1887

Abstract

Summary

The proper optimisation of fields under waterflooding under uncertainty might require the evaluation of multiple scenarios over a set of reservoir models designed to incorporate geological, structural and stratigraphic uncertainties. Nowadays, reservoir models might have several millions of grid cells and a larger computing infrastructure is needed in order to achieve a near-optimal solution for the net present value objective function given the large uncertainties.

In this work a methodology is presented where data-driven models, in the form of capacitance resistance methods, together with analytical fractional flow theory and the help of machine learning techniques are used to perform the optimization of a set of reservoir models under uncertainty.

The fractional flow parameters for the Buckley Leverett function are calculated on a well by well basis using iterative ensemble smoothers after a connectivity analysis is performed. The connectivity analysis is further conditioned initially to flow diagnostics averages and to averages for the time of flight.

The objective is to maximize the net present value by using proxy models that better match the reservoir and to provide insights on drainage areas and possible infill drilling locations for better field development plans using a fraction of the time required.

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/content/papers/10.3997/2214-4609.202035116
2020-09-14
2024-03-29
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