奥纬咨询_人工智能将如何重塑上游石油和天然气的前沿

Reasoning, AgenticOrchestration, and Pedro AlcalaJordi SerraNadim Haddad AI has crossed from theback o¾ce into the¼eld EXECUTIVE BRIEFING Artificial Intelligence (AI) is moving beyond back-oޱce automation into the operational coreof upstream oil and gas. The new wave — reasoning-capable models, agentic orchestration, This is not incremental improvement; it is an operating-model shift. The leadershipchallenge is no longer whether to use AI, but how to scale from pilots to governed, safe WHAT’S NEW Reasoning-capable models:AI that can work through problems step-by-step — like anengineer — testing options, weighing trade-oޮs, and updating its recommendations as Agentic orchestration:Software “agents” that coordinate drilling, subsurface, production,and logistics across existing tools, collapsing silos and optimizing to asset-level value — Multimodal AI (vision + data):Systems that combine live video, sensor data, andengineering schematics to understand field environments in context and provide real- IMPLICATIONS FOR UPSTREAM LEADERS Shift metrics:Move from discipline-only measures (such as feet drilled or equipmentuptime) to system-level performance anchored to the profit and loss(P&L). Governance ࢉrst:Keep human-in-the-loop oversight with safe operating envelopes, tiered Early movers lead:Reduce operational risk, accelerate time to first oil, and shape industry WHERE IT APPLIES NOW Exploration —AI agents accelerate seismic interpretation, connecting legacysoftware and tools with no intermediaries, and contextualizing resp onses for fully Drilling —Reasoning models and multimodal vision to complete automation cycleswith current drilling equipment and to provide enhanced responses that considerall context factors: such as hydraulics, ROP (rate of penetration), hole cleaning, and Production —Agentic layers optimize gas lift, surface equipment performance, Logistics —AI agents integrate marine routes, weather, and demand forecastsfor optimal dispatch, considering all potential factors, and with no requirement for Health, Safety, and Environment (HSE) —Multimodal vision systems with nospecific instructions detect unsafe behavior, gas leaks, and anomalies beforeincidents escalate. The evolution is from specific “pre-defined incident detection” LEADERSHIP MANDATE •Embed AI into coreworkࢊows:Move beyond isolatedpilots. •Build orchestration layers over current •Invest in talent, governance, andpartnerships:Enable decisive, •Orchestrate the asset, not the silo:Anchor outcomes to the profit and FROM PILOTS TO CORE: WHY AI MATTERS INUPSTREAM NOW AI has moved beyond the back oޱce and is now emerging in frontline upstream operations.In many industries, particularly oil and gas, AI adoption is rapidly becoming a matter ofoperational competitiveness and corporate survival. Yet for leaders in traditional, asset-heavy Where do we start, how do we scale, and which advances matter most today? In upstream, the stakes of AI deployment are higher: workflows are more complex, risk toleranceis lower, and resistance to change is often embedded in the field. At the same time, the rapid paceof AI advancement, with new models, tools, and frameworks emerging every quarter, makesit increasingly diޱcult for upstream leaders to know where to begin or how to focus. What’s Just a year ago, LLMs (large-language models, such as the foundational model for ChatGPT)were primarily used as smarter search engines and to automate time consuming back-oޱcetasks. In oil and gas, their impact was largely administrative rather than operational — but that We are witnessing breakthrough advancements every quarter, with hundreds of companiesracing to embed AI into upstream operations. Digitalization has progressed from dashboards The technical trajectory mirrors this shift: from engineers building descriptive dashboards,to data scientists deploying machine learning models for prediction, to advanced physicsinformed AI models for subsurface reinterpretation and production optimization. And now, Three field-ready capabilities —(1)reasoning-capable models, (2) agentic orchestration, and(3) multimodal vision— can now be layered over existing systems, with human-in-the-loop This article translates these three AI advancements into direct, high-impact applications 1. REASONING-CAPABLE MODELS: ENGINEERING LOGIC AI models have steadily advanced in accuracy and scale, but the true breakthrough is theirgrowing ability to reason. This leap in reasoning — not just raw intelligence — marks a newinflection point, where models begin to simulate how engineers think, evaluate trade-oޮs, Capability comparison uses twodimensions across model generations: Machine learning and AI in upstream oil and gas have evolved into highly complex,state-of-the-art solutions — yet they often remained siloed, solving niche problems likegas-lift optimization or torque-and-drag prediction, while missing broader integration. Traditionally, these models have lar