Complex lithology prediction using probabilistic neural network improved by continuous restricted Boltzmann machine and particle swarm optimization

Lithology prediction, especially for reservoirs consisting of complex lithologies, is universally considered as a critical underlying task for petroleum exploration, because lithological data is indispensable for the analysis of some geological work, such as stratigraphic correlation or sedimentation modeling. Hence, how to acquire the reliable lithological information gradually becomes a hot topic in the geoscience. Probabilistic neural network (PNN)is an excellent approach for lithology prediction since it can efficiently complete pattern recognition by determining characteristics of each kind of learning data. However, its computation performance is severely limited by two factors, which are quality of raw data and selection of parameter. High correlation of raw data could be an obstacle for the determination of data characteristics because partial probability density distributions established by PNN would be merged. As window length of each probability density distribution shows great impact on the accuracy of calculated results, the selection of this parameter must be addressed optimally before prediction. In order to improve the calculation capability of PNN, two techniques, continuous restricted Boltzmann machine (CRBM)and particle swarm optimization (PSO), are introduced. CRBM has the special function of extracting features from raw data and the features generally present with low correlation, thus can be viewed as an ideal preprocessing segment for PNN. PSO is one of the most efficient algorithms used for solving optimization problem, and the optimal parameter setting of PNN, therefore, can be revealed. Due to the advantages of CRBM and PSO for PNN, a new method for complex lithology prediction is proposed, which is referred as CRBM-PSO-PNN. Data for new method validation is recorded by two wells which are located in the IARA oilfield. Moreover, three experiments are well designed in order to verify the computing capability of new method comprehensively. Experiment results manifest that the prediction accuracies provided by new method in three experiments are highest, all of which are over 75%. High prediction accuracies fully demonstrate that the proposed method is effective to predict complex lithology, and the predicted results are reliable to serve other geological work.