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Journal Articles Journal of Petroleum Science and Engineering Year : 2019

Oil production uncertainty assessment by predicting reservoir production curves and confidence intervals from arbitrary proxy responses.

Abstract

Underground fluid flow in hydrocarbon reservoirs (or aquifers) is difficult to predict accurately due to geological and petrophysical uncertainties. To quantify that uncertainty, several spatial statistical methods are often used to generate an ensemble of subsurface models representing and sampling these uncertainties. However, to predict the uncertainties in terms of flow responses, one needs to run a forward flow simulator (often multiphase flow in transient state) on every model of this ensemble and this generally entails intractable computational costs. Approximate solutions (flow proxies) can help addressing this challenge but introduce physical simplifications whose impact on the uncertainty quantification is difficult to characterize. This paper proposes a workflow to assess the dynamic reservoir behavior uncertainties from an input ensemble of realizations sampling geological and geophysical uncertainties. Analytical reservoir production curves are estimated from proxy distances computed between all ensemble members and from a few accurate flow responses computed on a subset of the ensemble. A randomization process accounting for proxy quality and for model selection is used to assess confidence intervals about reservoir production quantile curves. The process can use both static and dynamic proxies and also permits to compare their efficiency. A case study on a real turbiditic reservoir shows the applicability of the method, and highlights the value of even a simple proxy to increase the confidence about future reservoir production
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Dates and versions

hal-02136465 , version 1 (24-05-2019)

Licence

Attribution - NonCommercial - NoDerivatives

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Gaétan Bardy, Pierre Biver, Guillaume Caumon, Philippe Renard. Oil production uncertainty assessment by predicting reservoir production curves and confidence intervals from arbitrary proxy responses.. Journal of Petroleum Science and Engineering, 2019, 176, pp.116-125. ⟨10.1016/j.petrol.2019.01.035⟩. ⟨hal-02136465⟩
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