Optimisation and economical evaluation of infill drilling in CSG reservoirs using a multi-objective genetic algorithm

2013 ◽  
Vol 53 (1) ◽  
pp. 381
Author(s):  
Alireza Salmachi ◽  
Mohammad Sayyafzadeh ◽  
Manouchehr Haghighi
Keyword(s):  
Genetic Algorithm ◽  
Water Treatment ◽  
Objective Function ◽  
Treatment Cost ◽  
Coal Bed ◽  
Water Production ◽  
Infill Drilling ◽  
Water Treatment Cost ◽  
The Cost ◽  
Gas Price

Water production in the early life of Coal Seam Gas (CSG) recovery makes these reservoirs different from conventional gas reservoirs. Normally, a large amount of water is produced during the early production period, while the gas-rate is negligible. It is essential to drill infill wells in optimum locations to reduce the water production and increase the gas recovery. To optimise infill locations in a CSG reservoir, an integrated framework is developed to couple the reservoir flow simulator (ECLIPSE) and the genetic algorithm (GA) optimisation toolbox of (MATLAB). In this study, the desired objective function is the NPV of the infill drilling. To obtain the economics of the infill drilling project, the objective function is split into two objectives. The first objective is the gas income; the second objective is the cost associated with water production. The optimisation problem is then solved using the multi-objective solver. The economics of the infill drilling program is investigated for a case study constructed based on the available data from the Tiffany unit in San Juan basin when gas price and water treatment cost are variable. Best obtained optimal locations of 20 new wells in the reservoir are attained using this optimisation framework to maximise the profit of this project. The results indicate that when the gas price is less than $2/Mscf, the infill plan, regardless of the cost of water treatment, is not economical and drilling additional wells cannot be economically justified. When the cost of water treatment and disposal increases from $0.01/STB to $4/STB, the optimisation framework intelligently distributes the infill wells across the reservoir in a way that the total water production of infill wells is reduced by 26%. Simulation results also indicate that when water treatment is an expensive operation, lower water production is attained by placing the infill wells in depleted sections of the coal bed, close to the existing wells. When water treatment cost is low, however, infill wells are freely allocated in virgin sections of the coal bed, where both coal gas content and reservoir pressure are high.

scholarly journals Optimized Placement of Connecting the Distributed Generationswork Stand Alone to Improve the Distribution Systems Reliability

2013 ◽  
Vol 64 (2) ◽  
pp. 76-83
Author(s):  
Hamed Hashemi-Dezaki ◽  
Ali Agheli ◽  
Behrooz Vahidi ◽  
Hossein Askarian-Abyaneh
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
System Reliability ◽  
Distribution Systems ◽  
Analytical Approach ◽  
Distributed Generations ◽  
Joint Point ◽  
Cost Of Energy ◽  
Optimum Solution ◽  
The Cost

The use of distributed generations (DGs) in distribution systems has been common in recent years. Some DGs work stand alone and it is possible to improve the system reliability by connecting these DGs to system. The joint point of DGs is an important parameter in the system designing. In this paper, a novel methodology is proposed to find the optimum solution in order to make a proper decision about DGs connection. In the proposed method, a novel objective function is introduced which includes the cost of connector lines between DGs and network and the cost of energy not supplied (CENS) savings. Furthermore, an analytical approach is used to calculate the CENS decrement. To solve the introduced nonlinear optimization programming, the genetic algorithm (GA) is used. The proposed method is applied to a realistic 183-bus system of Tehran Regional Electrical Company (TREC). The results illustrate the effectiveness of the method to improve the system reliability by connecting the DGs work stand alone in proper placements.

scholarly journals Hybrid water treatment cost prediction model for raw water intake optimization

2016 ◽  
Vol 75 ◽  
pp. 230-242 ◽  
Author(s):  
Edoardo Bertone ◽  
Rodney A. Stewart ◽  
Hong Zhang ◽  
Kelvin O'Halloran
Keyword(s):  
Water Treatment ◽  
Prediction Model ◽  
Water Intake ◽  
Treatment Cost ◽  
Raw Water ◽  
Cost Prediction ◽  
Water Treatment Cost

scholarly journals Assessment of Urban Water Supply System Based on Query Optimization Strategy

