Abstract

When a limited number of wells are drilled at the early stages of developing unconventional fields, the available data is insufficient for developing data-driven models. To compensate for the lack of data in new fields, transfer learning may be adopted by using a previously learned model/knowledge from similar fields (source data) to build a predictive model for the new field. To be effective, transfer learning requires the source and target fields to have similarities and to ensure relevant information/knowledge is transferred. The transfer of irrelevant knowledge may impede the training process and lead to a negative knowledge transfer. When multiple source data are available, it is important to identify each source data's relevance and potential contribution to the target data. We introduce a framework to rank different source datasets and determine their capability for transfer learning. The methodology relies on …

Date

March 7, 2023

Authors

Jodel Cornelio, Syamil Mohd Razak, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour

Conference

SPE Middle East Oil and Gas Show and Conference

Pages

D021S084R003

Publisher

SPE