公开(公告)号：US20230142230A1

公开(公告)日：2023-05-11

申请号：US17982878

申请日：2022-11-08

Applicant: ConocoPhillips Company

Inventor： Amir Nejad ,  Christopher S. Olsen ,  Bo Hu ,  Xin Luo ,  Qing Chen ,  Alexander J. Wagner ,  Liu Chao Zhang ,  Iman Shahim ,  Curt E. Schneider ,  David D. Smith ,  Andy Flowers ,  Richard Barclay

IPC: E21B43/16

CPC classification number: E21B43/16

Abstract: Implementations described and claimed herein provide systems and methods for dynamic waterflood forecast modeling utilizing deep thinking computational techniques to reduce the processing time for generating the forecast model and improving the accuracy of resulting forecasts. In one particular implementation, a dataset of a field may be restructured into the spatio-temporal framework and data driven deep neural networks may be utilized to learn the nuances of data interactions to make more accurate forecasts for each well in the field. Further, the generated model may forecast a single time segment and build the complete forecast through recursive prediction instances. The temporal component of the restructured data may include all or a portion of the production history of the field divided into spaced time intervals. The spatial component of the restructure data may include, within each epoch, a computed or estimated spatial relationships of all existing wells.