Applications of Honey Bee Optimization in Reservoir Engineering Assisted History Matching

2021 ◽  
Author(s):  
Mohamed Shams ◽  
Ahmed El-Banbi ◽  
M. Helmy Sayyouh
Keyword(s):  
Optimization Algorithm ◽  
History Matching ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Reservoir Engineering ◽  
Bee Colony ◽  
Assisted History Matching ◽  
Bee Colony Optimization ◽  
Matching Procedure

Abstract Bee colony optimization technique is a stochastic population-based optimization algorithm inspired by the natural optimization behavior shown by honey bees during searching for food. Bee colony optimization algorithm has been successfully applied to various real-world optimization problems mostly in routing, transportation, and scheduling fields. This paper introduces the bee colony optimization method as the optimization technique in reservoir engineering assisted history matching procedure. The superiority of the proposed optimization algorithm is validated by comparing its performance with two other advanced nature-inspired optimization techniques (genetic and particle swarm optimization algorithms) in three synthetic assisted history matching problems. In addition, this paper presents the application of the bee colony optimization technique in assisting the history match of a full field reservoir simulation model of a mature gas-cap reservoir with 28 years of history. The resultant history matched model is compared with those obtained using a manual history matching procedure and using the most widely applied optimization algorithm used in assisted history matching commercial software tools. The results of this work indicate that employing the bee colony algorithm as the optimization technique in the assisted history matching workflow yields noticeable enhancement in terms of match quality and time required to achieve a reasonable match.

Successful Application of Honey-Bee Optimization Technique in Reservoir Engineering Assisted History Matching: Case Study

2021 ◽  
Author(s):  
Mohamed Shams
Keyword(s):  
Simulation Model ◽  
Reservoir Simulation ◽  
Optimization Algorithm ◽  
History Matching ◽  
Optimization Technique ◽  
Food Sources ◽  
Bee Colony ◽  
Assisted History Matching ◽  
Bee Colony Optimization ◽  
Reservoir Simulation Model

Abstract This paper provides the field application of the bee colony optimization algorithm in assisting the history match of a real reservoir simulation model. Bee colony optimization algorithm is an optimization technique inspired by the natural optimization behavior shown by honeybees during searching for food. The way that honeybees search for food sources in the vicinity of their nest inspired computer science researchers to utilize and apply same principles to create optimization models and techniques. In this work the bee colony optimization mechanism is used as the optimization algorithm in the assisted the history matching workflow applied to a reservoir simulation model of WD-X field producing since 2004. The resultant history matched model is compared with with those obtained using one the most widely applied commercial AHM software tool. The results of this work indicate that using the bee colony algorithm as the optimization technique in the assisted history matching workflow provides noticeable enhancement in terms of match quality and time required to achieve a reasonable match.

Significance of Biologically Inspired Optimization Techniques in Real-Time Applications

2017 ◽  
pp. 150-180
Author(s):  
Sushruta Mishra ◽  
Brojo Kishore Mishra ◽  
Hrudaya Kumar Tripathy
Keyword(s):  
Real Time ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Biologically Inspired ◽  
Swarm Optimization ◽  
Genetic Search ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

The techniques inspired from the nature based evolution and aggregated nature of social colonies have been promising and shown excellence in handling complicated optimization problems thereby gaining huge popularity recently. These methodologies can be used as an effective problem solving tool thereby acting as an optimizing agent. Such techniques are called Bio inspired computing. Our study surveys the recent advances in biologically inspired swarm optimization methods and Evolutionary methods, which may be applied in various fields. Four real time scenarios are demonstrated in the form of case studies to show the significance of bio inspired algorithms. The techniques that are illustrated here include Differential Evolution, Genetic Search, Particle Swarm optimization and artificial bee Colony optimization. The results inferred by implanting these techniques are highly encouraging.

Significance of Biologically Inspired Optimization Techniques in Real-Time Applications

2020 ◽  
pp. 224-248
Author(s):  
Sushruta Mishra ◽  
Brojo Kishore Mishra ◽  
Hrudaya Kumar Tripathy
Keyword(s):  
Real Time ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Biologically Inspired ◽  
Swarm Optimization ◽  
Genetic Search ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

The techniques inspired from the nature based evolution and aggregated nature of social colonies have been promising and shown excellence in handling complicated optimization problems thereby gaining huge popularity recently. These methodologies can be used as an effective problem solving tool thereby acting as an optimizing agent. Such techniques are called Bio inspired computing. Our study surveys the recent advances in biologically inspired swarm optimization methods and Evolutionary methods, which may be applied in various fields. Four real time scenarios are demonstrated in the form of case studies to show the significance of bio inspired algorithms. The techniques that are illustrated here include Differential Evolution, Genetic Search, Particle Swarm optimization and artificial bee Colony optimization. The results inferred by implanting these techniques are highly encouraging.

From Optimization to Clustering

2015 ◽  
pp. 594-619 ◽  
Author(s):  
Megha Vora ◽  
T. T. Mirnalinee
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Efficient Solution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Ant Colony Optimization Algorithm ◽  
Swarm Optimization ◽  
Insight Into

In the past two decades, Swarm Intelligence (SI)-based optimization techniques have drawn the attention of many researchers for finding an efficient solution to optimization problems. Swarm intelligence techniques are characterized by their decentralized way of working that mimics the behavior of colony of ants, swarm of bees, flock of birds, or school of fishes. Algorithmic simplicity and effectiveness of swarm intelligence techniques have made it a powerful tool for solving global optimization problems. Simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the particle swarm optimization algorithm. Studies of the foraging behavior of ants resulted in the development of ant colony optimization algorithm. This chapter provides insight into swarm intelligence techniques, specifically particle swarm optimization and its variants. The objective of this chapter is twofold: First, it describes how swarm intelligence techniques are employed to solve various optimization problems. Second, it describes how swarm intelligence techniques are efficiently applied for clustering, by imposing clustering as an optimization problem.

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