Wellbore Stability System Using Clustering Correlation Models
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Solution Overview
Problem
Wellbore instability during drilling leads to issues like hole enlargement, poor hole conditions, stuck pipes, and increased risk of casing-casing or tubing-casing pressure, which can result in project delays and reduced well performance.
Innovation Solution
A wellbore stability system that analyzes post-drilling parameters to generate a correlation model, identifying root causes of instability and providing real-time advisory actions by clustering and weighting failure influences, thus minimizing wellbore failure through optimized drilling fluid properties and operational practices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If post-drilling analysis with correlation models is implemented, then wellbore stability is improved, but device complexity increases
Solution Approach 1:
The system segments wellbore failure analysis into distinct clusters of influencing factors (geological parameters, drilling operational parameters, drilling fluid parameters). Each cluster is analyzed separately through correlation models, allowing complex multi-factor analysis to be broken down into manageable components while maintaining overall system reliability
Solution Approach 2:
The system performs preliminary clustering and correlation model generation using historical wellbore failure data before actual drilling operations. This pre-established knowledge base enables real-time stability assessment without requiring complex computational resources during critical drilling operations, thus improving reliability without proportionally increasing operational complexity
2Reliability
If real-time monitoring and advisory actions are implemented, then wellbore failure is reduced, but use of energy increases
Solution Approach 1:
Correlation models and parameter clusters are pre-computed from historical data before real-time monitoring begins. During actual drilling operations, the system only needs to input current parameter values into these pre-established models, dramatically reducing real-time computational energy requirements while maintaining accurate wellbore stability prediction
Solution Approach 2:
The system automatically monitors drilling parameters, compares them against correlation model thresholds, and generates advisory actions without requiring continuous human intervention. This automation reduces the energy burden of manual analysis while providing continuous real-time wellbore stability assessment
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for improving wellbore stability and minimizing wellbore failure issues. In one aspect, a method includes receiving, for a well site, post-drilling parameters; clustering the received post-drilling parameters into groups based on an information type of each post-drilling parameter; generating a correlation model by applying a numerical analysis or a statistical analysis to the post-drilling parameters included in each of the groups; determining a resolution to avoid wellbore failure at a new well site by processing information regarding the new well site through the correlation model; and transmitting the resolution for implementation at the well site.