scholarly journals A compositional function hybridization of PSO and GWO for solving well placement optimization problem

2021 ◽  
Author(s):  
Daniel Ocran ◽  
Sunday Sunday Ikiensikimama ◽  
Eric Broni-Bediako
Keyword(s):  
Optimization Problem ◽  
Well Placement ◽  
Placement Optimization ◽  
Well Placement Optimization

scholarly journals A Modified Niching Crow Search Approach to Well Placement Optimization

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 857
Author(s):  
Jahedul Islam ◽  
Md Shokor A. Rahaman ◽  
Pandian M. Vasant ◽  
Berihun Mamo Negash ◽  
Ahshanul Hoqe ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Multimodal Optimization ◽  
Test Functions ◽  
Well Placement ◽  
Suggested Approach ◽  
Placement Optimization ◽  
Well Placement Optimization ◽  
Search Approach

Well placement optimization is considered a non-convex and highly multimodal optimization problem. In this article, a modified crow search algorithm is proposed to tackle the well placement optimization problem. This article proposes modifications based on local search and niching techniques in the crow search algorithm (CSA). At first, the suggested approach is verified by experimenting with the benchmark functions. For test functions, the results of the proposed approach demonstrated a higher convergence rate and a better solution. Again, the performance of the proposed technique is evaluated with well placement optimization problem and compared with particle swarm optimization (PSO), the Gravitational Search Algorithm (GSA), and the Crow search algorithm (CSA). The outcomes of the study revealed that the niching crow search algorithm is the most efficient and effective compared to the other techniques.

A holistic review on artificial intelligence techniques for well placement optimization problem

2020 ◽  
Vol 141 ◽  
pp. 102767 ◽  
Author(s):  
Jahedul Islam ◽  
Pandian M. Vasant ◽  
Berihun Mamo Negash ◽  
Moacyr Bartholomeu Laruccia ◽  
Myo Myint ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Optimization Problem ◽  
Well Placement ◽  
Artificial Intelligence Techniques ◽  
Placement Optimization ◽  
Holistic Review ◽  
Well Placement Optimization

A Survey of Nature-Inspired Algorithms With Application to Well Placement Optimization

2020 ◽  
pp. 32-45 ◽  
Author(s):  
Jahedul Islam ◽  
Pandian M. Vasant ◽  
Berihun Mamo Negash ◽  
Moacyr Bartholomeu Laruccia ◽  
Myo Myint
Keyword(s):  
Optimization Problem ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Economic Factors ◽  
Well Placement ◽  
Field Development ◽  
Placement Optimization ◽  
Well Placement Optimization ◽  
Nature Inspired Algorithms ◽  
Metaheuristic Techniques

Well placement optimization is one of the major challenging factors in the field development process in the oil and gas industry. This chapter aims to survey prominent metaheuristic techniques, which solve well the placement optimization problem. The well placement optimization problem is considered as high dimensional, discontinuous, and multi-model optimization problem. Moreover, the computational expenses further complicate the issue. Over the last decade, both gradient-based and gradient-free optimization methods were implemented. Gradient-free optimization, such as the particle swarm optimization, genetic algorithm, is implemented in this area. These optimization techniques are utilized as standalone or as the hybridization of optimization methods to maximize the economic factors. In this chapter, the authors survey the two most popular nature-inspired metaheuristic optimization techniques and their application to maximize the economic factors.

Well Placement Optimization With Cat Swarm Optimization Algorithm Under Oilfield Development Constraints

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Chen Hongwei ◽  
Feng Qihong ◽  
Zhang Xianmin ◽  
Wang Sen ◽  
Zhou Wensheng ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Well Placement ◽  
Swarm Optimization ◽  
Penalization Method ◽  
De Algorithm ◽  
Placement Optimization ◽  
Well Placement Optimization ◽  
Oilfield Development ◽  
Handling Method

Proper well placement can improve the oil recovery and economic benefits during oilfield development. Due to the nonlinear and complex properties of well placement optimization, an effective optimization algorithm is required. In this paper, cat swarm optimization (CSO) algorithm is applied to optimize well placement for maximum net present value (NPV). CSO algorithm, a heuristic algorithm that mimics the behavior of a swarm of cats, has characteristics of flexibility, fast convergence, and high robustness. Oilfield development constraints are taken into account during well placement optimization process. Rejection method, repair method, static penalization method, dynamic penalization method and adapt penalization method are, respectively, applied to handle well placement constraints and then the optimal constraint handling method is obtained. Besides, we compare the CSO algorithm optimization performance with genetic algorithm (GA) and differential evolution (DE) algorithm. With the selected constraint handling method, CSO, GA, and DE algorithms are applied to solve well placement optimization problem for a two-dimensional (2D) conceptual model and a three-dimensional (3D) semisynthetic reservoir. Results demonstrate that CSO algorithm outperforms GA and DE algorithm. The proposed CSO algorithm can effectively solve the constrained well placement optimization problem with adapt penalization method.

Embedded discrete fracture modeling: Flow diagnostics, non-Darcy flow, and well placement optimization

2022 ◽  
Vol 208 ◽  
pp. 109477
Author(s):  
Shuai Ma ◽  
Binshan Ju ◽  
Lin Zhao ◽  
Knut-Andreas Lie ◽  
Yintao Dong ◽  
...  
Keyword(s):  
Darcy Flow ◽  
Well Placement ◽  
Flow Diagnostics ◽  
Placement Optimization ◽  
Fracture Modeling ◽  
Discrete Fracture ◽  
Well Placement Optimization ◽  
Modeling Flow

Selection of Representative Scenarios Using Multiple Simulation Outputs for Robust Well Placement Optimization in Greenfields

2021 ◽  
Author(s):  
Seyed Kourosh Mahjour ◽  
Antonio Alberto Souza Santos ◽  
Susana Margarida da Graca Santos ◽  
Denis Jose Schiozer
Keyword(s):  
Reference Model ◽  
Simulation Models ◽  
Cumulative Distribution ◽  
Small Subset ◽  
Fine Grid ◽  
Well Placement ◽  
Placement Optimization ◽  
Well Placement Optimization ◽  
Selection Of ◽  
Dynamic Uncertainties

Abstract In greenfield projects, robust well placement optimization under different scenarios of uncertainty technically requires hundreds to thousands of evaluations to be processed by a flow simulator. However, the simulation process for so many evaluations can be computationally expensive. Hence, simulation runs are generally applied over a small subset of scenarios called representative scenarios (RS) approximately showing the statistical features of the full ensemble. In this work, we evaluated two workflows for robust well placement optimization using the selection of (1) representative geostatistical realizations (RGR) under geological uncertainties (Workflow A), and (2) representative (simulation) models (RM) under the combination of geological and reservoir (dynamic) uncertainties (Workflow B). In both workflows, an existing RS selection technique was used by measuring the mismatches between the cumulative distribution of multiple simulation outputs from the subset and the full ensemble. We applied the Iterative Discretized Latin Hypercube (IDLHC) to optimize the well placements using the RS sets selected from each workflow and maximizing the expected monetary value (EMV) as the objective function. We evaluated the workflows in terms of (1) representativeness of the RS in different production strategies, (2) quality of the defined robust strategies, and (3) computational costs. To obtain and validate the results, we employed the synthetic UNISIM-II-D-BO benchmark case with uncertain variables and the reference fine- grid model, UNISIM-II-R, which works as a real case. This work investigated the overall impacts of the robust well placement optimization workflows considering uncertain scenarios and application on the reference model. Additionally, we highlighted and evaluated the importance of geological and dynamic uncertainties in the RS selection for efficient robust well placement optimization.

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