Process systems engineering perspective on the planning and development of oil fields

Tavallali, M. Sadegh; Karimi, Iftekhar A.; Baxendale, David

doi:10.1002/aic.15209

Cited by 44 publications

(28 citation statements)

References 135 publications

(236 reference statements)

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“…Optimisation problems of production systems over short and long term horizons have been formulated as MINLPs and MILPs in several published studies (Tavallali et al, 2016). These studies incorporate the flow dynamics of certain elements of the entire production system, thus making them differ significantly in complexity.…”

Section: Relevant Literaturementioning

confidence: 99%

A computational performance comparison of MILP vs. MINLP formulations for oil production optimisation

Epelle

Gerogiorgis

2020

Computers & Chemical Engineering

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Model-based oil production systems optimisation under pressure and facility routing constraints is a testing challenge, especially in presence of complex downhole wellbore phenomena (water coning, slugging, phase separation). Nonlinearities and nonconvexities from underlying physics and binary decisions exacerbate model complexity, yielding Mixed Integer Nonlinear Programs (MINLP). To guarantee solvability of optimisation formulations and reduce MINLP complexity, piecewise linearisation techniques based on Special Ordered Sets of type 2 (SOS2) constraints are developed towards approximating nonlinear functions and transforming models to Mixed Integer Linear Programs (MILP). Nevertheless, computational analyses of MILP vs. MINLP formulations for oil production optimisation are scarce. This study explores the benefits of an MILP reformulation applied to three case studies of varying complexity. We compare MILP model results to original MINLP formulation solutions with multiple solvers, evaluating the impact of the number of linearisation breakpoints used on solution time, accuracy, robustness, model development effort and ease of automation.

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Section: Relevant Literaturementioning

confidence: 99%

A computational performance comparison of MILP vs. MINLP formulations for oil production optimisation

Epelle

Gerogiorgis

2020

Computers & Chemical Engineering

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“…Tavallali et al have tackled well placement, production planning, and facility allocation optimization problems 3,[19][20][21] using an open-box approach which involves a problem-specific modification of the outer approximation algorithm proposed by Grossmann and coworkers. [22][23][24] Furthermore, long-term infrastructure planning and production schedul- formulations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Tavallali et al have tackled well placement, production planning, and facility allocation optimization problems using an open‐box approach which involves a problem‐specific modification of the outer approximation algorithm proposed by Grossmann and coworkers . Furthermore, long‐term infrastructure planning and production scheduling challenges have also been addressed in several publications by Grossmann and coworkers via complex Mixed‐Integer Linear Programming (MILP) and Mixed‐Integer Nonlinear Programming (MINLP) formulations .…”

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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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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“…Tavallali et al 21,22 have performed perspective studies on the O&G industry from the process systems engineering point of view.…”

Section: Literature Reviewmentioning

confidence: 99%

Supply chain networks servicing upstream operations in oil and gas fields after the shale revolution

Montagna

Cafaro

2019

AIChE Journal

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The recent irruption of shale resources in the oil and gas (O&G) industry has dramatically changed the paradigm for managing upstream operations. Unconventional production is largely driven by drilling new wells, yielding a much larger scale of material and service flows that need to be efficiently planned. This article presents an optimization framework for the design of integrated supply chain networks (SCNs) servicing upstream operations. Novel aspects addressed in this work are the integrated planning of material and service supplies; the economies of scale in operating, transportation and capital expenditures; the concept of waiting cost to quantify production losses; and the reverse flow of materials within closed‐loop supply chains. The merits of the proposed approach are assessed through real‐world case studies from the Argentine O&G industry. Optimal designs yield significant cost savings and confirm that the integrated planning of the SCN is critical for O&G operators after the shale revolution.

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Process systems engineering perspective on the planning and development of oil fields

Cited by 44 publications

References 135 publications

A computational performance comparison of MILP vs. MINLP formulations for oil production optimisation

A computational performance comparison of MILP vs. MINLP formulations for oil production optimisation

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

Supply chain networks servicing upstream operations in oil and gas fields after the shale revolution

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