Simultaneous and sequential approaches to joint optimization of well placement and control

Humphries, Thomas; Haynes, Ronald D.; James, Lesley

doi:10.1007/s10596-013-9375-x

Cited by 80 publications

(63 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically for optimal well placement problems, due to complex reservoir geometry and heterogeneity in reservoir rock properties, objective functions tend to be highly irregular with multiple local optima in the parameter space (Bayer and Finkel, 2004;Humphries et al, 2013). Global optimization methods that involve derivative-free algorithms are generally preferred to solve optimization problems including optimal well placement.…”

Section: Introductionmentioning

confidence: 99%

Optimal well placement and brine extraction for pressure management during CO2 sequestration

Cihan

Birkhölzer

Bianchi

2015

International Journal of Greenhouse Gas Control

View full text Add to dashboard Cite

a b s t r a c tLarge-scale pressure increases resulting from carbon dioxide (CO 2 ) injection in the subsurface can potentially impact caprock integrity, induce reactivation of critically stressed faults, and drive CO 2 or brine through conductive features into shallow groundwater. Pressure management involving the extraction of native fluids from storage formations can be used to reduce such pressure increases. However, dealing with large volumes of extracted brine can be technically challenging and expensive. Selection of optimal well locations and pumping rates are critical for maximizing CO 2 storage and minimizing brine extraction during geologic CO 2 sequestration (GCS). Robust and efficient computerized algorithms combining reservoir models and optimization methods are needed to make proper decisions on well placement and pumping rates. This study presents a constrained differential evolution (CDE) algorithm for solving global optimization problems involving pressure management of GCS projects. Application of the CDE optimization methodology was demonstrated for a hypothetical CO 2 storage scenario in a deep sandstone reservoir in the Southern San Joaquin Basin in California, USA. Industrial-scale storage of CO 2 would generate significant pressure buildup in this formation, which in turn would raise concerns about induced seismicity due to presence of multiple faults surrounding the injection site. Through the CDE optimization algorithm coupled to a vertically-averaged reservoir simulator, we successfully estimated optimal solutions for brine extraction wells in the reservoir that would limit the local pressure along the faults to a prescribed threshold. Multiple realizations of the reservoir permeability field were created to understand the impact of reservoir heterogeneity on optimization results. Our results indicate that the reservoir slope and heterogeneity have significant impact on optimum extraction rates. Reservoir heterogeneity is also a significant factor for extraction well locations, suggesting that in practice decisions about extraction well placement through optimization should be made at later project stages when data from a few years of CO 2 injection have allowed iterative updating and refining of the reservoir forward models. Although the study focused on optimization of brine extraction, the CDE optimization methodology presented in this paper has also potential to solve other complex optimization problems related to GCS, such as increasing storage efficiency by enhancing injectivity and capillary and dissolution trapping.Published by Elsevier Ltd.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal well placement and brine extraction for pressure management during CO2 sequestration

Cihan

Birkhölzer

Bianchi

2015

International Journal of Greenhouse Gas Control

View full text Add to dashboard Cite

show abstract

“…This makes coupled optimization difficult to implement, particularly on larger fields and models. Humphries et al [125] compared sequential and simultaneous solutions to the coupled well placement and well control problem. This study found that solving the coupled problem separately often led to better solutions than the simultaneous solution, potentially because the smaller solution space of the decoupled problem makes finding good solutions easier.…”

Section: Coupled Well Placement and Well Control Optimizationmentioning

confidence: 99%

Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms

et al. 2017

View full text Add to dashboard Cite

This paper presents a review of history matching and oil field development optimization techniques with a focus on optimization algorithms. History matching algorithms are reviewed as a precursor to production optimization algorithms. Techniques for history matching and production optimization are reviewed including global and local methods. Well placement, well control, and combined well placement-control optimization using both secondary and tertiary oil production techniques are considered. Secondary and tertiary recovery techniques are commonly referred to as waterflooding and enhanced oil recovery (EOR), respectively. Benchmark models for comparison of methods are summarized while other applications of methods are discussed throughout. No single optimization method is found to be universally superior. Key areas of future work are combining optimization methods and integrating multiple optimization processes. Current challenges and future research opportunities for improved model validation and large scale optimization algorithms are also discussed.

