Efficient reservoir history matching using subspace vectors

In this paper, we describe a method of history matching in which changes to the reservoir model are constructed from a limited set of basis vectors. The purpose of this reparameterization is to reduce the cost of a Newton iteration, without altering the final estimate of model parameters and without substantially slowing the rate of convergence. The utility of a subspace method depends on several factors, including the choice and number of the subspace vectors to be used. Computational gains in efficiency result partly from a reduction in the size of the matrix system that must be solved in a Newton iteration. More important contributions, however, result from a reduction in the number of sensitivity coefficients that must be computed, reduction in the dimensions of the matrices that must be multiplied, and elimination of matrix products involving the inverse of the prior model covariance matrix. These factors affect the efficiency of each Newton iteration. Although computation of the optimal set of subspace vectors may be expensive, we show that the rate of convergence and the final results are somewhat insensitive to the choice of subspace vectors. We also show that it is desirable to start with a small number of subspace vectors and gradually increase the number at each Newton iteration until an acceptable level of data mismatch is obtained.     

Efficient updating of kriging estimates and variances

This short note presents a method for efficiently updating ordinary kriging estimates and variances when one or more additional samples are incorporated into the kriging system. First, the foundation linear algebra result is presented. Then the update equations are derived. Finally, an illustrative application of updating is briefly discussed.     

分时电价下多用途水库的运用方式研究

提出了考虑丰枯、峰谷分时电价因子的多用途水库水电站优化调度模型,并应用遗传算法求解.通过四川省紫坪铺水库的运用研究表明,是否考虑分时电价对水库的运用方式会产生较大影响.分时电价调度模型可使电站可靠出力提高,能够达到水资源合理利用,有利于调节水电站开发,并能较好地协调发电与供水矛盾,具有推广应用价值.     

Real-time reservoir management: A multiscale adaptive optimization and control approach

We propose a decision-making approach for optimizing the profitability of hydrocarbon reservoirs. The proposed approach addresses the overwhelming complexity of the overall optimization problem by suggesting an oilfield operations hierarchy that entails different time scales. We discuss system identification, optimization, and control that are appropriate at various levels of the hierarchy and capitalize on the abilities of permanently instrumented and remotely actuated fields. Optimization is performed in real-time and is based on feedback. We provide details on real-time identification of hybrid models and their use at the scheduling and supervisory control levels. Case studies using field-calibrated simulation data demonstrate the applicability and value of the proposed approach. Directions for future development are given.     

Intelligent model selection with updating parameters during staged excavation using optimization method

Various constitutive models have been proposed, and previous studies focused on identifying parameters of specified models. To develop the smart construction, this paper proposes a novel optimization-based intelligent model selection procedure in which parameter identification is also performed during staged excavation. To conduct the model selection, a database of seven constitutive models accounting for isotropic or anisotropic yield surface, isotropic or anisotropic elasticity, or small strain stiffness for clayey soils is established, with each model numbered and deemed as one additional parameter for optimization. A newly developed real-coded genetic algorithm is adopted to evaluate the performance of simulation against field measurement. As the process of optimization goes on, the soil model exhibiting good performance during simulation survives from the database and model parameters are also optimized. For each excavation stage, with the selected model and optimized parameters, wall deflection and ground surface settlement of the subsequent unexcavated stage are predicted. The proposed procedure is repeated until the entire excavation is finished. This proposed procedure is applied to a real staged excavation with field data, which demonstrates its effectiveness and efficiency in engineering practice with highlighting the importance of anisotropic elasticity and small strain stiffness in simulating excavation. All results demonstrate that the current study has both academic significance and practical significance in providing an efficient and effective approach of adaptive optimization-based model selection with parameters updating in engineering applications.

     

复杂防洪系统联合优化调度模型

建立以水库群系统安全度最大、行蓄洪区系统损失最小为目标函数,将河道堤防安全行洪考虑为约束条件的复杂防洪系统多目标递阶优化调度模型(MoHOOM),以行蓄洪区总分洪流量为协调变量,将河道水流连续方程解耦,基于大系统分解协调法建立协调层和基于粒子群算法求解底层子系统优化问题,形成三级递阶分解协调结构和相应求解方法。以淮河中游防洪系统为背景进行了实例研究,给出了水库群泄流和行蓄洪区分洪最优方案,在相同初始计算条件下,优化模型结果比实际调度降低了鲇鱼山和梅山水库0.37和0.01的安全度指标,减小下游蒋家集和润河集河段超过安全泄量以上100 m3/s和720 m3/s的洪峰流量,启用南润段行洪区致损1 256.1万元;比规则调度降低了鲇鱼山和梅山水库0.24和0.21的安全度指标,减小下游蒋家集河段超过安全泄量以上750 m3/s的洪峰流量,避免南润段行洪区损失341.6万元。模型有利于挖掘上游水库群的防洪能力,在保障河道堤防安全行洪条件下,减少下游不必要的行蓄洪区分洪损失,以系统全局寻优方式进行复杂防洪系统联合调度。     

