共查询到20条相似文献，搜索用时 31 毫秒
1.
Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models
The amount of hydrocarbon recovered can be considerably increased by finding optimal placement of non-conventional wells.
For that purpose, the use of optimization algorithms, where the objective function is evaluated using a reservoir simulator,
is needed. Furthermore, for complex reservoir geologies with high heterogeneities, the optimization problem requires algorithms
able to cope with the non-regularity of the objective function. In this paper, we propose an optimization methodology for
determining optimal well locations and trajectories based on the covariance matrix adaptation evolution strategy (CMA-ES)
which is recognized as one of the most powerful derivative-free optimizers for continuous optimization. In addition, to improve
the optimization procedure, two new techniques are proposed: (a) adaptive penalization with rejection in order to handle well
placement constraints and (b) incorporation of a meta-model, based on locally weighted regression, into CMA-ES, using an approximate
stochastic ranking procedure, in order to reduce the number of reservoir simulations required to evaluate the objective function.
The approach is applied to the PUNQ-S3 case and compared with a genetic algorithm (GA) incorporating the Genocop III technique
for handling constraints. To allow a fair comparison, both algorithms are used without parameter tuning on the problem, and
standard settings are used for the GA and default settings for CMA-ES. It is shown that our new approach outperforms the genetic
algorithm: It leads in general to both a higher net present value and a significant reduction in the number of reservoir simulations
needed to reach a good well configuration. Moreover, coupling CMA-ES with a meta-model leads to further improvement, which
was around 20% for the synthetic case in this study. 相似文献
2.
Obiajulu J. Isebor Louis J. Durlofsky David Echeverría Ciaurri 《Computational Geosciences》2014,18(3-4):463-482
In oil field development, the optimal location for a new well depends on how it is to be operated. Thus, it is generally suboptimal to treat the well location and well control optimization problems separately. Rather, they should be considered simultaneously as a joint problem. In this work, we present noninvasive, derivative-free, easily parallelizable procedures to solve this joint optimization problem. Specifically, we consider Particle Swarm Optimization (PSO), a global stochastic search algorithm; Mesh Adaptive Direct Search (MADS), a local search procedure; and a hybrid PSO–MADS technique that combines the advantages of both methods. Nonlinear constraints are handled through use of filter-based treatments that seek to minimize both the objective function and constraint violation. We also introduce a formulation to determine the optimal number of wells, in addition to their locations and controls, by associating a binary variable (drill/do not drill) with each well. Example cases of varying complexity, which include bound constraints, nonlinear constraints, and the determination of the number of wells, are presented. The PSO–MADS hybrid procedure is shown to consistently outperform both stand-alone PSO and MADS when solving the joint problem. The joint approach is also observed to provide superior performance relative to a sequential procedure. 相似文献
3.
Mansoureh Jesmani Mathias C. Bellout Remus Hanea Bjarne Foss 《Computational Geosciences》2016,20(6):1185-1209
This work considers the well placement problem in reservoir management and field development optimization. In particular, it emphasizes embedding realistic and practical constraints into a mathematical optimization formulation. Such constraints are a prerequisite for the wider use of mathematical optimization techniques in well placement problems, since constraints are a way to incorporate reservoir engineering knowledge into the problem formulation. There are important design limitations that are used by the field development team when treating the well placement problem, and these limitations need to be articulated and eventually formalized within the problem before conducting the search for optimal well placements. In addition, these design limitations may be explicit or implicit. In this work, various design limitations pertaining to well locations have been developed in close collaboration with a field operator on the Norwegian Continental Shelf. Moreover, this work focuses on developing constraint-handling capability to enforce these various considerations during optimization. In particular, the Particle Swarm Optimization (PSO) algorithm is applied to optimize for the well locations, and various practical well placement constraints are incorporated into the PSO algorithm using two different constraint-handling techniques: a decoder procedure and the penalty method. The decoder procedure maps the feasible search space onto a cube and has the advantage of not requiring parameter tuning. The penalty method converts the constrained optimization problem into an unconstrained one by introducing an additional term, which is called a penalty function, to the objective function. In contrast to the penalty method, only feasible solutions are evaluated in the decoder method. Through numerical simulations, a comparison between the penalty method and the decoder technique is performed for two cases. We show that the decoder technique can easily be implemented for the well placement problem, and furthermore, that it performs better than the penalty method in most of the cases. 相似文献
4.
