Abstract

Waterflooding is among the oldest and perhaps most economical of Enhanced Oil Recovery (EOR) processes to extend field life and increase ultimate oil recovery from naturally depleting reservoirs. High oil prices provide incentive for companies to look deeper into their reservoir portfolios for additional waterflooding opportunities. Time and information constraints can limit the depth and rigor of such a screening evaluation. Time is reflected by the effort of screening a vast number of reservoirs for the applicability of implementing a waterflood, whereas information is reflected by the availability of data (consistency of measured and modeled data) with which to extract significant knowledge necessary to make good development decisions.

A new approach to screening a large number of reservoirs uses a wide variety of input information and satisfies a number of constraints such as physical, financial, geopolitical, and human constraints. In a fully stochastic workflow that includes stochastic back-population of incomplete datasets, stochastic proxy models over time series, and stochastic ranking methods such as Bayesian belief networks, more than 1,500 reservoirs were screened to reduce their number by one order of magnitude to about 100 potential candidates that are suitable for a more detailed phase of evaluation. Numerical models were used to create response surfaces that capture the sensitivity and uncertainty of the influencing input parameters on the output. Reservoir uncertainties were combined with expert knowledge and environmental variables and were used as proxy model states in the formulation of objective functions. The input parameters were assigned in a stochastic manner. The output is represented by a ranking of potential waterflood candidates.

The benefit of this approach is in the inclusion of a wide range of influencing parameters while at the same time speeding up the screening process without jeopardizing the quality of the results.

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