Real-Time Caving Volume Estimation for Drilling Operations
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Solution Overview
Problem
Wellbore drilling operations face challenges due to excessive caving volumes caused by shear failure, leading to drilling problems such as stuck pipe and reduced efficiency, as existing methods fail to accurately predict and mitigate caving volumes in real-time.
Innovation Solution
Real-time caving volume estimation using logging data and geomechanical analysis, combining image log and caliper log data to determine breakout angular width and depth, with adjustments to drilling parameters like mud weight and well trajectory to minimize risks and improve efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If drilling operations continue without real-time caving volume monitoring, then drilling efficiency is maintained, but drilling risks such as stuck pipe and wellbore collapse increase
Solution Approach 1:
The system implements real-time feedback by continuously monitoring logging data (image logs and caliper logs) during drilling operations, calculating caving volume estimates, and providing immediate alerts when caving volumes exceed predetermined thresholds. This enables operators to adjust drilling parameters proactively, preventing wellbore collapse and stuck pipe events while maintaining drilling efficiency through timely interventions rather than reactive responses.
2Measurement precision
If real-time logging data acquisition and geomechanical analysis are implemented, then caving volume prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex geomechanical analysis into distinct, manageable modules: (1) image log data acquisition and breakout detection, (2) caliper log data acquisition and depth measurement, (3) caving volume calculation using segmented wellbore intervals, and (4) threshold-based alerting. Each module processes specific data types independently and feeds results to the next stage, reducing overall system complexity while maintaining high prediction accuracy through specialized processing at each step.
3Reliability
If caving volume monitoring is performed continuously, then drilling risk mitigation improves, but data processing time and computational resources increase
Solution Approach 1:
The system applies partial monitoring by focusing computational resources on critical parameters only: breakout angular width from image logs and breakout depth from caliper logs. Rather than processing all available logging data, the system selectively extracts and processes only the specific measurements needed for caving volume calculation. This selective approach maintains continuous monitoring capability while significantly reducing data processing time and computational resource requirements.
Data Source
AI summary
Methods and systems for determining caving volume estimations based on logging data and geomechanical models are provided. For example, a system can receive image log data measured during a drilling operation in a wellbore. The system can receive an identification of a breakout in a subterranean formation around the wellbore. The system can determine, using the image log data, a breakout angular width for the breakout. The system can determine a breakout depth for the breakout. The system can determine a caving volume based on the breakout depth and the breakout angular width substantially contemporaneously with the drilling operation. The system can output the caving volume estimation for use in substantially contemporaneously adjusting a drilling parameter for the drilling operation.