Production Optimization under Uncertainty with Constraint Handling Kristian

Hanssen, Grete; Foss, Bjarne A.; Teixeira, Anne Kayline Soares

doi:10.1016/j.ifacol.2015.08.011

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…CVaR has shown to be more robust in mitigating risks as compared to VaR [55], [56]. 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].…”

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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Section: Gradient Based Waterflood Optimizationmentioning

confidence: 99%

Untitled

2023

JESTR

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“…The fulfillment of the constraint can be increased by improving the estimate of the statistical properties of the load variations, e.g., better prediction of the variance or more correct probability distribution. Other types of chance constraints were tested such as cVaR [25], which is able to handle nonlinear models, but increased the computational demand. However, we did not see any significant reduction in the violation of the chance constraints.…”

Section: Simulation Studymentioning

confidence: 99%

“…For a linear system with Gaussian disturbance, it has been shown that the constraints can be converted to an explicit second-order cone constraints [24]. The use of scenarios and conditional value at risk (cVaR) as an approximation to the probabilistic constraint has also been suggested [25]. In [26], an approximation of chance constraints using scenarios and mixed integer quadratic programming is presented.…”

Section: Introductionmentioning

confidence: 99%

Battery Power Smoothing Control in a Marine Electric Power Plant Using Nonlinear Model Predictive Control

Bø

Johansen

2017

IEEE Trans. Contr. Syst. Technol.

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Abstract-This paper presents a power variation smoothing method using batteries on a weak ship grid. For some marine vessels, power fluctuations on the ac grid are large. This results in large variations in the electrical frequency of the grid and excessive wear and tear of the power producers. Adding batteries connected to a DC/AC drive to smooth out the power fluctuations has been suggested. However, due to the large amount of fluctuation, batteries can overheat. Therefore, we suggest using a band-pass filter with cutoff frequency parameters optimized by model predictive control based on a power spectral density estimate of power consumption for disturbance prediction.

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“…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%

“…CVaRO is a fairly new concept in the production optimization setting and has only recently begun to receive attention from the oil production community (Hanssen et al, 2015;Capolei et al, 2015a). CVaRO is well known in financial mathematics where it has been used in risk management as a tool specifically designed to target the paramount risks of low returns in terms of the risk measure known as value-at-risk (Jorion, 2006).…”

Section: Conditional Value-at-risk Optimizationmentioning

confidence: 99%