Application of Locality Preserving Projection-Based Unsupervised Learning in Predicting the Oil Production for Low-Permeability Reservoirs

Abstract

Summary Predicting well production in unconventional reservoirs has attracted much attention in recent years. However, it is still a challenge because of the heterogeneity of the unconventional formation and the uncertain physical properties of rock and the fluid-flow mechanism. Therefore, the objective of our study is to develop an efficient method for production forecast in low-permeability reservoirs. In this paper, we applied a locality preserving projection (LPP) -based machine-learning method to provide a novel way of predicting production in low-permeability reservoirs. Our model preserves the local geometric information inherent in the oil data and hence is capable of seizing its intrinsic nonlinear feature. Through LPP-based analysis, specific parameters (e.g., the total fluid pumped) are verified to have more important impact than others on estimating oil production. We conducted both parameter testing and comparison experiments, with results indicating that the LPP-based method has good applicability and efficiency in reasonably forecasting well production from low-permeability reservoirs. This work provides petroleum engineers an effective method for the prediction of oil production.