C3.ai, Inc.

Riyad Sabir Muradov

Henrik Ohlsson

Thomas M Siebel

Nikhil Krishnan

Mehdi Maasoumy Haghighi

Romain Florian Juban

Louis Poirier

Michael Haines

Amir Hossein Delgoshaie

Sina Khoshfetratpakazad

Yushi Homma

Philippe Ivan S Wellens

Systems and methods managing, by an orchestrator, a plurality of agents to generate a response to an input. The orchestrator employs one or more multimodal models such as a large language models to process or deconstruct the prompt into a series of instructions for different agents. Each agent employs one or more machine-learning models to process disparate inputs or different portions of an input associated with the prompt. The system generates, by the orchestrator, a natural language summary of the structured and unstructured data records. The system formulates output and transmits the natural language summary of the data records.

Enterprise generative artificial intelligence architecture

A method of waterflood management for reservoir(s) having production hydrocarbon-containing well(s) including injector well(s). A reservoir model has model parameters in a mathematical relationship relating a water injection rate to a total production rate of the production well including at least one of a hydrocarbon production rate and water production rate. A solver implements automatic differentiation utilizing training data regarding the reservoir including operational data that includes recent sensor and/or historical data for the water injection rate and the hydrocarbon production rate, and constraints for the model parameters. The solver solves the reservoir model to identify values or value distributions for the model parameters to provide a trained reservoir model. The trained reservoir model uses water injection schedule(s) for the injector well to generate predictions for the total production rate.

Waterflood management of production wells

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Redwood City, CA, United States of America

Riyad Sabir Muradov