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PETROMAKS2-Stort program petroleum

REMEDY: Reduced Emissions and Improved Energy Efficiency on the NCS

Alternative title: REMEDY: Reduserte CO2 utslipp og bedre energieffektivitet på NCS

Awarded: NOK 16.0 mill.

Ensemble-based closed-loop reservoir management systems (CLRM) are now the standard modeling tool for developing and managing oil reservoirs. REMEDY aims to take a significant step towards reaching the industry's 2030 and 2050 climate goals on NCS by developing CLRM workflows and methods for reducing energy use and CO2 emissions during field development and production. REMEDY will extend a traditional CLRM to an ensemble reservoir-emission-management workflow. In a CLRM, we mutually combine theoretical understanding represented by models with information from observations while accounting for uncertainties to make optimal decisions. We apply history-matching tools to keep a reservoir model up to date. After that, we use the up-to-date model in an optimization algorithm to update the optimal production strategy while including emission constraints. The recursive use of the resulting production data in the history-matching algorithm to further improve the model closes the loop. REMEDY addresses the development of ensemble methods for history matching, optimization, and decision support under uncertainty. We use the decision-driven CLRM workflow to optimize the decision alternatives over the ensemble of history-matched geological models and evaluate each alternative's expected outcome. The project results will be ensemble-based decision methods and workflows that allow for more efficient sub-surface data processing and application to field development and reservoir production when accounting for requirements of reduced greenhouse-gasses emissions.

Ensemble-based closed-loop reservoir management systems (CLRM) are now the standard modeling tool for developing and managing oil reservoirs. REMEDY aims to take a significant step towards reaching the industry's 2030 and 2050 climate goals on NCS by developing CLRM workflows and methods for reducing energy use and CO2 emissions during field development and production. REMEDY will extend a traditional CLRM to an ensemble reservoir-emission-management workflow. In a CLRM, we mutually combine theoretical understanding represented by models with information from observations while accounting for uncertainties to make optimal decisions. We apply history-matching tools to keep a reservoir model up to date. After that, we use the up-to-date model in an optimization algorithm to update the optimal production strategy while including emission constraints. The recursive use of the resulting production data in the history-matching algorithm to further improve the model closes the loop. REMEDY addresses the development of ensemble methods for history matching, optimization, and decision support under uncertainty. We use the decision-driven CLRM workflow to optimize the decision alternatives over the ensemble of history-matched geological models and evaluate each alternative's expected outcome. The project results will be ensemble-based decision methods and workflows that allow for more efficient sub-surface data processing and application to field development and reservoir production when accounting for requirements of reduced greenhouse-gasses emissions.

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Funding scheme:

PETROMAKS2-Stort program petroleum