Robust Unilateral Alignment for Subsurface Lithofacies Classification

Abstract

Subsurface lithofacies classification refers to the way of establishing a classifier on the interpreted well logging data to predict the lithofacies types corresponding to the uninterpreted ones. Such a task has become a research focus by applying machine learning technologies under the assumption of independent and identical distribution. However, due to the differences in, such as sedimentary environments, reservoir heterogeneity, and logging equipments, the same lithofacies might exhibit different logging characteristics between two different wells or even two different strata in one well. Therefore, motivated by the data drift issue and inspired by the domain adaptation methods, we propose the robust unilateral alignment (RUA) for lithofacies classification. The characteristics of the proposed RUA are as follows: 1) the projected maximum mean discrepancy (PMMD) is designed to reduce the marginal and conditional distribution discrepancy; 2) the random data mapping and target domain information preserving constraint is adopted to embody the data transformation model; and 3) the weighting mechanism and risk-aware constraint are introduced to solve the class imbalance and iterative risk problems. The experiments conducted on the data sets from Jiyang Depression, Bohai Bay Basin, verify the superior performances in accuracy and stability over the existing work.