Automated Drilling Path Planning and Parameter Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Subterranean drilling for hydrocarbon extraction faces challenges due to unknown subterranean formations, leading to drill bit damage and reduced rate of penetration, as the weight-on-bit and drilling parameters are often not optimally managed, resulting in increased wear and prolonged drilling times.
Innovation Solution
Automated concurrent path planning and drill parameter optimization using sensors, machine-learning models, and Bayesian optimization to select the best drill path trajectory and parameters, such as weight-on-bit and revolutions-per-minute, to minimize drilling distance, wear, and non-productive time, while avoiding subterranean formations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If weight-on-bit is increased to improve rate of penetration, then drilling speed improves, but drill bit wear and damage increases when drilling through dense subterranean formations
Solution Approach 1:
The system dynamically adjusts weight-on-bit in real-time based on sensor feedback from the subterranean environment. The automated drilling system continuously monitors formation characteristics and modifies drilling parameters to optimize rate of penetration while preventing excessive wear, transitioning from static to dynamic parameter control
Solution Approach 2:
The system implements closed-loop feedback control where sensor data from the subterranean environment is continuously fed back to the control system. This feedback mechanism enables real-time adjustment of weight-on-bit and other drilling parameters to maintain optimal performance and prevent drill bit damage during encounters with dense formations
2Reliability
If weight-on-bit is decreased to reduce drill bit wear, then drill bit durability improves, but rate of penetration suffers causing drilling operations to stall
Solution Approach 1:
The system employs dynamic parameter adjustment where weight-on-bit is continuously modified based on real-time sensor feedback. This dynamic control allows the system to maintain sufficient weight for adequate penetration rate while preventing excessive loads that would cause drill bit wear, adapting continuously to changing formation conditions
Solution Approach 2:
The system changes drilling parameters including weight-on-bit, revolutions-per-minute, and mud flow rate based on real-time sensor data and machine-learning model predictions. These parameter adjustments optimize the balance between rate of penetration and drill bit durability by adapting to the specific characteristics of encountered subterranean formations
3Device complexity
If traditional drilling path planning is used without real-time updates, then planning simplicity is maintained, but drilling distance increases and non-productive time increases due to excessive wear and stalling
Solution Approach 1:
The system performs preliminary path planning based on available sensor data and probability distributions before drilling operations begin. This preliminary action establishes an initial drilling trajectory that can be executed while allowing for real-time updates, balancing planning complexity with operational efficiency
Solution Approach 2:
The system implements continuous feedback-driven path updates where sensor data from the subterranean environment is used to recalculate and optimize the drilling trajectory in real-time. This feedback mechanism enables the system to adjust the drill path to avoid dense formations and optimize drilling efficiency without requiring overly complex pre-planning
4Productivity
If automated concurrent path planning and parameter optimization is implemented, then drilling efficiency improves, but system complexity increases
Solution Approach 1:
The automated drilling system integrates multiple functions including path planning, parameter optimization, sensor data processing, and real-time control into a single unified system. This multi-functionality improves drilling efficiency by coordinating all drilling operations under one automated framework while managing system complexity through integration
Solution Approach 2:
The system employs machine-learning models and Bayesian optimization algorithms that enable the drilling system to automatically optimize its own performance without external intervention. This self-service capability improves drilling efficiency by continuously adapting to subterranean conditions while the complexity is managed through autonomous operation
Data Source
AI summary
A drilling device may use a concurrent path planning process to create a path from a starting location to a destination location within a subterranean environment. The drilling device can receive sensor data. A probability distribution can be generated from the sensor data indicating one or more likely materials compositions that make up each portion of the subterranean environment. The probability distribution can be sampled, and for each sample, a drill path trajectory and drill parameters for the trajectory can be generated. A trained neural network may evaluate each trajectory and drill parameters to identify the most ideal trajectory based on the sensor data. The drilling device may then initiate drilling operations for a predetermined distance along the ideal trajectory.