Maximizing Oil Recovery in a Naturally Fractured Carbonate Reservoir Using Computational Intelligence based on Particle Swarm Optimization

2018 ◽  
Author(s):  
Mohammad Rasheed Khan ◽  
Ahmed Sadeed ◽  
Abdul Asad ◽  
Zeeshan Tariq ◽  
Muhammad Tauqeer
Keyword(s):  
Particle Swarm Optimization ◽  
Oil Recovery ◽  
Computational Intelligence ◽  
Particle Swarm ◽  
Carbonate Reservoir ◽  
Swarm Optimization ◽  
Naturally Fractured ◽  
Fractured Carbonate

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.

Gas Injection Enhanced Oil Recovery Application in a Mature Naturally-Fractured-Carbonate Reservoir

2014 ◽  
Author(s):  
J. E. Buenaventura ◽  
R. S. Alvarez ◽  
J. G. Flores ◽  
I. E. Martinez
Keyword(s):  
Enhanced Oil Recovery ◽  
Oil Recovery ◽  
Gas Injection ◽  
Carbonate Reservoir ◽  
Naturally Fractured ◽  
Fractured Carbonate

The Impact Of Gas Injection On The Oil Recovery Of A Giant Naturally Fractured Carbonate Reservoir

1993 ◽  
Author(s):  
A. Badakhshan ◽  
H. Golshan ◽  
H.R. Musavi-Nezhad ◽  
F.A. Sobbi
Keyword(s):  
Oil Recovery ◽  
Gas Injection ◽  
Carbonate Reservoir ◽  
Naturally Fractured ◽  
Fractured Carbonate ◽  
The Impact

The Binary Differential Search Algorithm Approach for Solving Uncapacitated Facility Location Problems

2017 ◽  
Vol 14 (1) ◽  
pp. 670-684 ◽  
Author(s):  
Mehmet Akif Sahman ◽  
Adem Alpaslan Altun ◽  
Abdullah Oktay Dündar
Keyword(s):  
Particle Swarm Optimization ◽  
Computational Intelligence ◽  
Artificial Bee Colony ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Benchmark Problems ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Binary Optimization ◽  
Bee Colony

Recently, many Computational-Intelligence algorithms have been proposed for solving continuous problem. The Differential Search Algorithm (DSA), a computational-intelligence based algorithm inspired by the migration movements of superorganisms, is developed to solve continuous problems. However, DSA proposed for solving problems with continuous search space proposed for solving should be modified for solving binary structured problems. When the DSA is intended for use in binary problems, continuous variables need to be converted into binary format due to solution space structure of this type of problem. In this study, the DSA is modified to solve binary optimization problems by using a conversion approach from continuous values to binary values. The new algorithm has been designated as the binary DSA or BDSA for short. First, when finding donors with the BDSA, four search methods (Bijective, Surjective, Elitist1 and Elitist2) with different iteration numbers are used and tested on 15 UFLP benchmark problems. The Elitist2 approach, which provides the best solution of the four methods, is used in the BDSA, and the results are compared with Continuous Particle Swarm Optimization (CPSO), Continuous Artificial Bee Colony (ABCbin), Improved Binary Particle Swarm Optimization (IBPSO), Binary Artificial Bee Colony (binABC) and Discrete Artificial Bee Colony (DisABC) algorithms using UFLP benchmark problems. Results from the tests and comparisons show that the BDSA is fast, effective and robust for binary optimization.

scholarly journals Gravity Drainage Mechanism in Naturally Fractured Carbonate Reservoirs; Review and Application

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3699 ◽  
Author(s):  
Faisal Awad Aljuboori ◽  
Jang Hyun Lee ◽  
Khaled A. Elraies ◽  
Karl D. Stephen
Keyword(s):  
Gravity Model ◽  
Oil Recovery ◽  
Fractured Reservoirs ◽  
Carbonate Reservoir ◽  
Real Field ◽  
Gravity Drainage ◽  
Naturally Fractured ◽  
Fractured Carbonate ◽  
Matrix Blocks ◽  
The Matrix

Gravity drainage is one of the essential recovery mechanisms in naturally fractured reservoirs. Several mathematical formulas have been proposed to simulate the drainage process using the dual-porosity model. Nevertheless, they were varied in their abilities to capture the real saturation profiles and recovery speed in the reservoir. Therefore, understanding each mathematical model can help in deciding the best gravity model that suits each reservoir case. Real field data from a naturally fractured carbonate reservoir from the Middle East have used to examine the performance of various gravity equations. The reservoir represents a gas–oil system and has four decades of production history, which provided the required mean to evaluate the performance of each gravity model. The simulation outcomes demonstrated remarkable differences in the oil and gas saturation profile and in the oil recovery speed from the matrix blocks, which attributed to a different definition of the flow potential in the vertical direction. Moreover, a sensitivity study showed that some matrix parameters such as block height and vertical permeability exhibited a different behavior and effectiveness in each gravity model, which highlighted the associated uncertainty to the possible range that often used in the simulation. These parameters should be modelled accurately to avoid overestimation of the oil recovery from the matrix blocks, recovery speed, and to capture the advanced gas front in the oil zone.

scholarly journals Support Vector Regression Based on the Particle Swarm Optimization Algorithm for Tight Oil Recovery Prediction

ACS Omega ◽  
2021 ◽  
Author(s):  
Shihui Huang ◽  
Leng Tian ◽  
Jinshui Zhang ◽  
Xiaolong Chai ◽  
Hengli Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Support Vector Regression ◽  
Oil Recovery ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Support Vector ◽  
Tight Oil ◽  
Swarm Optimization

A Comparative Study of Genetic and Particle Swarm Optimization Algorithms and Their Hybrid Method in Water Flooding Optimization

2018 ◽  
Vol 140 (10) ◽  
Author(s):  
Majid Siavashi ◽  
Mohsen Yazdani
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Newton Method ◽  
Oil Recovery ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Water Flooding ◽  
Swarm Optimization

Optimization of oil production from petroleum reservoirs is an interesting and complex problem which can be done by optimal control of well parameters such as their flow rates and pressure. Different optimization techniques have been developed yet, and metaheuristic algorithms are commonly employed to enhance oil recovery projects. Among different metaheuristic techniques, the genetic algorithm (GA) and the particle swarm optimization (PSO) have received more attention in engineering problems. These methods require a population and many objective function calls to approach more the global optimal solution. However, for a water flooding project in a reservoir, each function call requires a long time reservoir simulation. Hence, it is necessary to reduce the number of required function evaluations to increase the rate of convergence of optimization techniques. In this study, performance of GA and PSO are compared with each other in an enhanced oil recovery (EOR) project, and Newton method is linked with PSO to improve its convergence speed. Furthermore, hybrid genetic algorithm-particle swarm optimization (GA-PSO) as the third optimization technique is introduced and all of these techniques are implemented to EOR in a water injection project with 13 decision variables. Results indicate that PSO with Newton method (NPSO) is remarkably faster than the standard PSO (SPSO). Also, the hybrid GA-PSO method is more capable of finding the optimal solution with respect to GA and PSO. In addition, GA-PSO, NPSO, and GA-NPSO methods are compared and, respectively, GA-NPSO and NPSO showed excellence over GA-PSO.

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