LLM Prompt Generator for Drilling Trajectory Control
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Solution Overview
Problem
Current drilling operations lack a comprehensive physics-based model to relate controllable drilling parameters to dogleg severity, relying heavily on intuition and experience, which can lead to inefficiencies and suboptimal drilling trajectories.
Innovation Solution
A system incorporating a bottom hole assembly with a rotary steering system and a large language model (LLM) recommendation system to analyze drilling data, generate prompts, and provide recommendations for adjusting drilling parameters in real-time to optimize drilling efficiency and trajectory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a physics-based model is implemented to relate drilling parameters to dogleg severity, then manufacturing precision and control accuracy improve, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent introduces an intermediary AI model that acts as a mediator between the complex physics-based relationships and the drilling control system. This AI intermediary processes drilling parameters (weight on bit, RPM, flowrate) and predicts dogleg severity without requiring the drilling system to directly implement complex physics calculations, thus improving trajectory control precision while managing system complexity.
Solution Approach 2:
The patent replaces traditional mechanical/intuitive drilling control methods with an AI-based computational system. Instead of relying on human intuition or simple mechanical feedback, the system uses machine learning models to analyze drilling parameters and predict trajectory outcomes, substituting physical intuition with data-driven computational intelligence.
2Productivity
If real-time data analysis and AI recommendations are implemented, then productivity and drilling efficiency improve, but use of energy and computational resources increase
Solution Approach 1:
The patent applies partial action by implementing AI recommendations selectively rather than continuously. The system analyzes drilling parameters and provides recommendations only when significant deviations or critical situations are detected, rather than processing and acting on every data point in real-time, thus improving drilling efficiency while reducing unnecessary computational energy consumption.
3Manufacturing precision
If reliance on intuition and experience is reduced in favor of data-driven methods, then manufacturing precision and trajectory control improve, but ease of operation and adaptability to new situations worsen
Solution Approach 1:
The patent implements feedback mechanisms where the AI system continuously monitors drilling parameters and compares actual outcomes with predicted trajectories. When deviations occur, the system provides corrective recommendations and learns from the results, creating a closed-loop system that maintains precision while adapting to new situations through continuous feedback rather than relying solely on pre-programmed rules or human intuition.
Data Source
AI summary
A system and method for modifying operation of a drilling platform controller. A prompt generator receives drilling operations data relevant to drilling platform controller operation, wherein the drilling operations data includes current drilling parameters of a selected drilling platform controller. The prompt generator generates a prompt for recommended changes in operation of the selected drilling platform controller and applies the prompt to a large language model (LLM) trained with drilling operations domain knowledge. The LLM generates a recommendation for one or more changes in operation of the selected drilling platform controller. Feedback on efficacy of the recommendation is received from the selected drilling platform controller and is used to modify operation of the prompt generator.