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Abstract

Produced Water Chemistry has been included in the history matching of reservoir simulations. Generally, in conventional history matching, the water chemistry is not considered as an extra constraint. The chemistry of the different types of water in a reservoir, such as aquifer, connate and seawater is very different, and can be traceable. Produced Water Chemistry is the main source of information to monitor scale precipitation in oil field operations. The objective of this paper is to evaluate the effect of adding produced water chemistry information as an extra constraint history matching a modified version of the PUNQ-S3 reservoir model. The PUNQ-S3 model is a synthetic benchmark case that has been used previously for history matching uncertainty quantification. Conventional historical production data (gas, oil rate and pressure) from six production wells are supported by the water chemistry tracer data from the wells that produce water in the history period. The different types of water are traced through their distinctive chemistries, namely aquifer, connate (formation) and sea (injection) water. Geological model is matched by varying porosity and permeability, both horizontal (kh) and vertical (kv) according to the prior beliefs about the reservoir geology (layering, spatial correlation and anisotropy). Two history matching scenarios are considered: including and not-including the Produced Water Chemistry (PWC) as extra matching constraints. Stochastic Particle Swarm Optimization (PSO) algorithm is used to generate ensembles of history matched models, which characterise the uncertainty of the reservoir prediction. The confidence intervals for the forecast are computed using NA-Bayes (Neighborhood Algorithm) technique, which evaluates the posterior probability of the generated models. Finally, to evaluate the effect of adding PWC in the history matching, the Bayesian confidence intervals (P10-P50-P90) generated by each method were compared.

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/content/papers/10.3997/2214-4609-pdb.293.H020
2012-06-04
2024-04-29
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609-pdb.293.H020
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