Petro & Agri Intelligence

Where the earth
yields its intelligence

AI-driven reservoir science for India's petroleum basins and satellite-fused crop intelligence for its agro-climatic zones — two domains, one underlying methodology: turning physical-world sensor data into decisions that matter before it is too late to act on them.

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Petroleum
Reservoir & formation intelligence
LLM-assisted well log interpretation, seismic analysis, and predictive drilling models for the KG, Cauvery, and Mumbai Offshore basins.
Agriculture
Crop yield prediction systems
Multi-spectral satellite fusion and ML yield forecasting for Kharif and Rabi crops across Andhra Pradesh's five agro-climatic zones.
KG basin
Primary focus basin
AP zones
Agri-climatic coverage
2 domains
One methodology
AI+SAR
Core technology stack
The connecting thread

Two domains, one discipline

Petroleum science and precision agriculture look like different worlds. They share a fundamental problem: making consequential decisions about what lies beneath the surface — in the ground, in the soil — based on incomplete, noisy, multi-source data, under time pressure. iQuantra applies the same AI and sensor fusion methodology to both.

Petroleum intelligence
From raw logs to reservoir decisions
India's legacy well datasets represent decades of exploration investment that has never been fully interrogated. iQuantra's AI-assisted interpretation pipeline — built on large language models augmented with basin-specific geological context — makes that interrogation possible at a scale and speed no manual workflow can match.
LLM-assisted well log interpretation with reasoning traces
Formation identification and net pay flagging
Seismic attribute analysis and horizon mapping
Cross-well correlation and basin-scale pattern recognition
Legacy dataset revaluation for overlooked pay potential
Agricultural intelligence
From satellite data to harvest decisions
Andhra Pradesh's Kharif season determines procurement requirements, credit exposure, and food price dynamics for the following quarter — yet the forecasting tools available to most stakeholders lag weeks behind the moment when intervention is still possible. Satellite-derived soil moisture changes that.
Sentinel-1 SAR soil moisture retrieval through monsoon cloud cover
Multi-spectral fusion of Sentinel-2, SMAP, and IMD data
Sub-district Kharif yield prediction 4–6 weeks before harvest
Crop stress identification and intervention mapping
Historical yield trend analysis across agro-climatic zones
The shared methodology
Multi-source sensor fusion + domain-contextualised AI inference
Whether the sensor is a wireline tool reading resistivity at 3,000 metres below the sea floor, or a Sentinel-1 radar pulse reading soil backscatter from 700 kilometres above the earth — the data science challenge is identical: fuse multiple imperfect signals, inject domain knowledge, and produce an interpretation that a field specialist can trust and act on. That is what iQuantra builds.
Full capability set

What we build

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LLM well log interpretation pipeline
Retrieval-augmented generation architecture that combines foundation model reasoning with basin-specific geological knowledge — producing annotated formation evaluation drafts for expert review.
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Seismic attribute AI analysis
Machine learning models applied to seismic attribute volumes for automated horizon identification, anomaly detection, and direct hydrocarbon indicator mapping.
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Legacy dataset revaluation
Rapid AI-assisted re-evaluation of legacy well portfolios that have not been fully interrogated — identifying overlooked pay potential that was uneconomic to assess manually at the time of drilling.
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Satellite soil moisture fusion
Multi-source remote sensing pipeline combining Sentinel-1 SAR, Sentinel-2 multispectral, and NASA SMAP data into a unified soil water status model for Kharif crop monitoring.
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Sub-district yield prediction
Ensemble ML models trained on fused satellite and meteorological data, delivering mandal-level Kharif yield forecasts 4–6 weeks before harvest — ahead of any intervention window closing.
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Human-in-the-loop review systems
Every iQuantra model output is a structured draft for expert review — not an autonomous decision. Confidence scores, uncertainty flags, and reasoning traces are standard outputs across both domains.
Who we serve

Organisations we work with

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E&P operators & NOCs
ONGC, Oil India, and joint-venture operators seeking to unlock value from legacy KG, Cauvery, and Mumbai Offshore well portfolios through AI-assisted interpretation.
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State agriculture departments
Andhra Pradesh and Telangana agricultural authorities seeking mandal-level Kharif yield forecasts to improve procurement planning and MSP intervention timing.
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Agri-commodity traders & banks
Commodity procurement organisations and rural lenders who need sub-district crop intelligence to manage procurement exposure and credit risk through the Kharif season.
Working with earth data?

Whether you hold a legacy well portfolio, manage agricultural procurement, or are building a precision farming platform — we welcome the conversation.

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