Modeling and Optimizing the Offshore Production of Oil and Gas under Uncertainty
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The topic of this thesis is optimizing the offshore production of oil and gas on a day-to-day basis. Optimization is based on a model of the production. Designing such production models is very complex and challenging, and as a result models will always be subject to some uncertainty in practice. In this thesis a data-driven approach to production modeling and model updating is suggested. Simple model structures are inferred from observations of measured production, motivated by the concepts of system identification and a desire for models which are sufficiently accurate while being easily solved numerically. A production model needs to be updated periodically to reflect changes in reservoirs and wells. Updating models against observations of recently measured data describing normal operations is suggested, motivated by a desire for an updating scheme which requires little human intervention and does not require frequent additional experimentation, for instance in the form of well-tests. The suggested approach to model-updating is based on parameter estimation against production data stemming from normal operations. As data describing normal operations may have low information content, parameters estimated in this manner may be subject to significant uncertainty. Bootstrapping is considered as a means of estimating uncertainty in fitted parameters. Methods for the explicit treatment of such uncertainty based on Monte-Carlo analysis are investigated. Methods for estimating lost potential due to uncertainty, result analysis, excitation planning and choosing active decision variables are suggested. Since uncertainty will never be completely eliminated in practice, some method is needed to adapt to uncertainty when implementing changes in production suggested by optimization. Motivated by how operators implement such changes in practice, an “operational strategy” approach is suggested to link optimization with implementation and monitoring by iteratively implementing smaller changes while monitoring profits and constraints. The models considered are low in complexity, which may simplify designing and updating models and may reduce numerical issues. Since the suggested methods rely on measurements, the success of the suggested methods will depend on measurement quality. Also, although the methods suggested do not require exotic measurements, the suggested methods could benefit from additional instrumentation on some fields. The methods suggested together constitute a novel, structured approach to optimization of offshore oil and gas production in the presence of uncertainty. Modeling methods are applied to real-world production data from two offshore oil fields. Implementation of the suggested methods are studied by simulations against an analog of one of the fields considered.