A Novel Approach for Risk Minimization in Life-Cycle Oil Production Optimization

Capolei, Andrea; Christiansen, Lasse Hjuler; Jørgensen, John Bagterp

doi:10.1016/b978-0-444-63965-3.50028-3

Cited by 3 publications

(3 citation statements)

References 34 publications

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“…(For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) approximations of CVaR exist (Rockafellar and Royset, 2010;Basova et al, 2011;Capolei et al, 2016). (Fig.…”

Section: Conditional Value-at-risk Optimizationmentioning

confidence: 96%

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Time-explicit methods for joint economical and geological risk mitigation in production optimization

Christiansen

Capolei

Jørgensen

2016

Journal of Petroleum Science and Engineering

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Section: Conditional Value-at-risk Optimizationmentioning

confidence: 96%

“…Popular methods based on this assumption include RO, MVO and conditional value-at-risk optimization (CVaRO) (Hanssen et al, 2015;Capolei et al, 2016;Siraj et al, 2015a). …”

Section: Geological Uncertainties and Risk Mitigation Strategiesmentioning

confidence: 99%

Time-explicit methods for joint economical and geological risk mitigation in production optimization

Christiansen

Capolei

Jørgensen

2016

Journal of Petroleum Science and Engineering

Self Cite

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“…However, to implement the optimization problem, there must be a function to map the outcomes to a single scalar. As the choice of objective function directly affects the optimization solution [9], using a proper objective seems crucial. Decision-makers usually use a specified risk measure as the objective, and the optimization problem is reduced to the minimization of this risk measure.…”

Section: Introductionmentioning

confidence: 99%

Comparative study of different risk measures for robust optimization of oil production under the market uncertainty: a regret-based insight

2020

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Model-based production optimization relies on a dynamic model that simulates the fluid flow in the oil reservoirs, and an economic objective function that assigns an economic measure to the recoverable oil reserves. An optimization algorithm utilizes the dynamic model to find the production scenario which maximizes the economic measure of profit. However, due to incompleteness and doubtfulness of available data, the reservoir model describing the complex subsurface geology is quite uncertain. Moreover, the definition of the economic objective functions such as net present value (NPV) requires economic variables such as oil price, interest rate, and production costs which unpredictably vary with time. In recent years, robust optimization (RO) has been widely used as an appropriate tool for handling the uncertainties in production optimization problems. However, previous works on robust optimization paid less attention to economic uncertainties arising from market volatility. Instead, they are mostly focused on geological uncertainties. This paper is devoted to production optimization under oil market uncertainty. To narrow down the range of economic uncertainties, a Bayesian framework for oil price history matching and forecasting has been developed which allows generating more reliable realizations of oil price future trend. It is common to include a measure of risk-averse in the objective function of RO problems. However, the quality of the solutions depends directly on the used risk measure. In the oil industry, risk measures such as worst-case scenario and CVaR (Conditional Value at Risk) have been used to mitigate the risk of low-profit realizations. These risk measures are appropriate in many cases for measuring the robustness. Though, they are inadequate in evaluating robustness in a relative sense in cases where the worst-case realizations have an undue effect on the final decisions. The risk measure defined based on the minimax regret approach takes into account all realizations instead of just considering the worst-case realizations. In this research, RO has been performed to maximize NPV using the minimax regret approach. In addition, the results are compared with the common risk measures used in the oil industry including expected profit, CVaR, and worst-case. Results show that while worstcase scenario and CVaR perform better than other risk measures in lower-profit realizations, they give inappropriate results for other scenarios. In contrast, regret-based approach and expected profit give nearly optimum solutions for all realizations. In this paper, the minimax regret approach was compared with other risk measures in the presence of oil price uncertainty. However, the results might be extended to optimization under geological uncertainty.

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Experimental characterization and mechanism of hydraulic pulsation waves driving microscopic residual oil

YANG

et al. 2022

Petroleum Exploration and Development

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A Novel Approach for Risk Minimization in Life-Cycle Oil Production Optimization

Cited by 3 publications

References 34 publications

Time-explicit methods for joint economical and geological risk mitigation in production optimization

Time-explicit methods for joint economical and geological risk mitigation in production optimization

Comparative study of different risk measures for robust optimization of oil production under the market uncertainty: a regret-based insight

Experimental characterization and mechanism of hydraulic pulsation waves driving microscopic residual oil

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