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Abstract

Summary

Reservoir monitoring or surveillance is crucial for a responsible and efficient use of subsurface reservoirs. In both production and storage systems, operators need to demonstrate that their assets can be managed in a safe way. Effective monitoring practices also help operators unlock additional value from their assets, by revising expectations, gaining confidence on their projected potentials and allowing development strategies to be adjusted.

The design of monitoring systems can be a challenge for both subsurface operators and regulators. The main difficulties range from the technical uncertainties on subsurface characterization and the unavailability of direct measurements to the lack of technologies to support monitoring design decisions.

As a step to bridge this gap, we have developed a quantitative value-of-information (VOI) methodology within the context of conformance management in CO2 storage operations. It is a practical model-based approach that uses a Bayesian framework to derive a measure of the expected contribution of (future) measurements from candidate monitoring strategies for discrimination of conformance and non-conformance situations.

In this work we integrate our practical VOI approach into an optimization workflow. This novel workflow is applied to determine the optimal design of time-lapse seismic surveys in a realistic CO2 storage case study. Obviously, the larger the spatial coverage of the survey, the more informative it will be. Therefore, the search for cheaper sparse survey designs is by nature a bi-objective problem which needs to consider both the accuracy requirements and the costs of the survey in the same optimization. Our optimization workflow also accounts for the uncertainties associated with the reservoir system by using ensembles of plausible measurement outcomes and model realizations.

Our results show that sparse survey designs can be optimized to reduce costs while keeping accuracy levels comparable to denser designs. Our results also suggest that optimal survey configurations lie on a Pareto front of the two objectives considered, corroborating the idea that the design of cost-efficient monitoring strategies has a multi-objective character.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202035155
2020-09-14
2024-04-26

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Optimization of Reservoir Surveillance Strategies Under Uncertainty: An Application to the Design of Sparse Monitoring Surveys
Conference Proceedings 2020, 1 (2020); https://doi.org/10.3997/2214-4609.202035155
/content/papers/10.3997/2214-4609.202035155
/content/papers/10.3997/2214-4609.202035155
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