show abstract

“…However, joint optimization results in a higher computational cost than the sequential algorithm. Humphries et al (2013) compared both the simultaneous and the sequential optimization approaches for unconstrained (in terms of production and injection rates) and constrained problems. A hybrid algorithm that combined the global search capabilities of PSO and the local search characteristic of GPS was used for the simultaneous optimization case.…”

Section: Joint Optimization Problemmentioning

confidence: 99%

“…As pointed out in the results of the previous studies, solving the joint optimization problem is computationally expensive even for the case of vertical wells, e.g., from few thousands in the Bellout et al (2012) algorithm to more than ten thousand simulation runs in the Humphries et al (2013) algorithm. In this paper, we investigate the joint optimization of well trajectories and controls using simultaneous and sequential algorithms.…”

Section: Joint Optimization Problemmentioning

confidence: 99%

A Covariance Matrix Adaptation Algorithm for Simultaneous Estimation of Optimal Placement and Control of Production and Water Injection Wells

Forouzanfar

Poquioma

Reynolds

2015

SPE Reservoir Simulation Symposium

View full text Add to dashboard Cite

In this paper, we present both simultaneous and sequential algorithms for the joint optimization of well trajectories and their lifecycle controls. The trajectory of a well is parameterized in terms of six variables which define a straight line in 3D. In the simultaneous joint optimization algorithm, the set of controls of a well throughout the life-cycle of the reservoir is parameterized in terms of the singular vectors corresponding to the largest singular values of a pre-specified temporal covariance matrix which imposes a temporal correlation on the controls at each well. In this approach, well controls are parameterized in terms of a few optimization parameters which significantly reduces the dimension of the joint optimization problem. Moreover, the imposed smoothness on the well controls will result in temporally smooth well controls. We use an implementation of the Covariance Matrix Adaptation -Evolution Strategy (CMA-ES) optimization algorithm to solve the defined optimization problem. In the sequential optimization algorithm, first the trajectories of the wells are optimized using CMA-ES optimization algorithm while the controls of the wells are pre-specified. Once the optimum trajectories of the wells are obtained, the life cycle production optimization step is performed in order to find the optimal well controls for the specified well trajectories. For the production optimization step, we compare the performance of three production optimization algorithms which are: the standard EnOpt algorithm, the standard CMA-ES algorithm for production optimization and, a variant of the CMA-ES algorithm for production optimization in which we set the initial covariance matrix equal to a pre-specified covariance which imposes a temporal correlation on the controls of each well. The performance of the proposed algorithms is tested for the joint optimization of well trajectories and controls of injectors and producers for the PUNQ reservoir model. The proposed simultaneous well placement-well control optimization algorithm obtained slightly better results than the sequential optimization framework. CMA-ES algorithm showed good performance for both well placement and production optimization purposes. Moreover, the CMA algorithm with a pre-specified covariance that imposes a temporal correlation on the well controls showed a superior performance compared to EnOpt for the life cycle production optimization step of the sequential framework. General well placement optimization problemWell placement optimization is the key element in the decision making process for preparing the optimal development plan for a reservoir. To solve a "general well placement optimization" problem, the number of wells, their trajectory and controls should be optimized in order to maximize a prescribed objective function, which is typically the net present value (NPV) of the hydrocarbon production from the reservoir. However, very few papers have addressed this general problem as it is too computationally expensive. Yeten et al. (2002) proposed ...

show abstract

Simultaneous and sequential approaches to joint optimization of well placement and control

Cited by 80 publications

References 37 publications

Optimal well placement and brine extraction for pressure management during CO2 sequestration

Optimal well placement and brine extraction for pressure management during CO2 sequestration

Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms

A Covariance Matrix Adaptation Algorithm for Simultaneous Estimation of Optimal Placement and Control of Production and Water Injection Wells

Contact Info

Product

Resources

About