scholarly journals Generalized Multi-Scale Stochastic Reservoir Opportunity Index for enhanced well placement optimization under uncertainty in green and brownfields

2021 ◽  
Vol 76 ◽  
pp. 41
Author(s):  
Forough Vaseghi ◽  
Mohammad Ahmadi ◽  
Mohammad Sharifi ◽  
Mario Vanhoucke
Keyword(s):  
Optimization Algorithm ◽  
Dynamic Properties ◽  
Computational Time ◽  
Optimum Number ◽  
Static Properties ◽  
Search Region ◽  
Well Placement ◽  
Multi Scale ◽  
Placement Optimization ◽  
Well Placement Optimization

Well placement planning is one of the challenging issues in any field development plan. Reservoir engineers always confront the problem that which point of the field should be drilled to achieve the highest recovery factor and/or maximum sweep efficiency. In this paper, we use Reservoir Opportunity Index (ROI) as a spatial measure of productivity potential for greenfields, which hybridizes the reservoir static properties, and for brownfields, ROI is replaced by Dynamic Measure (DM), which takes into account the current dynamic properties in addition to static properties. The purpose of using these criteria is to diminish the search region of optimization algorithms and as a consequence, reduce the computational time and cost of optimization, which are the main challenges in well placement optimization problems. However, considering the significant subsurface uncertainty, a probabilistic definition of ROI (SROI) or DM (SDM) is needed, since there exists an infinite number of possible distribution maps of static and/or dynamic properties. To build SROI or SDM maps, the k-means clustering technique is used to extract a limited number of characteristic realizations that can reasonably span the uncertainties. In addition, to determine the optimum number of clustered realizations, Higher-Order Singular Value Decomposition (HOSVD) method is applied which can also compress the data for large models in a lower-dimensional space. Additionally, we introduce the multiscale spatial density of ROI or DM (D2ROI and D2DM), which can distinguish between regions of high SROI (or SDM) in arbitrary neighborhood windows from the local SROI (or SDM) maxima with low values in the vicinity. Generally, we develop and implement a new systematic approach for well placement optimization for both green and brownfields on a synthetic reservoir model. This approach relies on the utilization of multi-scale maps of SROI and SDM to improve the initial guess for optimization algorithm. Narrowing down the search region for optimization algorithm can substantially speed up the convergence and hence the computational cost would be reduced by a factor of 4.

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.

scholarly journals Quantum-Based Analytical Techniques on the Tackling of Well Placement Optimization

2020 ◽  
Vol 10 (19) ◽  
pp. 7000
Author(s):  
Jahedul Islam ◽  
Berihun Mamo Negash ◽  
Pandian Vasant ◽  
Nafize Ishtiaque Hossain ◽  
Junzo Watada
Keyword(s):  
Shale Gas ◽  
Optimization Algorithm ◽  
Bat Algorithm ◽  
Computational Techniques ◽  
Diversity Analysis ◽  
Gas Reservoir ◽  
Well Placement ◽  
Shale Gas Reservoir ◽  
Placement Optimization ◽  
Well Placement Optimization

The high dimensional, multimodal, and discontinuous well placement optimization is one of the main difficult factors in the development process of conventional as well as shale gas reservoir, and to optimize this problem, metaheuristic techniques still suffer from premature convergence. Hence, to tackle this problem, this study aims at introducing a dimension-wise diversity analysis for well placement optimization. Moreover, in this article, quantum computational techniques are proposed to tackle the well placement optimization problem. Diversity analysis reveals that dynamic exploration and exploitation strategy is required for each reservoir. In case studies, the results of the proposed approach outperformed all the state-of-the-art algorithms and provided a better solution than other algorithms with higher convergence rate, efficiency, and effectiveness. Furthermore, statistical analysis shows that there is no statistical difference between the performance of Quantum bat algorithm and Quantum Particle swarm optimization algorithm. Hence, this quantum adaptation is the main factor that enhances the results of the optimization algorithm and the approach can be applied to locate wells in conventional and shale gas reservoir.

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.

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