Optimizing smart well controls under geologic uncertainty

Alhuthali, Ahmed; Datta‐Gupta, Akhil; Yuen, Bevan; Fontanilla, Jerry

doi:10.1016/j.petrol.2010.05.012

Cited by 39 publications

(13 citation statements)

References 9 publications

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“…This is a coherent and averse measure of risk used for example in Alhuthali et al (2010). However, often it provides a too conservative risk measure.…”

Section: Worst-case Scenariomentioning

confidence: 99%

“…3. The standard deviation was used as an uncertainty measure, for example in Yeten et al (2003); Bailey et al (2005); Alhuthali et al (2010); Capolei et al (2013) and interepreted as a risk measure in Capolei et al (2015). The variance, σ 2 , compared to σ, misses also the positive homogeneity and subadditivity.…”

Section: Standard Deviation and Variancementioning

confidence: 99%

“…The safety margin risk measure (15) was used by Yeten et al (2003); Bailey et al (2005); Alhuthali et al (2010). However, they did not use (15) directly to measure the risk.…”

Section: Safety Margin R(ψ)mentioning

confidence: 99%

“…in classical portfolio theory (Markowitz, 1959), the standard deviation σ(ψ) is used to quantify uncertainty in returns of financial portfolios. In the oil community, Bailey et al (2005); Alhuthali et al (2010); Yeten et al (2003) propose to reduce the uncertainty in profit by including the standard deviation in the cost function. In many decision problems dealing with safety and reliability, risk is often interpreted as the probability of a dreadful event or disaster (Ditlevsen and Madsen, 1996;Rockafellar and Royset, 2010), and minimizing the probability of a highly undesirable event is known as the safety-first principle (Roy, 1952).…”

Section: Introductionmentioning

confidence: 99%

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Profit and Risk Measures in Oil Production Optimization∗∗This research project is financially supported by the Danish Research Council for Technology and Production Sciences. FTP Grant no. 274-06-0284 and the Center for Integrated Operations in the Petroleum Industry at NTNU.

2015

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In oil production optimization, we usually aim to maximize a deterministic scalar performance index such as the profit over the expected reservoir lifespan. However, when uncertainty in the parameters is considered, the profit results in a random variable that can assume a range of values depending on the value of the uncertain parameters. In this case, a problem reformulation is needed to properly define the optimization problem. In this paper we describe the concept of risk and we explore how to handle the risk by using appropriate risk measures. We provide a review on various risk measures reporting pro and cons for each of them. Finally, among the presented risk measures, we identify two of them as appropriate risk measures when minimizing the risk.

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“…This is a coherent and averse measure of risk used for example in Alhuthali et al (2010). However, often it provides a too conservative risk measure.…”

Section: Worst-case Scenariomentioning

confidence: 99%

Section: Standard Deviation and Variancementioning

confidence: 99%

“…The safety margin risk measure (15) was used by Yeten et al (2003); Bailey et al (2005); Alhuthali et al (2010). However, they did not use (15) directly to measure the risk.…”

Section: Safety Margin R(ψ)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Profit and Risk Measures in Oil Production Optimization∗∗This research project is financially supported by the Danish Research Council for Technology and Production Sciences. FTP Grant no. 274-06-0284 and the Center for Integrated Operations in the Petroleum Industry at NTNU.

2015

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“…Chen (2010) tackles the problem of closed-loop production optimization using the optimizer EnOpt ) which is applied to the geological model ensemble updated by either EnKF (Evensen, 1994) or EnRML (Gu & Oliver, 2007). In Yeten et al (2003), Aitokhuehi et al (2004) and Alhuthali et al (2010), multiple geostatistical realizations of the reservoir are considered in the formulation of the objective function:…”

Section: Petroleum Communitymentioning

confidence: 99%

Well Placement Optimization under Uncertainty with CMA-ES Using the Neighborhood

Bouzarkouna¹,

Ding²,

Auger³

2012

ECMOR XIII - 13th European Conference on the Mathematics of Oil Recovery

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In the well placement problem, as well as in many other field development optimization problems, geological uncertainty is a key source of risk affecting the viability of field development projects. Well placement problems under geological uncertainty are formulated as optimization problems in which the objective function is evaluated using a reservoir simulator on a number of possible geological realizations. The existing approaches to cope with geological uncertainty require multiple reservoir simulations (on the possible realizations) to estimate the expected field performance for a given well configuration, which is often very demanding in CPU time and impractical when dealing with real field cases.In this paper, we present a new approach to handle geological uncertainty for the well placement problem with a reduced number of reservoir simulations. The proposed approach uses already simulated well configurations in the neighborhood of each well configuration for the objective function evaluation. We use thus only one single reservoir simulation performed on a randomly chosen realization together with the neighborhood of each well configuration to estimate the objective function instead of using multiple simulations on multiple realizations. This approach is combined with the stochastic optimizer CMA-ES (Covariance Matrix Adaptation -Evolution Strategy).The proposed approach is shown on the benchmark reservoir case PUNQ-S3 to be able to capture the geological uncertainty using a smaller number of reservoir simulations. This approach is compared to the reference approach using all the possible realizations for each well configuration. It is shown that the proposed approach is able to reduce significantly the number of reservoir simulations by more than 80% for the reservoir case in this study. Now with TOTAL. ECMOR XIII -13th European Conference on the Mathematics of Oil Recovery

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Multiple shooting applied to robust reservoir control optimization including output constraints on coherent risk measures

et al. 2017

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Optimizing smart well controls under geologic uncertainty

Cited by 39 publications

References 9 publications

Profit and Risk Measures in Oil Production Optimization∗∗This research project is financially supported by the Danish Research Council for Technology and Production Sciences. FTP Grant no. 274-06-0284 and the Center for Integrated Operations in the Petroleum Industry at NTNU.

Profit and Risk Measures in Oil Production Optimization∗∗This research project is financially supported by the Danish Research Council for Technology and Production Sciences. FTP Grant no. 274-06-0284 and the Center for Integrated Operations in the Petroleum Industry at NTNU.

Well Placement Optimization under Uncertainty with CMA-ES Using the Neighborhood

Multiple shooting applied to robust reservoir control optimization including output constraints on coherent risk measures

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