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The use of proxy models for optimisation of expensive functions has demonstrated its value since the 1990's in many industries. Within reservoir engineering, similar techniques have been used for over a decade for history matching, both in commercial tools and in-house software.In addition to efficient history matching, proxy models have a distinct advantage when performing uncertainty quantification of probabilistic forecasts. Markov Chain Monte Carlo (MCMC) methods cannot realistically be applied directly with reservoir simulations, and even fast proxy models can fail dramatically to adequately represent the range of uncertainty if implemented without due care.A pitfall of the use of proxy models is that they are considered 'black box' and their quality is difficult to measure. Engineers prefer to deal with deterministic simulation models which they can evaluate and understand.The main pitfall of simple random walk MCMC techniques, which have begun to appear within reservoir engineering workflows, is a focus on theoretical properties which are not observed in practical implementations. This gives rise to potential gross errors, which are not generally appreciated by practitioners. Advances in recent years within the field of Bayesian statistics have significantly improved this situation, but have not yet been disseminated within the oil and gas industry. This paper describes the limitations of random walk MCMC techniques which are currently used for reservoir prediction studies, and shows how Hamiltonian MCMC techniques, together with an efficient implementation of proxy models, can lead to a more reliable and validated probabilistic uncertainty quantification, whilst also generating a suitable ensemble of deterministic reservoir models. Scientific comparison studies are performed for both an analytical case and a realistic reservoir simulation case to demonstrate the validity of the approach.The benefit of this methodology is to allow asset teams to effectively manage reservoir decisions using a robust and validated understanding of uncertainty. It lays the scientific foundations for the next generation of uncertainty tools and workflows.
The use of proxy models for optimisation of expensive functions has demonstrated its value since the 1990's in many industries. Within reservoir engineering, similar techniques have been used for over a decade for history matching, both in commercial tools and in-house software.In addition to efficient history matching, proxy models have a distinct advantage when performing uncertainty quantification of probabilistic forecasts. Markov Chain Monte Carlo (MCMC) methods cannot realistically be applied directly with reservoir simulations, and even fast proxy models can fail dramatically to adequately represent the range of uncertainty if implemented without due care.A pitfall of the use of proxy models is that they are considered 'black box' and their quality is difficult to measure. Engineers prefer to deal with deterministic simulation models which they can evaluate and understand.The main pitfall of simple random walk MCMC techniques, which have begun to appear within reservoir engineering workflows, is a focus on theoretical properties which are not observed in practical implementations. This gives rise to potential gross errors, which are not generally appreciated by practitioners. Advances in recent years within the field of Bayesian statistics have significantly improved this situation, but have not yet been disseminated within the oil and gas industry. This paper describes the limitations of random walk MCMC techniques which are currently used for reservoir prediction studies, and shows how Hamiltonian MCMC techniques, together with an efficient implementation of proxy models, can lead to a more reliable and validated probabilistic uncertainty quantification, whilst also generating a suitable ensemble of deterministic reservoir models. Scientific comparison studies are performed for both an analytical case and a realistic reservoir simulation case to demonstrate the validity of the approach.The benefit of this methodology is to allow asset teams to effectively manage reservoir decisions using a robust and validated understanding of uncertainty. It lays the scientific foundations for the next generation of uncertainty tools and workflows.
At first, the risk assessment of dynamic model parameters based on uncertainty analysis was conducted to evaluate the impact of the reservoir uncertainties in a carbonate reservoir, offshore Abu Dhabi. The study showed that the evolution of gas to oil ratio (GOR) was a main reservoir response that affected the future production profile; hence it is the focus of this paper. It is important to track the GOR behavior in the field, to compare the actual reservoir performance with the risk assessment results and to evaluate the proper reservoir management and development options. Uncertainty analysis was used to evaluate future GOR performance and its probability of occurrence. The range of production plateau duration was also quantified. Therefore, future GOR performance impact on production for all probability ranges can be estimated. Finally, "GOR performance indicator" was developed by use of GOR upside and downside performance curves generated as a result of uncertainty studies. More than three years of monitoring GOR evolution by use of the developed GOR performance indicator allows the assessment of GOR behavior trends against the expected performance, which permits the development of adequate reservoir management plans. The GOR trend can be explained as a result of reservoir management actions such as optimizing gas injection rate, adjusting well production rate and closing high GOR (HGOR) oil producers. Accordingly, the GOR trend can indicate the current reservoir performance compared to the range of uncertainty analysis. It also can put light on required reservoir management action plans for operations and assist building assurance of the reservoir development. Consequently, the developed "GOR performance indicator" is based on the reservoir simulation outputs for uncertainty analysis. In case that the GOR trend is deviating from the range of the GOR performance indicator, the reservoir simulation inputs needs to be modified. The developed GOR performance indicator is considered useful to foresee required reservoir management action plans and to suggest alternative development plan needs in case of unfavorable actual reservoir performance. It is noted that the GOR performance indicator can be updated based on new information available. Additional data gathering can support to narrow uncertainty ranges.
Reservoir simulation is widely used for field development planning in many fields and the evaluation of uncertainty range in production forecast is indispensable to make decision for further investment. Reservoir simulation model consists of geological, petrophysical and reservoir engineering parameters for each cell and cell boundary. These reservoir model parameters are usually defined based on limited available data in consideration of their uncertainty range. Therefore, the identification of influential parameters and the reduction of uncertainty range for these parameters are key components to mitigate the prediction uncertainty. An Upper Jurassic carbonate reservoir in Field A located in offshore Abu Dhabi has long production history for more than 30 years. Field A experienced several development schemes including natural depletion, crestal gas injection and crestal water injection. The current reservoir simulation model reasonably replicates historical performance on pressure, water cut evolution and GOR trend in field and well-by-well scales. On the other hand, we identified some reservoir model parameters have high uncertainty due to reservoir complexity and lack of reliable data. In this study, we focused on the identification of influential parameters on production forecast and the reduction of parameter uncertainty range using an experimental design approach. More than 200 simulation cases were generated with different combination of selected parameters using Latin Hypercube Sampling method. In each case, we evaluated history matching quality in field scale and relationship between history matching quality and each parameter. We found some parameters have correlation with history matching quality independently from the other parameters settings. This means that the uncertain range of those parameters can be reduced to achieve an acceptable history match irrespective of the other parameters. Furthermore, the prediction uncertain range was analyzed using the selected cases showing reasonable history matching quality to investigate the relationship between cumulative oil production and each parameter. The results indicated some parameters have a stronger impact on production forecast and their uncertainty range need to be reduced by further data gathering or considering other mitigation plans. This study successfully demonstrated that the proposed multiple parameter sensitivity analysis by effective use of experimental design approach enables to reduce the parameter uncertain range and identify the key influential parameters. Furthermore, this study result contributes to the prioritization and optimization of future data gathering plan in Field A.
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