Optimal producer well placement and production planning in an oil reservoir

Tavallali, M. Sadegh; Karimi, Iftekhar A.; Teo, Kwong Meng; Baxendale, David; Ayatollahi, Shahab

doi:10.1016/j.compchemeng.2013.04.002

Cited by 58 publications

(72 citation statements)

References 49 publications

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“…The authors develop a single-period model for a limited planning horizon of one week and consider gas quality specifications at delivery nodes. Tavallali et al 9 integrate critical elements of upstream oil production and spatiotemporal subsurface dynamics in a multi-period mathematical programming approach. Knudsen and Foss 10 consider late-life shale gas wells producing at low erratic rates due to reservoir depletion and liquid loading.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi‐period planning, design, and strategic models for long‐term, quality‐sensitive shale gas development

Drouven

Grossmann

2016

AIChE Journal

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In this work we address the long-term, quality-sensitive shale gas development problem. This problem involves planning, design, and strategic decisions such as where, when, and how many shale gas wells to drill, where to lay out gathering pipelines, as well as which delivery agreements to arrange. Our objective is to use computational models to identify the most profitable shale gas development strategies. For this purpose we propose a large-scale, nonconvex, mixed-integer nonlinear programming model. We rely on generalized disjunctive programming to systematically derive the building blocks of this model. Based on a tailor-designed solution strategy we identify near-global solutions to the resulting large-scale problems. Finally, we apply the proposed modeling framework to two case studies based on real data to quantify the value of optimization models for shale gas development. Our results suggest that the proposed models can increase upstream operators' profitability by several million U.S. dollars.

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Section: Literature Reviewmentioning

confidence: 99%

Multi‐period planning, design, and strategic models for long‐term, quality‐sensitive shale gas development

Drouven

Grossmann

2016

AIChE Journal

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“…Although it is primarily to determine the spatial location of the new drillings, it is sometimes upscaled to only a reservoir selection (for new drillings) which is due to the low resolution of the subsurface models employed. The only mathematical programming study that we know in this category is by Tavallali et al [9]. Well placement studies should also determine the optimal flow rates of the wells.…”

Section: Well Placementmentioning

confidence: 99%

“…Therefore (1) the subsurface conditions and (2) scheduling horizon contrast the studies in this category. However, the works of Tavallali et al [9,39] include production scheduling and higher level planning decisions for integrated surface network and subsurface formation. The reservoir dynamics is ignored in the first group, and surface operation, production scheduling, and flow-line routing over a short term are studied.…”

Section: Flow Schedulingmentioning

confidence: 99%

“…The novel methodologies such as the modified OA-ER-AP algorithm [9] for this non-continuous problem can be applied to problems from other application domains, particularly dynamic optimization problems that include some types of location/allocation/timing decisions, e.g. wind mill placement, underground water well placement, fin placement in heating and cooling problems, or locationallocation in coal bed methane developments.…”

Section: Research Opportunitiesmentioning

confidence: 99%

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Perspectives on the Design and Planning of Oil Field Infrastructure

Tavallali

Karimi

2014

Computer Aided Chemical Engineering

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“…However, most papers published before the 1980s did not pay enough attention to optimization algorithms, and successful applications were very rare (Aronofsky and Williams 1962;Wattenbarger 1970;McFarland et al 1984). With the advances in optimization algorithms and computing power, research has increased greatly since the 1980s (Sequeira et al 2002;Chacón et al 2004;Barragán et al 2005;Gunnerud and Foss 2010;Knudsen and Foss 2013;Tavallali et al 2013).…”

Section: Introductionmentioning

confidence: 99%

A review of closed-loop reservoir management

Hou

Zhou

Zhang³

et al. 2015

Pet. Sci.

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The closed-loop reservoir management technique enables a dynamic and real-time optimal production schedule under the existing reservoir conditions to be achieved by adjusting the injection and production strategies. This is one of the most effective ways to exploit limited oil reserves more economically and efficiently. There are two steps in closed-loop reservoir management: automatic history matching and reservoir production optimization. Both of the steps are large-scale complicated optimization problems. This paper gives a general review of the two basic techniques in closed-loop reservoir management; summarizes the applications of gradient-based algorithms, gradient-free algorithms, and artificial intelligence algorithms; analyzes the characteristics and application conditions of these optimization methods; and finally discusses the emphases and directions of future research on both automatic history matching and reservoir production optimization.

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Optimal producer well placement and production planning in an oil reservoir

Cited by 58 publications

References 49 publications

Multi‐period planning, design, and strategic models for long‐term, quality‐sensitive shale gas development

Multi‐period planning, design, and strategic models for long‐term, quality‐sensitive shale gas development

Perspectives on the Design and Planning of Oil Field Infrastructure

A review of closed-loop reservoir management

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