Geosteering Copilot AI Assistant for Historical Data Decisions
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Solution Overview
Problem
Geosteering decisions in wellbore drilling are often hindered by the lack of readily available and reliable historical data, requiring geosteers to rely on their knowledge and experience, which can be inconsistent and insufficient for optimal drilling outcomes.
Innovation Solution
An AI assistant engine utilizing machine learning algorithms is employed to analyze historical data, generate reports, and provide real-time recommendations for geosteering decisions, integrating downhole measurements and historical data to enhance drilling precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If geosteers rely on their knowledge and experience to make decisions, then decisions can be made without extensive historical data, but the reliability and consistency of decisions deteriorate due to subjective human judgment
Solution Approach 1:
An AI assistant engine is introduced as an intermediary between historical data and geosteering decisions. The AI engine processes historical data, downhole measurements, and geological models to generate objective recommendations, mediating the gap between available information and decision-making needs.
Solution Approach 2:
The patent replaces the mechanical system of human cognitive processing with an AI-based information processing system. The AI assistant engine automatically analyzes data, identifies patterns, and generates recommendations, substituting human judgment with algorithmic decision support.
2Manufacturing precision
If extensive historical data is collected and analyzed to improve decision accuracy, then drilling precision improves, but the complexity of data processing and system requirements increase
Solution Approach 1:
The AI assistant engine is designed as a multi-functional system that performs data collection, historical data analysis, downhole measurement processing, geological model integration, and recommendation generation within a single unified platform, reducing the need for multiple separate systems.
Solution Approach 2:
The system automatically collects, processes, and analyzes data without requiring manual intervention for each decision. The AI engine self-manages the complex data processing tasks, including querying historical data, integrating downhole measurements, and generating recommendations autonomously.
3Measurement precision
If real-time downhole measurements are integrated with historical data analysis, then geosteering decision accuracy improves, but the time required for data processing and decision generation increases
Solution Approach 1:
The system pre-loads and indexes historical data before it is needed for decision-making. Historical data is organized and prepared in advance, allowing the AI engine to quickly retrieve and analyze relevant information when real-time decisions are required, reducing processing delays.
Solution Approach 2:
The AI assistant engine continuously receives feedback from downhole measurements and adjusts its analysis in real-time. The system processes measurements as they become available and provides ongoing recommendations, creating a continuous feedback loop that maintains accuracy without requiring batch processing delays.
Data Source
AI summary
A method and a system comprising: disposing a bottom hole assembly (BHA) into a wellbore, wherein the BHA comprises a measurement assembly; acquiring one or more measurements with the measurement assembly; acquiring historical data from the wellbore; extracting relevant information from the historical data; training a machine learning (ML) model with the relevant information to form a trained ML model; and providing an answer to a question utilizing the trained ML model.