共查询到20条相似文献，搜索用时 15 毫秒
1.
Lianlin Li Behnam Jafarpour M. Reza Mohammad-Khaninezhad 《Computational Geosciences》2013,17(1):167-188
Development of subsurface energy and environmental resources can be improved by tuning important decision variables such as well locations and operating rates to optimize a desired performance metric. Optimal well locations in a discretized reservoir model are typically identified by solving an integer programming problem while identification of optimal well settings (controls) is formulated as a continuous optimization problem. In general, however, the decision variables in field development optimization can include many design parameters such as the number, type, location, short-term and long-term operational settings (controls), and drilling schedule of the wells. In addition to the large number of decision variables, field optimization problems are further complicated by the existing technical and physical constraints as well as the uncertainty in describing heterogeneous properties of geologic formations. In this paper, we consider simultaneous optimization of well locations and dynamic rate allocations under geologic uncertainty using a variant of the simultaneous perturbation and stochastic approximation (SPSA). In addition, by taking advantage of the robustness of SPSA against errors in calculating the cost function, we develop an efficient field development optimization under geologic uncertainty, where an ensemble of models are used to describe important flow and transport reservoir properties (e.g., permeability and porosity). We use several numerical experiments, including a channel layer of the SPE10 model and the three-dimensional PUNQ-S3 reservoir, to illustrate the performance improvement that can be achieved by solving a combined well placement and control optimization using the SPSA algorithm under known and uncertain reservoir model assumptions. 相似文献
2.
Nadav Sorek Eduardo Gildin Fani Boukouvala Burcu Beykal Christodoulos A. Floudas 《Computational Geosciences》2017,21(2):247-266
The objective of this paper is to introduce a novel paradigm to reduce the computational effort in waterflooding global optimization problems while realizing smooth well control trajectories amenable for practical deployments in the field. In order to overcome the problems of slow convergence and non-smooth impractical control strategies, often associated with gradient-free optimization (GFO) methods, we introduce a generalized approach which represent the controls by smooth polynomial approximations either by a polynomial function or by a piecewise polynomial interpolation, which we denote as function control method (FCM) and interpolation control method (ICM), respectively. Using these approaches, we aim to optimize the coefficients of the selected functions or the interpolation points in order to represent the well-control trajectories along a time horizon. Our results demonstrate significant computational savings, due to a substantial reduction in the number of control parameters, as we seek the optimal polynomial coefficients or the interpolation points to describe the control trajectories as opposed to directly searching for the optimal control values (bottom hole pressure) at each time interval. We demonstrate the efficiency of the method on two and three-dimensional models, where we found the optimal variables using a parallel dynamic-neighborhood particle swarm optimization (PSO). We compared our FCM-PSO and ICM-PSO to the traditional formulation solved by both gradient-free and gradient-based methods. In all comparisons, both FCM and ICM show very good to superior performances. 相似文献
3.
Oilfield development involves several key decisions, including the number, type (injection/production), location, drilling schedule, and operating control trajectories of the wells. Without considering the coupling between these decision variables, any optimization problem formulation is bound to find suboptimal solutions. This paper presents a unified formulation for oilfield development optimization that seeks to simultaneously optimize these decision variables. We show that the source/sink term of the governing multiphase flow equations includes all the above decision variables. This insight leads to a novel and unified formulation of the field development optimization problem that considers the source/sink term in reservoir simulation equations as optimization decision variables. Therefore, a single optimization problem is formulated to simultaneously search for optimal decision variables by determining the complete dynamic form of the source/sink terms. The optimization objective function is the project net present value (NPV), which involves discounted revenue from oil production, operating costs (e.g. water injection and recycling), and capital costs (e.g., cost of drilling wells). A major difficulty after formulating the generalized field development optimization problem is finding an efficient solution approach. Since the total number of cells in a reservoir model far exceeds the number of cells that are intersected by wells, the source/sink terms tend to be sparse. In fact, the drilling cost in the NPV objective function serves as a sparsity-promoting penalty to minimize the number of wells while maximizing the NPV. Inspired by this insight, we solve the optimization problem using an efficient gradient-based method based on recent algorithmic developments in sparse reconstruction literature. The gradients of the NPV function with respect to the source/sink terms is readily computed using well-established adjoint methods. Numerical experiments are presented to evaluate the feasibility and performance of the generalized field development formulation for simultaneous optimization of the number, location, type, controls, and drilling schedule of the wells. 相似文献
4.
