An automation system for gas-lifted oil wells: Model identification, control, and optimization

Camponogara, Eduardo; Plucenio, Agustinho; Teixeira, Anne Kayline Soares; Campos, Sthener

doi:10.1016/j.petrol.2009.11.003

Cited by 37 publications

(23 citation statements)

References 28 publications

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“…However, the pressure drop due to fl uid level drop would be domininat over the pressure drop due to friction when the tubing size is more than the optimum tubing size. This would result in production rate to decrease (Camponogara et al, 2010;Mahmudi and Sadeghi, 2013;Allyeva and Novruzaliyev, 2015;Elldakli and Soliman, 2017). Therfore, there always must be an optimum value for the tubing size as it was shown in Figures 4-6 in this study.…”

Section: Discussionmentioning

confidence: 66%

A case study on simulation and optimization of artifi cial lift methods in one of the Iranian oil fi elds

Azdarpour¹,

Karaei²,

Dabiri³

2017

Biosci. Biotech. Res. Comm

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Oil production from reservoirs usually occurs by natural fl ow of the fl uid out of the formation. This oil recovery is called primary recovery, where the production is solely controlled by the natural energy of the formation. However, after some times of production reservoir pressure declines, which causes a decline in oil production rate. Thus, regaining the reservoir pressure to enhance oil production is of great importance. Gas lift as one of the best methods of oil recovery when reservoir pressure declines has been implemented for decades. The reservoir pressure of one of the oil fi elds in Iran has been dropped to a level where no natural fl uid fl ow occurs form the reservoir. Gas lift has been proposed to compensate the natural pressure of the reservoir and ease the petroleum production from the reservoir. PIPESIM software was used to study the effectiveness of the gas lift system. Different parameters including tubing diameter, injected gas rates, and injection depth and their effect on infl ow performance relationship (IPR) were investigated. The simulation results showed that natural energy of the reservoir is not suffi cient for producing oil. Thus, gas lift as of the best methods of increasing the production rate in this fi eld could be implemented successfully.

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Section: Discussionmentioning

confidence: 66%

A case study on simulation and optimization of artifi cial lift methods in one of the Iranian oil fi elds

Azdarpour¹,

Karaei²,

Dabiri³

2017

Biosci. Biotech. Res. Comm

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“…Well head flowing pressure is registered using a pressure recorder installed at the wellhead. The oil rate, the GLR, the water cut, the API gravity, and the oil formation volume factor have been determined using a track or skid mounted Portable Test (Oglesby et al 2006;Camponogara et al 2010).…”

Section: Data Descriptionmentioning

confidence: 99%

A pragmatic approach for optimizing gas lift operations

Garrouch

Al-Dousari

Al-Sarraf

2019

J Petrol Explor Prod Technol

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The oil flow rate in a single vertical well undergoing gas lift operations is complicated by three factors: (1) The flow is driven by gas injection, in addition to the fluid flow potential gradient applied along the well, (2) the well is interfaced with a porous and permeable reservoir contributing with a fluid feed, and (3) the wellbore geometry may consist of concentric pipes of varying diameters and lengths, rather than a single-diameter pipe. Dimensional analysis is applied to this complex, highly nonlinear production problem, in order to develop empirical models for predicting the optimal gas injection rate and the maximum oil production rate that may be produced from continuous gas lift operations. Two pairs of coupled dimensionless groups are revealed. The first pair consists of a dimensionless pressure drop (π 1) adjusted to the complex wellbore geometry, and a dimensionless ratio of kinetic to viscous forces (π 2) which accounts for the porous medium feed. A constructed database for 388 vertical wells producing by continuous gas lift operations has been used to validate the dimensionless groups. A power-law relation is revealed between the dimensionless groups π 1 and π 2 , allowing to construct an analytical model for predicting the maximum oil production rate that corresponds to the optimal gas injection rate. The second pair consists of two groups denoted χ 1 and χ 2. The group χ 1 is a dimensionless pressure drop with adjustment being augmented to account for the temperature effects on gas flow. Similar to π 2 , the dimensionless group χ 2 is a ratio of kinetic to viscous forces, adjusted to account for the porous medium feed. However, χ 2 is a function of the injection rate, instead of the oil production rate. Likewise, a power-law relation is revealed between χ 1 and χ 2 , allowing to construct an analytical model for predicting the optimal gas injection rate. All power-law relations yield high correlation coefficients when the validation data are segregated according to a discrete productivity index. The analytical models developed by applying dimensional analysis appear to capture the physical controls of gas lift operations. Intuitively, the optimal gas injection rate depends on the pressure gradient along the pipe, the wellbore geometry, the temperature conditions at the bottom of the well and in the stock-tank, the oil density, and on the productivity index. Similarly, the maximum oil production rate, corresponding to the optimal gas injection rate, depends on the pressure gradient along the pipe, the wellbore geometry, the oil density, the productivity index which is implicitly affected by the oil permeability, and viscosity. Unlike multivariate nonlinear regression analysis, the application of dimensional analysis for deriving the analytical models, presented in this study, does not require a presumed functional relationship. In retrospect, dimensional analysis evades the guessing process associated with nonlinear regression analysis.

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“…Aiming at the deficiencies when a single well is as the research object, the overall optimization model of the oil and gas production block is established by Dong et al, and it is superior to the single well optimization strategy. For a lift‐gas well, studies had addressed the optimization problem of oil field production process with rate capacity constraints . These studies did not consider pressure and temperature constraints.…”

Section: Introductionmentioning

confidence: 99%

A novel optimization approach for oil and gas production process considering model parameters uncertainties

et al. 2016

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Through analyzing the integrated oil and gas production process, a multi-objective optimization model for the integrated oil and gas production process is established with considering nonlinear reservoir behaviour, multiphase flow in wells and constraints from the surface facilities. In order to reduce the influence of model parameter uncertainty in oil and gas production process, an error compensation method based on Gaussian mixture model (GMM) is proposed to compensate the model. Non-dominated sorting genetic algorithm-II (NSGA-II) is used as the optimization algorithm. Moreover, an operational strategy by using post-optimization is applied to solve the optimization model, so as to ensure the feasibility of obtained optimal set-point. Finally, a novel optimization approach for oil and gas production process considering model parameter uncertainty is proposed. Simulation results indicate that the proposed optimization method is feasible and effective. This article is protected by copyright.All rights reserved

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An automation system for gas-lifted oil wells: Model identification, control, and optimization

Cited by 37 publications

References 28 publications

A case study on simulation and optimization of artifi cial lift methods in one of the Iranian oil fi elds

A case study on simulation and optimization of artifi cial lift methods in one of the Iranian oil fi elds

A pragmatic approach for optimizing gas lift operations

A novel optimization approach for oil and gas production process considering model parameters uncertainties

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