Adjoint-based well placement optimisation for Enhanced Oil Recovery (EOR) under geological uncertainty: From seismic to production

Epelle, Emmanuel I.; Gerogiorgis, Dimitrios I.

doi:10.1016/j.petrol.2020.107091

Cited by 16 publications

(3 citation statements)

References 42 publications

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“…Other studies which directly incorporate details of the reservoir's conditions in their model apply both gradient-based and derivative-free methods for optimising water flooding operations, especially in the context of closed-loop reservoir management (Chen et al, 2010;Asadollahi et al, 2012;Epelle and Gerogiorgis, 2020). However, the focus herein is the fast-paced dynamics of the wells, manifolds, pipelines and separator components of the production system.…”

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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“…Water flooding plays an important role in field development; − however, the oil production of many oilfields has declined and entered a high water-cut period in recent years. − To increase the production of high water-cut oilfields while reducing the production cost at a low oil price is an urgent problem. − For example, the PL oilfield in China has many vertical layers and strong heterogeneity. The single-layer water emergence and water content increase rapidly due to the initial joint injection and recovery with large cross-sections.…”

Section: Introductionmentioning

confidence: 99%

Optimization Method of Three-Dimensional Equilibrium Displacement in Thin Interbed Reservoirs

Jiang,

Cheng,

et al. 2023

ACS Omega

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Most thin interbed reservoirs face a common problem that a nonequilibrium injection and production relationship in plane and vertical directions results in quick water breakthrough, rapid water-cut rise, and a poor water flooding efficiency in a single layer. A finer injection-production strategy should be developed to avoid serious water channeling and an ineffective water cycle. To narrow this gap, this work presents a three-dimensional intelligent equilibrium displacement model (3D-IEDM) to optimize water flooding in thin interbed reservoirs. A water-injection splitting model is first established to determine the water-injection rate of each layer based on displacement pressure and flow resistance. Then, water saturation is calculated for the injection-production well group based on the material balance principle. To achieve three-dimensional equilibrium flooding, the minimum water saturation variance is chosen as the optimization target and the improved particle swarm optimization algorithm is employed to reduce the optimization time caused by iterative calculations. Finally, the 3D-IEDM is programmed as software to provide a quantitative equilibrium flooding optimization scheme in an actual oilfield. The implementation in the pilot B36 well group test of the PL oilfield indicates that the optimization velocity of the 3D-IEDM can optimize the vertical water injection profile of thin interbed reservoirs and improve the sweep efficiency, and the length of time is approximately 14 times less than that of conventional simulator-based methods. Compared with the conventional injection-production scheme, the initial productivity of the pilot well group using the 3D-IEDM increases by 6.45%, and the utilization factor of water injection improves by 15%.

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“…Several gradient-based methods have been devised and utilized to solve different optimization problems such as well placement and control problems (Hutahaean et al, 2019;Liu et al, 2019;Sun et al, 2019;Epelle and Gerogiorgis, 2020). In terms of efficiency and accuracy, the adjoint method is ranked number one on the list of gradient-based methods because of its efficient and accurate gradient computation.…”

Section: Introductionmentioning

confidence: 99%

Efficient Optimization and Robust Value Quantification of Enhanced Oil Recovery Strategies

Oguntola¹

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With an increasing demand for hydrocarbon reservoir produces such as oil, etc., and difficulties in finding green oil fields, the use of Enhanced Oil Recovery (EOR) methods such as polymer, Smart water, and solvent flooding for further development of existing fields can not be overemphasized. For reservoir profitability and reduced environmental impact, it is crucial to consider appropriate well control settings of EOR methods for given reservoir characterization. Moreover, finding appropriate well settings requires solving a constrained optimization problem with suitable numerical solution methods. Conventionally, the solution method requires many iterations involving several computationally demanding function evaluations before convergence to the appropriate near optimum. The major subject of this thesis is to develop an efficient and accurate solution method for constrained optimization problems associated with EOR methods for their value quantifications and ranking in the face of reservoir uncertainties. The first contribution of the thesis develops a solution method based on the inexact line search method (with Ensemble Based Optimization (EnOpt) for approximate gradient computation) for robust constrained optimization problems associated with polymer, Smart water, and solvent flooding. Here, the objective function is the expectation of the Net Present Value (NPV) function over given geological realizations. For a given set of well settings, the NPV function is defined based on the EOR simulation model, which follows from an appropriate extension of the black-oil model. The developed solution method is used to find the economic benefits and also the ranking of EOR methods for different oil reservoirs developed to mimic North Sea reservoirs. Performing the entire optimization routine in a transformed domain along with truncations has been a common practice for handling simple linear constraints in reservoir optimization. Aside from the fact that this method has a negative impact on the quality of gradient computation, it is complicated to use for non-linear constraints. The second contribution of this thesis proposes a technique based on the exterior penalty method for handling general linear and non-linear constraints in reservoir optimization problems to improve gradient computation quality by the EnOpt method for efficient and improved optimization algorithm. Because of the computationally expensive NPV function due to the costly reservoir simulation of EOR methods, the solution method for the underlying EOR optimization problem becomes inefficient, especially for large reservoir problems. To speedup the overall computation of the solution method, this thesis introduces a novel full order model (FOM)-based certified adaptive machine learning optimization procedures to locally approximate the expensive NPV function. A supervised feedforward deep neural network (DNN) algorithm is employed to locally create surrogate model. In the FOM-based optimization algorithm of this study, several FOM NPV function evaluations are required by the EnOpt method to approximate the gradient function at each (outer) iteration until convergence. To limit the number FOM-based evaluations, we consider building surrogate models locally to replace the FOM based NPV function at each outer iteration and proceed with an inner optimization routine until convergence. We adapt the surrogate model using some FOM-based criterion where necessary until convergence. The demonstration of methodology for polymer optimization problem on a benchmark model results in an improved optimum and found to be more efficient compared to using the full order model optimization procedures.

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Adjoint-based well placement optimisation for Enhanced Oil Recovery (EOR) under geological uncertainty: From seismic to production

Cited by 16 publications

References 42 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

Optimization Method of Three-Dimensional Equilibrium Displacement in Thin Interbed Reservoirs

Efficient Optimization and Robust Value Quantification of Enhanced Oil Recovery Strategies

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