Dynamic Self-Optimizing Control for Oil Reservoir Waterflooding

Grema, Alhaji Shehu; Landa, Amaia C.; Cao, Yi

doi:10.1016/j.ifacol.2015.08.009

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…This proposed method could be seen in the works of [138], [140], [148]. Self-optimizing control for waterflooded reservoir optimization has being reported by Grema et al [149], [150]. Grema and Cao [32] applied a data driven self-optimizing control.…”

Section: Data-driven Optimization Approachmentioning

confidence: 98%

“…The CVs were investigated from measured production data in an offline manner and it was then implemented online in a closed loop feedback approach, and then compared to the open loop control (OC) scheme. This same approach was applied to multivariable waterflooding optimal control by Grema et al [150]. Recently, SOC was extended for smart well problems [151].…”

Section: Data-driven Optimization Approachmentioning

confidence: 99%

See 1 more Smart Citation

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: Data-driven Optimization Approachmentioning

confidence: 98%

Section: Data-driven Optimization Approachmentioning

confidence: 99%

Untitled

2023

JESTR

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“…Many types of disturbances can be applied such as the increase of the water flow rate through the distillation column that can affect the process [7], a combination of white noise and sine-wave noise [11] and changes to the composition of raw materials. Numerous works applied the robustness test to check the capability and performance of their proposed controller for the process [3,[12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Real time robustness test evaluation performance for intelligent fuzzy controller in extraction process

Yusoff

Ashar

Muhammad

et al. 2019

IJEECS

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This paper presents the real time robustness test using intelligent fuzzy based controller in extraction process of essential oil is discussed in this study. Previous finding shows that the quality of the essential oil is affected by the steam temperature that acts as the control variable in this study. The robustness test is applied to the system during running process to show the system is robust to any operation conditions t make sure the controller is able to give a smooth control output response. The dynamic performance of the system are represented by applying standard performance criteria such as rise time Tr, settling time Ts, overshoot <em>%OS</em>, root mean square error (RMSE) and time on recovering load disturbance. Generally, the objectives in designing the controllers have been achieved because all intelligent fuzzy based controllers capable to regulate the desired set point by acting on the change in the output compared with the set point.

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