Transforming Decision-Making with Agentic AI for Dynamic Business and Analytics Dashboards in Hydrocarbon Asset Management

Abstract The conventional dashboard paradigm in upstream oil and gas relies on static, pre‑built visualizations tied to specific software platforms. While useful, these dashboards cannot answer ad‑hoc, cross‑domain questions that arise during day‑to‑day asset management. Engineers and managers are forced to manually pull and reconcile data from multiple systems, which slows decisions and leads to fragmented views of asset performance. This paper presents a data‑centric framework that uses Agentic AI to address these constraints. The system goes beyond static reporting by creating an interactive, generative dashboard environment. A purpose‑built AI agent interprets natural language queries, retrieves and correlates relevant data across disciplines (drilling and completion performance, production surveillance, reservoir simulation outcomes, and economic metrics), and dynamically generates visualizations on demand from a centralized corporate data bank. A field case study from a Niger Delta asset demonstrates the application in a production surveillance scenario, where the AI agent synthesized production data, historical decline behavior, and well performance metrics to generate dynamic dashboards that would conventionally require hours of specialist effort. Results show that time from question to insight dropped from hours to under a minute, with corresponding improvements in decision quality through more holistic, data‑driven analysis.