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
5.
Kai Zhang Wenjuan Zhang Liming Zhang Jun Yao Yuxue Chen Ranran Lu 《Computational Geosciences》2014,18(2):139-156
An improper well pattern will have considerable adverse effects on the ultimate recovery of oil and gas considering the geological complexities usually associated with reservoirs. Designing an optimal well pattern for a given reservoir is often challenging because of following two categories of reasons: static factors including strong heterogeneities of reservoirs, the existence of outer boundaries, faults and pinch-out belts, variations of sedimentary facie physical properties; dynamic factors including the producers and injectors drilled previously, the multitudes of well patterns and the transformation among them. To overcome the difficulties of designing well patterns under complex conditions, a new method of constructing triangular adaptive well pattern is proposed in this paper. This new triangular adaptive well pattern can adjust the locations of wells spontaneously according to the conditions of reservoirs, achieving optimal effects using fewest wells. Inspired by the similarities between triangular well pattern often encountered in the industry and the triangulation of domains in computational geometry, the well-known Delaunay triangulation is employed to determine the locations of wells. By taking full advantage of the properties of Delaunay triangulation, the construction of triangular adaptive well pattern on the basis of boundaries, faults, and existing wells can be easily obtained and the number of control variables is greatly decreased in the optimization process. Therefore, a gradient-based algorithm coupled with reservoir numerical simulator is used to optimize the well pattern. Compared with conventional regular well patterns, the well pattern proposed here differs in that the scale and orientation of local flooding units are not the same in different parts of the reservoir depending on the geological conditions and the distribution of oil and water in the reservoir. Additionally, the heterogeneity of permeability is taken into account and a uniform displacement of oil in each flooding unit is realized by adjusting the locations of injectors. Detailed results are present ed for two different examples. The results show that the method proposed here can be successfully applied to the construction and optimization of well pattern for large-scale reservoirs. 相似文献
6.
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
7.
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
8.
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
9.
为定量描述稠油油藏蒸汽辅助重力泄油(SAGD)开采过程中注采井间夹层对双水平井泄油通道与开发效果的影响,针对新疆油田A区块,利用CMG数值模拟软件建立了表征SAGD注采井间夹层的数值模拟模型,对注采井间存在不同大小、间距及物性夹层情况下的SAGD蒸汽腔发育形态与开发效果进行对比,得到了稠油油藏夹层非均质影响SAGD开发的临界特征参数。矿场实际的非均质SAGD井组夹层展布特征、蒸汽腔发育特征及生产效果对比结果表明,得到的结论与矿场实践一致,因此可用于指导并实现经济、高效的稠油油藏双水平井SAGD开发。 相似文献
10.
Tailai Wen Marco R. Thiele David Echeverría Ciaurri Khalid Aziz Yinyu Ye 《Computational Geosciences》2014,18(3-4):483-504
Waterflooding is a common secondary oil recovery process. Performance of waterfloods in mature fields with a significant number of wells can be improved with minimal infrastructure investment by optimizing injection/production rates of individual wells. However, a major bottleneck in the optimization framework is the large number of reservoir flow simulations often required. In this work, we propose a new method based on streamline-derived information that significantly reduces these computational costs in addition to making use of the computational efficiency of streamline simulation itself. We seek to maximize the long-term net present value of a waterflood by determining optimal individual well rates, given an expected albeit uncertain oil price and a total fluid injection volume. We approach the optimization problem by decomposing it into two stages which can be implemented in a computationally efficient manner. We show that the two-stage streamline-based optimization approach can be an effective technique when applied to reservoirs with a large number of wells in need of an efficient waterflooding strategy over a 5 to 15-year period. 相似文献
11.
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
12.
