公开(公告)号：US20230140905A1

公开(公告)日：2023-05-11

申请号：US17982799

申请日：2022-11-08

Applicant: ConocoPhillips Company

Inventor： Bo Hu ,  Qing Chen ,  Amir Nejad ,  Xin Luo ,  Christopher S. Olsen ,  Robert C. Burton ,  Liang Zhou ,  Xin Jun Gou ,  Liu Chao Zhang ,  Junjing Zhang ,  Iman Shahim ,  Curt E. Schneider ,  David D. Smith ,  Andy Flowers

IPC: G06F30/28 ,  G06N3/08

CPC classification number: G06F30/28 ,  G06N3/08

Abstract: Implementations described and claimed herein provide systems and methods for a framework to achieve completion optimization for waterflood field reservoirs. The proposed methodology leverages adequate data collection, preprocessing, subject matter expert knowledge-based feature engineering for geological, reservoir and completion inputs, and state-of-the-art machine-learning technologies, to indicate important production drivers, provide sensitivity analysis to quantify the impacts of the completion features, and ultimately achieve completion optimization. In this analytical framework, model-less feature ranking based on mutual information concept and model-dependent sensitivity analyses, in which a variety of machine-learning models are trained and validated, provides comprehensive multi-variant analyses that empower subject-matter experts to make a smarter decision in a timely manner.