A real-time operation optimization model for flood management in river-reservoir systems

In this paper, a new methodology has been developed for real-time flood management in river-reservoir systems. This methodology is based upon combining a Genetic Algorithm (GA) reservoir operation optimization model for a cascade of two reservoirs, a hydraulic-based flood routing simulation model in downstream river system, a Geographical Information System (GIS) based database, and application of K-Nearest Neighbor (K-NN) algorithm for development of optimal operating rules. The GA optimization model estimates the optimal hourly reservoirs’ releases to minimize the flood damages in the downstream river. GIS tools have also been used for specifying different land-uses and damage functions in the downstream floodplain and it has been linked to the unsteady module of HEC-RAS flood routing model using Hec-GeoRAS module. An innovative approach has also been developed using K-NN algorithm to formulate the optimal operating rules for a system of two cascade reservoirs based on optimal releases obtained from the optimization model. During a flood event, the K-NN algorithm searches through the historical flood hydrographs and optimal reservoir storages determined by the optimization model to find similar situations. The similarity between the hydrographs is quantified based on the slopes of rising and falling limbs of inflow hydrographs and reservoir storages at the beginning of each hourly time step during the flood events for two cascade reservoirs. The developed methodology have been applied to the Bakhtiari and Dez River-Reservoir systems in southwest of Iran. The results show that the proposed models can be effectively used for flood management and real-time operation of cascade river-reservoir systems.     

Reduced-order optimal control of water flooding using proper orthogonal decomposition

Model-based optimal control of water flooding generally involves multiple reservoir simulations, which makes it into a time-consuming process. Furthermore, if the optimization is combined with inversion, i.e., with updating of the reservoir model using production data, some form of regularization is required to cope with the ill-posedness of the inversion problem. A potential way to address these issues is through the use of proper orthogonal decomposition (POD), also known as principal component analysis, Karhunen–Loève decomposition or the method of empirical orthogonal functions. POD is a model reduction technique to generate low-order models using ‘snapshots’ from a forward simulation with the original high-order model. In this work, we addressed the scope to speed up optimization of water-flooding a heterogeneous reservoir with multiple injectors and producers. We used an adjoint-based optimal control methodology that requires multiple passes of forward simulation of the reservoir model and backward simulation of an adjoint system of equations. We developed a nested approach in which POD was first used to reduce the state space dimensions of both the forward model and the adjoint system. After obtaining an optimized injection and production strategy using the reduced-order system, we verified the results using the original, high-order model. If necessary, we repeated the optimization cycle using new reduced-order systems based on snapshots from the verification run. We tested the methodology on a reservoir model with 4050 states (2025 pressures, 2025 saturations) and an adjoint model of 4050 states (Lagrange multipliers). We obtained reduced-order models with 20–100 states only, which produced almost identical optimized flooding strategies as compared to those obtained using the high-order models. The maximum achieved reduction in computing time was 35%.     

嵌入水安全调控因子的短期梯调优化模型

为在梯级水库短期优化调度中更有效地考虑河流水安全需求,建立一种通过调控因子对水安全需求进行表征的短期梯调优化模型。在该模型构建中,应用Vague集对河流水安全进行度量,并引入水安全调控因子,推导得到考虑水安全需求的梯级下泄流量约束计算公式,通过下泄流量约束将调控因子嵌入短期梯调优化模型。通过该模型,可根据河流水安全需求确定调控因子,并求解得到相应的短期梯调优化方案。嘉陵江中游航电三梯级的应用分析表明,该模型能在满足河流水安全需求情况下,实现短期梯调较优的经济效益。在短期梯调优化模型中嵌入水安全调控因子,与未嵌入调控因子情形相比,既能提高梯级发电效益,又能不降低梯级运行相关的河流水安全度。     

水库防洪优化调度的恒定出流模型及应用

现行水库洪水优化调度数学模型存在泄流闸门开度不断调整及时段间流量突变的问题。针对这些问题,将水库防洪优化调度数学模型分为时段内出库流量线性变化的瞬时出流模型和时段内出流不变的恒定出流模型。对于1个水库和1个防洪控制点所组成的基本防洪系统,应用矩形入流条件的河道洪水演进方法,以时段内恒定的出库流量为决策变量,构建水库防洪优化补偿调度数学模型。实例计算结果表明:恒定出流模型比瞬时出流模型占用的防洪库容减小0.05%~0.18%、最大下泄流量有增有减,即恒定出流模型既不劣于瞬时出流模型,也能很好地解决瞬时出流模型存在的问题。同时,实例揭示的水库二次补偿调节比等蓄量调度方式减小防洪库容10.6%,为确定水库的防洪库容提供了新的方法。     

