Generative AI Drilling Framework for Well Trajectory Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing drilling operations lack efficient and accurate methods for constructing boreholes that penetrate reservoirs, particularly in complex geologic environments with lateral variations and fractures, leading to challenges in resource extraction and production optimization.
Innovation Solution
A system utilizing generative artificial intelligence engines to generate configuration settings for graphical user interfaces of computational frameworks, enabling enhanced visualization and control of drilling operations, including tools like DRILLPLAN, DRILLOPS, PETREL, TECHLOG, PETROMOD, ECLIPSE, and INTERSECT, to optimize well construction and resource extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional drilling methods are used in complex geologic environments, then drilling operations can be performed, but the accuracy of borehole construction and trajectory control deteriorates
Solution Approach 1:
The patent introduces an AI-based computational framework as an intermediary system that processes complex geologic data and generates optimized drilling trajectories. This framework acts as a mediator between geologic complexity and drilling control, translating complex subsurface conditions into actionable drilling parameters while maintaining high trajectory accuracy.
Solution Approach 2:
The patent replaces traditional mechanical drilling control systems with an AI-driven computational framework. Instead of relying solely on mechanical adjustments and human expertise, the system uses machine learning models to analyze geologic data and automatically generate optimized drilling paths, reducing the impact of geologic complexity on drilling precision.
2Productivity
If real-time data analysis is implemented, then drilling operation efficiency improves, but system complexity increases
Solution Approach 1:
The patent implements a self-service data analysis system where the AI computational framework automatically processes drilling data, generates insights, and adjusts drilling parameters without requiring manual intervention. The system self-updates its models based on new data, continuously improving efficiency while managing its own complexity through automated learning processes.
Solution Approach 2:
The patent establishes real-time feedback loops where drilling data is continuously analyzed by the AI framework, and the system automatically adjusts drilling parameters based on the analysis. This closed-loop feedback mechanism enables real-time optimization of drilling efficiency while the AI system manages complexity through automated decision-making based on feedback from sensors and operational data.
3Manufacturing precision
If dynamic planning is used, then well trajectory control accuracy improves, but computational requirements increase
Solution Approach 1:
The patent employs preliminary action by pre-computing multiple potential drilling trajectories and preparing computational models in advance. The AI framework analyzes geologic data beforehand to generate a library of optimized paths, allowing the drilling system to quickly select and adjust the best trajectory without requiring intensive real-time computational power, thus reducing energy consumption while maintaining high accuracy.
Data Source
AI summary
A method may include receiving a prompt by a generative artificial intelligence engine, where the prompt describes a drilling analysis; responsive to the prompt, generating configuration settings for one or more graphical user interfaces of one or more computational frameworks; and transmitting instructions for rendering at least one of the graphical user interfaces to a display according to its configuration settings.