Search for a command to run...
_ Digital transformation in upstream operations has long promised greater efficiency and uptime, but traditional efforts often stall due to siloed systems and expensive infrastructure overhauls. Integrated Operations Center as a Service (IOCaaS) represents a new approach to enable the rapid adoption of an artificial intelligence (AI)-enabled operations model that represents an alternative to physical control rooms or large CAPEX investments. IOCaaS provides a remote, cloud- or edge-hosted operations center that leverages existing field data streams and domain expertise. Early deployments of IOCaaS in North America are yielding results that include lower operating costs and higher production. This has been achieved by combining advanced analytics with real-time field data. This case study shows how the IOCaaS, as developed by OPX Ai, is applied in oil and gas fields to optimize artificial lift and flow assurance. The discussion examines key technical aspects, case histories from Chevron and ConocoPhillips assets in Canada, implementation timelines, and quantifiable outcomes achieved. The focus is on how self-learning models and edge microservices are deployed, how they integrate with supervisory control and data acquisition (SCADA) and data historian systems, and what results they have delivered in the field. IOCaaS Architecture and Approach IOCaaS reimagines the traditional operations center as a service layer that sits atop an operator’s existing automation and IT infrastructure. Instead of building a centralized physical control room, operators subscribe to modular microservices (either cloud-connected or deployed at the edge) that continuously monitor and optimize assets. These microservices interface with existing SCADA systems, historians, and enterprise resource planning (ERP) databases, i.e., there is no need to “rip-and-replace” legacy systems. Data from wells, compressors, pipelines, and facilities flow into the IOCaaS platform, where AI-driven analytics turn raw readings into actionable insights. This architecture (Fig. 1) typically involves: field data acquisition from sensors and SCADA, an edge-computing layer for real-time analytics, cloud dashboards for engineers, and integration hooks into maintenance and business planning systems. By building on existing field infrastructure, IOCaaS enables the activation of integrated operations in a matter of weeks to a few months, rather than years. Critically, IOCaaS is designed for exception-based workflows. Instead of personnel watching screens 24 hours a day, 7 days a week to catch problems, the system monitors every well and piece of equipment autonomously. Engineers and operators are alerted only when anomalies or suboptimal conditions are detected, allowing teams to shift from reactive firefighting to proactive surveillance. In Chevron’s Kaybob Duvernay Formation operation and ConocoPhillips’ Montney Formation asset, this translated to a leaner operating model. Field staff could take their eyes off routine wells and focus on higher-value tasks, confident that the AI will flag the exceptions. The end goal was a hybrid intelligence approach that relied on human expertise with guidance from AI analysis. This framework improved both the speed and quality of operational decisions.