There is no gainsaying that determining the optimal number, type, and location of hydrocarbon reservoir wells is a very important aspect of field development planning. The reason behind this fact is not farfetched—the objective of any field development exercise is to maximize the total hydrocarbon recovery, which for all intents and purposes, can be measured by an economic criterion such as the net present value of the reservoir during its estimated operational life-cycle. Since the cost of drilling and completion of wells can be significantly high (millions of dollars), there is need for some form of operational and economic justification of potential well configuration, so that the ultimate purpose of maximizing production and asset value is not defeated in the long run. The problem, however, is that well optimization problems are by no means trivial. Inherent drawbacks include the associated computational cost of evaluating the objective function, the high dimensionality of the search space, and the effects of a continuous range of geological uncertainty. In this paper, the differential evolution (DE) and the particle swarm optimization (PSO) algorithms are applied to well placement problems. The results emanating from both algorithms are compared with results obtained by applying a third algorithm called hybrid particle swarm differential evolution (HPSDE)—a product of the hybridization of DE and PSO algorithms. Three cases involving the placement of vertical wells in 2-D and 3-D reservoir models are considered. In two of the three cases, a max-mean objective robust optimization was performed to address geological uncertainty arising from the mismatch between real physical reservoir and the reservoir model. We demonstrate that the performance of DE and PSO algorithms is dependent on the total number of function evaluations performed; importantly, we show that in all cases, HPSDE algorithm outperforms both DE and PSO algorithms. Based on the evidence of these findings, we hold the view that hybridized metaheuristic optimization algorithms (such as HPSDE) are applicable in this problem domain and could be potentially useful in other reservoir engineering problems. 相似文献
5.
Determination of well locations and their operational settings (controls) such as injection/production rates in heterogeneous subsurface reservoirs poses a challenging optimization problem that has a significant impact on the recovery performance and economic value of subsurface energy resources. The well placement optimization is often formulated as an integer-programming problem that is typically carried out assuming known well control settings. Similarly, identification of the optimal well settings is usually formulated and solved as a control problem in which the well locations are fixed. Solving each of the two problems individually without accounting for the coupling between them leads to suboptimal solutions. Here, we propose to solve the coupled well placement and control optimization problems for improved production performance. We present an alternating iterative solution of the decoupled well placement and control subproblems where each subproblem (e.g., well locations) is resolved after updating the decision variables of the other subproblem (e.g., solving for the control settings) from previous step. This approach allows for application of well-established methods in the literature to solve each subproblem individually. We show that significant improvements can be achieved when the well placement problem is solved by allowing for variable and optimized well controls. We introduce a well-distance constraint into the well placement objective function to avoid solutions containing well clusters in a small region. In addition, we present an efficient gradient-based method for solving the well control optimization problem. We illustrate the effectiveness of the proposed algorithms using several numerical experiments, including the three-dimensional PUNQ reservoir and the top layer of the SPE10 benchmark model. 相似文献
6.
Determining the optimum placement of new wells in an oil field is a crucial work for reservoir engineers. The optimization problem is complex due to the highly nonlinearly correlated and uncertain reservoir performances which are affected by engineering and geologic variables. In this paper, the combination of a modified particle swarm optimization algorithm and quality map method (QM + MPSO), modified particle swarm optimization algorithm (MPSO), standard particle swarm optimization algorithm (SPSO), and centered-progressive particle swarm optimization (CP-PSO) are applied for optimization of well placement. The SPSO, CP-PSO, and MPSO algorithms are first discussed, and then the modified quality map method is discussed, and finally the implementation of these four methods for well placement optimization is described. Four example cases which involve depletion drive model, water injection model, and a real field reservoir model, with the maximization of net present value (NPV) as the objective function are considered. The physical model used in the optimization analyses is a 3-dimensional implicit black-oil model. Multiple runs of all methods are performed, and the results are averaged in order to achieve meaningful comparisons. In the case of optimizing placement of a single producer well, it is shown that it is not necessary to use the quality map to initialize the position of well placement. In other cases considered, it is shown that the QM + MPSO method outperforms MPSO method, and MPSO method outperforms SPSO and CP-PSO method. Taken in total, the modification of SPSO method is effective and the applicability of QM + MPSO for this challenging problem is promising 相似文献
7.
