOC by traditional methods due to the non-linear relationship between TOC content and wireline log data. However, support vector regress machine is an efficient supervised learning artificial intelligence algorithm, and performs well to deal with non-linear relationship dataset. Therefore, in order to explore a more efficient way for TOC content prediction, the support vector regress machine was tried to predict TOC content through wireline log data. To further verify this, some data tests have been conducted on an offshore exploration prior well data in the northern of Beibu Gulf Basin. After analyzing various kernel functions and optimization methods by cross-validation and error analysis, it can be indicated that the Gaussian kernel function and the particle swarm optimization (PSO) algorithm which used the linear decreasing inertia coefficient and mutated particles are suitable for SVR model and model parameter selection, respectively. Finally, the prediction result of SVR with the best model and model parameters is compared with the results of an improved Delta logR method and multilayer perceptron method. The comparison results show that SVR has a higher correlation coefficient than other methods. In conclusion, the prediction result of SVR can be thought of as an efficient and reliable method for TOC content prediction. Through the continuous prediction of organic carbon content, the qualitative and quantitative evaluation of high-quality source rocks in this area can be realized, which is of great practical significance for determining the exploration field and the search for favorable target reservoirs.