HOPSPACK 2.0 user manual.

Plantenga, Todd

doi:10.2172/1000278

Cited by 49 publications

(38 citation statements)

References 6 publications

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“…In this work, the well placement problem is solved using HJDS, GPS, and a hybrid optimization parallel search package (HOPSPACK; Plantenga, 2009). HOPSPACK is a distributed computing implementation of GPS which can be run in a so-called asynchronous mode to balance the computational load of each node in the cluster (Plantenga, 2009).…”

Section: Well Placement Optimizationmentioning

confidence: 99%

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Joint optimization of oil well placement and controls

et al. 2012

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In this thesis we optimize the drilling location and operational controls of wells in a joint manner to improve the overall development strategy for a petroleum field. In particular, in this thesis we treat the integrated problem of searching for an improved well placement configuration while also taking into account the control settings of the production and/or injector wells planned for the development of the hydrocarbon asset. In oil field development, the well placement and well control problems are commonly performed in a sequential manner. However, this type of sequential approach cannot be expected to yield optimal solutions because it relies on handling well production controls using heuristic techniques during the well placement part of the procedure. In this work, we develop a nested (joint) optimization approach that seeks to capture the interdependency between the well configuration and the associated controls during the optimization search.This thesis summarizes the development of the joint approach; from establishing the methodology while using relatively simple cases and performing thorough comparisons against sequential approaches, to further extending and finally testing the methodology using a real field case model. This progression naturally divides the work in this thesis into two parts with different research focus. The first part of this work (Chapter 2) focuses chiefly on creating proper definitions and on establishing the proposed methodology against common approaches. The second part of this thesis (Chapters 3 and 4), on the other hand, focuses mainly on applying the developed methodology within a real field case scenario involving the North Sea Martin Linge oil reservoir. The dual aim of this application work is both to further develop the methodology, and to produce and test optimization solutions that may serve as decision-support to engineering efforts within the development work process of the Martin Linge field.Chapter 2 establishes the core of the methodology followed in this thesis. This chapter introduces the joint and sequential approaches as different ways to solve for the coupled well placement and control problem. The joint approach embeds the well control optimization within the search for optimum well placement configurations. Derivativefree methods based on pattern search are used to solve for the well-positioning part of the problem, while the well control optimization is solved by sequential quadratic programming using gradients efficiently computed through adjoints. Compared to reasonable sequential approaches, the joint optimization yields a significant increase in net present value of up to 20%. Compared to the sequential procedures, though, the joint approach requires about an order of magnitude increase in the total number of reservoir simulations performed during optimization. This increase, however, is somewhat mitigated by i the parallel implementation of some of the pattern search algorithms used in this work.Chapter 3 focuses on extending and applying ...

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Section: Well Placement Optimizationmentioning

confidence: 99%

“…HOPSPACK is a distributed computing implementation of GPS which can be run in a so-called asynchronous mode to balance the computational load of each node in the cluster (Plantenga, 2009). In asynchronous mode, HOPSPACK avoids "idle" cores by continuously sending new polling points for evaluation.…”

Section: Well Placement Optimizationmentioning

confidence: 99%

Joint optimization of oil well placement and controls

et al. 2012

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“…We compared our algorithm against a C ++ implementation of an Augmented Lagrangian derivative-free algorithm [22], called HOPSPACK [30], and a Fortran implementation of DFO. For now on, Algorithm 1.1, with the implementation described in the previous section, will be called Skinny.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…We compared the new method against two well-established derivativefree algorithms: the first is based on Augmented Lagrangians [22,30] and the second one is supported on trust-region ideas [11,12]. In principle, these algorithms are able to handle thin feasible regions.…”

Section: Final Remarksmentioning

confidence: 99%

Constrained derivative-free optimization on thin domains

Martínez

Sobral

2012

J Glob Optim

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Many derivative-free methods for constrained problems are not efficient for minimizing functions on "thin" domains. Other algorithms, like those based on Augmented Lagrangians, deal with thin constraints using penalty-like strategies. When the constraints are computationally inexpensive but highly nonlinear, these methods spend many potentially expensive objective function evaluations motivated by the difficulties of improving feasibility. An algorithm that handles efficiently this case is proposed in this paper. The main iteration is splitted into two steps: restoration and minimization. In the restoration step the aim is to decrease infeasibility without evaluating the objective function. In the minimization step the objective function f is minimized on a relaxed feasible set. A global minimization result will be proved and computational experiments showing the advantages of this approach will be presented.

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“…This was accomplished by using HOPSPACK [33], a Hybrid Optimization Parallel Search PACKage developed at Sandia. HOPSPACK is a computer program that solves derivative-free optimization problems using an open source, C++ software framework.…”

Section: Numentioning

confidence: 99%

Transient PVT measurements and model predictions for vessel heat transfer. Part II.

Report¹,

Rice²,

Paradiso³

et al. 2010

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Part I of this report focused on the acquisition and presentation of transient PVT data sets that can be used to validate gas transfer models. Here in Part II we focus primarily on describing models and validating these models using the data sets. Our models are intended to describe the high speed transport of compressible gases in arbitrary arrangements of vessels, tubing, valving and flow branches. Our models fall into three categories: (1) network flow models in which flow paths are modeled as one-dimensional flow and vessels are modeled as single control volumes, (2) CFD (Computational Fluid Dynamics) models in which flow in and between vessels is modeled in three dimensions and (3) coupled network/CFD models in which vessels are modeled using CFD and flows between vessels are modeled using a network flow code. In our work we utilized NETFLOW as our network flow code and FUEGO for our CFD code. Since network flow models lack three-dimensional resolution, correlations for heat transfer and tube frictional pressure drop are required to resolve important physics not being captured by the model. Here we describe how vessel heat transfer correlations were improved using the data and present direct model-data comparisons for all tests documented in Part I. Our results show that our network flow models have been substantially improved. The CFD modeling presented here describes the complex nature of vessel heat transfer and for the first time demonstrates that flow and heat transfer in vessels can be modeled directly without the need for correlations. 4 ACKNOWLEDGMENTS

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HOPSPACK 2.0 user manual.

Cited by 49 publications

References 6 publications

Joint optimization of oil well placement and controls

Joint optimization of oil well placement and controls

Constrained derivative-free optimization on thin domains

Transient PVT measurements and model predictions for vessel heat transfer. Part II.

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