AI Identifies Missing Reserves in Reservoirs Using ML
Summary
The USPTO published patent application US20260099794A1 covering an AI/ML system for identifying missing reserves in oil and gas reservoirs. The system ingests well site data and uses multiple machine learning models to generate reservoir quality indicators, determine missing reserves between wells, identify candidate wells for intervention, and predict economic outcomes for intervention options.
What changed
The USPTO published patent application US20260099794A1 for an augmented intelligence system that identifies missing reserves in oil and gas reservoirs using machine learning. The system uses four ML models: one generates behind casing opportunities and reservoir quality indicators from well logs, a second determines missing reserves based on quality indicators, a third identifies candidate wells, and a fourth predicts economic outcomes for intervention options.
Energy companies and oilfield operators may benefit from this AI-driven approach to reservoir management, as it could improve reserve identification accuracy and economic forecasting for intervention decisions. The patent does not create compliance obligations but represents a potential technology for future adoption in reservoir analysis and extraction optimization.
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Apr 9, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
AUGMENTED INTELLIGENCE (AI) DRIVEN MISSING RESERVES OPPORTUNITY IDENTIFICATION
Application US20260099794A1 Kind: A1 Apr 09, 2026
Inventors
Shripad BINIWALE, Sanjoy KHATANIAR, Mohamed Osman Mahgoub AHMED
Abstract
A method, computer system, and computer program product are provided for identifying missing reserves in a reservoir. Well site data for well sites in a reservoir are ingested. A first machine learning model generates behind casing opportunities and reservoir quality indicators from the plurality of logs. A second machine learning model determines missing reserves based on the reservoir quality indicators for the well sites and in between the wells. A third machine learning model determines candidate wells based on the missing reserves. A fourth machine learning model predicts economic outcomes for intervention options for the candidate wells. An oilfield decision is supported based on the predicted economic outcomes.
CPC Classifications
G06Q 10/06334 E21B 47/003 G06Q 50/02
Filing Date
2023-09-22
Application No.
19113935
Named provisions
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