Data-driven prediction for carbonate lithologies based on new ensemble learning of logging sequences

Abstract

Lithology prediction, especially for carbonate and volcanic reservoirs, is universally viewed as a crucial job in early petroleum exploration because the predicted lithology information is significant to some basic geological work such as stratigraphic correlation or sedimentation modeling. Although being capable to provide effective results in many practices, the typical methods normally cannot reach the goals when dealing with the complex lithology prediction. In order to solve such problem perfectly, a new hybrid neural network, a transformation of probabilistic neural network (PNN) by integrating CRBM (continuous restricted Boltzmann machine) and (PSO) particle swarm optimization, is proposed in this article. The raw data with high correlation can be filtered by CRBM, and then changes to be a new data set with low correlation. The utilization of PSO is to optimize the window length of probability density distribution for each kind of pattern. Then, with the help of CRBM and PSO, PNN becomes potential to make accurate judgments for test points. The data used for validation is collected from 8 cored wells of A oilfield. In two designed experiments, the proposed method gives higher prediction accuracies compared with other validated models, all of which are over 90%. Test results convincingly prove that the new hybrid network is capable to provide an effective solution for the complex lithology prediction, and the predicted results are reliable enough to serve as the reference data for further geological studies.