Meeting the Challenges of Real Time Production Optimization— A Parametric Model-based Approach

Awasthi, Ankur; Nikolaou, Michael; Saputelli, Luigi; Mijares, Gerardo

doi:10.2118/111853-ms

Cited by 9 publications

(4 citation statements)

References 28 publications

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“…Nikolaou and coworkers have extensively addressed the dire need to bridge the gap between petroleum reservoir modeling and realtime production optimization, exploring successfully several fruitful ideas, most notably the moving-horizon concept, and the parametric and automated adaptive modeling approaches. [10][11][12][13][14] Gunnerud et al proposed a disaggregated framework for optimizing an entire production network over a short-term horizon at steady state conditions. 2 This computational framework which is also adopted in the present article analyses the entire system as a combination of wells, pipelines, and separators.…”

Section: Literature Reviewmentioning

confidence: 99%

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Optimal rate allocation for production and injection wells in an oil and gas field for enhanced profitability

Epelle¹,

Gerogiorgis²

2019

AIChE Journal

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An oil and gas field requires careful operational planning and management via production optimisation for increased recovery and long-term project profitability. This paper addresses the challenge of production optimisation in a field undergoing secondary recovery by water flooding. The field operates with limited processing capacity at the surface separators, pipeline pressure constraints and water injection constraints; an economic indicator (Net Present Value -NPV) is used as the objective function. The formulated optimisation framework adequately integrates slow-paced subsurface dynamics using reservoir simulation and the fast-paced surface dynamics using sophisticated multiphase flow simulation in the upstream facilities. Optimisation of this holistic longterm model is made possible by developing accurate second order polynomial proxy models at each time step. The resulting formulation is solved as a Nonlinear Program (NLP) using commercially available solvers. A comparative analysis is performed using MATLAB's fmincon solver and the IPOPT solver for their robustness, speed and convergence stability in solving the proposed problem.By implementing 2 synthetic case studies, our mathematical programming approach determines the optimal production and injection rates of all wells and further demonstrates considerable improvement to the NPV obtained by simultaneously applying the tools of streamline, reservoir and surface facility simulation for well rate allocation.

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Section: Literature Reviewmentioning

confidence: 99%

“…Nikolaou and coworkers have extensively addressed the dire need to bridge the gap between petroleum reservoir modeling and real‐time production optimization, exploring successfully several fruitful ideas, most notably the moving‐horizon concept, and the parametric and automated adaptive modeling approaches …”

Section: Literature Reviewmentioning

confidence: 99%

Optimal rate allocation for production and injection wells in an oil and gas field for enhanced profitability

Epelle¹,

Gerogiorgis²

2019

AIChE Journal

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“…al [2,3] discretize and linearize the full model to arrive at a linear time dependent state space model:…”

Section: Figure 4 Scheme For Selecting Appropriate Model Type For Cmentioning

confidence: 99%

The Use of Model Predictive Control for Asset Production Optimization: Application to a Thin-Rim Oil Field Case

Linden¹,

Leemhuis²

2010

All Days

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Increasingly the upstream oil & gas industry is using active flow control (e.g. feedback loops) or passive flow control (e.g. passive ICD) technologies to optimize asset production. They are used, for example, to commingle production, stabilize production in case of water or gas breakthrough, and to mitigate the effect of slugging in wells. While the merits of such flow control technologies are becoming clear, so do their limitations.One main limitation of using traditional flow control technologies is that, in the presence of complex and changing process dynamics, it is difficult to come up with a controller design that optimizes asset production. Advanced control, specifically Model Predictive Control (MPC), is widely used in the industry to optimize complex downstream processes. In this paper we will start to explore the merits of MPC for upstream applications by means of a realistic test case of a thin oil rim that suffers from gas breakthrough. In the paper three aspects of MPC will be explored into detail.Firstly, we take a look at the dynamic process models, which lie at the basis of MPC. In MPC the dynamic model is used to compute control actions that optimize the future behavior of the production process. In order to do this, the model has to be sufficient accurate and needs to have a computational burden that allows for real-time optimization. We will discuss how a mixture of physical modeling and data driven models may be used for this purpose.Secondly, we give concrete examples of such fast dynamic models for the production process of the thin oil rim case. The models describe the dynamic behavior of the gas cone in the reservoir, as well as the effect that gas breakthrough has on well performance. Using such a model, we will evaluate 2 different thin oil rim production strategies that are based on downhole and topside flow control.Thirdly, we develop a production strategy using the MPC framework. In this approach the dynamic well-reservoir model is used to determine optimal settings of wellhead and ICD valve positions that optimize production. This advanced production control production strategy is benchmarked in terms Net Present Value (NPV) and barrels produced, and compared to cases that use only conventional feedback wellhead flow control. Production improvement in oil production over the first 1.5 years is 17% in the test case.Since beginning of the 1990's, starting in refineries and base chemical plants, MPC has become the standard advanced control methodology in downstream industries, putting more process knowledge (models) into production control and giving rise to optimal production. A similar development may be expected in the upstream industry. As the paper shows MPC has the potential for bringing real-time production optimization a step closer.

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“…In 2006, the method combining empirical formula with optimization theory is proposed by Saputelli [3,4], Elgsaeter [5], and Awasthi [6] and etc. Their goal focused on the short-term optimization of production and gained the optimized development program in time with convenient empirical formula.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Real-time Optimization of Reservoir Production

Zhang¹,

Ye²,

Zhang³

et al. 2011

JCP

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Water flooding is a common technology of enhanced oil recovery and has been widely used to the world's oil fields. But it is difficult to maintain a uniform displacement of water, which causes the ineffective circulation of injected water. Therefore,  the real-time dynamic control strategy is studied to work out development plans scientifically and make better economic profits in this paper. The net present value of crude oil exploitation during a certain period of time is choosen to performance index of the control problem. Hydrodynamic equations of oil, water and gas, which describe the displacement process, are used as governed equations. Meanwhile, the boundary constraints of wells and balance relationship of injection-production are taken into account. Based on the above equations, a control model of oil field development is established. The control vector parameterization method is illustrated in detail to deal with the unconstrained, control constrained and state constrained optimal control problem. <span style="font-size: 10pt; color: black; font-family: "Times New Roman","serif"; mso-fareast-font-family: 宋体; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">Finally, <span style="font-size: 10pt; color: black; font-family: "TimesNewRoman","serif"; mso-fareast-font-family: 宋体; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-bidi-font-family: TimesNewRoman;" lang="EN-US">a simulation example is employed to verify the validity of the proposed control algorithm.

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Meeting the Challenges of Real Time Production Optimization— A Parametric Model-based Approach

Cited by 9 publications

References 28 publications

Optimal rate allocation for production and injection wells in an oil and gas field for enhanced profitability

Optimal rate allocation for production and injection wells in an oil and gas field for enhanced profitability

The Use of Model Predictive Control for Asset Production Optimization: Application to a Thin-Rim Oil Field Case

Dynamic Real-time Optimization of Reservoir Production

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