Optimal Multi-Reservoir Operation for Hydropower Production in the Nam Ngum River Basin

This research aims to investigate optimal hydropower production of multi-reservoirs in Lao PDR and develop optimal reservoir rule curves. The Nam Ngum 1 and 2 (NN1 and NN2, respectively) reservoirs in the Nam Ngum River basin (NNRB), which is located in the middle of Laos, are selected as study areas. Mixed integer nonlinear programming (MINLP) is developed as an optimization model to maximize the hydropower production of joint reservoir operation of NN1 and NN2. The optimal operation rule curves are established by using the storage level estimated by the optimization model. Given the limited sideflow data, an integrated flood analysis system (IFAS) and water balance equation are used to simulate the sideflow into NN1 reservoir. A good fit is observed between the monthly streamflow simulated by IFAS and that calculated by the water balance equation. Compared with the observed data, the MINLP model can increase the annual and monthly hydropower production by 20.22% (6.01% and 14.21% for NN1 and NN2, respectively). The water storage level estimated by the MINLP model is used to build the operation rule curves. Results show that the MINLP model of multi-reservoir is a useful and effective approach for multi-reservoir operations and is expected to hold high application value for similar reservoirs in NNRB

Assessing water reservoirs management and development in Northern Vietnam

Abstract. In many developing countries water is a key renewable resource to complement carbon-emitting energy production and support food security in the face of demand pressure from fast-growing industrial production and urbanization. To cope with undergoing changes, water resources development and management have to be reconsidered by enlarging their scope across sectors and adopting effective tools to analyze current and projected infrastructure potential and operation strategies. In this paper we use multi-objective deterministic and stochastic optimization to assess the current reservoir operation and planned capacity expansion in the Red River Basin (Northern Vietnam), and to evaluate the potential improvement by the adoption of a more sophisticated information system. To reach this goal we analyze the historical operation of the major controllable infrastructure in the basin, the HoaBinh reservoir on the Da River, explore re-operation options corresponding to different tradeoffs among the three main objectives (hydropower production, flood control and water supply), using multi-objective optimization techniques, namely Multi-Objective Genetic Algorithm. Finally, we assess the structural system potential and the need for capacity expansion by application of Deterministic Dynamic Programming. Results show that the current operation can only be relatively improved by advanced optimization techniques, while investment should be put into enlarging the system storage capacity and exploiting additional information to inform the operation

Performance of Deterministic and Probabilistic Hydrological Forecasts for the Short-Term Optimization of a Tropical Hydropower Reservoir

Hydropower is the most important source of electricity in Brazil. It is subject to the natural variability of water yield. One building block of the proper management of hydropower assets is the short-term forecast of reservoir inflows as input for an online, event-based optimization of its release strategy. While deterministic forecasts and optimization schemes are the established techniques for short-term reservoir management, the use of probabilistic ensemble forecasts and multi-stage stochastic optimization techniques is receiving growing attention. The present work introduces a novel, mass conservative scenario tree reduction in combination with a detailed hindcasting and closed-loop control experiments for a multi-purpose hydropower reservoir in a tropical region in Brazil. The case study is the hydropower project Três Marias, which is operated with two main objectives: (i) hydroelectricity generation and (ii) flood control downstream. In the experiments, precipitation forecasts based on observed data, deterministic and probabilistic forecasts are used to generate streamflow forecasts in a hydrological model over a period of 2 years. Results for a perfect forecast show the potential benefit of the online optimization and indicate a desired forecast lead time of 30 days. In comparison, the use of actual forecasts of up to 15 days shows the practical benefit of operational forecasts, where stochastic optimization (15 days lead time) outperforms the deterministic version (10 days lead time) significantly. The range of the energy production rate between the different approaches is relatively small, between 78% and 80%, suggesting that the use of stochastic optimization combined with ensemble forecasts leads to a significantly higher level of flood protection without compromising the energy production