Simultaneous and sequential approaches to joint optimization of well placement and control 总被引：1，自引：0，他引：1
Thomas D. Humphries Ronald D. Haynes Lesley A. James 《Computational Geosciences》2014,18(3-4):433-448
Determining optimal well placement and control is essential to maximizing production from an oil field. Most academic literature to date has treated optimal placement and control as two separate problems; well placement problems, in particular, are often solved assuming some fixed flow rate or bottom-hole pressure at injection and production wells. Optimal placement of wells, however, does depend on the control strategy being employed. Determining a truly optimal configuration of wells thus requires that the control parameters be allowed to vary as well. This presents a challenging optimization problem, since well location and control parameters have different properties from one another. In this paper, we address the placement and control optimization problem jointly using approaches that combine a global search strategy (particle swarm optimization, or PSO) with a local generalized pattern search (GPS) strategy. Using PSO promotes a full, semi-random exploration of the search space, while GPS allows us to locally optimize parameters in a systematic way. We focus primarily on two approaches combining these two algorithms. The first is to hybridize them into a single algorithm that acts on all variables simultaneously, while the second is to apply them sequentially to decoupled well placement and well control problems. We find that although the best method for a given problem is context-specific, decoupling the problem may provide benefits over a fully simultaneous approach. 相似文献
8.
9.
W. Bangerth H. Klie M. F. Wheeler P. L. Stoffa M. K. Sen 《Computational Geosciences》2006,10(3):303-319
Determining optimal locations and operation parameters for wells in oil and gas reservoirs has a potentially high economic impact. Finding these optima depends on a complex combination of geological, petrophysical, flow regimen, and economical parameters that are hard to grasp intuitively. On the other hand, automatic approaches have in the past been hampered by the overwhelming computational cost of running thousands of potential cases using reservoir simulators, given that each of these runs can take on the order of hours. Therefore, the key issue to such automatic optimization is the development of algorithms that find good solutions with a minimum number of function evaluations. In this work, we compare and analyze the efficiency, effectiveness, and reliability of several optimization algorithms for the well placement problem. In particular, we consider the simultaneous perturbation stochastic approximation (SPSA), finite difference gradient (FDG), and very fast simulated annealing (VFSA) algorithms. None of these algorithms guarantees to find the optimal solution, but we show that both SPSA and VFSA are very efficient in finding nearly optimal solutions with a high probability. We illustrate this with a set of numerical experiments based on real data for single and multiple well placement problems. 相似文献
10.
Abeeb A. Awotunde 《Computational Geosciences》2016,20(1):213-230
Of concern in the development of oil fields is the problem of determining the optimal locations of wells and the optimal controls to place on the wells. Extraction of hydrocarbon resources from petroleum reservoirs in a cost-effective manner requires that the producers and injectors be placed at optimal locations and that optimal controls be imposed on the wells. While the optimization of well locations and well controls plays an important role in ensuring that the net present value of the project is maximized, optimization of other factors such as well type and number of wells also plays important roles in increasing the profitability of investments. Until very recently, improving the net worth of hydrocarbon assets has been focused primarily on optimizing the well locations or well controls, mostly manually. In recent times, automatic optimization using either gradient-based algorithms or stochastic (global) optimization algorithms has become increasingly popular. A well-control zonation (WCZ) approach to estimating optimal well locations, well rates, well type, and well number is proposed. Our approach uses a set of well coordinates and a set of well-control variables as the optimization parameters. However, one of the well-control variables has its search range extended to cover three parts, one part denoting the region where the well is an injector, a second part denoting the region where there is no well, and a third part denoting the region where the well is a producer. By this, the optimization algorithm is able to match every member in the set of well coordinates to three possibilities within the search space of well controls: an injector, a no-well situation, or a producer. The optimization was performed using differential evolution, and two sample applications were presented to show the effectiveness of the method. Results obtained show that the method is able to reduce the number of optimization variables needed and also to identify simultaneously, optimal well locations, optimal well controls, optimal well type, and the optimum number of wells. Also, comparison of results with the mixed integer nonlinear linear programming (MINLP) approach shows that the WCZ approach mostly outperformed the MINLP approach. 相似文献
11.
Determining the optimum type and location of new wells is an essential component in the efficient development of oil and gas
fields. The optimization problem is, however, demanding due to the potentially high dimension of the search space and the
computational requirements associated with function evaluations, which, in this case, entail full reservoir simulations. In
this paper, the particle swarm optimization (PSO) algorithm is applied for the determination of optimal well type and location.
