Identification of Critical Operational Uncertainties in Field Development Planning Using Stochastic Gradients
conference paper
The development of oil and gas fields requires asset teams to make complex decisions in the presence of uncertainties. Studies of reservoir management strategies typically focus exclusively on geological uncertainties by working with an ensemble of reservoir model realizations. However, the development of hydrocarbon reservoirs is often also subject to operational uncertainties such as rig delays or drilling operation delays (which may in turn be related to unpredictable externalities of technical, economical, political or meteorological nature). Production attainment aims to minimize the associated economic risks by employing appropriate mitigation strategies that should ensure that targets are realized. In this work we use concepts from model-based robust optimization to quantify the impact of operational uncertainties on development strategies. In particular, we employ the stochastic simplex approximate gradient to obtain sensitivities of crucial production metrics with respect to operational factors. The StoSAG gradient has favorable theoretical and computational properties that allow the inclusion in this calculation of for example geological and petrophysical uncertainties. The factors can be sorted based on the associated sensitivity magnitudes to enable a robust ranking and identification of operational parameters with the highest impact on production attainment.
Topics
TNO Identifier
953186
Source title
17th European Conference on the Mathematics of Oil Recovery 14-17 September 2020, Online Event
Pages
1-12
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