Identification of sedimentary facies with well logs: an indirect approach with multinomial logistic regression and artificial neural network

Taking K-successions of the H-Zone of the Pearl River Mouth Basin as a testing example, we used two kinds of approaches to implement the microfacies identification. One is a direct identification, the other is an indirect approach in which we conducted the lithofacies classification first and then identified the microfacies based on previously estimated lithofacies. Both approaches were trained and checked by interpretations of experienced geologists from real subsurface core data. Multinomial logistic regression (MLR) and artificial neural network (ANN) were used in these two approaches as classification algorithms. Cross-validations were implemented. The source data set was randomly divided into training subset and testing subset. Four models, namely, MLR_direct, ANN_direct, MLR_indirect, and ANN_indirect, were trained with the training subset. The result of the testing set shows that the direct approaches (MLR_direct and ANN_direct) perform relatively poor with a total accuracy around 75%. While the indirect approaches (MLR_indirect and ANN_indirect) perform much better with a total accuracy of around 89 and 82%, respectively. This indirect method is simple and reproducible, and it could lead to a robust way of analyzing sedimentary microfacies of horizontal wells with little core data or even are almost never cored while core data are available for nearby vertical wells.