泥质夹层对油砂SAGD的开发效果具有一定影响。以加拿大麦凯河某区块为研究对象,利用数值模拟手段,对位于注入井上方和注采井间的泥质薄夹层进行表征,模拟对比了泥质薄夹层的位置、延展长度、渗透率、厚度以及条数对SAGD开发效果的影响,得到了油砂SAGD开发的泥质薄夹层的临界参数。研究表明,泥质薄夹层位于注入井上方时对SAGD开发效果影响较小,位于注采井间时有不利影响,且越靠近生产井,蒸汽腔发育越晚,累产油量越低。注采井间泥质薄夹层超过7条、延展长度超过20 m时蒸汽腔几乎不发育,而渗透率与厚度对SAGD开发效果影响不明显。因此油砂SAGD布井时注采井间应尽量避开泥质薄夹层,从而有效降低泥质薄夹层对SAGD的影响。该研究结果,对于指导油砂SAGD部署、规避地质风险、实现SAGD高效开发具有重要指导意义。 相似文献
13.
Waterflooding using closed-loop control 总被引：2，自引：0，他引：2
To fully exploit the possibilities of “smart” wells containing both measurement and control equipment, one can envision a
system where the measurements are used for frequent updating of a reservoir model, and an optimal control strategy is computed
based on this continuously updated model. We developed such a closed-loop control approach using an ensemble Kalman filter
to obtain frequent updates of a reservoir model. Based on the most recent update of the reservoir model, the optimal control
strategy is computed with the aid of an adjoint formulation. The objective is to maximize the economic value over the life
of the reservoir. We demonstrate the methodology on a simple waterflooding example using one injector and one producer, each
equipped with several individually controllable inflow control valves (ICVs). The parameters (permeabilities) and dynamic
states (pressures and saturations) of the reservoir model are updated from pressure measurements in the wells. The control
of the ICVs is rate-constrained, but the methodology is also applicable to a pressure-constrained situation. Furthermore,
the methodology is not restricted to use with “smart” wells with down-hole control, but could also be used for flooding control
with conventional wells, provided the wells are equipped with controllable chokes and with sensors for measurement of (wellhead
or down hole) pressures and total flow rates. As the ensemble Kalman filter is a Monte Carlo approach, the final results will
vary for each run. We studied the robustness of the methodology, starting from different initial ensembles. Moreover, we made
a comparison of a case with low measurement noise to one with significantly higher measurement noise. In all examples considered,
the resulting ultimate recovery was significantly higher than for the case of waterflooding using conventional wells. Furthermore,
the results obtained using closed-loop control, starting from an unknown permeability field, were almost as good as those
obtained assuming a priori knowledge of the permeability field. 相似文献
14.
Oleg Volkov Vladislav Bukshtynov Louis J. Durlofsky Khalid Aziz 《Computational Geosciences》2018,22(6):1465-1485
The advantages of the simultaneous integration of production and time-lapse seismic data for history matching have been demonstrated in a number of previous studies. Production data provide accurate observations at particular spatial locations (wells), while seismic data enable global, though filtered/noisy, estimates of state variables. In this work, we present an efficient computational tool for bi-objective history matching, in which data misfits for both production and seismic measurements are minimized using an adjoint-gradient approach. This enables us to obtain a set of Pareto optimal solutions defining the optimal trade-off between production and seismic data misfits (which are, to some extent, conflicting). The impact of noise structure and noise level on Pareto optimal solutions is investigated in detail. We discuss the existence of the “best” trade-off solution, or least-conflicting posterior model, which corresponds to the history-matched model that is expected to provide the least-conflicting forecast of future reservoir performance. The overall framework is successfully applied in 2D and 3D compositional simulation problems to provide a single least-conflicting posterior model and, for the 2D case, multiple samples from the posterior distribution using the randomized maximum likelihood method. 相似文献
15.
Adriano Petry André Grahl Pereira Jonas Rodrigues de Souza 《Earth Science Informatics》2017,10(2):183-196
We propose a new algorithm for the problem of approximate nearest neighbors (ANN) search in a regularly spaced low-dimensional grid for interpolation applications. It associates every sampled point to its nearest interpolation location, and then expands its influence to neighborhood locations in the grid, until the desired number of sampled points is achieved on every grid location. Our approach makes use of knowledge on the regular grid spacing to avoid measuring the distance between sampled points and grid locations. We compared our approach with four different state-of-the-art ANN algorithms in a large set of computational experiments. In general, our approach requires low computational effort, especially for cases with high density of sampled points, while the observed error is not significantly different. At the end, a case study is shown, where the ionosphere dynamics is predicted daily using samples from a mathematical model, which runs in parallel at 56 different longitude coordinates, providing sampled points not well distributed that follow Earth’s magnetic field-lines. Our approach overcomes the comparative algorithms when the ratio between the number of sampled points and grid locations is over 2849:1. 相似文献
16.
