Hybrid LLM Drilling Program Generator for Geomechanical Data
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Solution Overview
Problem
Current methods for generating drilling programs for subterranean wellbore construction are time-consuming and require extensive manual analysis, often failing to incorporate all relevant data in a timely manner, especially when updating programs during operations.
Innovation Solution
The use of hybrid data generators that combine Large Language Models with physics-based and machine learning models to create and update drilling programs, leveraging multi-disciplinary datasets for optimizing drilling operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis methods are used to generate drilling programs, then comprehensive data analysis can be performed, but the process is time-consuming and reduces productivity
Solution Approach 1:
The patent replaces manual mechanical analysis methods with automated computer-based systems that use machine learning algorithms and data processing tools. This substitution enables comprehensive multi-disciplinary data analysis (geological, geomechanical, engineering parameters) to be performed automatically, eliminating the time-consuming nature of manual review while maintaining or improving analysis comprehensiveness.
Solution Approach 2:
The patent introduces an intermediary automated analysis system that acts as a bridge between raw multi-disciplinary data and drilling program recommendations. This intermediary system processes and synthesizes data from multiple sources (well logs, seismic data, formation tests) before presenting results to engineers, thereby accelerating the overall process while preserving analytical depth.
2Loss of information
If extensive manual analysis is performed to incorporate all relevant data, then data completeness improves, but the time required for updating programs during operations increases
Solution Approach 1:
The patent implements continuous automated data processing that operates throughout the drilling operation. Rather than performing batch updates, the system continuously ingests new data from sensors and operations, automatically updates the drilling program in real-time, and provides recommendations without interruption. This continuous action ensures all relevant data is incorporated while minimizing time loss.
Solution Approach 2:
The patent performs preliminary automated processing and validation of data streams before they require human review. By pre-processing multi-disciplinary data, identifying anomalies, and preparing synthesis results in advance, the system reduces the time needed for final program updates while ensuring no critical information is missed.
3Reliability
If traditional methods are used for drilling program generation, then expert judgment can be applied, but manual labor requirements increase and expert time efficiency decreases
Solution Approach 1:
The patent enables the system to perform self-service automated analysis, data synthesis, and program generation without requiring extensive manual intervention. The automated system handles routine data processing, parameter optimization, and program drafting tasks independently, freeing subject matter experts from manual labor while preserving their judgment for high-level decision-making and validation.
Solution Approach 2:
The patent creates a universal automated system that handles multiple functions (data integration, analysis, program generation, and recommendation) that previously required different specialists. This multi-functional system consolidates various expert tasks into a single integrated platform, reducing overall manual labor while maintaining the quality of expert judgment through automated synthesis.
Data Source
AI summary
A method may include providing one or more inputs to a hybrid data generator, wherein one of the one or more inputs is based at least in part on a wellsite location, wherein the hybrid data generator comprises a large language model, and wherein the large language model is based at least in part on a machine learning algorithm. The method may further include utilizing an information handling system to generate a drilling program based at least in part on the one or more inputs and the hybrid data generator. The method may further include performing at least a portion of a drilling operation based at least in part on the drilling program and collecting at least one measurement from at least one sensor during the drilling operation.