Directional Drilling Trajectory Ranking Using Offset Well Context
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Solution Overview
Problem
Existing directional drilling methods lack consistency and efficiency, often relying on expert intuition and failing to leverage advanced data analytics and machine learning for optimized trajectory planning and execution.
Innovation Solution
An autonomous directional drilling framework combines planning and execution, utilizing trajectory optimization models, machine learning, and downhole intelligence to enhance decision-making, ensuring accurate and efficient drilling operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If expert intuition is used for directional drilling, then flexibility in decision-making is maintained, but consistency and efficiency deteriorate
Solution Approach 1:
The patent replaces expert human judgment with an automated machine learning-based trajectory optimization system. The system processes geological data, well logs, and seismic information through computational models to automatically determine optimal drilling trajectories, eliminating reliance on individual expert intuition while maintaining high decision-making flexibility through adaptive algorithms.
Solution Approach 2:
The directional drilling system incorporates self-service capabilities through autonomous trajectory optimization. The machine learning models continuously learn from drilling data and geological information to autonomously adjust drilling parameters and trajectory in real-time, reducing the need for external expert intervention while maintaining optimal drilling performance.
2Loss of information
If traditional drilling methods are used, then equipment simplicity is maintained, but data analytics capability deteriorates
Solution Approach 1:
The patent implements a universal data processing framework that handles multiple data types (well logs, seismic data, geological information) through a single integrated machine learning platform. This multi-functional system consolidates data analytics capabilities while maintaining system manageability through unified processing architecture.
Solution Approach 2:
The system introduces an intermediary data processing layer that bridges raw geological data and drilling decisions. Machine learning models act as intermediaries that transform complex, multi-source data into actionable trajectory recommendations, making the system more data-driven without overwhelming complexity in the decision-making interface.
3Manufacturing precision
If autonomous trajectory optimization is implemented, then drilling precision is improved, but computational requirements worsen
Solution Approach 1:
The patent performs preliminary trajectory optimization during the planning phase using machine learning models that process geological data and predict optimal paths before drilling begins. This preliminary computation reduces real-time computational burden during actual drilling operations, as the trajectory framework is pre-established through sophisticated data analytics.
Solution Approach 2:
The system dynamically adjusts computational intensity based on drilling conditions. The machine learning models adapt their processing requirements in real-time based on encountered geological formations, allowing high computational precision when needed and reduced computation when conditions are stable, thereby optimizing energy consumption while maintaining trajectory accuracy.
Data Source
AI summary
A method can include generating candidate trajectories for drilling a borehole in a subsurface environment; contextualizing each of the candidate trajectories using data from offset wells in the subsurface environment to assign contexts to portions of each of the candidate trajectories; assigning weights to the contexts; ranking the candidate trajectories utilizing the weights; based on the ranking, selecting one of the candidate trajectories; and issuing control instructions to equipment for drilling at least a portion of the borehole using the selected one of the candidate trajectories.