Landmark Graphics Corporation

Halliburton Energy Services, Inc.

Kalyan Saikia

Jeffrey Marc Yarus

Rae Mohan Srivastava

Siva Rama Krishna Jandhyala

Samiran Roy

Jordan Michael Yarus

Katherine Evans

Florence Mazouzi

A method of designing a cement blend for a wellbore isolation barrier based on the analysis of a stress state of the wellbore isolation barrier from the injection of COinto a porous formation. The analysis software may determine an optimized cement blend for a future COinjection schedule. The analysis software may determine a current near wellbore stress state for a current COinjection schedule. The analysis software may optimize a COinjection schedule based on the analysis of a future near wellbore stress state of the wellbore isolation barrier. The near wellbore stress state of the isolation barrier may be determined by at least one model accessed by the analysis software. The inputs into the model comprise periodic COinjection pressure and flowrate datasets, cement properties, and formation properties.

Method to tailor cement properties and optimize injection schedule for near wellbore integrity in carbon storage/injection wells

A system can determine a location for future wells using machine-learning techniques. The system can receive seismic data about a subterranean formation and may determine a set of seismic attributes from the seismic data. The system can block the set of seismic attributes into a set of blocked seismic attributes by distributing the set of seismic attributes onto a geo-cellular grid representative of the subterranean formation. The system can apply a trained machine-learning model to the set of blocked seismic attributes to generate a composite seismic parameter. The system can distribute the composite seismic parameter in the subterranean formation to characterize formation locations based on a predicted presence of hydrocarbons.

Reservoir characterization using machine-learning techniques

The disclosed embodiments include a method, apparatus, and computer program product configured to provide a probabilistic approach for real time drilling. In particular, the disclosed embodiments are configured to obtain real-time data gathered during the drilling operation to update a probability model of a formation that is used in determining whether to alter a direction of a drill path. The disclosed embodiments may be configured to provide a notification to a user indicating or suggesting that certain adjustments be made to the drill path based on the updated probability model. Additionally, the disclosed embodiments may be configured to automatically make the adjustments to the drill path based on the updated probability model.

Probabilistic methodology for real time drilling

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Houston, TX, United States of America

Kalyan Saikia