Application of an Integrated Ensemble-Based History Matching Approach - An Offshore Field Case Study

Abstract

Abstract The Internet of Things has popularized the notion of a digital twin - a virtual representation of a physical system. There are substantial risks associated with designing a development plan for an oilfield and the industry has been making use of reservoir models - digital twins - to improve the decision-making process for many years. With an increase in the availability of computational resources, the industry is moving towards ensemble-based workflows to estimate risk in field development plans. In this paper, we demonstrate the use of an integrated ensemble-based approach to assess uncertainties in the reservoir models and quantify their impact on the decision-making process. An important feature of a digital twin is its ability to use sensor data to update the virtual model, more commonly known as history matching or data assimilation. We demonstrate how production data can be used to identify and constrain the uncertainties in the reservoir model. Production data is incorporated using Bayesian statistics and state-of-the-art supervised machine learning techniques to create an ensemble of models that capture the range of uncertainties in the reservoir model. This ensemble of calibrated models with an improved predictive ability provides a realistic assessment of the uncertainty associated with production forecasts. The ensemble-based approach is demonstrated through its application on an offshore oilfield located in the North Sea. The field is highly compartmentalized and has high structural uncertainty following the interpretation and depth conversion. An integrated cross-domain model is set up to incorporate typically ignored structural uncertainty in addition to the uncertainties and their dependencies in the dynamic parameters, including fault transmissibility, pore-volume, fluid contacts, saturation, and relative permeability endpoints, etc. Results from the history matched ensemble of models show a significa nt reduction in uncertainty in these parameters and the predicted production. An advantage of the proposed technique is that the automated, repeatable, and auditable ensemble-based workflow can assimilate the newly acquired measured data into the reservoir model at any time, keeping the model up-to-date and evergreen.