AI in oil and gas: use cases and the companies behind them

Start from the operating decision, not the model

Artificial intelligence in oil and gas is not one market. A machine-learning model that adjusts a rod pump, a platform that streams drilling data to a remote center, a production forecast used in an acquisition, and a knowledge graph that links engineering documents to plant equipment solve different problems for different owners. Grouping them under a single AI label hides the questions that determine whether a deployment is useful: what decision changes, who approves it, which data feeds it, and how the result is checked.

This guide organizes the market by use case. Each section names companies whose BTU Graph profiles carry dated public evidence for that work. It complements BTU Graph's energy AI companies collection, which defines inclusion boundaries across the wider energy system. Here the focus is narrower: the upstream and oilfield workflows where AI is already tied to wells, rigs, reservoirs, and production assets.

A practical test for any proposal is to write down the decision in one sentence before reviewing the technology. Examples include changing a pump's stroke rate, setting a drilling parameter, choosing a forecast for a reserves report, or deciding which equipment to inspect first. If that sentence cannot be written, the evaluation is likely to drift toward demonstrations rather than measurable operating change.

Production and artificial lift

Producing wells generate continuous time-series data, which makes artificial lift one of the most concrete AI applications in the industry. Ambyint applies physics-informed machine learning to rod lift, plunger lift, and electric-submersible-pump operations; its platform monitors well behavior, flags anomalies, and recommends or automates operating changes. The value depends on connections to SCADA and field-control systems, reliable sensor data, and a clear boundary between automated actions and operator oversight.

When evaluating production AI, ask how the system behaves when data is missing or a sensor drifts, whether recommendations can be traced to the inputs that produced them, and who can override an automated change in the field. A pilot should compare outcomes against a control group of similar wells over a defined period, with downtime, lifting cost, and production measured the same way on both sides.

Drilling, completions, and real-time operations

Well construction is a second cluster. Corva provides real-time applications and analytics for drilling, completions, wireline, and drillout operations, with predictive guidance and collaboration between office and field users; APIs and custom-application hosting extend it beyond packaged dashboards. Baker Hughes has published an offshore case study involving Corva's platform. Large service companies also build their own digital and AI offerings: SLB's portfolio includes digital and AI platforms alongside its reservoir, drilling, and production technologies, and Baker Hughes participates in digital asset-management workflows.

The important distinction is between guidance and control. A system that suggests drilling parameters to a human driller carries different risks from one that writes setpoints to rig equipment. Buyers should test data latency, how each well's context is mapped, who owns an alert, how the system works on mobile devices at the wellsite, and how model changes are released and reviewed.

Subsurface interpretation, forecasting, and engineering data

Forecasting and valuation sit closer to the office. Novi Labs organizes proprietary and public well-level data with machine-learning production forecasts and no-code analytical workflows for development planning, capital allocation, and acquisitions; a Journal of Petroleum Technology case study documents an operator using Novi-developed software to evaluate development scenarios. ComboCurve develops cloud software for forecasting, reserves, economics, and scheduling, and Enverus connects asset screening, forecasting, and economics in PRISM. In each case, users should separate observed data, modeled output, and analyst judgment.

Interpretation of physical samples is another use case. GeologicAI deploys multi-sensor scanning systems for drill core and chips and adds AI-assisted interpretation; its current focus is critical minerals, but the workflow of turning rock measurements into a governed geological dataset is directly comparable to subsurface work in oil and gas. Buyers should test calibration, chain of custody, resolution, and how expert review changes the final interpretation.

Underneath many AI projects is a data problem. Cognite Data Fusion integrates industrial IT, operational technology, and engineering data and adds contextual relationships that support digital twins and AI applications. Eigen builds operational-data integration, reporting automation, analytics, and knowledge-graph digital twins for oil and gas operations, and an SPE Germany article identifies Eigen consultants as collaborators on an AI and rule-based well-integrity monitoring project. These platforms rarely make an operating decision on their own; their job is to make data trustworthy enough for other tools and people to use.

How to compare AI providers without a ranking

The companies in this guide are listed alphabetically on their profiles and in the mapped-company grid. Inclusion reflects public evidence of relevant work, not an endorsement, a ranking, or a commercial relationship. Coverage is incomplete: many operators build AI in-house and never describe it publicly, and private deployments are not visible to BTU Graph.

A useful shortlist mixes company types. Compare one specialist that owns a narrow workflow, one platform that improves the underlying data, and the in-house option. Ask each to reproduce a past decision with your own data, to show how a model's output is reviewed before it changes an operation, and to export the records you would need for an audit. Where commodity prices enter the economics, keep the source and timestamp of every price series outside the model so forecasts and valuations can be rerun.