Optimization of Nonconventional Well Type, Location, and Trajectory

Yeten, B.; Durlofsky, Louis J.; Aziz, Khalid

doi:10.2118/86880-pa

Cited by 271 publications

(97 citation statements)

References 14 publications

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“…Because of the inherent uncertainty in the geological characterization of the subsurface, a non-deterministic approach is necessary. Robust life-cycle optimization uses one or more ensembles of geological realizations (reservoir models) to account for uncertainties and to determine the production strategy that maximizes a given objective function over the ensemble (see, e.g., Yeten et al [21] or Van Essen et al [20]). …”

Section: Robust Optimizationmentioning

confidence: 99%

Value of information in closed-loop reservoir management

Barros

Hof

2015

Comput Geosci

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This paper proposes a new methodology to perform value of information (VOI) analysis within a closedloop reservoir management (CLRM) framework. The workflow combines tools such as robust optimization and history matching in an environment of uncertainty characterization. The approach is illustrated with two simple examples: an analytical reservoir toy model based on decline curves and a water flooding problem in a two-dimensional fivespot reservoir. The results are compared with previous work on other measures of information valuation, and we show that our method is a more complete, although also more computationally intensive, approach to VOI analysis in a CLRM framework. We recommend it to be used as the reference for the development of more practical and less computationally demanding tools for VOI assessment in real fields.

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Section: Robust Optimizationmentioning

confidence: 99%

Value of information in closed-loop reservoir management

Barros

Hof

2015

Comput Geosci

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“…Determining optimal drilling locations for production and injection wells in an oil reservoir is a problem of considerable industrial interest (see for instance Yeten et al (2003); Bangerth et al (2006); Onwunalu and Durlofsky (2010)). The variables in this problem correspond to positional parameters for each well; in this example, we will consider only vertical wells, each of which can be parameterized by its (x 1 , x 2 ) co-ordinates, representing a grid location in the discrete reservoir model.…”

Section: Oil Reservoir Simulator Examplementioning

confidence: 99%

Efficient optimization of the likelihood function in Gaussian process modelling

Butler

Haynes

Humphries

et al. 2014

Computational Statistics & Data Analysis

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Gaussian Process (GP) models are popular statistical surrogates used for emulating computationally expensive computer simulators. The quality of a GP model fit can be assessed by a goodness of fit measure based on optimized likelihood. Finding the global maximum of the likelihood function for a GP model is typically very challenging as the likelihood surface often has multiple local optima, and an explicit expression for the gradient of the likelihood function is typically unavailable. Previous methods for optimizing the likelihood function (e.g., MacDonald et al. (2013)) have proven to be robust and accurate, though relatively inefficient. We propose several likelihood optimization techniques, including two modified multi-start local search techniques, based on the method implemented by MacDonald et al. (2013), that are equally as reliable, and significantly more efficient. A hybridization of the global search algorithm Dividing Rectangles (DIRECT) with the local optimization algorithm BFGS provides a comparable GP model quality for a fraction of the computational cost, and is the preferred optimization technique when computational resources are limited. We use several test functions and a real application from an oil reservoir simulation to test and compare the performance of the proposed methods with the one implemented by MacDonald et al. (2013) in the R library GPfit. The proposed method is implemented in a Matlab package, GPMfit.

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“…Y is the number of discount periods (years). C d is the drilling and completing cost for all wells defined based on the approximate formula used by Yeten (2003) …”

Section: Problem Definitionmentioning

confidence: 99%

“…Hence, the well placement optimization problem is nowadays a major focus in the petroleum industry. Many studies already investigated the well placement using different optimization methods: stochastic methods such as genetic algorithms (e.g., Bukhamsin et al, 2010, Emerick et al, 2009, Guyaguler and Horne, 2000, Montes et al, 2001, Morales et al, 2010, Yeten et al, 2003) and deterministic methods in particular adjoint methods (e.g., Sarma & Chen, 2008, Forouzanfar et al, 2010, Zandvliet et al, 2008, Vlemmix et al, 2009). …”

Section: Introductionmentioning

confidence: 99%

Partially Separated Metamodels With Evolution Strategies for Well-Placement Optimization

Bouzarkouna¹,

Ding

Auger

2013

SPE Journal

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Finding the optimal location of non-conventional wells increases significantly the project's Net Present Value (NPV). This problem is nowadays one of the most challenging problems in oil and gas fields development. When dealing with complex reservoir geology and high reservoir heterogeneities, stochastic optimization methods are the most suitable approaches for optimal well placement. However, these methods require in general a considerable computational effort (in terms of number of reservoir simulations, which are CPU time demanding). This paper presents the use of the CMA-ES (Covariance Matrix Adaptation -Evolution Strategy) optimizer, which is recognized as one of the most powerful derivative free optimizers, to optimize well locations and trajectories. A local regression based meta-model is incorporated into the optimization process in order to reduce the computational cost. The objective function (e.g., the NPV) can usually be split into local components referring to each of the wells: it depends in general on a smaller number of principal parameters, and thus can be modeled as a partially separable function. In this paper, we propose to exploit the partial separability of the objective function into CMA-ES coupled with meta-models, by building partially separated meta-models. Thus, different meta-models are built for each well or set of wells, which results in a more accurate modeling.An example is presented. Results show that taking advantage of the partial separability of the objective function leads to a significant decrease in the number of reservoir simulations needed to find the "optimal" well configuration, given a restricted budget of reservoir simulations. This approach is practical and promising when dealing with a large number of wells to be located. IntroductionNowadays, the environment and the conditions in which oil and gas fields are discovered are more and more complex and challenging. Both existing fields and new discoveries need an optimal production scheme to be economically viable. One of the most important issues that must be addressed by reservoir engineers to maximize a given project's asset value is to optimally decide where to drill wells. Hence, the well placement optimization problem is nowadays a major focus in the petroleum industry. Many studies already investigated the well placement using different optimization methods: stochastic methods such as genetic algorithms (e.g., Bukhamsin et al., 2010, Emerick et al., 2009, Guyaguler and Horne, 2000, Montes et al., 2001, Morales et al., 2010, Yeten et al., 2003) and deterministic methods in particular adjoint methods (e.g., Sarma & Chen, 2008, Forouzanfar et al., 2010, Zandvliet et al., 2008, Vlemmix et al., 2009).Stochastic optimization methods were shown to be an appropriate approach for the well placement problem given the multiple local optima and the non-smoothness of the objective function. A promising method is the Covariance Matrix AdaptationEvolution Strategy (CMA-ES), a state of the art stochastic optimizer first...

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Optimization of Nonconventional Well Type, Location, and Trajectory

Cited by 271 publications

References 14 publications

Value of information in closed-loop reservoir management

Value of information in closed-loop reservoir management

Efficient optimization of the likelihood function in Gaussian process modelling

Partially Separated Metamodels With Evolution Strategies for Well-Placement Optimization

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