渤海油田低效井侧钻是稳产增产的重要手段之一。优化侧钻方案实现降本增效是精细化侧钻的研究重点。本文系统介绍了侧钻井设计方法,基于对低效井与侧钻井位进行合理匹配,优选侧钻井段,确定深部侧钻和浅部侧钻方案,针对浅部侧钻方案提出了隔水导管重入方案、隔水导管鞋下裸眼侧钻方案、表层套管段开窗侧钻方案、表层套管鞋下裸眼侧钻方案的适用条件与难点,针对深部侧钻方案提出了生产套管开窗侧钻方案、生产套管鞋下裸眼侧钻方案的优选流程。并以渤海某油田2口低效井A井与B井为例,进行了槽口优选、侧钻井段优选、侧钻点优选,最终通过互换2口井的侧钻井位,节约进尺1428 m。现场实施结果表明本次2口低效井侧钻方案合理,能够实现降本增效的目的。本文提出的低效井侧钻优选方法对渤海油田低效井侧钻设计具有一定的指导意义。 相似文献
17.
Ivar Aavatsmark 《Computational Geosciences》2016,20(5):1029-1042
Peaceman’s equivalent well-cell radius for 2D square grids has been generalized to 2D grids consisting of regular hexagons. The development consists of the following steps. Firstly, the analytical solution for the pressure drop between injector and producer for wells in a seven-spot pattern is determined. Secondly, this solution is compared with the numerical solution on hexagonal grids for a sixth of a seven-spot pattern. Finally, the equivalent well-cell radius is calculated, and its asymptotic behavior for infinitely fine grids is derived. The results are valid for both steady-state and unsteady-state conditions. 相似文献
18.
鄂尔多斯盆地北缘地球化学大数据样本优选分析 总被引：1，自引：1，他引：0
众所周知,地球化学数据携带有众多地质噪音,这些噪音严重影响地球化学数据信息的客观性与可靠性;对于地球化学大数据融合分析而言,确定样品的有效性及变量优选是滤除地质噪音、建立最优样本集合的必要性工作,因而在地球化学大数据处理分析前需首先进行大样本优选,从而更加客观、真实的揭示地球化学大数据信息及相关地质意义。本文以鄂尔多斯盆地北缘1∶20万地球化学土壤测量数据为例,考虑元素之间的地球化学亲和力与组合匹配关系,建立非线性大样本优选模型。具体做法是基于优选后的样品矩阵,将39个元素变量分解成若干独立因子向量,将最优独立因子向量作为元素组合,其向量各分量作为元素变量的权重,依权重大小进行变量优选;优选后的样本集合可以作为该区地球化学数据分析与信息识别的有效地学信息集合,运用这种集合可以有效开展鄂尔多斯盆地外围铀地球化学分析,并为盆地铀资源预测奠定基础。 相似文献
19.
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
20.
Kai Zhang Hao Zhang Liming Zhang Ping Li Xiaoming Zhang Jun Yao 《Computational Geosciences》2017,21(3):499-518
A proper well pattern will have considerable effects on the oil field production, with the ultimate recovery of hydrocarbon enhanced and the water production rate reduced. Comparing with triangular well pattern, quadrangular well pattern has more advantages in some cases and has broader application prospects. However, constructing an optimal quadrangular well pattern is more complicated and has not gain enough research. Facing this situation, a new method of constructing quadrangular adaptive well pattern is proposed in this paper. This quadrangular adaptive well pattern is generated using frontal Delaunay quad-mesh generation method. Boundaries, faults, and existing wells can be constrained and treated as control variables to determine the well spacing, a gradient-based algorithm coupled with reservoir numerical simulator is used to optimize the well pattern. Comparing to conventional regular well pattern, the new well pattern will adjust its shape according to the heterogeneity in different parts of the reservoir, achieving the optimal effect using fewest wells. Two different examples are applied to demonstrate the proposed methodology. The results show that the method proposed can be successfully applied to the construction and optimization of well pattern for large-scale reservoirs and improve the ultimate recovery significantly. 相似文献