Approaches to optimal aquifer management and intelligent control in a multiresolutional decision support system

Despite remarkable new developments in stochastic hydrology and adaptations of advanced methods from operations research, stochastic control, and artificial intelligence, solutions of complex real-world problems in hydrogeology have been quite limited. The main reason is the ultimate reliance on first-principle models that lead to complex, distributed-parameter partial differential equations (PDE) on a given scale. While the addition of uncertainty, and hence, stochasticity or randomness has increased insight and highlighted important relationships between uncertainty, reliability, risk, and their effect on the cost function, it has also (a) introduced additional complexity that results in prohibitive computer power even for just a single uncertain/random parameter; and (b) led to the recognition in our inability to assess the full uncertainty even when including all uncertain parameters. A paradigm shift is introduced: an adaptation of new methods of intelligent control that will relax the dependency on rigid, computer-intensive, stochastic PDE, and will shift the emphasis to a goal-oriented, flexible, adaptive, multiresolutional decision support system (MRDS) with strong unsupervised learning (oriented towards anticipation rather than prediction) and highly efficient optimization capability, which could provide the needed solutions of real-world aquifer management problems. The article highlights the links between past developments and future optimization/planning/control of hydrogeologic systems.

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Resumen  A pesar de nuevos avances notables en hidrología estocástica y las adaptaciones de métodos avanzados de investigación de operaciones, control estocástico, e inteligencia artificial, las soluciones de problemas complejos del mundo real en hidrogeología han sido bastante limitadas. La principal razón es la dependencia definitiva en modelos de primer-principio que conducen a ecuaciones parciales diferencias de parámetro distribuido complejas (PDE) a una escala dada. Mientras que la adición de incertidumbre, y por lo tanto, estocasticidad o aleatoriedad ha incrementado la profundidad y resaltado relaciones importantes entre la incertidumbre, confiabilidad, riesgo, y su efecto en la función de costo, la adición también ha permitido (a) introducir complejidad adicional que resulta en potencia computacional excesiva aún para un solo parámetro incierto/aleatorio; y (b) llevar a reconocer nuestra discapacidad para evaluar la incertidumbre completa aún cuando se incluyen todos los parámetros inciertos. Se introduce un cambio paradigmático: una adaptación de nuevos métodos de control de inteligencia que relajará la dependencia en PDE estocásticas, rígidas y de uso computacional intensivo, cambiando el énfasis hacia un sistema de apoyo de decisiones de propósitos múltiples (MRDS) adaptivo, flexible, y orientado a objetivos con fuerte aprendizaje sin supervisión (orientado a la anticipación más que a la predicción) con fuerte capacidad de optimización eficiente, lo cual podría aportar las soluciones necesarias a los problemas de manejo reales con los acuíferos. El artículo resalta los vínculos entre desarrollos pasados y control/planificación/optimización futura de sistemas hidrogeológicos.

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Résumé  Malgré de remarquables nouveaux développements en hydrologie stochastique ainsi que de remarquables adaptations de méthodes avancées pour les opérations de recherche, le contrôle stochastique, et lintelligence artificielle, solutions pour les problèmes complexes en hydrogéologie sont restées assez limitées. La principale raison est lultime confiance en les modèles qui conduisent à des équations partielles complexes aux paramètres distribués (PDE) à une échelle donnée. Alors que laccumulation dincertitudes et, par conséquent, la stockasticité ou laléat a augmenté la perspicacité et a mis en lumière dimportantes relations entre lincertitude, la fiabilité, le risque, et leur effet sur les coûts de fonctionnement, il a également (a) introduit une complexité additionnelle qui résulte dans un pouvoir prohibitif des moyens de calcul informatique même pour une simple estimation de lincertitude; et (b) a conduit a une reconnaissance de notre manque daptitude à maîtriser lincertitude totale même en introduisant tous les paramètres connus de lincertitude. La représentation du changement est introduit: une adaptation de nouvelles méthodes de contrôle intelligent qui va relâcher la dépendance à la rigidité des algorithmes, aux calculs informatiques intensifs, à la PDE stockastique, et qui modifiera lemphase entre les MRDS—systèmes interactifs daide à la décision de multiresolutionelle (flexibles, adaptables et orientables selon les objectifs)—avec un fort apprentissage non (orienté vers lanticipation plutôt que la prédiction), et une capacité doptimisation efficiente très élevée, qui pourrait apporter le besoin de solutions pour la modélisation des problèmes de management des aquifères réalistes. Cet article met en lumière les liens entre les développements passés et les futurs moyens doptimisation, de gestion et de contrôle des systèmes hydrogéologiques.

