Selection time for workover candidate reduced by 89% for PGN Saka
Using AI embedded in digital solutions from Schlumberger, PGN Saka automated its well workover and intervention programs to vastly accelerate the selection process. The automated system screens and ranks high-well-count assets in a fraction of the time to detect, diagnose and recommend the appropriate actions required to ensure that wells remain healthy, for an evergreen opportunity pipeline.
PGN Saka used a hybrid physics- data-centric automated decision support system anchored in a knowledge-based framework to identify well performance signature and opportunities that we best aligned with the operator’s economic considerations. Machine learning (ML) models ensured the system keeps improving as decisions are made.
The data science profile in the Delfi digital platform enables customization of AI-powered well portfolio optimization systems by petrotechnical engineers.
