Containerized AI-Driven Well Completion, Workover, and Production Optimization Systems Using Kubernetes–OpenStack Infrastructure.

Abstract

The oil and gas industry faces pressure to optimize well delivery, reduce non-productive time, and improve production across dispersed fields. Traditional workflows rely on sequential, compute-heavy simulations for well design and optimization, causing decision-making bottlenecks. This paper introduces a containerized AI system built on Kubernetes–OpenStack that enables scalable, GPU-accelerated well engineering. It integrates AI models for well completion, workover ranking, and forecasting in a multi-tenant setup. Based on performance benchmarks, the system shows how infrastructure improvements boost operational efficiency. It supports parallel torque, hydraulic, stress, and forecast simulations for large well portfolios. Case studies highlight faster planning, better intervention success, and increased production. The approach allows quick deployment of AI, automated workflows, and real-time digital twins for extended drilling and field optimization. This bridges cloud infrastructure and petroleum engineering by enabling scalable AI workflows for well management in multi-tenant environments