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Shibao Lu ◽  
Yizi Shang ◽  
Wei Li ◽  
Zhimin Wang
Keyword(s):  
Water Treatment ◽  
Water Supply ◽  
Query Optimization ◽  
Treatment Cost ◽  
Distributed Database ◽  
Urban Water ◽  
Optimization Strategy ◽  
Urban Water Supply ◽  
Treatment Efficiency ◽  
Water Treatment Cost

As a complicated water treatment system project, an urban water supply plays a significant role in enhancing ecological civilization construction, promoting social-economic sustainable development, and improving the living environment of humans. This paper has the goal of improving water treatment efficiency and reducing water treatment cost based on comparative studies by applying two types of distributed database query optimization methods, including the system for a distributed database (SDD-1) and all reduction algorithms. The approach involves the components of partial relations and deletes the database components that are unrelated to a query, using only attributes related to a query. Thereby, the realization process becomes simple, with a small amount of transmitted data. The results show that the validity and convenience of all reduction algorithms involve the whole query process. An algorithm is independent of the chart of static characteristics and can realize all reduction states without depending on analyzing the benefits from intermediate semijoin results, which will ultimately contribute to reductions in the transmission of useless data in the network and communication costs. Implementation of the query optimization strategy can improve water treatment efficiency, reduce water treatment cost, lower water treatment expense, and implement effective communication.

scholarly journals OPTIMUM ALLOCATION OF WATER TO THE CULTIVATION FARMS USING GENETIC ALGORITHM

2015 ◽  
Vol XL-1-W5 ◽  
pp. 631-638 ◽  
Author(s):  
B. Saeidian ◽  
M. Saadi Mesgari ◽  
M. Ghodousi
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Production Function ◽  
Water Scarcity ◽  
Deficit Irrigation ◽  
Optimum Allocation ◽  
Water Production ◽  
Irrigation Planning ◽  
Available Water ◽  
Crop Type

The water scarcity crises in the world and specifically in Iran, requires the proper management of this valuable resource. According to the official reports, around 90 percent of the water in Iran is used for agriculture. Therefore, the adequate management and usage of water in this section can help significantly to overcome the above crises. The most important aspect of agricultural water management is related to the irrigation planning, which is basically an allocation problem. The proper allocation of water to the farms is not a simple and trivial problem, because of the limited amount of available water, the effect of different parameters, nonlinear characteristics of the objective function, and the wideness of the solution space. Usually To solve such complex problems, a meta-heuristic method such as genetic algorithm could be a good candidate.<br><br> In this paper, Genetic Algorithm (GA) is used for the allocation of different amount of water to a number of farms. In this model, the amount of water transferable using canals of level one, in one period of irrigation is specified. In addition, the amount of water required by each farm is calculated using crop type, stage of crop development, and other parameters. Using these, the water production function of each farm is determined. Then, using the water production function, farm areas, and the revenue and cost of each crop type, the objective function is calculated. This objective function is used by GA for the allocation of water to the farms. The objective function is defined such that the economical profit extracted from all farms is maximized. Moreover, the limitation related to the amount of available water is considered as a constraint. In general, the total amount of allocated water should be less than the finally available water (the water transferred trough the level one canals). Because of the intensive scarcity of water, the deficit irrigation method are considered. In this method, the planning is on the basis of the optimum and limited allocation of water, and not on the basis of the each crop water requirement. According to the available literature, in the condition of water scarcity, the implementation of deficit irrigation strategy results in higher economical income. <br><br> The main difference of this research with others is the allocation of water to the farms. Whilst, most of similar researches concentrate on the allocation of water to different water consumption sections (such as agriculture, industry etc.), networks and crops.<br><br> Using the GA for the optimization of the water allocation, proper solutions were generated that maximize the total economical income in the entire study area. In addition, although the search space was considerably wide, the results of the implementation showed an adequate convergence speed. The repeatability test of the algorithm also proved that the algorithm is reasonably stable. In general the usage of GA algorithm can be considered as an efficient and trustable method for such irrigation planning problems.<br><br> By optimum allocation of the water to the farms with different areas and crop types, and considering the deficit irrigation method, the general income of the entire area can be improved substantially.

