Blog · 12 October 2026 · 11 min read

Implementing AI in the petroleum industry: a practical 90-day plan

Most AI projects in oil and gas fail for the same reasons: they start with a platform instead of a problem, they wait for perfect data, and nobody measures whether engineers actually use the result. This plan takes a petroleum company from first idea to daily use in 90 days, starting with the documents and spreadsheets you already have.

Start with work, not with a platform

Pick one piece of work that is frequent, document-heavy and painful. In drilling and maintenance the best candidates are almost always the same:

• Daily drilling report review and the morning summary for management
• Overhaul and maintenance planning: scope, cost per equipment item, manpower and timeline
• Answering questions from mud programs, end-of-well reports and procedures
• Inspection and certification tracking for drill string and handling equipment
• Preparing the same presentation for management or investors every month

Choose one. Write down how long it takes today and who does it. That is your baseline.

Days 1–30: one team, one project, real files

1. Create a project for one real well or one real overhaul, and upload its files: reports, programs, plans and the spreadsheets people actually use.
2. Write down the project facts once: scope, currency, exclusions, dates, constraints. The assistant should apply them to every answer.
3. Give three to five engineers access and ask them to use it for that work only, every day, for two weeks.
4. Collect every answer that was wrong or unhelpful. These become your test set.

Days 31–60: measure and fix

Fix what the test set shows: missing files, unclear project facts, rates that should come from your own rate card instead of planning estimates. Replace placeholder figures with real workshop, manpower and inspection rates as soon as you have them.

Days 61–90: widen carefully

• Add the next project of the same kind, reusing the same setup.
• Add the next team: production, HSE or procurement.
• Decide what may connect later: historian, SAP PM, document management. Read-only first.
• Write the rules: who may upload what, what may leave the network, who approves outputs used in operations.

Data: you have more than you think

Waiting for a data lake is the most common reason AI never starts in a petroleum company. PDFs, Word files and spreadsheets are enough for the first 90 days. Daily drilling reports alone hold depth, operations by the hour, mud properties and problems for every day of every well.

Safety, security and approvals

• Keep AI read-only to control systems. It may draft; people decide.
• Label general industry knowledge separately from what your own records say.
• For live operations, any calculation or procedure must be confirmed against the approved program by the responsible engineer.
• Decide where data is stored. For many operators in the Gulf, storage inside the country is a requirement.

Common mistakes

• Buying a platform before choosing the first piece of work.
• A pilot with no baseline, so nobody can say whether it worked.
• Letting every chat invent its own numbers instead of one source of truth.
• Training nobody: engineers need fifteen minutes and three good example questions.
• Ignoring language: if your engineers think in Urdu or Arabic, the tool has to understand them.

ChatPetroleum is designed for this path: projects with memory, your own files, one set of numbers, files you can use, and voice in English, Arabic and Urdu. Start free at chatpetroleum.space.