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Abstract

Nowadays, the majority of the world’s most important oil and gas provinces have reached a mature stage. A proper exploitation and recovery maximisation primarily rely on the ability to foresee the consequences of different reservoir management decisions and production scenarios. <br><br>Because of the inherent non-uniqueness of the history match process, using only one data-conditioned reservoir model to forecast hydrocarbon production may lead to erroneous interpretations and discrepancies with reality. A possible solution to this inverse problem and the related uncertainty quantification has been recently introduced by a novel methodology based on a stochastic search technique, called the Neighbourhood Algorithm (NA). The above methodology consists of two main steps: an optimization phase and an uncertainty assessment phase carried out in a Bayesian framework (NA-Bayes). After sampling acceptable data-fit regions of the parameter space, the posterior probabilities are calculated and a quantitative inference is performed.<br><br>In this paper the NA-NAB approach was firstly evaluated by means of four analytical test functions (Branin, Six-Hump Camel Back, Goldstein and Price, Levy) with the aim of assessing its efficiency in searching and sampling the parameters space, verifying its stability and robustness and examining the effects of the algorithm control parameters.<br><br>Two case studies are reported in order to investigate the suitability of the methodology for real fields. The hydrocarbon production, the pressure and the water cut were selected as match variables for the considered oil and gas reservoirs. The approach was promising and the results obtained were similar to those of the manually history matched model though achieved with a significant time reduction. <br><br>In addition to the increased speed of history matching, the procedure allowed a proper uncertainty quantification by means of multiple production forecasts that better quantify risk and uncertainty in reservoir performance, which is crucial for economical evaluation and decision making.<br><br>

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/content/papers/10.3997/2214-4609.201402501
2006-09-04
2020-07-03
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