AI Wellbore Stability Monitoring for Mud Weight Uncertainty
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Solution Overview
Problem
Oilfield operations involve large amounts of data that are often reviewed and analyzed manually, which is a time-consuming and error-prone process, and there is a need for an improved system and method to process this data efficiently.
Innovation Solution
A method and system utilizing a large language model (LLM) assisted by plug-ins/agents to extract parameter-value pairs from unstructured data, perform statistical analysis, and detect anomalies, enabling real-time monitoring of wellbore stability by determining pore and fracture gradients and updating mud weight profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data review and analysis is used, then human judgment and flexibility are maintained, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical data review processes with an AI-based system that automatically processes large datasets. The system uses natural language processing and machine learning models to extract insights from wellbore data, substituting human manual analysis with automated computational processes that are both faster and more consistent.
Solution Approach 2:
The system enables self-service analysis by allowing users to query the AI model in natural language without requiring complex data processing knowledge. The AI automatically processes the data, extracts relevant information, and provides insights, making the system accessible to users without specialized expertise in data analysis.
2Productivity
If manual data analysis is performed, then detailed examination of data can be conducted, but the process becomes error-prone and inefficient
Solution Approach 1:
The patent implements automated data processing using AI models that systematically analyze wellbore data without human intervention. The system automatically processes large datasets, extracts patterns, and generates insights, eliminating the errors and inefficiencies associated with manual analysis while maintaining high accuracy through computational methods.
Solution Approach 2:
The system incorporates feedback mechanisms where the AI model continuously learns from new data and refines its analysis. The model can be trained on historical wellbore data to improve its accuracy over time, and the system provides feedback loops that allow for iterative improvement of analysis results based on actual wellbore performance.
Data Source
AI summary
A method for monitoring a risk to a stability of a wellbore in a subsurface formation includes receiving first input data 2024/243558 representing the wellbore or the subsurface formation. The method also includes extracting parameter-value pairs from the first input data. The method also includes determining an expected pore pressure gradient based upon the parameter-value pairs. The method also includes determining an expected fracture gradient based upon the parameter-value pairs. The method also includes determining a mud weight uncertainty profile for the wellbore based upon the expected pore pressure gradient and the expected fracture gradient.