The PSO algorithm is a stochastic procedure that uses a population of solutions, called particles, which move in the search
space. Particle positions are updated iteratively according to particle fitness (objective function value) and position relative
to other particles. The general PSO procedure is first discussed, and then the particular variant implemented for well optimization
is described. Four example cases are considered. These involve vertical, deviated, and dual-lateral wells and optimization
over single and multiple reservoir realizations. For each case, both the PSO algorithm and the widely used genetic algorithm
(GA) are applied to maximize net present value. Multiple runs of both algorithms are performed and the results are averaged
in order to achieve meaningful comparisons. It is shown that, on average, PSO outperforms GA in all cases considered, though
the relative advantages of PSO vary from case to case. Taken in total, these findings are very promising and demonstrate the
applicability of PSO for this challenging problem. 相似文献
12.
Computational Geosciences - Well placement optimization is commonly performed using population-based global stochastic search algorithms. These optimizations are computationally expensive due to... 相似文献
13.
Hamidreza Hamdi Ivo Couckuyt Mario Costa Sousa Tom Dhaene 《Computational Geosciences》2017,21(2):267-287
The process of reservoir history-matching is a costly task. Many available history-matching algorithms either fail to perform such a task or they require a large number of simulation runs. To overcome such struggles, we apply the Gaussian Process (GP) modeling technique to approximate the costly objective functions and to expedite finding the global optima. A GP model is a proxy, which is employed to model the input-output relationships by assuming a multi-Gaussian distribution on the output values. An infill criterion is used in conjunction with a GP model to help sequentially add the samples with potentially lower outputs. The IC fault model is used to compare the efficiency of GP-based optimization method with other typical optimization methods for minimizing the objective function. In this paper, we present the applicability of using a GP modeling approach for reservoir history-matching problems, which is exemplified by numerical analysis of production data from a horizontal multi-stage fractured tight gas condensate well. The results for the case that is studied here show a quick convergence to the lowest objective values in less than 100 simulations for this 20-dimensional problem. This amounts to an almost 10 times faster performance compared to the Differential Evolution (DE) algorithm that is also known to be a powerful optimization technique. The sensitivities are conducted to explain the performance of the GP-based optimization technique with various correlation functions. 相似文献
14.
Vincent Artus Louis J. Durlofsky Jérôme Onwunalu Khalid Aziz 《Computational Geosciences》2006,10(4):389-404
The determination of the optimal type and placement of a nonconventional well in a heterogeneous reservoir represents a challenging optimization problem. This determination is significantly more complicated if uncertainty in the reservoir geology is included in the optimization. In this study, a genetic algorithm is applied to optimize the deployment of nonconventional wells. Geological uncertainty is accounted for by optimizing over multiple reservoir models (realizations) subject to a prescribed risk attitude. To reduce the excessive computational requirements of the base method, a new statistical proxy (which provides fast estimates of the objective function) based on cluster analysis is introduced into the optimization process. This proxy provides an estimate of the cumulative distribution function (CDF) of the scenario performance, which enables the quantification of proxy uncertainty. Knowledge of the proxy-based performance estimate in conjunction with the proxy CDF enables the systematic selection of the most appropriate scenarios for full simulation. Application of the overall method for the optimization of monobore and dual-lateral well placement demonstrates the performance of the hybrid optimization procedure. Specifically, it is shown that by simulating only 10% or 20% of the scenarios (as determined by application of the proxy), optimization results very close to those achieved by simulating all cases are obtained. 相似文献
15.
16.
局部线性化的反演方法不仅在初始模型远离真值时收敛速度慢,而且往往易陷入局部极大值中,而遗传算法(GA)便能解决这一问题,它是一种全局搜索法。虽然它同模拟退火法一样利用转移概率进行搜索,但其收敛速度一般比模拟退火法快。本文引进了多个目标函数进行综合评价和灾变过程,用单道地震道对一维声波介质的速度和密度同时进行了反演,并反演了加噪地震道和含薄层介质模型,取得了满意的效果。 相似文献
17.
Assessing spatial uncertainty in mapping soil erodibility factor using geostatistical stochastic simulation 总被引：1，自引：0，他引：1
Soil erosion is one of most widespread process of degradation. The erodibility of a soil is a measure of its susceptibility
to erosion and depends on many soil properties. Soil erodibility factor varies greatly over space and is commonly estimated
using the revised universal soil loss equation. Neglecting information about estimation uncertainty may lead to improper decision-making.
