Artificial Intelligence for Production Optimization in Schoonebeek Thermal EOR Field

Abstract

Production from the Schoonebeek heavy oil steam flood in northeast Netherlands was historically curtailed because of limits on H2S and water production. The interdependence of various permit and facility constraints makes production optimisation for Schoonebeek extremely challenging. So much so, that the conventional IPSM approach does not apply. To understand the field's production potential and to reach it, the team developed a novel Production System Optimisation (PSO) workflow using techniques from machine learning and operations research. In this paper we explain the details of this PSO workflow, the mathematics behind it, and share our results and learnings. The algorithm runs in 5 minutes and is used in daily optimisation. The application of this new workflow in combination with the successful deployment of a novel H2S scavenger, resulted in a Schoonebeek production uplift of 50%.