Shriram Finance
less than 3–4 hours — Onboarding Turn-Around-Time (TAT)
Independently reportedBanking & finance · Process automation · Classical ML · In production
Process automation
Oil and Natural Gas Corporation (ONGC)
Vendor-reportedEnergy & utilitiesIndiaScaledEnterpriseAI agents
Vendor-reported. The customer is named and the numbers are quoted from the source page, but the account comes from the vendor. No independent confirmation.
Reported by spe.org (vendor self-report). Checked against the source page on 2026-09-06. 4 of 4 figures below appear on that page word for word.
The problem
Manually building and calibrating well models for approximately 600 wells required several months of continuous engineering effort.
What was deployed
- Products
- Pipesim engine, well-analysis Python library
- Models
- small language model (SLM)
- Technique
- AI agents
- Build or buy
- Built in-house or to order
- Deployment
- Custom build
- Data used
- validated input data, Excel or CSV files containing well and survey data
- Scale
- approximately 600 wells (Case Study 1, offshore stimulation-planning); 370 tubing-sensitivity simulations across 3 offshore fields (Case Study 2)
What changed
Each row is quoted from the source. Figures we could not find on the page in those words are marked — they are kept, not deleted, so you can judge them.
exceeded 700engineering hours saved (Case Study 1, ~600-well stimulation planning)
“Conservatively, the total time saved in this case study exceeded 700 engineering hours, without compromise in modeling consistency or quality.”
Quoted word for word from the source
under 1 hoursimulation execution timefrom large manual modeling effort
“All 370 simulations were executed automatically in under 1 hour of wall-clock time.”
Quoted word for word from the source
exceeded 320engineering hours saved (Case Study 2, 370-simulation tubing sensitivity)
“The estimated net saving exceeded 320 engineering hours, while enabling comprehensive tubing-sensitivity evaluation across the full well population.”
Quoted word for word from the source
exceeded 1,000combined engineering hours saved (both case studies)
“Combined savings across the two projects exceeded 1,000 engineering hours.”
Quoted word for word from the source
within a single daytime to complete modelingfrom several months
“What would normally have required months of distributed manual effort was completed within a single day of supervised automation.”
Quoted word for word from the source
Difficulties and limits
- Even at an optimistic rate of four to five wells per engineer per day, the task would have exceeded 1,000 engineering hours.
- Engineers must remain in the supervisory loop. The agent flags incomplete data, but final validation remains the responsibility of the production engineer.
- Automation succeeds only with disciplined data management. Early efforts focused heavily on standardizing units, naming conventions, and FGS metadata.
Sources
- spe.org — vendor self-report, 2026-04-01
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