One geostatistical approach to spatial analysis is sequential Gaussian simulation, which draws alternative, equally probable,
joint realizations of a regionalised variable. Differences between the realizations provide a measure of spatial uncertainty
and allow us to carry out an error analysis. The objective of this paper was to assess the model output error of soil erodibility
resulting from the uncertainties in the input attributes (texture and organic matter). The study area covers about 30 km2 (Calabria, southern Italy). Topsoil samples were collected at 175 locations within the study area in 2006 and the main chemical
and physical soil properties were determined. As soil textural size fractions are compositional data, the additive-logratio
(alr) transformation was used to remove the non-negativity and constant-sum constraints on compositional variables. A Monte
Carlo analysis was performed, which consisted of drawing a large number (500) of identically distributed input attributes
from the multivariable joint probability distribution function. We incorporated spatial cross-correlation information through
joint sequential Gaussian simulation, because model inputs were spatially correlated. The erodibility model was then estimated
for each set of the 500 joint realisations of the input variables and the ensemble of the model outputs was used to infer
the erodibility probability distribution function. This approach has also allowed for delineating the areas characterised
by greater uncertainty and then to suggest efficient supplementary sampling strategies for further improving the precision
of K value predictions. 相似文献
18.
Based on the algorithm for gradual deformation of Gaussian stochastic models, we propose, in this paper, an extension of this method to gradually deforming realizations generated by sequential, not necessarily Gaussian, simulation. As in the Gaussian case, gradual deformation of a sequential simulation preserves spatial variability of the stochastic model and yields in general a regular objective function that can be minimized by an efficient optimization algorithm (e.g., a gradient-based algorithm). Furthermore, we discuss the local gradual deformation and the gradual deformation with respect to the structural parameters (mean, variance, and variogram range, etc.) of realizations generated by sequential simulation. Local gradual deformation may significantly improve calibration speed in the case where observations are scattered in different zones of a field. Gradual deformation with respect to structural parameters is necessary when these parameters cannot be inferred a priori and need to be determined using an inverse procedure. A synthetic example inspired from a real oil field is presented to illustrate different aspects of this approach. Results from this case study demonstrate the efficiency of the gradual deformation approach for constraining facies models generated by sequential indicator simulation. They also show the potential applicability of the proposed approach to complex real cases. 相似文献
19.
Multiobjective optimization deals with mathematical optimization problems where two or more objective functions (cost functions) are to be optimized (maximized or minimized) simultaneously. In most cases of interest, the objective functions are in conflict, i.e., there does not exist a decision (design) vector (vector of optimization variables) at which every objective function takes on its optimal value. The solution of a multiobjective problem is commonly defined as a Pareto front, and any decision vector which maps to a point on the Pareto front is said to be Pareto optimal. We present an original derivation of an analytical expression for the steepest descent direction for multiobjective optimization for the case of two objectives. This leads to an algorithm which can be applied to obtain Pareto optimal points or, equivalently, points on the Pareto front when the problem is the minimization of two conflicting objectives. The method is in effect a generalization of the steepest descent algorithm for minimizing a single objective function. The steepest-descent multiobjective optimization algorithm is applied to obtain optimal well controls for two example problems where the two conflicting objectives are the maximization of the life-cycle (long-term) net-present-value (NPV) and the maximization of the short-term NPV. The results strongly suggest the multiobjective steepest-descent (MOSD) algorithm is more efficient than competing multiobjective optimization algorithms. 相似文献
20.
覃伟 《水文地质工程地质》2021,48(6):161-170
针对传统的优化算法难以在具有变量多、约束条件复杂、局部极值点多的边坡临界滑动面搜索中取得较好效果的问题,提出双重变异遗传算法（DMGA）。一方面,该算法通过探测变异操作提升算法的局部寻优能力,通过直接变异操作提升算法的全局寻优能力,两者的结合使算法能够在搜索的广度与深度上达到较好的平衡;另一方面,算法采用考虑个体适应度值与进化代数的自适应交叉概率及自适应变异概率,使算法在进化的早期能够增加种群的多样性,在进化的后期能够保护较优的个体不受破坏。将该算法与简化Bishop法相结合,对澳大利亚计算机应用协会（ACADS）提供的考核题及一个海堤边坡工程实例进行分析,计算结果表明:（1）对于均质边坡和非均质边坡,该方法均能准确搜索到边坡的临界滑动面及相应的安全系数;（2）与仅进行直接变异或探测变异的遗传算法相比,双重变异遗传算法具有更强的全局搜索能力及更好的鲁棒性,具有广阔的应用前景。 相似文献