Well integrity/control

AI-Based Well-Integrity Monitoring Shows Promise

This paper presents the proof of concept of artificial-intelligence-based well-integrity monitoring for gas lift, natural flow, and water-injector wells.

Statistical metrics.
Statistical metrics.
Source: SPE 211093.

This paper presents the proof of concept (PoC) of artificial intelligence (AI)-based well-integrity monitoring for gas-lift, natural-flow, and water-injector wells. AI-model prototypes were built to detect annulus leakage as incident-relevant anomalies from time-series sensor data. The AI models for gas-lift and natural-flow wells achieved a sufficient level of performance, with a minimum of 75% of historical events detected and less than one false positive per month per well.

Problem Statement

In the exploration and production industry, historical well-integrity events are rare because systems are designed as robustly as possible to prevent incidents. For the authors’ study, there were only 12 historical wellbore leakage incidents spanning 13 years of well operations in the assets considered.

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