Xi H Wang

20220300812, Sep 22, 2022

Apr 6, 2022

17/714247

- Houstion TX, US
- Hopkinton MA, US
Angelo CIARLINI - Rio de Janeiro, BR
Romulo D. Pinho - Niteroi, BR
Vinicius GOTTIN - Rio de Janeiro, BR
Andre MAXIMO - Rio de Janeiro, BR
Edward PACHECO - Rio de Janeiro, BR
David HOLMES - Guildford, GB
Keshava RANGARAJAN - Sugar Land TX, US
Scott David SENFTEN - Sugar Land TX, US
Joseph Blake WINSTON - Houston TX, US
Xi WANG - Houston TX, US
Clifton Brent WALKER - Richmond TX, US
Ashwani DEV - Katy TX, US
Nagaraj SIRINIVASAN - Sugar Land TX, US

Landmark Graphics Corporation - Houston TX
EMC IP Holding Company LLC - Hopkinton MA

G06N 3/08
G06F 9/50
G06N 3/04
G06N 3/02
G06F 9/48

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.

20210115778, Apr 22, 2021

Aug 2, 2018

17/047837

- Houston TX, US
Joseph Blake Winston - Houston TX, US
Srinath Madasu - Houston TX, US
Xi Wang - Houston TX, US
Yogendra Narayan Pandey - Houston TX, US
Wei Chiu - Sugar Land TX, US
Jeffery Padgett - The Woodlands TX, US
Aimee Jackson Taylor - Bogota, CO

E21B 44/02
H04Q 9/00
E21B 49/00
E21B 47/07
G05B 19/4155

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.

20200182036, Jun 11, 2020

Apr 5, 2018

16/637263

- Houston TX, US
Joseph Blake WINSTON - Houston TX, US
Anuj JIAN - Katy TX, US
Xi WANG - Katy TX, US

E21B 44/00
E21B 47/12
G05B 13/04
G05B 13/02
H04L 29/08
G06N 20/00
G06N 3/08

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.

20200057675, Feb 20, 2020

Aug 16, 2018

15/998728

- Hopkinton MA, US
Angelo Ciarlini - Rio de Janeiro, BR
Romulo D. Pinho - Niterol, BR
Vinicius Gottin - Rio de Janeiro, BR
Andre Maximo - Rio de Janeiro, BR
Edward Pacheco - Rio de Janeiro, BR
David Holmes - Guildford, GB
Keshava Rangarajan - Sugar Land TX, US
Scott David Senften - Sugar Land TX, US
Joseph Blake Winston - Houston TX, US
Xi Wang - Houston TX, US
Clifton Brent Walker - Richmod TX, US
Ashwani Dev - Katy TX, US
Chandra Yeleshwarapu - Sugar Land TX, US
Nagaraj Srinivasan - Sugar Land TX, US

G06F 9/50
G06N 3/04
G06N 3/08

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.