Optimal feedback control for reservoir waterflooding

Grema, Alhaji Shehu; Cao, Yi

doi:10.1109/iconac.2014.6935454

Cited by 4 publications

(7 citation statements)

References 20 publications

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“…Ref. [3] ‐ (Lorentzen et al., 2006)) for Iran's offshore reservoirs (3D reservoirs), the proposed closed loop OR‐based decision system can increase the total water injected. Under the condition simulated, the total water injected using the proposed OR‐based decision system has increased by 21.79% compared with the available IOR technique.…”

Section: Resultsmentioning

confidence: 99%

“…Note that most of the relevant papers in the literature, for example, Refs. [3][4][5][6][7][8] do not implement this model update strategy and assume the primary model of the reservoir ( 5) is the legitimate model for the entire reservoir life time, which is an unrealistic assumption. The ensemble Kalman filter is described next.…”

Section: Non-linear Model Predictive Controlmentioning

confidence: 99%

“…Ref. [3] - (Lorentzen et al, 2006)). The aforementioned references also show that the optimal multi-valued injection rate technique can increase the oil production by 30%…”

Section: Comparisonmentioning

confidence: 99%

“…This computer server is equipped with optimisation, estimation, machine learning or artificial intelligent algorithms. The input to these algorithms is the real time data collected by sensors and the output is the generated proper commands for distributed actuators computed so that the efficiency and productivity are enhanced [1][2][3][4][5][6][7][8][9][10]. Due to the uncertainty in exchange of information between distributed sensors and actuators and gateways, as well as complexity in the environment equipped with the network of these sensors and actuators, we need to develop a large scale stochastic non-linear operation research model that is obviously much difficult to develop with respect to the commonly used small scale deterministic models.…”

Section: Introduction 1| Motivation and Backgroundsmentioning

confidence: 99%

“…Under the unrealistic assumption that the reservoir primary mathematical model is the true representation of the reservoir dynamics for the entire life of the reservoir, Refs. [3][4][5][6][7][8] show that the optimal multi-valued injection rate technique enhances the oil production by 30% and the oil recovery factor by 3%. It can also reduce the water injection rate for the pressure stabilisation by 25% compared with the currently used IOR technique.…”

Section: Introduction 1| Motivation and Backgroundsmentioning

confidence: 99%

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Application of the closed loop industrial internet of things (IIoT)‐based control system in enhancing the oil recovery factor and the oil production

Naghneh,

Amani,

Farhadi

et al. 2023

IET Cyber-Phy Sys Theory & Ap

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A non‐linear large scale stochastic optimisation model for enhancing the oil production and the recovery factor of the offshore oil reservoirs is proposed. The model aims at minimising the miss‐match between mathematical model and the actual dynamic behaviour of the reservoir and the exploitation time, while maximising the oil production and the recovery factor. The model involves the three dimension (3D) oil reservoirs equipped with a few vertical injection and production wells. The limited number of wells is one of the major features of the common oil reservoirs in the middle‐east region. The proposed model consists of the primarily mathematical model of the 3D reservoir, a model update algorithm and a large scale constrained non‐linear optimisation algorithm. The input to this model is the daily production rate of the oil, natural gas and water produced from the oil reservoir and the output is the optimal injection rate to be injected to the injection wells in order to maximise the oil production and the recovery factor. In order to evaluate the performance of this model, the authors apply this model on part of one of the Iran's offshore oil reservoirs and study the performance improvement due to the proposed model and compare its performance with the performance of the available Improved Oil Recovery (IOR) technique. It is illustrated that the proposed model can increase the oil production from the reservoir up to 47.96% and reduce the exploitation period up to 66.66% compared with those of the available technique.

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Section: Resultsmentioning

confidence: 99%

Section: Non-linear Model Predictive Controlmentioning

confidence: 99%

“…Ref. [3] - (Lorentzen et al, 2006)). The aforementioned references also show that the optimal multi-valued injection rate technique can increase the oil production by 30%…”

Section: Comparisonmentioning

confidence: 99%

Section: Introduction 1| Motivation and Backgroundsmentioning

confidence: 99%

Section: Introduction 1| Motivation and Backgroundsmentioning

confidence: 99%

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Application of the closed loop industrial internet of things (IIoT)‐based control system in enhancing the oil recovery factor and the oil production

Naghneh,

Amani,

Farhadi

et al. 2023

IET Cyber-Phy Sys Theory & Ap

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Dynamic Self-Optimizing Control for Oil Reservoir Waterflooding

2015

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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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Optimal feedback control for reservoir waterflooding

Cited by 4 publications

References 20 publications

Application of the closed loop industrial internet of things (IIoT)‐based control system in enhancing the oil recovery factor and the oil production

Application of the closed loop industrial internet of things (IIoT)‐based control system in enhancing the oil recovery factor and the oil production

Dynamic Self-Optimizing Control for Oil Reservoir Waterflooding

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