AI in Oil and Gas and Energy
Subsurface, reliability and land data intelligence, without sending proprietary data offsite.
Energy operators sit on decades of unstructured technical data: well logs, completion reports, daily drilling reports, inspection records and land files, most of it as scanned PDFs nobody can search. It is also data that is commercially sensitive enough that operators will not send it to a third-party API. Senteras is based in Houston and builds this on the operator's own infrastructure.
Book a free consultationWhat makes this hard
- Decades of well files, completion reports and inspection records exist only as scanned documents nobody can query
- Subsurface and completion data is competitively sensitive and routinely excluded from third-party services by contract
- Equipment failure in the field is expensive and often predictable from data already being collected and discarded
- Regulatory and HSE reporting is manual, repetitive and carries real penalties when it slips
Where AI earns its place
Technical Document Intelligence
Make decades of scanned well files, completion reports and daily drilling reports searchable in natural language, with page-level citation back to the original document.
Days of file review to minutesEquipment Reliability
Predict failures on rotating equipment and artificial lift from sensor and maintenance history, prioritising by deferred production rather than by asset value.
Failures caught before deferred productionHSE and Regulatory Reporting
Assemble incident, emissions and compliance reporting from field records, with the source of every figure traceable.
Reporting cycles measured in hoursLand and Lease Analysis
Extract terms, obligations, depth severances and expiry triggers from lease and title documents across a position.
Full-position obligation visibilityFigures are drawn from Senteras engagements and are illustrative of typical results. Outcomes vary by data quality, infrastructure and scope.
The rules that shape the build
These are the constraints that decide the architecture, usually before anyone has picked a model. This is general information about the regulatory landscape, not legal advice on your obligations.
Export controls on technical data
Some subsurface and equipment data is export-controlled, which constrains where inference may physically run and who may access it.
PHMSA and state reporting obligations
AI-assisted regulatory reporting still needs a named human attesting to accuracy, and the audit trail has to survive an inspection.
EPA emissions reporting (Subpart W)
Calculation methodology has to be reproducible and documented. A model that cannot show its arithmetic cannot be in the calculation path.
The data never leaves the operator
Every energy engagement Senteras has scoped has ended on-premise or in the operator's own cloud tenancy, without exception. Subsurface data is the asset. Operators will trade some model capability for the certainty that it stays inside their perimeter, and open-weight models are now close enough on document tasks that the trade costs very little.
Where this applies
The same core systems, with the differences that matter in each setting.
- Upstream operators
- Well file retrieval and reliability; the payback is measured in deferred production avoided.
- Midstream
- Pipeline integrity records and right-of-way document analysis.
- Utilities and power
- Outage prediction and vegetation management planning; regulated cost recovery changes the business case.
- Renewables
- Asset performance and warranty claim substantiation across a distributed fleet.
- Mining
- Same reliability and document patterns, with heavier geological survey corpora.
Common questions
Will our subsurface data leave our environment?
No. Every energy engagement we have scoped has ended on-premise or in the operator's own cloud tenancy. Subsurface data is the asset, and open-weight models are now close enough on document work that keeping it inside costs very little capability.
Can this read decades of scanned well files?
That is the highest-value use case in the sector. Poor scans and handwritten annotations are handled, with low-confidence extractions flagged rather than guessed, and every answer cites the page it came from.
Does AI help with emissions reporting?
It can assemble and structure the inputs. The calculation methodology must remain reproducible and documented, so a model does not belong in the arithmetic path for Subpart W reporting.
How do you handle export-controlled technical data?
It constrains where inference physically runs and who may access it, which is another reason these deployments end up on-premise. It is worth establishing which of your data is controlled before scoping, not during.
How we build it
Local & On-Prem LLM Deployment
The most powerful AI models, running entirely on your hardware.
Custom AI Agents & Automation
AI that doesn't just answer questions. It gets things done.
Model Fine-Tuning & Integration
Models that speak your industry's language, trained on your data.
Document Processing
Extraction, classification and validation across formats you do not control.
Predictive Maintenance
Prioritised by production impact, not by failure probability alone.
Internal Knowledge Base
Answers from your own documents, under your existing permissions.
Find out what this looks like for your organisation
A 30-minute call. We will tell you plainly whether AI is the right tool for the problem you have, including when it is not.
Book a free consultation