Optimisation of decision making under uncertainty throughout field lifetime: A fractured reservoir example

• Workflow for effective propagation of uncertainty throughout field lifetime.
• Workflow aims to achieve results of MCMC approaches but with fewer simulations.
• Model classification using MDS to try and reduce number of realisation.
• Optimisation under uncertainty using multi-objective PSO (MOPSO) and Bayesian inference.
• MO history matching and Bayesian inference reduces compute time in post-production phase.
• MOO under uncertainty finds optimal across chosen ensemble of models.
• Clustering reduces workload but removes possible scenarios which impacts on results.
• Applied to 2 case studies, 1 a sector model, 2 a full field example.

Assessing the change in uncertainty in reservoir production forecasts over field lifetime is rarely undertaken because of the complexity of joining together the individual workflows. This becomes particularly important in complex fields such as naturally fractured reservoirs. The impact of this problem has been identified in previous and many solutions have been proposed but never implemented on complex reservoir problems due to the computational cost of quantifying uncertainty and optimising the reservoir development, specifically knowing how many and what kind of simulations to run.This paper demonstrates a workflow that propagates uncertainty throughout field lifetime, and into the decision making process by a combination of a metric-based approach, multi-objective optimisation and Bayesian estimation of uncertainty. The workflow propagates uncertainty estimates from appraisal into initial development optimisation, then updates uncertainty through history matching and finally propagates it into late-life optimisation. The combination of techniques applied, namely the metric approach and multi-objective optimisation, help evaluate development options under uncertainty. This was achieved with a significantly reduced number of flow simulations, such that the combined workflow is computationally feasible to run for a real-field problem.This workflow is applied to two synthetic naturally fractured reservoir (NFR) case studies in appraisal, field development, history matching and mid-life EOR stages. The first is a simple sector model, while the second is a more complex full field example based on a real life analogue. This study infers geological uncertainty from an ensemble of models that are based on the carbonate Brazilian outcrop which are propagated through the field lifetime, before and after the start of production, with the inclusion of production data significantly collapsing the spread of P10-P90 in reservoir forecasts. The workflow links uncertainty estimation with the appropriate optimisation at appraisal, development and reservoir management stages to maximise oil recovery under uncertainty.