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.

Capolei, Andrea; Foss, Bjarne A.; Jørgensen, John Bagterp

doi:10.1016/j.ifacol.2015.08.034

Cited by 29 publications

(5 citation statements)

References 25 publications

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“…Control risks relate to the setting of production rates from individual wells as well as daily production targets. Capolei, Foss and Jørgensen (2015, p.215) identified that, for new wells, these risks are related to the high uncertainty in seismic data, core samples, and borehole logs (Capolei, Foss et Jørgensen, 2015). Svensson (2000) identified that demand risks are related to downstream supply chain activities, including market risk due to price instability and market fluctuations (Svensson, 2000).…”

Section: Supply Chain Planning Under Uncertaintymentioning

confidence: 99%

Tactical supply chain planning after mergers under uncertainty with an application in oil and gas

ALNAQBI

Trochu

Dweiri

et al. 2023

Computers & Industrial Engineering

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Section: Supply Chain Planning Under Uncertaintymentioning

confidence: 99%

Tactical supply chain planning after mergers under uncertainty with an application in oil and gas

ALNAQBI

Trochu

Dweiri

et al. 2023

Computers & Industrial Engineering

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“…Hanssen et al [57] formulated a stochastic reservoir optimization problem based on CVaR to handle oil production constraints. It was further extended to consider multiple risk scenarios [58]. For large ensemble realizations, retrospective optimization was found to be an optional technique [59].…”

Section: Gradient Based Waterflood Optimizationmentioning

confidence: 99%

Untitled

2023

JESTR

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Waterflooding is the most common secondary recovery technique used in oil and gas industries today, owing to its cheap investment cost and easy implementation. However, major challenges are encountered in terms of oil sweep efficiency and breakthrough time which poses a risk to production and economic lifecycle of reservoirs. As a result, reservoir engineers are tasked with improvising optimal production strategies with the goal of maximizing profit. This review extensively describes some common optimization techniques reported in improving oil reservoir production. Also, their formulation, limitations and advantages with respect to production rates, oil well placement and control, inter-well connectivity and reservoir sweep efficiency were reviewed. While there are several optimization algorithms used in waterflooding, the emphasis in this work involves only the gradient and data driven optimizers since it is impossible to cover all optimization technique in a single review paper. Basically, no algorithm has been globally accepted as superior to the other since the sole aim is to improve productivity and economic profit, and each of these techniques has its unique practicability. However, when considering factors like design limitations, computational and economic cost, implementation timeframe, availability of data, some technique may suffice.

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“…MO includes uncertainty in the optimization framework, but the optimized solution does not lead to a reduced uncertainty in the achieved NPV. For better uncertainty handling of these MOOs, we are inspired by the seminal work of "risk-return" portfolio management (Markowitz 1952), which has been applied to robust waterflooding optimization under geological uncertainty by Capolei et al (2015b), to consider a riskaverse MVO objective. In MVO, the risk is quantified by the variance of the NPV distribution.…”

Section: Introductionmentioning

confidence: 99%

“…By changing the NPV objective, these optimization methods provide an indirect or ad hoc way of balancing the short-term and the long-term economic objectives. In Capolei et al (2015b), a bicriterion MVO function has been optimized and key economic indicators have been given. We consider a single-objective MVO to investigate whether, by explicit handling of uncertainty in model-based economic optimization, the balance between shortterm and long-term gains can be naturally obtained.…”

Section: Introductionmentioning

confidence: 99%

Handling Geological and Economic Uncertainties in Balancing Short-Term and Long-Term Objectives in Waterflooding Optimization

Siraj

Hof

2017

SPE Journal

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Model-based economic optimization of oil production has a significant scope to increase financial life-cycle performance. The net-present-value (NPV) objective in this optimization, because of its nature, focuses on long-term gains, whereas short-term production is not explicitly addressed. At the same time, the achievable NPV is highly uncertain because of strongly varying economic conditions and limited knowledge of the reservoirmodel parameters. The prime focus of this work is to develop optimization strategies that balance both long-term and short-term economic objectives and also offer robustness to the long-term NPV. An earlier robust hierarchical optimization method honoring geological uncertainty with robust long-term and short-term NPV objectives serves as a starting base of this work. We address the issue of extending this approach to include economic uncertainty and aim to analyze how the optimal solution reduces the uncertainty in the achieved average NPV. An ensemble of varying oil prices is used to model economic uncertainty with average NPVs as robust objectives in the hierarchical approach. A weighted-sum approach is used with the same objectives to quantify the effect of uncertainty. To reduce uncertainty, a mean-variance-optimization (MVO) objective is then considered to maximize the mean and also minimize the variance. A reduced effect of uncertainty on the long-term NPV is obtained compared with the uncertainty in the mean-optimization (MO) objectives. Last, it is investigated whether, because of the better handling of uncertainty in MVO, a balance between short-term and long-term gains can be naturally obtained by solving a single-objective MVO. Simulation examples show that a faster NPV buildup is naturally achieved by choosing appropriate weighting of the variance term in the MVO objective.

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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.

Cited by 29 publications

References 25 publications

Tactical supply chain planning after mergers under uncertainty with an application in oil and gas

Tactical supply chain planning after mergers under uncertainty with an application in oil and gas

Untitled

Handling Geological and Economic Uncertainties in Balancing Short-Term and Long-Term Objectives in Waterflooding Optimization

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