AI-Driven Well Surveillance Using Low-Code Tools: Automating Anomaly Detection and Actionable Insights on Gas Lift Wells

Abstract

Automation, AI, and modern monitoring equipment has made the dream of surveillance staff having a near-omniscient view of oilfield issues closer to reality. This paper tackles a segment of this vision and presents a low-code well surveillance workflow developed to reduce the manual monitoring by well performance specialists and production engineers while extending surveillance coverage across gas lift wells. The proof-of-concept system was built by Occidental using Microsoft Power Automate, Power Apps, SharePoint, OneDrive, Azure OCR, and multimodal large language model (LLM) calls. The workflow captures well images, extracts structured information, prompts an LLM to identify abnormal operating conditions and propose follow-up actions, and then presents those outputs in a user-facing application alongside the original surveillance view. The prototype established a complete end-to-end path from data capture to anomaly review and user feedback. In testing, the system demonstrated that a low-code architecture can support daily surveillance screening, highlight wells that merit attention, and place draft recommendations directly in front of users. This work is not presented as a final enterprise architecture. Instead, it documents the decisions, tradeoffs, performance metrics comparing different LLMs, and lessons from an accelerated proof of concept effort. The results show that low-code tools can be used to create a practical, AI-assisted surveillance workflow, create a testing ground for multimodal AI in production operations, and provide a bridge toward future model-driven surveillance systems that rely on structured data and purpose-built machine learning models.