DIGIRES is a joint Petromaks-2 and industry project that aims to develop the next-generation digital workflows for sub-surface field development and reservoir management. As such, DIGIRES addresses new challenges in the petroleum industry related to the processing and integration of vast amount of data with models for reservoir characterization.
NORCE (Norwegian Research Centre AS) coordinates the project which has industry support from Equinor, Aker BP, Vår Energy, Neptune Energy, DEA Wintershall, Petrobras, and Lundin. The research partners are NORCE and the University of Stavanger.
The project builds on an integrated reservoir-management philosophy for sub-surface modeling. We use multiple model realizations to characterize uncertainty together with sub-surface analytics and digitalization to handle big data. The objective of the project is to "improve decision making and uncertainty analysis for well-planning and field development by using a decision-driven ensemble-based approach."
Thus, an essential element of DIGIRES is the transition from data-driven to decision-driven workflows and the transformation into big-data analytics and digitalization. DIGIRES combines data analytics with model predictions and expert knowledge. A particular outcome of the project will be the implementation and demonstration of ensemble-based probabilistic decision making for reservoir management.
DIGIRES will mature existing technology and tools. Simplified workflows and interfaces will allow for efficient processing of big sub-surface data sets. The project will improve reservoir understanding and decision making to maximize future value creation.
DIGIRES integrates industrial experience and technology solutions, real field data, and forefront research, by independent institutes and academia. As such, DIGIRES applies the most up-to-date technological solutions and methods to actual petroleum reservoirs with big data.
Better knowledge of uncertainty reduces risks and leads to better decisions. The problem is first to create a consistent uncertainty basis and after that to use this knowledge in a mathematically coherent and computationally efficient manner in the decision process.
This project builds on our previous experience from ensemble-based conditioning and optimization, from which we postulate the hypothesis that "it is possible to develop computationally efficient ensemble methods for probabilistic decision making in high-dimensional and nonlinear dynamical systems," taking the uncertainty into account.
The approach taken is to use multiple realizations and ensemble methods for generating the best possible uncertainty basis and then develop decision methods that use the ensemble of realizations as input in the decision-making process.