Artificial Intelligence (AI) is moving decisively into the operational heart of upstream oil and gas. The new wave — reasoning-capable models, agentic orchestration, and multimodal vision — goes beyond dashboards and isolated predictive analytic use cases to deliver actionable integrated recommendations to front lines.
Just a year ago, large-language models (LLMs) were primarily used for smarter search and back-office tasks; that is changing as reasoning-capable models and agentic orchestration begin to reach the operational core. Digitalization has progressed from dashboards and isolated descriptive analytics to governed decision engines and orchestration. This transformation is forcing leaders to focus on how to scale from AI pilots to governed, safe deployment across core workflows, anchoring outcomes to the profit and loss (P&L ).
Reasoning AI, agentic orchestration, and multimodal vision
AI is evolving rapidly — and in upstream oil and gas, three new capabilities are beginning to redefine how teams make decisions, coordinate workflows, and respond to field conditions in real time.
- Reasoning-capable models: These mimic engineers by working through problems step by step — testing options, weighing trade-offs, and continuously updating recommendations as conditions change. Unlike previous AI limited to linear predictions, they apply multi-step logical reasoning and dynamic contextual assessment.
- Agentic orchestration: This approach uses software agents to coordinate processes — drilling, subsurface, production, and logistics — across existing tools. It breaks down silos and optimizes operations at the asset level, all while enforcing human approvals, safety limits, and full audit trails.
- Multimodal AI: These systems integrate live video, sensor data, and engineering schematics to provide real-time, contextual field awareness. They interpret operational environments deeply, enabling precise and timely recommendations beyond mere visual pattern recognition.
How AI is powering today’s upstream workflows
AI is no longer experimental in upstream oil and gas — it’s actively reshaping core workflows across exploration, drilling, production, logistics, and HSE. Here’s where it’s already making an impact:
Exploration: AI agents accelerate seismic interpretation, connecting legacy software and tools directly, and generating contextualizing insights for fully automated and more accurate workflows.
Drilling: Reasoning models and multimodal vision to complete automation cycles with current drilling equipment and to provide enhanced responses that consider all context factors: such as hydraulics, rate of penetration, hole cleaning, and formation target positional accuracy. Drilling centers are evolving from monitoring and alarms to automated engineering analysis and prescriptions.
Production: Agentic layers optimize gas lift, surface equipment performance, and field production, optimizing P&L while operating within subsurface model envelopes.
Logistics: AI agents integrate marine routes, weather, and demand forecasts for optimal dispatch, considering all potential factors, and with no requirement for specific instructions. They act on requests and coordinate the full value chain from delivery request through inventory and third parties to delivery.
Health, Safety, and Environment (HSE): Multimodal vision systems with no specific instructions detect unsafe behavior, gas leaks, and anomalies before incidents escalate. The evolution is from specific “pre-defined incident detection” to a fully trained eye that’s always looking to improve HSE.
Leadership priorities for scaling AI safely in upstream oil and gas
Leaders should shift metrics from discipline-based KPIs to system-level performance anchored to the P&L, while also putting governance first with human-in-the-loop oversight, safe operating envelopes, tiered approvals, and auditable actions. To move beyond pilots, it’s key to embed AI into core workflows and orchestrate the asset rather than the silo. It’s also important to build orchestration layers over current systems to scale quickly and safely, and invest in talent, governance, and partnerships to enable a decisive, compliant rollout. Additionally, development must be shifted from sequential handoffs to continuous orchestration across exploration, reservoir management, drilling, completions, production, and delivery.
AI has evolved into a powerful enabler for upstream oil and gas operations. Early adoption promises accelerated time to first oil, safer operations, more resilient value chains, and the ability to shape future standards of digital-physical convergence in oil and gas.
Written in partnership with Quantrue