Oil and gasWhat we do, and an illustrative case
Oil and gas operations can be improved massively, safely, starting with one site in 10 weeks.
We map how the work really runs from your own systems, measure the gap to best practice, and check every case as it happens.
What a site can gainPublished results, with their sources
The gains are documented.
$84M
lost per site a year to unplanned downtime, oil & gas average
Siemens, “The True Cost of Downtime 2022” (32 hours a month at $220K an hour)
30–50%
less unplanned downtime with mature predictive maintenance
McKinsey, “Manufacturing: Analytics unleashes productivity and profitability,” 2017
30%
of incidents in maintenance: process plants, with permit failures in 20%+
UK HSE, “Dangerous Maintenance,” as cited in IChemE Symposium Series
2 in 3
turnarounds overrun 10%+ on cost and schedule, or trip at startup
AP-Networks turnaround benchmarks (more than two-thirds of turnarounds), via Digital Refining
What oil and gas operators needSafety-driven and business-driven
Built for how you really run.
Safety first
Permit-to-work and MOC gaps caught before incidents.
Reliable assets
Fewer unplanned shutdowns, maintenance on condition.
More throughput
Higher uptime and turnarounds on time and budget.
Low-risk start
One site, 10 weeks. Runs on-prem with open-source models.
Illustrative caseOil & gas site: maintenance, permits, uptime
$4.2M saved a year.
One site, 10 weeks. An illustrative case, not an AIx2 client result: Illustrative. Average oil & gas site: 32 h of unplanned downtime a month at $220K an hour (Siemens, True Cost of Downtime 2022). Assumes 5% fewer hours: 1.6 h × 12 × $220K = $4.2M a year.
32 h/month → 30.4 h
unplanned downtime
Siemens, True Cost of Downtime 2022; assumes 5% fewer hours
30% → 35%
wrench time
Typical wrench time is 25–35%, best practice about 55% (D. Palmer, Maintenance Planning and Scheduling Handbook). Assumes a 5-point gain.
spot checks → 100%
permits checked for gaps
30% of reported chemical-industry incidents are maintenance-related, with permit-to-work failures in over 20% of them (UK HSE, Dangerous Maintenance).
How we get thereThe ten weeks, step by step
From your own data to live checks on every case.
Step 01: Collecting Data
Connect SAP PM, DCS and PI historian; add permit, MOC and inspection records; capture shift logs, Excel and e-mail
Weeks 1–2
Step 02: Mapping Processes and Data Flow
Map work orders from notification to close; map permits and MOCs as they really run; trace data from sensor to decision
Weeks 3–4
Step 03: Gap Analysis
Find repeat failures and what they cost; flag permit, MOC and backlog risks
Weeks 5–6
Step 04: Optimal Design
Fix the costliest repeat failures first; redesign permit and work-order flow
Weeks 7–8
Step 05: Implementation Live Assurance
Run live QC on every work order and permit; flag safety and reliability risk early; track savings against the baseline
Weeks 9–10