Real-Time Production Optimization of Offshore Oil and Gas Production Systems: A Technology Survey

Bieker, Hans Petter; Slupphaug, Olav; Johansen, Tor Arne

doi:10.2118/99446-ms

Cited by 35 publications

(3 citation statements)

References 41 publications

(38 reference statements)

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“…Handling uncertainty has been identified as a key challenge in real‐time optimization of oil and gas production . Monte Carlo simulations have been previously used by Bieker et al, to analyze the uncertainty in production optimization.…”

Section: Optimization For Oil and Gas Production Process Considering mentioning

confidence: 99%

“…Handling uncertainty has been identified as a key challenge in real-time optimization of oil and gas production. [28] Monte Carlo simulations have been previously used by Bieker et al, [29] to analyze the uncertainty in production optimization. Bieker et al [30] also mention that the uncertainty in production optimization should be related to the production profit.…”

Section: Description Of Model Parameter Uncertainty In Oil and Gas Prmentioning

confidence: 99%

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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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Section: Optimization For Oil and Gas Production Process Considering mentioning

confidence: 99%

Section: Description Of Model Parameter Uncertainty In Oil and Gas Prmentioning

confidence: 99%

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

et al. 2016

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“…Typically, processing facilities are modelled as a constant pressure node with constraints on oil, gas and water processing capacity (Bieker et al [13]). The model should be updated whenever the capacity changes.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Model-Based Production Optimization for a Gas Field in North Africa

Hoffmann¹

2018

J. Model. Optim.

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This paper presents a model-based optimization solution suitable for short-term production optimization of large gas fields with wells producing into a common surface network into a shared gas treatment plant. The proposed methodology is applied to a field consisting of one dry gas reservoir with a CO2 content of 7.3% and one wet gas reservoir with a CO2 content of 2.8% and initial CGR of 15 stb/MMscf. 23 wells are producing, and all gas production is processed in a common gas treatment plant where condensates and CO2 are extracted from the reservoir gas. The final sales gas must honor compositional constraints (CO2 content and heating value). The proposed solution consists of a bi-level optimization algorithm. A Mixed Integer Linear Programming (MILP) formulation of the optimization problem is solved, assuming some key parameters in the gas plant to be constant. Hydraulic performances of the system, approximated using SOS2 piecewise linear models, and condensates and CO2 extraction, captured using simplified models, are included in the MILP. After solving the MILP, the values of the key parameters are calculated using a full simulation model of the gas plant and the new values are substituted in the MILP input data. This iterative procedure continues until convergence is achieved. Results show that the proposed methodology can find the optimum choke openings for all wells to maximize the total gas rate while honoring numerous surface constraints. The solution runs in 30 sec. and an average of 3-4 iterations is needed to achieve convergence. It is therefore a suitable solution for short-term production optimization and daily operations.

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Challenges in Enterprise Wide Optimization for the Process Industries

Grossmann

Furman

2009

Springer Optimization and Its Applications

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Real-Time Production Optimization of Offshore Oil and Gas Production Systems: A Technology Survey

Cited by 35 publications

References 41 publications

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

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

Short-Term Model-Based Production Optimization for a Gas Field in North Africa

Challenges in Enterprise Wide Optimization for the Process Industries

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