Accelerating Mature Field EOR Evaluation Using Machine Learning Uncertainty Workflows Integrating Subsurface And Economics

Abstract

Field re-development planning for tertiary recovery projects in mature fields traditionally involves a comprehensive subsurface evaluation circle, including static/dynamic modeling, scenario assessment and candidate selection based on economic models. The aforementioned sequential approach is time-consuming and includes the risk of delaying project maturation. This work introduces a novel approach which integrates subsurface geological and dynamic modeling as well as economics and uses machine learning augmented uncertainty workflows to achieve project acceleration. In the elaborated enhanced oil recovery (EOR) evaluation process, a machine learning assisted approach is used in order to narrow geological and dynamic parameter ranges both for model initialization and subsequent history matching. The resulting posterior parameter distributions are used to create the input models for scenario evaluation under uncertainty. This scenario screening comprises not only an investigation of qualified EOR roll-out areas, but also includes detailed engineering such as well spacing optimization and pattern generation. Eventually, a fully stochastic economic evaluation approach is performed in order to rank and select scenarios for EOR implementation. The presented workflow has been applied successfully for a mature oil field in Central/Eastern Europe with 60+ years of production history. It is shown that by using a fully stochastic approach, integrating subsurface engineering and economic evaluation, a considerable acceleration of up to 75% in project maturation time is achieved. Moreover, the applied workflow stands out due to its flexibility and adaptability based on changes in the project scope. In the course of this case study, a sector roll-out of chemical EOR is elaborated, including a proposal for 27 new well candidates and 17 well conversions, resulting in an incremental oil production of 4.7MM bbl. The key findings were: A workflow is introduced that delivers a fully stochastic economic evaluation while honoring the input and measured data.The delivered scenarios are conditioned to the geological information and the production history in a Bayesian Framework to ensure full consistency of the selected subsurface model advanced to forecasting.The applied process results in substantial time reduction for an EOR re-development project evaluation cycle.