A Self-Adaptive Surrogate-Assisted Evolutionary Algorithm for Well Placement Optimization Problems

2015 ◽  
Author(s):  
Mohammad Sayyafzadeh
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Well Placement ◽  
Placement Optimization ◽  
Well Placement Optimization ◽  
Self Adaptive

A robust proxy for production well placement optimization problems

Fuel ◽  
2017 ◽  
Vol 206 ◽  
pp. 467-481 ◽  
Author(s):  
Behzad Pouladi ◽  
Sahar Keshavarz ◽  
Mohammad Sharifi ◽  
Mohammad Ali Ahmadi
Keyword(s):  
Optimization Problems ◽  
Production Well ◽  
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.

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.

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
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