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Default risk-based probabilistic decision model for risk management and control

This study describes how risk-based risk control allocation models work. We begin by discussing the economic rationale for allocating risk control in a diversified organization such as an enterprise. For a probability model for risk control decision making under uncertainty and risk, we propose a model involving stochastic total loss amount constraints with respect to various tolerable default levels. Our main objective is to develop a method that will allow shaping of the risk associated with risk control outcomes. The direct and indirect losses caused by simulated disasters can be estimated using an engineering and financial analysis model. Based on this model, we can generate an exceeding probability curve and then calculate how much of the loss can be eliminated or transferred to other entities should funds be allocated to risk control. The optimal natural disaster risk control arrangement with a probabilistic formulation is explained in this paper. Results from the proposed formulations are compared in case studies. The model attempts to apply risk-based budget guidelines to risk reduction measurement within a portfolio-based risk framework.     

Bayesian updating of KJHH model for prediction of maximum ground settlement in braced excavations using centrifuge data

In this paper, a Bayesian approach for updating a semi-empirical model for predicting excavation-induced maximum ground settlement using centrifuge test data is presented. The Bayesian approach involves three steps: (1) prior estimate of the maximum ground settlement and model bias factor, (2) establishment of the likelihood function and posterior distribution of the model bias factor using the settlement measurement in the centrifuge test, and (3) development of posterior distribution of the predicted maximum settlement. This Bayesian approach is demonstrated with a case study of a well-documented braced excavation, and the results show that the accuracy of the maximum settlement prediction can be improved and the model uncertainty can be reduced with Bayesian updating.     

Efficient and flexible Bayesian updating of embankment settlement on soft soils based on different monitoring datasets

Acta Geotechnica - In situ monitoring provides valuable information to update the predictions of the embankment settlement on soft soils. Observational data obtained at different monitoring moments...     

应用最优控制理论自动率定二维浅水方程的糙率参数

应用偏微分方程最优化控制理论,导出了二维浅水方程糙率自动率定的最优化系统,实现了二维水力学模型计算中分布式糙率参数的自动率定算法。结合实际应用的可操作性,按区块设定糙率,对恒定流和非恒定流两种流态分别进行了常数糙率和随水深变化的糙率自动率定实验。数值实验表明,在观测数据包含足够的信息时,该方法不仅能够准确地自动率定出相关的糙率参数,还能成功地将糙率随水深的变化关系式识别出来。但是,糙率水深变化关系识别迭代收敛速度显著变慢,因此,建议采用相应的实现对策来考虑变糙率的洪水预测问题。     

水库群供调水系统实时调度研究

以大连市复杂水库群供、调水系统为背景,在对系统特性分析的基础上,提出了系统实时调度框架及二层耦合结构模式,设计了以"总量控制、耦合嵌套、多维决策、滚动修正"为核心的实时调度流程。在径流预报方面,提出了基于超越概率的水库群供、调水系统长期入库径流预报方法,结合中期GFS(Global Forecasting System)数值预报技术对不同时段入库径流进行滚动预报。在调度模型方面,采用基于动态规划的建模求解新方法和常规调度方法,为调度决策提供参考。实例研究表明,建立的实时调度系统实现了滚动预报和滚动调度,具备了实时性;实现了宏观总控与局部调整相结合的调度目标,具备了调度决策不同时间尺度耦合嵌套特性,证明了系统的适用性。研究成果对于同类系统实时调度具有重要参考价值。     

有限供水条件下水库和田间配水整合优化调度

针对多水源多作物灌区,研究有限供水条件下灌区优化配水问题。模型中既考虑水库优化调度,又考虑田间优化配水。模型思路:根据灌区水土资源分布状况,将全灌区划分为多个子区,每个子区种植有若干种作物。如果把每个子区每种作物所在的田块看作一个土壤水库,则可采用水库群调度的方法研究这类灌区优化配水问题,文中采用的是优化控制方法。为了证明模型的正确性,同时建立了3个模型。模型1:既不考虑水库优化调度又不考虑田间优化配水;模型2:只考虑水库优化调度;模型3:只考虑田间优化配水。实例计算表明,整合调度模型能产生较多的效益(特别在干旱年份),优化控制方法计算性能良好。