Efficient Particle Swarm Optimization of Well Placement to Enhance Oil Recovery Using a Novel Streamline-Based Objective Function

2016 ◽  
Vol 138 (5) ◽  
Author(s):  
Majid Siavashi ◽  
Mohammad Rasoul Tehrani ◽  
Ali Nakhaee
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Oil Recovery ◽  
Hybrid Approach ◽  
Particle Swarm ◽  
Full Time ◽  
Well Placement ◽  
Swarm Optimization ◽  
Simulation And Optimization ◽  
Optimal Well Placement

One of the main reservoir development plans is to find optimal locations for drilling new wells in order to optimize cumulative oil recovery. Reservoir simulation is a necessary tool to study different configurations of well locations to investigate the future of the reservoir and determine the optimal places for well drilling. Conventional well-known numerical methods require modern hardware for the simulation and optimization of large reservoirs. Simulation of such heterogeneous reservoirs with complex geological structures with the streamline-based simulation method is more efficient than the common simulation techniques. Also, this method by calculation of a new parameter called “time-of-flight” (TOF) offers a very useful tool to engineers. In the present study, TOF and distribution of streamlines are used to define a novel function which can be used as the objective function in an optimization problem to determine the optimal locations of injectors and producers in waterflooding projects. This new function which is called “well location assessment based on TOF” (WATOF) has this advantage that can be computed without full time simulation, in contrast with the cumulative oil production (COP) function. WATOF is employed for optimal well placement using the particle swarm optimization (PSO) approach, and its results are compared with those of the same problem with COP function, which leads to satisfactory outcomes. Then, WATOF function is used in a hybrid approach to initialize PSO algorithm to maximize COP in order to find optimal locations of water injectors and oil producers. This method is tested and validated in different 2D problems, and finally, the 3D heterogeneous SPE-10 reservoir model is considered to search locations of wells. By using the new objective function and employing the hybrid method with the streamline simulator, optimal well placement projects can be improved remarkably.

Comparison of SVC and TCSC Installation in Transmission Line with Loss Minimization and Cost of Installation via Particle Swarm Optimization

2015 ◽  
Vol 785 ◽  
pp. 495-499
Author(s):  
Siti Amely Jumaat ◽  
Ismail Musirin
Keyword(s):  
Particle Swarm Optimization ◽  
Transmission Line ◽  
Objective Function ◽  
Transmission Loss ◽  
Particle Swarm ◽  
Static Var Compensator ◽  
Loss Minimization ◽  
Swarm Optimization ◽  
The Cost ◽  
Thyristor Controlled Series Compensator

The paper presents a comparison of performance Static Var Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) with objective function to minimize the transmission loss, improve the voltage and monitoring the cost of installation. Simulation performed on standard IEEE 30-Bus RTS and indicated that EPSO a feasible to achieve the objective function.

Inverse estimation of time-varying heat transfer coefficients for a hollow cylinder by using self-learning particle swarm optimization

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Kun-Yung Chen ◽  
Te-Wen Tu
Keyword(s):  
Heat Transfer ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
Hollow Cylinder ◽  
General Type ◽  
Particle Swarm ◽  
Time History ◽  
Time Varying ◽  
Swarm Optimization ◽  
Self Learning

Abstract An inverse methodology is proposed to estimate a time-varying heat transfer coefficient (HTC) for a hollow cylinder with time-dependent boundary conditions of different kinds on inner and outer surfaces. The temperatures at both the inner surface and the interior domain are measured for the hollow cylinder, while the time history of HTC of the outer surface will be inversely determined. This work first expressed the unknown function of HTC in a general form with unknown coefficients, and then regarded these unknown coefficients as the estimated parameters which can be randomly searched and found by the self-learning particle swarm optimization (SLPSO) method. The objective function which wants to be minimized was found with the absolute errors between the measured and estimated temperatures at several measurement times. If the objective function converges toward the null, the inverse solution of the estimated HTC will be found eventually. From numerical experiments, when the function of HTC with exponential type is performed, the unknown coefficients of the HTC function can be accurately estimated. On the contrary, when the function of HTC with a general type is conducted, the unknown coefficients of HTC are poorly estimated. However, the estimated coefficients of an HTC function with the general type can be regarded as the equivalent coefficients for the real function of HTC.

