Black Hole Particle Swarm Optimization for Well Placement
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Solution Overview
Problem
Well placement optimization in field development planning is challenging due to the large number of optimization variables and over-parameterization issues, making existing evolutionary optimization solutions like Genetic Algorithm and Particle Swarm Optimization impractical for large-scale field development cases.
Innovation Solution
The Black Hole Particle Swarm Optimization (BHPSO) method, which combines Particle Swarm Optimization with a static Black Hole Optimizer, optimizes well count, location, type, trajectory, and horizontal section length, reducing CPU requirements and making the optimization workflow feasible for large-scale field development projects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the position of every well is considered a decision variable in evolutionary optimization algorithms, then the optimization can explore the multidimensional space of well parameters, but the number of optimization variables becomes excessively large leading to over-parameterization and making the workflow practically infeasible
Solution Approach 1:
The patent segments the well placement optimization into two distinct phases: a static phase using Black Hole Optimization to determine well positions and types, and a dynamic phase using Particle Swarm Optimization to optimize production parameters. This segmentation reduces the number of variables in each phase, making the overall optimization feasible while maintaining comprehensive exploration capability.
Solution Approach 2:
The patent applies preliminary action by first using the Black Hole Optimization algorithm to determine the optimal well positions and types before proceeding to the production parameter optimization phase. This preliminary well placement decision reduces the complexity of subsequent optimization by fixing the well configuration variables.
2Productivity
If standard Particle Swarm Optimization is used for well placement optimization, then the algorithm can handle the optimization problem, but it requires excessive CPU time and becomes impractical for large-scale field development cases
Solution Approach 1:
The patent divides the optimization process into two segments: a static well placement phase using Black Hole Optimization that is independent of well count, and a dynamic production optimization phase using PSO. This segmentation allows the computationally intensive PSO to operate with fewer variables, dramatically reducing CPU time while maintaining optimization effectiveness.
Solution Approach 2:
The patent introduces the Black Hole Optimization algorithm as an intermediary that handles the well placement decisions separately from the production optimization. This intermediary approach allows PSO to focus only on production parameter optimization rather than the entire well placement problem, reducing computational burden.
3Adaptability or versatility
If the number of wells in a field development case increases to hundreds, then the field development planning becomes more comprehensive, but the over-parameterization problem worsens making the optimization workflow practically infeasible
Solution Approach 1:
The patent segments the optimization into well placement (static) and production parameter optimization (dynamic). The Black Hole Optimization handles well placement independently of the number of wells, while PSO optimizes production parameters for the configured wells. This segmentation maintains scalability despite increasing field size.
Solution Approach 2:
The patent performs preliminary well placement optimization using Black Hole Algorithm before production parameter optimization. This preliminary action establishes the well configuration once, and subsequent PSO optimization works with this fixed configuration, making the process feasible even for large numbers of wells.
Data Source
AI summary
Provided herein are systems, methods and apparatuses for a Black Hole Particle Swarm Optimization for Optimal Well Placement in Field Development Planning.