Hybrid Drilling Advisory System Optimizing ROP
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Solution Overview
Problem
Current drilling technologies face limitations in optimizing drilling performance by focusing solely on Rate-of-Penetration (ROP) and are prone to getting stuck at local optimum points, lacking adaptability to changing drilling conditions and requiring extensive downhole data for optimization.
Innovation Solution
Integration of global and local search methods with a data fusion approach to optimize multiple controllable drilling parameters, using a computer-based system that receives real-time data to generate operational updates and implement them during ongoing drilling operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If local search methods are used to optimize drilling parameters, then optimization speed is improved, but the system gets stuck at local optimum points
Solution Approach 1:
The patent combines global search methods (genetic algorithms) with local search methods (gradient descent) into a hybrid optimization system. The global search explores the entire parameter space to find promising regions, while the local search refines solutions within those regions. This merging allows the system to maintain both exploration capability (avoiding local optima) and exploitation efficiency (fast convergence), directly resolving the contradiction between optimization speed and avoiding local optimum traps.
2Adaptability or versatility
If global search methods are used to optimize drilling parameters, then adaptability to changing conditions is improved, but computational complexity increases
Solution Approach 1:
The patent implements a two-stage optimization process where global search is performed first to identify promising parameter regions, and then local search is applied to refine solutions. This preliminary action of global exploration followed by local refinement allows the system to adapt to changing drilling conditions effectively while managing computational complexity by avoiding exhaustive global search at every optimization step.
Solution Approach 2:
The hybrid optimization system dynamically switches between global and local search modes based on drilling conditions and optimization progress. When drilling conditions change significantly, the system activates global search to explore new parameter spaces; when conditions are stable, it uses local search for efficient refinement. This dynamic adaptation balances computational complexity with adaptability to changing conditions.
3Ease of operation
If single parameter optimization is used to increase ROP, then simplicity of operation is maintained, but overall drilling performance deteriorates
Solution Approach 1:
The patent optimizes multiple drilling parameters simultaneously (weight on bit, rotary speed, drill bit type, etc.) rather than single parameter optimization. The hybrid optimization system evaluates combinations of parameter changes to maximize ROP while considering interdependencies between parameters. This multi-parameter optimization approach significantly improves overall drilling performance compared to simple single-parameter control, while the automated system maintains ease of operation.
Data Source
AI summary
Combined methods and systems for optimizing drilling related operations include global search engines and local search engines to find the optimal value for at least one controllable drilling parameter, and a data fusion module to combine or select the operational recommendations from global and local search engines. The operational recommendations are used to optimize the objective function, mitigate dysfunctions, and improve drilling efficiency.