Developing Schedule With Linear Programming (Case Study: STTF II Project Komplek Sukamukti Banjaran)

2020 ◽  
Vol 4 (02) ◽  
pp. 34-45
Author(s):  
Naufal Dzikri Afifi ◽  
Ika Arum Puspita ◽  
Mohammad Deni Akbar
Keyword(s):  
Linear Programming ◽  
Objective Function ◽  
Project Scheduling ◽  
Time Limit ◽  
Minimum Time ◽  
Service Cost ◽  
Trade Off ◽  
Overtime Work ◽  
The Cost

Shift to The Front II Komplek Sukamukti Banjaran Project is one of the projects implemented by one of the companies engaged in telecommunications. In its implementation, each project including Shift to The Front II Komplek Sukamukti Banjaran has a time limit specified in the contract. Project scheduling is an important role in predicting both the cost and time in a project. Every project should be able to complete the project before or just in the time specified in the contract. Delay in a project can be anticipated by accelerating the duration of completion by using the crashing method with the application of linear programming. Linear programming will help iteration in the calculation of crashing because if linear programming not used, iteration will be repeated. The objective function in this scheduling is to minimize the cost. This study aims to find a trade-off between the costs and the minimum time expected to complete this project. The acceleration of the duration of this study was carried out using the addition of 4 hours of overtime work, 3 hours of overtime work, 2 hours of overtime work, and 1 hour of overtime work. The normal time for this project is 35 days with a service fee of Rp. 52,335,690. From the results of the crashing analysis, the alternative chosen is to add 1 hour of overtime to 34 days with a total service cost of Rp. 52,375,492. This acceleration will affect the entire project because there are 33 different locations worked on Shift to The Front II and if all these locations can be accelerated then the duration of completion of the entire project will be effective

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

Modernization of Water Treatment System at Agricultural Enterprises Due to Introduction of Solar Energy

2020 ◽  
Vol 67 (1) ◽  
pp. 142-147
Author(s):  
Alina A. Aleksandrova ◽  
Maksim S. Zhuzhin ◽  
Yuliya M. Dulepova
Keyword(s):  
Mathematical Model ◽  
Water Treatment ◽  
Solar Energy ◽  
Energy Saving ◽  
Electric Energy ◽  
Solar Water Heater ◽  
Water Heater ◽  
Agricultural Enterprises ◽  
Solar Water ◽  
The Cost

Energy saving today is an integral part of the development strategy of agricultural organizations. Considerable attention is paid to the modernization and automation of technological processes in agricultural enterprises, which can improve the quality of work and reduce the cost of production. The direction of modernization is to reduce the consumption of electric energy by improving the water treatment system in livestock complexes. (Research purpose) The research purpose is to determine the potential of solar energy used in the Nizhny Novgorod region and to determine the possibility of its use for water heating in livestock complexes and to consider the cost-effectiveness of using a device to heat water through solar energy. (Materials and methods) Authors used an improved algorithm of Pixer and Laszlo, applied in the NASA project «Surface meteorology and Energy», which allows to calculate the optimal angle of inclination of the device for heating water. (Results and discussion) Designed a mock-up of a livestock complex with a solar water heater installed on the roof, protected by patent for invention No. 2672656. A mathematical model was designed experimentally to predict the results of the plant operation in non-described modes. (Conclusions) The article reveales the optimal capacity of the circulation pump. Authors have created a mathematical model of the device that allows to predict the water heating in a certain period of time. The article presents the calculations on the energy and economic efficiency of using a solar water heater. An electric energy saving of about 30 percent, in the economic equivalent of 35 percent.

scholarly journals Hybrid Seven-bar Press Mechanism

2018 ◽  
Vol 12 (3) ◽  
pp. 181-187
Author(s):  
M. Erkan Kütük ◽  
L. Canan Dülger
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Optimization ◽  
Hybrid System ◽  
Degrees Of Freedom ◽  
Dimensional Synthesis ◽  
Design Variables ◽  
Optimization Study ◽  
Metal Stamping

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

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