Landmark Graphics Corporation

Emc IP Holding Company LLC

Nagaraj Srinivasan

Keshava Prasad Rangarajan

Robello Samuel

Vinicius Michel Gottin

Jeffrey Marc Yarus

Jonas Furtado Dias

Scott David Senften

Ashwani Dev

Joseph Blake Winston

Yogendra Narayan Pandey

Romulo D Pinho

David Michael Holmes

Xi Wang

Edward Pacheco

Chandra Yeleshwarapu

Angelo Ciarlini

Andre Maximo

Clifton Brent Walker

Naresh Chaudhary

James Etienne

Embodiments of the subject technology for deep learning based reservoir modelling provides for receiving input data comprising information associated with one or more well logs in a region of interest. The subject technology determines, based at least in part on the input data, an input feature associated with a first deep neural network (DNN) for predicting a value of a property at a location within the region of interest. Further, the subject technology trains, using the input data and based at least in part on the input feature, the first DNN. The subject technology predicts, using the first DNN, the value of the property at the location in the region of interest. The subject technology utilizes a second DNN that classifies facies based on the predicted property in the region of interest.

Deep learning based reservoir modeling

A system can assign a value to one or more sustainability factors for a wellbore operation based on historical data. The system can determine, for each of the one or more sustainability factors, a weight. The system can determine a sustainability index corresponding to a predicted carbon footprint for the wellbore operation based on the weight and the value for each of the one or more sustainability factors. The system can output a command for adjusting the wellbore operation based on the sustainability index.

Metric-based sustainability index for wellbore life cycle

A computer implemented method, computer program product, and system for managing execution of a workflow comprising a set of subworkflows, comprising optimizing the set of subworkflows using a deep neural network, wherein each subworkflow of the set of subworkflows has a set of tasks, wherein each task of the sets of tasks has a requirement of resources of a set of resources; wherein each task of the sets of tasks is enabled to be dependent on another task of the sets of tasks, training the deep neural network by: executing the set of subworkflows, collecting provenance data from the execution, and collecting monitoring data that represents the state of said set of resources, wherein the training causes the neural network to learn relationships between the states of said set of resources, the said sets of tasks, their parameters and the obtained performance, optimizing an allocation of resources of the set of resources to each task of the sets of tasks to ensure compliance with a user-defined quality metric based on the deep neural network output.

Workflow optimization

Growing community of inventors

Sugar Land, TX, United States of America

Nagaraj Srinivasan