Next generation intelligent completions for multi-stacked brownfield in Malaysia

Multi-stacked brownfield in Malaysia is known to have zonal contrast reservoir pressure and water cut. Commingled production without any flow control such as conventional on-off sliding sleeve will induce cross flow of production from a high pressure reservoir to lower pressure reservoir which disables optimum oil production. Having high zonal water cut contrast will cause early or excessive water production translates to deferred oil production. To pro-actively prevent these occurrences, adaptation of intelligent completion components such as Permanent Downhole Gauge (PDG) and surface-controlled Flow Control Valve (FCV) can be used. Downhole FCV choke is designed to cater for the dynamic changes of reservoir properties predicted over well life. In order to standardize the FCV choke sizing by well or by campaign, the choke sizing will be averaged to fit for all layers which is not the ultimate optimized design for maximum oil production. Latest in market today, electrical driven infinite position FCV is the solution to conventional hydraulic actuated FCV. Having infinite position enables optimized choke sizing for all reservoir layers and flexible to tackle uncertainties and dynamic changes of reservoir properties over time which enables the ultimate optimum oil production and water cut reduction. Besides choke sizing, deployment method and operating method also contribute to installation and operating efficiency. Conventional multi-position FCVs in market today are either fully hydraulic operated or electro-hydraulic operated which require hydraulic pump units at surface to enable pressuring up hydraulic control lines to change the position of FCV. It is also time consuming during deployment due to the requirement of electrical splicing, hydraulic splicing and FCV actuation sequence. Infinite position FCV is electrically operated using single downhole cable that can be multi-dropped to more than 25 FCV which reduces deployment time. With WellWatcher Advisor software that provides real time optimization features, operating efficiency is improved significantly with infinite position FCV as compared to conventional multi-position FCV and on-off sliding sleeve

Optimal irrigation water allocation using a genetic algorithm under various weather conditions

Growing water scarcity, due to growing populations and varying natural conditions, puts pressure on irrigation systems, which often are the main consumptive water users. Therefore, water resources management to improve the allocation of limited water supplies is essential. In this study, a non-linear programming optimization model with an integrated soil/water balance is developed to determine the optimal reservoir release policies and the optimal cropping pattern around Doroudzan Dam in the South-West of Iran. The proposed model was solved using a genetic algorithm (GA). Four weather conditions were identified by combining the probability levels of rainfall, evapotranspiration and inflow. Moreover, two irrigation strategies, full irrigation and deficit irrigation were modeled under each weather condition. The results indicate that for all weather conditions the total farm income and the total cropped area under deficit irrigation were larger than those under full irrigation. In addition, our results show that when the weather conditions and the availability of water changes the optimal area under corn and sugar beet decreases sharply. In contrast, the change in area cropped with wheat is small. It is concluded that the optimization approach has been successfully applied to Doroudzan Dam region. Thus, decision makers and water authorities can use it as an effective tool for such large and complex irrigation planning problems

Study of Gas Production from Shale Reservoirs with Multi-Stage Hydraulic Fracturing Horizontal Well Considering Multiple Transport Mechanisms

Development of unconventional shale gas reservoirs (SGRs) has been boosted by the advancements in two key technologies: horizontal drilling and multi-stage hydraulic fracturing. A large number of multi-stage fractured horizontal wells (MsFHW) have been drilled to enhance reservoir production performance. Gas flow in SGRs is a multi-mechanism process, including: desorption, diffusion, and non-Darcy flow. The productivity of the SGRs with MsFHW is influenced by both reservoir conditions and hydraulic fracture properties. However, rare simulation work has been conducted for multi-stage hydraulic fractured SGRs. Most of them use well testing methods, which have too many unrealistic simplifications and assumptions. Also, no systematical work has been conducted considering all reasonable transport mechanisms. And there are very few works on sensitivity studies of uncertain parameters using real parameter ranges. Hence, a detailed and systematic study of reservoir simulation with MsFHW is still necessary. In this paper, a dual porosity model was constructed to estimate the effect of parameters on shale gas production with MsFHW. The simulation model was verified with the available field data from the Barnett Shale. The following mechanisms have been considered in this model: viscous flow, slip flow, Knudsen diffusion, and gas desorption. Langmuir isotherm was used to simulate the gas desorption process. Sensitivity analysis on SGRs\u27 production performance with MsFHW has been conducted. Parameters influencing shale gas production were classified into two categories: reservoir parameters including matrix permeability, matrix porosity; and hydraulic fracture parameters including hydraulic fracture spacing, and fracture half-length. Typical ranges of matrix parameters have been reviewed. Sensitivity analysis have been conducted to analyze the effect of the above factors on the production performance of SGRs. Through comparison, it can be found that hydraulic fracture parameters are more sensitive compared with reservoir parameters. And reservoirs parameters mainly affect the later production period. However, the hydraulic fracture parameters have a significant effect on gas production from the early period. The results of this study can be used to improve the efficiency of history matching process. Also, it can contribute to the design and optimization of hydraulic fracture treatment design in unconventional SGRs

Modelling of a Gas Cap Gas Lift System

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A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs

Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach. In this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome. Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin