Landmark Graphics Corporation

Emc IP Holding Company LLC

Xi Wang

Keshava Prasad Rangarajan

Joseph Blake Winston

Vinicius Michel Gottin

Jonas Furtado Dias

Scott David Senften

Ashwani Dev

Romulo D Pinho

David Michael Holmes

Angelo Ciarlini

Edward Pacheco

Chandra Yeleshwarapu

Andre Maximo

Clifton Brent Walker

Srinath Madasu

Yogendra Narayan Pandey

Nagaraj Srinivasan

Aimee Jackson Taylor

Anuj Jain

Nagaraj Sirinivasan

Wei Chiu

Jeffery Padgett

Aspects of the present disclosure relate to projecting control parameters of equipment associated with forming a wellbore, stimulating the wellbore, or producing fluid from the wellbore. A system includes the equipment and a computing device. The computing device is operable to project a control parameter value of the equipment using an equipment control process, and to receive confirmation that the projected control parameter value is within an allowable operating range. The computing device is also operable to adjust the equipment control process based on the confirmation, and to control the equipment to operate at the projected control parameter value. Further, the computing device is operable to receive real-time data associated with the forming of the wellbore, the stimulating of the wellbore, or the producing fluid from the wellbore. Furthermore, the computing device is operable to adjust the equipment control process based on the real-time data.

Operating wellbore equipment using a distributed decision framework

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

A method of managing oilfield activity with a control system is provided having a plurality of virtual sensors and integrating the virtual sensors into a virtual sensor network. The method includes determining interdependencies among the virtual sensors, obtaining operational information from the virtual sensors, and providing virtual sensor output to the control system based on the determined interdependencies and the operational information.

Integrated surveillance and control

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Houston, TX, United States of America

Xi Wang