A dry gas reservoir offshore North Trinidad has been on production since 2002 with a high recovery to date. Notwithstanding this maturity, significant volumetric uncertainty was suspected and this had the potential to alter the field development plan. Hence, a robust quantification of hydrocarbon volumes was deemed necessary through a study that rigorously captured risks and uncertainties and provided higher confidence in the predicted remaining reserves.

Experimental design based uncertainty analysis, Markov Chain Monte Carlo for Bayesian optimization and proxy modelling assisted history matching methodologies were used. Scoping Latin Hypercube (LHC) simulations were run to test a wide range of volumetric scenarios. MCMC allowed Bayesian optimization to be done in an unbiased manner but being computationally expensive, a proxy modelling workflow was utilized. Top LHC cases were selected to train a proxy. The efficiency of the proxy workflow enabled hundreds of full field proxy simulations to be run in a short time. All experiments were combined and an optimum filtering criterion established to short list potentially matched cases. Finally, stochastic volumes were selected from this filtered pool.

Due to the use of Bayesian algorithm, posterior uncertainty distributions were often different, and thus quite insightful, when compared to the prior distributions. The shape of prior distributions had limited impact on the final result. A limited number of LHC runs scoping a wide sample space were sufficient to train a robust proxy model. Acceptable history matches for all wells could be achieved just after the second MCMC cycle. LHC workflow allowed wider uncertainty sampling but typically poor matches, whereas MCMC proved to be a rapid & highly effective history matching technique but it tended to smooth out extremes volumetric results and converged towards a high confidence median. A key result was the observation of a very strong correlation between the "global" history matching error and field gas-in-place or recoverable volumes.

Use of this Proxy based workflow allowed significant reduction in computational costs. A powerful cross-plot analysis methodology is presented that demonstrates convergence of hydrocarbon volumes to a narrow range. Results showed that irrespective of the deterministic starting inputs, reduction in history match error was only achieved for a narrow posterior range of uncertainties and volumes, even where a large initial uncertainty was believed to exist. These results were successful in achieving the objective by largely reducing the volumetric uncertainty and re-confirming the initial view.

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