Optimal Field Development Planning of Well Locations With Reservoir Uncertainty

Cullick, A.S.; Narayanan, Keshav; Gorell, Sheldon

doi:10.2523/96986-ms

Cited by 7 publications

(6 citation statements)

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“…(Cottini-Loureiro & Araujo, 2005) (Cullick, Heath, Narayanan, April, & Kelly, 2004) (Cullick, Narayanan, & Gorell, 2005) Onwunalu, & Aziz, 2006) (Bailey, Couet, & Wilkinson, 2005) (Osterloh & Menard, 2007) (Ozdogan, Sahni, Yeten, Guyaguler, & Chen, 2005) (Shelkov, et al) (Baris & Horne, 2001) (Guyaguler & Horne, 2001). A proxy based approach may be found in (Jutila & Goodwin, 2006).…”

Section: Approaches To History Matching and Uncertainty Quantificationmentioning

confidence: 99%

Bridging the Gap Between Deterministic and Probabilistic Uncertainty Quantification Using Advanced Proxy Based Methods

Goodwin

2015

SPE Reservoir Simulation Symposium

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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.

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Section: Approaches To History Matching and Uncertainty Quantificationmentioning

confidence: 99%

Bridging the Gap Between Deterministic and Probabilistic Uncertainty Quantification Using Advanced Proxy Based Methods

Goodwin

2015

SPE Reservoir Simulation Symposium

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“…The production history data obtained from the wells, as they are drilled, are integrated into the well placement decisions. Cullick et al (2005) developed an optimization methodology for locating wells with design constraints simultaneously for depletion drive and water injection based using dynamic simulator outputs based on predictions of ultimate recovery and NPV.…”

Section: Well Placementmentioning

confidence: 99%

Application of Assisted Optimization to Aid Oil Exploitation Strategy Selection for Offshore Fields

Gaspar

Barreto

Mazo

et al. 2014

Day 3 Fri, May 23, 2014

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Decision-making processes for selecting an oil exploitation strategy can be complex due to the high number of variables to be optimized. Many times, it can be unfeasible to search an optimal solution by evaluating a high quantity of variables simultaneously. In this case, assisted methods that involve engineering analyses and mathematical optimization algorithms are an alternative to obtain a good solution. This paper shows the application of an assisted method to optimize a large number of variables of an oil exploitation strategy. The proposed methodology is to order and combine different optimization procedures with practical engineering analysis. The optimization variables include number and position of wells, platform capacities, wells opening schedule and wells shut-in time. The methodology is applied to a reservoir model based on a Brazilian offshore oil field to discuss the results obtained. Results indicate an efficient procedure for evaluating deterministic scenarios, suggesting optimization procedures for each decision variable and enabling the achievement of good quality solutions with a reasonable number of simulation runs. This is useful in many practical cases, mainly those, which require runs with long simulation time.

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“…2,3 The objective function is usually taken as the NPV, but can be another or a combination of economic or production metrics such as ROR or reserves. Difficulty in applying classical optimization techniques to the development planning problem is the nonlinear solution space with many possible locally optimal solutions, a set of equations often not differentiable, and a mix of continuous and discrete variables.…”

Section: Uncertainty and Optimizer Calculatorsmentioning

confidence: 99%

Optimizing Field Development Concepts for Complex Offshore Production Systems

Cullick

Cude

Tarman

2007

Proceedings of Offshore Europe

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fax 01-972-952-9435. AbstractAn analysis framework is presented that unifies the subsurface and surface disciplines involved with developing offshore projects. The method treats the complex interactions between surface and subsurface aspects of a production problem as a single system. The framework optimizes and assesses the risks and uncertainties associated with facility concept selection and sizing, with consideration of relevant subsurface and surface uncertainties and risks.There is considerable uncertainty in the subsurface; this manifests itself in terms of a range of reservoir deliverability and reserves. In many projects, this subsurface uncertainty is not properly conveyed to the surface teams who are charged with selecting the best facilities design for the project. Often, the result of this disconnect yield suboptimal facilities followed by expensive retrofitting. The framework presented automates optimization of the concept design with rigorous reservoir and facilities modeling.Two case studies are presented, both with multiple reservoirs connected through a common pipeline network. The first case is a typical deepwater U.S. Gulf of Mexico project. The second case is more typical of a medium water depth development in northern Europe. Full scenario analyses of multiple production platform alternatives and number of wells optimization is evaluated within a context of reservoir volumetric and drive mechanism uncertainties. This framework accomplishes the following: it links physics-based reservoir deliverability and expert-system based facilities screening tools with the economics analysis in a unified workflow, and it applies advanced non-linear optimization to deliver better decisions regarding facilities selection and sizing.

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Optimal Field Development Planning of Well Locations With Reservoir Uncertainty

Cited by 7 publications

References 0 publications

Bridging the Gap Between Deterministic and Probabilistic Uncertainty Quantification Using Advanced Proxy Based Methods

Bridging the Gap Between Deterministic and Probabilistic Uncertainty Quantification Using Advanced Proxy Based Methods

Application of Assisted Optimization to Aid Oil Exploitation Strategy Selection for Offshore Fields

Optimizing Field Development Concepts for Complex Offshore Production Systems

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