AI in oil & gas: a value-chain map of credible use cases
The useful question is no longer whether oil and gas can use AI. It is where AI can change a technical or managerial workflow without weakening the evidence, physics, safety barriers or accountable authority that make the workflow trustworthy.
One industry, several kinds of AI
Oil and gas has used optimization, pattern recognition, advanced analytics, computer vision, predictive models and automation for years. Foundation models and agents add a different capability: they can work across unstructured technical knowledge, interpret mixed information, orchestrate tools and increasingly initiate bounded actions. Treating all of these as one category obscures both value and risk.
Recent deployments show the stack becoming more integrated. ADNOC and SLB’s AiPSO connects millions of real-time data points across wells and facilities and was deployed across eight fields in 2025, with wider deployment planned. SLB’s 2026 work on edge AI is explicitly aimed at real-time operational decision-making closer to wells and facilities. Halliburton has continued integrating AI, automation and execution at the wellsite. These are not generic chat deployments; they combine domain models, operational data, engineering constraints and workflow authority.
Value-chain map
| Domain | Credible AI contribution | What is already mature | Where newer GenAI / agents add value | Authority boundary |
|---|---|---|---|---|
| Subsurface | Seismic/well interpretation, reservoir characterization, analog retrieval, uncertainty exploration | ML-assisted interpretation, inversion, simulation, optimization | Cross-document technical synthesis; conversational access to models/data; workflow orchestration | Reserve/resource interpretation and development choices remain accountable technical decisions |
| Field development & wells | Well planning, trajectory/design alternatives, drilling performance, geosteering, rig scheduling | Optimization, predictive analytics, drilling automation | Plan generation, constraint retrieval, lessons-learned synthesis, multi-tool orchestration | Design acceptance and operational envelopes require competent human approval |
| Production | Surveillance, anomaly detection, allocation, lift optimization, network optimization | Advanced process control, ML, digital twins, optimization | Explain anomalies, coordinate reservoir-well-facility context, propose scenarios | Closed-loop action only inside engineered and monitored bounds |
| Maintenance & integrity | Condition monitoring, inspection analysis, failure prediction, work planning, technical knowledge | Predictive maintenance, vision inspection, condition analytics | Evidence synthesis, procedure retrieval, work-pack drafting, cross-asset learning | Integrity acceptance, deferral and safety-critical release stay with authorized roles |
| HSE | Hazard discovery, incident learning, barrier information, permit/document assistance | Vision, sensor analytics, anomaly detection | Search and synthesis across incidents, procedures and barrier records | Permit approval, isolation, override and safety-critical decisions cannot be delegated casually |
| Projects | Schedule/cost/risk/change intelligence, engineering knowledge, reporting | Analytics, forecasting, controls automation | Cross-system explanation, decision memory, document intelligence, bounded agents | Baseline/change/forecast commitments remain governed project decisions |
| Supply chain | Demand signals, inventory, supplier intelligence, document/contract analysis | Forecasting and optimization | Sourcing research, specification comparison, exception handling | Commercial commitment and material supplier decisions remain authorized actions |
| Management | Portfolio synthesis, operational briefing, scenario preparation, decision memory | BI and analytics | Multi-source synthesis and persistent decision support | AI can prepare the decision surface; management owns the decision |
Where the strongest evidence is emerging
1. Production-system optimization
Integrated production is a strong fit because the value is measurable and the workflow already has rich operational data, engineering models and defined constraints. AiPSO’s architecture links wells and facilities rather than optimizing isolated assets. SLB’s 2026 OptiFlow work similarly combines simulation with generative and physics-informed AI so engineers can test scenarios and diagnose issues across the production system.
2. Drilling and well delivery
Real-time data, repeatable decisions and costly variance make drilling fertile ground for AI and automation. ONGC’s 2026 enterprise deployment of SLB DrillOps covers up to 120 rigs with real-time monitoring, predictive analytics and performance insights. Halliburton’s acquisition of Sekal strengthens closed-loop drilling automation as the market moves from advice toward controlled execution.
3. Planning across fragmented technical domains
Petoro and SLB reported 2026 work combining generative AI, scheduling algorithms and domain expertise for field-development planning. The significance is architectural: AI becomes useful when it reduces the friction between documents, datasets, specialist models and planning decisions—not when it merely generates text.
Ready-made product or transformation?
The product landscape is changing quickly. SLB’s Tela is being embedded across upstream applications; its June 2026 Digital Marketplace launched with roughly 200 products spanning agents, domain models, skills, connectors and applications. This means an operator should now check existing domain-platform capability before commissioning a custom AI layer.
Use native capability
When the workflow already lives in a domain application and the vendor supplies governed AI against the relevant data and model context.
Configure and integrate
When value depends on connecting historian, maintenance, subsurface, ERP, project, document or operational systems and reconciling identity, semantics and permissions.
Build selectively
When the organization has a differentiated workflow, proprietary knowledge or decision logic that packaged capability cannot represent economically.
The build decision should therefore start with the workflow and evidence architecture—not with a desire to own a model.
The authority gradient matters more near the asset
The farther right a use case moves, the more the architecture must address engineering limits, independent protection layers, fail-safe behavior, observability, rollback, competency and explicit decision rights. RoboWell, deployed with ADNOC, illustrates the distinction: autonomous adjustment occurs within specified operating conditions. That boundedness is part of the system design, not a footnote.
Agentic AI increases this issue. An agent that retrieves well history is a different control object from an agent that can alter a production set point, issue a work order or modify an approved plan. Permission architecture must reflect the difference.
A practical portfolio sequence
Operators do not need to choose between experimentation and industrial deployment. They need a portfolio that matches technical consequence to evidence maturity.
Start where the workflow is consequential enough to matter but bounded enough to evaluate. Measure cycle time, decision quality, production or reliability outcome, error/exception rate, user effort and control performance. Only then increase scope or authority.
A useful first portfolio often combines three layers: a low-consequence knowledge workflow, a technical decision-support workflow with measurable engineering outcomes, and one tightly bounded automation case. That produces evidence about adoption, architecture and operational control without pretending every problem needs the same AI pattern.
What changes with agents
The emerging shift is from isolated prediction toward orchestration. SLB describes Tela around an observe–plan–generate–act–learn loop; its 2026 marketplace extends that environment with specialized agents and tools. The important organizational question is therefore becoming: which technical actions may an agent prepare or execute, against which evidence, inside which operating envelope, with whose authority?
That pushes AI transformation into familiar oil-and-gas disciplines: management of change, barrier thinking, operating envelopes, competency, assurance, traceability and learning from deviations. Agentic technology is new; the need to govern consequential work is not.
Sources and current signals
- ADNOC and SLB — AI-powered production system optimization, November 2025
- SLB and Qualcomm — edge AI for energy operations, June 2026
- SLB Digital Marketplace — agents, models and applications, June 2026
- SLB Tela agentic AI, November 2025
- SLB OptiFlow / OptiSite — integrated production workflows, March 2026
- SLB Q2 2026 — ONGC drilling and KOC AI deployments
- Petoro and SLB — AI-driven well planning, June 2026
- Halliburton / Sekal — drilling automation, April 2026
- AIQ / Halliburton / ADNOC — RoboWell autonomous well control