Artificial intelligence for the oil and gas industry in Kazakhstan

AI in oil and gas addresses four classes of problems: equipment failure prediction (pumps, compressors, gas-pumping units), production and waterflood optimization, industrial-safety monitoring through video analytics, and automation of work with technical documentation. According to Samruk-Kazyna, the AI waterflooding module of KazMunayGas's ABAI project alone delivered 12,000 tonnes of additional oil in 2025.

Industry challenges and our solutions

Pain

Artificial-lift well stock failures: one ESP failure means 3–10 days of well downtime and $30–100K in losses; a large oil and gas production unit sees hundreds of failures a year

Solution

Predictive diagnostics of the well stock from control-station telemetry and maintenance history: a 7/30-day risk list with root-cause explanation

MTBR up 10–15%, 30–40% of emergency repairs converted to planned

Pain

Flow rate measured through the group metering unit once every few days — the production engineer manages regimes “blind”, with a 2–5% shortfall against the well stock's potential

Solution

Virtual flow meter (a hybrid of physics and ML) plus an advisor of optimal ESP regimes across the well stock

+1–3% production on a mature well stock with no capital investment

Pain

Safety violations at fields and refineries: an inspector covers less than 5% of the territory

Solution

Video analytics on existing cameras: PPE, danger zones, hot work, leaks and smoke

Round-the-clock monitoring, automatic incident logging

Pain

Regime quality varies between shifts at refineries: 2–5% in light-product yield; full APC costs $1–3 million per unit

Solution

Unit operator advisor: quality soft sensors plus setpoint recommendations from the “best shifts”, read-only from the historian

+0.5–1.5% target-product yield, −3–7% furnace fuel

Pain

Heat-exchanger fouling: 1–3% of the plant's fuel consumption lost, cleanings by the calendar

Solution

Fouling forecast: heat-transfer coefficient calculation + ML forecast of degradation + cleaning-schedule optimization

$1–4 million a year per plant

Pain

Engineers spend 20–30% of their time searching through standards, procedures and P&IDs

Solution

A RAG assistant on a local LLM with citations down to the clause and revision — entirely inside the enterprise perimeter

0.5–1 hour a day per engineer, fewer errors from outdated revisions

Typical solution architecture

  1. Sources: process control system/SCADA, historians (PI, WinCC), CMMS (SAP PM, 1C), LIMS, video streams
  2. Data bus and data mart: OPC UA/Modbus connectors, historian exports — no intervention in the control loop
  3. On-premise ML platform: failure-prediction models, virtual flow meters, regime advisors
  4. Interfaces: production engineer's workstation, a mobile app for the field foreman, dashboards for the chief engineer

Solutions for the industry

Predictive equipment maintenanceComputer vision for industrial safety (HSE)Digital twins and process optimizationPrivate LLM assistants inside the enterprise perimeter

Why Kazakhstan's oil and gas industry is adopting AI right now

2026 has been declared the Year of Digitalization and Artificial Intelligence; Samruk-Kazyna's portfolio companies have been set a KPI of +5% EBITDA from AI. The group is running 62 AI projects with an expected effect of more than $1.3 billion by 2030. KazMunayGas has declared its predictive-analytics pilots at the Atyrau and Pavlodar refineries successful and in 2026 began full-scale roll-out — 100+ critical assets at each plant. Tengizchevroil operates more than 20 AI products, including an LLM assistant for engineers over 300+ technical specifications.

The question of whether to adopt AI is closed in the industry. The open question is who will deliver quickly, locally, inside a closed perimeter and cheaper than Western integrators. That is exactly the niche “105kz” LLP (105 Industrial AI) fills: a Kazakhstani engineering team, an on-premise architecture and a pilot in 8–12 weeks.

How AI predicts oilfield equipment failures

ESP control-station telemetry (current, intake pressure, temperature, vibration) is already collected by the process control system — but used reactively. The predictive model is trained on the combination of telemetry and failure history from the CMMS (1C, SAP PM) and identifies precursors of degradation: pump seizure, loss of flow, rising leakage, scale deposition. The output is a list of wells ranked by failure risk over 7/30 days with a root-cause explanation (SHAP) and a recommendation: change the operating regime or schedule a planned pull for repair.

Mean time between repairs is an official KPI of chief engineers at oil and gas production units, so the project's effect is measured in the terms the customer already reports on.

What the implementation sequence looks like for a production asset

  1. Data and process audit (2–3 weeks): inventory of telemetry, maintenance histories and laboratory data; interviews with the chief engineer, production engineer and chief mechanic; a ranked list of tasks with economics
  2. Retrospective check: before the pilot, models are run on historical data — we find precursors of failures that have already happened and calculate shift variability
  3. Pilot at one site (8–12 weeks): one shop, one unit or 100–200 wells; success criteria are fixed before the start
  4. Roll-out and integration (4–12 months): scaling to the well stock/plant, integration with the CMMS and procedures, staff training, SLA support

Who we work with in the industry

Production assets (oil and gas production units with an artificial-lift well stock), refineries, oilfield service companies, operators of mid-sized fields. For international operators (TCO, NCOC, KPO) we work under their prequalification procedures, with documentation in English.

Our positioning relative to the major vendors is honest: predictive-analytics programs for first-tier equipment are locked up by contracts with global vendors — we cover what is unprofitable for them: “second-tier” equipment (at a refinery that is 2,000+ items of rotating equipment), the artificial-lift well stock and narrow process tasks — locally and several times cheaper.

Which mistakes most often kill AI projects in oil and gas

  • Starting with the hardest task: optimizing a whole field takes years; a predictive model on 100 wells takes a quarter
  • Ignoring the people: the production engineer and mechanic must trust the system — explainable forecasts, training and joint alarm calibration are mandatory
  • The effect “not measured beforehand”: without a fixed baseline (current MTBR, failure statistics) a dispute over the pilot's results is inevitable
  • A model without support: regimes and equipment change — models without retraining degrade within 6–12 months

FAQ

Frequently asked questions

What chief engineers, security and procurement teams usually ask — with straight answers.

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Next step

Request an oilfield data audit

Tell us about your task — we'll come back with a data audit plan and an effect estimate within two business days.

  1. 1A 30-minute call: the task, the data, who signs off on security
  2. 2Data audit or a retrospective analysis on an anonymised export
  3. 3An 8–12 week pilot with success criteria agreed before the start
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