人工智能将如何重塑上游石油和天然气的前沿

Reasoning, AgenticOrchestration, andMultimodal Vision Pedro AlcalaJordi SerraNadim HaddadTommy Inglesby AI has crossed from theback office into thefield EXECUTIVE BRIEFING Artificial Intelligence (AI) is moving beyond back-office automation into the operational coreof upstream oil and gas. The new wave — reasoning-capable models, agentic orchestration,and multimodal vision — goes past dashboards and predictive analytics to deliver actionablerecommendations in thefield. 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, safedeployment across coreworkflows. WHAT’S NEW Reasoning-capable models:AI that can work through problems step-by-step — like anengineer — testing options, weighing trade-offs, and updating its recommendations asconditionschange. Agentic orchestration:Software “agents” that coordinate drilling, subsurface, production,and logistics across existing tools, collapsing silos and optimizing to asset-level value —operating under policies that enforce human approvals, safety limits, and full audittrails. Multimodal AI (vision + data):Systems that combine live video, sensor data, andengineering schematics to understand field environments in context and provide real-timerecommendations. 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 first:Keep human-in-the-loop oversight with safe operating envelopes, tieredapprovals, and auditable actions to meet safety and regulatoryrequirements. Early movers lead:Reduce operational risk, accelerate time to first oil, and shape industrystandards for digital-physicalconvergence. WHERE IT APPLIES NOW Exploration —AI agents accelerate seismic interpretation, connecting legacysoftware and tools with no intermediaries, and contextualizing responses for fullyautomated and more accurateworkflows. 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, andformation target positional accuracy. Drilling centers are evolving from monitoringand alarms to automated engineering analysis andprescriptions. Production —Agentic layers optimize gas lift, surface equipment performance,and field production, optimizing P&L while operating within subsurfacemodelenvelopes. Logistics —AI agents integrate marine routes, weather, and demand forecastsfor optimal dispatch, considering all potential factors, and with no requirement forspecific instructions. They act on requests and coordinate the full value chain fromdelivery request through inventory and third parties todelivery. 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”to a fully trained eye that’s always looking to improveHSE. LEADERSHIP MANDATE •Embed AI into coreworkflows:Move beyond isolatedpilots. •Build orchestration layers over currentsystems:Scale quickly andsafely. •Invest in talent, governance, andpartnerships:Enable decisive,compliantrollout. •Orchestrate the asset, not the silo:Anchor outcomes to the profit andlossstatement. FROM PILOTS TO CORE: WHY AI MATTERS INUPSTREAM NOW AI has moved beyond the back office 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-heavysectors, one of the most urgent and difficult questionsremains: 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 difficult for upstream leaders to know where to begin or how to focus. What’smissing is clarity on how these capabilities translate into tangible, high-impact applications inthis context. Even as AI evolves rapidly around us, the unique complexity of upstream operationsdemands tailored examples that feel credible, implementable, and technicallygrounded. 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-officetasks. In oil and gas, their impact was largely administrative rather than operational — but thatis now changing as reasoning-capable models and agentic orchestration begin to reach