Predictive Soil Characterization and Drillstring Instability Monitoring
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Solution Overview
Problem
Horizontal directional drilling (HDD) operations face challenges with drillstring instability, loss of directional control, and the risk of cross-bore incidents due to unreliable soil characterization and mapping of underground utilities, leading to costly and potentially fatal consequences.
Innovation Solution
A system and method utilizing predictive algorithms and sensors to characterize soil and quantify drillstring instability, allowing for real-time monitoring of drillstring behavior, load management, and position prediction, eliminating the need for operator 'feel' and enhancing situational awareness during HDD operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predictive algorithms and sensors are used to characterize soil and quantify drillstring instability, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The patent replaces traditional mechanical operator judgment ('feel') with predictive algorithms that process sensor data to characterize soil properties and predict drillstring instability. This substitution of mechanical intuition with computational analysis improves measurement precision while managing system complexity through software-based solutions.
Solution Approach 2:
The patent introduces predictive algorithms as an intermediary between raw sensor data and operational decisions. These algorithms process thrust, torque, and navigation data to provide interpreted soil characteristics and instability predictions, acting as a mediator that simplifies the complexity for operators while maintaining high measurement precision.
2Reliability
If real-time monitoring of drillstring behavior is implemented, then reliability is improved, but use of energy increases
Solution Approach 1:
The patent implements continuous real-time monitoring of drillstring behavior through sensors and predictive algorithms during HDD operations. This continuous action improves reliability by providing ongoing instability predictions, while energy consumption is managed through efficient sensor placement and processing only critical parameters.
3Measurement precision
If predictive algorithms are used to determine soil characteristics, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces physical soil sampling and laboratory analysis with predictive algorithms that determine soil characteristics from drillstring performance data. This substitution improves measurement precision by providing real-time soil type identification without the complexity of physical sampling equipment.
Solution Approach 2:
The patent uses predictive algorithms that analyze changes in drillstring parameters (thrust, torque, rate of penetration) to infer soil characteristics. By monitoring parameter changes rather than directly measuring soil properties, the system achieves high measurement precision with reduced device complexity.
4Reliability
If load management and position prediction are implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where predictive algorithms continuously monitor drillstring position, load, and soil characteristics, then provide real-time feedback to operators about potential cross-bore incidents. This feedback loop improves reliability by enabling proactive hazard avoidance while managing complexity through integrated software processing.
Solution Approach 2:
The patent introduces predictive algorithms as an intermediary that processes complex sensor data from multiple sources (thrust, torque, navigation) and provides simplified position prediction and hazard warnings. This intermediary approach improves reliability for cross-bore prevention while reducing the perceived complexity for operators.
Data Source
AI summary
Disclosed is a system and method for horizontal directional drilling (HDD). The system and method utilize predictive algorithms to both characterize the soil within the borehole and to quantify instability within the drillstring. The soil characteristics are represented by a soil coefficient (α) which relates the curvature of the borehole with the length of thrust of the drill rig as well as by comparison of thrust while thrusting with torque while drilling. The value of (α) is obtained by comparison of the historical orientation of the drilling head over the length of the borehole and the borehole shape as determined by an arbitrary navigation sensor. Drillstring instability is determined as a function of historical thrust and torque efficiencies and windup over the length of the borehole.