scholarly journals Self-adaptive potential-based stopping criteria for Particle Swarm Optimization with forced moves

2020 ◽  
Vol 14 (4) ◽  
pp. 285-311
Author(s):  
Bernd Bassimir ◽  
Manuel Schmitt ◽  
Rolf Wanka
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Particle Swarm ◽  
Upper Bounds ◽  
Local Optimum ◽  
Adaptive Potential ◽  
Stopping Criteria ◽  
Swarm Optimization ◽  
Self Adaptive

Abstract We study the variant of Particle Swarm Optimization that applies random velocities in a dimension instead of the regular velocity update equations as soon as the so-called potential of the swarm falls below a certain small bound in this dimension, arbitrarily set by the user. In this case, the swarm performs a forced move. In this paper, we are interested in how, by counting the forced moves, the swarm can decide for itself to stop its movement because it is improbable to find better candidate solutions than the already-found best solution. We formally prove that when the swarm is close to a (local) optimum, it behaves like a blind-searching cloud and that the frequency of forced moves exceeds a certain, objective function-independent value. Based on this observation, we define stopping criteria and evaluate them experimentally showing that good candidate solutions can be found much faster than setting upper bounds on the iterations and better solutions compared to applying other solutions from the literature.

A novel streamline-based objective function for well placement optimization in waterfloods

2020 ◽  
pp. 1-23
Author(s):  
Forouzan Naderi ◽  
Majid Siavashi ◽  
Ali Nakhaee
Keyword(s):  
Objective Function ◽  
Oil Recovery ◽  
Reservoir Simulation ◽  
Target Function ◽  
Water Flooding ◽  
Local Optimum ◽  
Full Time ◽  
Well Placement ◽  
Placement Optimization ◽  
Well Placement Optimization

Abstract In reservoir development plans, well placement optimization is usually performed to better sweep oil and reduce the amount of trapped oil inside reservoirs. Long term optimization of well placement requires multiple times simulation of reservoirs which makes these problems cumbersome, especially when a large number of decision variables exist. Cumulative oil production (COP) or net present value (NPV) functions are commonly used as the objective function of optimal enhance oil recovery projects. Use of these functions requires a full-time reservoir simulation and their convergence could be difficult with the chance to be trapped in local optimum solutions. In this study, the novel proportionally distributed streamlines (PDSL) target function is proposed that can be minimized to reach the optimal well placement. PDSL can be estimated even without full time reservoir simulation. PDSL tries to direct the appropriate number of streamlines toward the regions with larger amount of oil in the shortest time and hence can improve oil recovery. Particle swarm optimization (PSO) method linked to an in-house streamline-based reservoir simulator is implemented to optimize well placement of water flooding problems in a 2D heterogeneous reservoir model.

Optimal Capacitor Allocation in Distribution System Using Particle Swarm Optimization

2014 ◽  
Vol 699 ◽  
pp. 770-775
Author(s):  
Ihsan Jabbar Hasan ◽  
Chin Kim Gan ◽  
Meysam Shamshiri ◽  
Mohd Ruddin Ab Ghani ◽  
Ismadi bin Bugis
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Particle Swarm ◽  
Voltage Profile ◽  
Power Losses ◽  
Swarm Optimization ◽  
Standard Case ◽  
Capacitor Placement ◽  
Penalty Factor ◽  
Ieee Standard

Capacitor installation is one of the most commonly used methods for reactive power compensation in the distribution networks. In this paper, the optimum capacitor placement and its sizing has been applied in the distribution network in terms of power losses minimization and voltage profile improvement. The maximum and minimum bus voltage and the maximum possible capacitor size are the constraints of optimum capacitor placement and sizing problem. There are considered as the penalty factor in the objective function. In order to evaluate the obtained objective function, the Particle Swarm Optimization (PSO) is utilized to find the best possible capacitor placement and capacity. The OpenDSS software has then been utilized to solve the power flow through Matlab coding interface. To validate the functionality of the proposed method, the IEEE 13-bus test system is implemented and the obtained results have been compared with the IEEE standard case without capacitor compensation. The results show that the proposed algorithm is more cost effective and has lower power losses as compared to the IEEE standard case. In addition, the voltage profile has been improved, accordingly.

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