We propose a quantitative model-based workflow for conformance verification of CO2 storage projects. Bayesian inference is applied to update an ensemble of simulation models that capture prior uncertainty based on mismatches with measured data. Conformance assessments are derived by comparison of updated model predictions with storage permit requirements and confidence criteria. Two examples, one conceptual and one based on a real candidate storage site, are provided in which the quantitative workflow is applied to the a priori assessment of candidate monitoring strategies. The examples illustrate the limitations of pressure monitoring in the presence of realistic subsurface uncertainties, and the potential for cost saving by informed design of geophysical monitoring surveys. Approximate methods are discussed that could make the workflow also applicable for (quasi) real-time conformance monitoring.@en

The general field development optimization problem is complex due to the potentially large number of controls of mixed type and discontinuities in the objective function related to varying numbers and types of wells being placed in a discretized grid. This may make the problem challenging or even unsuitable for certain types of optimization methods that rely on, e.g., the availability of (adjoint) gradients. It is not yet clear which alternative approaches will be most useful. Here we investigate the application of stochastic gradient-based optimization techniques to field development optimization. Since their initial application to large-scale well rate and pressure control problems, such techniques have been shown to produce useful results of practical value also for other types of reservoir optimization problems such vertical well placement, well drilling scheduling, and water-alternating-gas strategy optimization. Here we introduce an efficient parameterization for well trajectory optimization and discuss a simple way to handle the number of wells that is placed. The full field development problem is split into subproblems that are addressed sequentially. The sequential workflow is applied to the Olympus benchmark model which represents a complex green field development optimization challenge. Initial experiments show that the proposed approach based on stochastic gradient methods is able to find much improved development strategies, as defined by the number and trajectories of wells, a platform location and a drilling sequence, at relatively low computational cost. We additionally identify a number of possible improvements to the applied workflow that are expected to make it applicable to other field cases of intermediate complexity.@en

We describe and evaluate a physics-based proxy model approach for reservoir prediction and optimization. It builds on the recent development of so-called flow-network models which represent flow paths between wells by discrete 1D grids with permeability and pore volume properties. These types of models represent an alternative to capacitance resistance and correlation-based models and have the benefit of allowing for all physics supported by regular 3D grid-based commercial simulators. The new model is different from a previously proposed model in that we include additional nodes in the network that allow for more and indirect flow paths between wells, as well as extra nodes to represent an aquifer. We describe the structure of our flow network and investigate the impact of design and training parameters on the performance of the network, both in history matching and prediction mode. Examples include the number and placement of network nodes, the treatment of aquifers, and the size and sampling of prior model property values. We distinguish between the accuracy of the history match and the generalizability by cross-validating the flow network performance on future well control strategies that are different from that encountered during the history period. Using this procedure, we aim to prevent overfitting of the model while ensuring sufficient predictive power. Results are presented for experiments based on phase rate and bottom hole pressure measurements and predictions generated with the Brugge benchmark model which is used as a synthetic truth. We subsequently present a first application of flow network models for well control optimization under uncertainty. To this end we employ a stochastic simplex gradient-based optimization approach and demonstrate that strategies that are expected to deliver improved NPV can be identified at much lower computational cost and within a much shorter time frame than would be required otherwise.@en