Patents by Inventor Keshava Rangarajan

Keshava Rangarajan has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11873707
    Abstract: A system and method for controlling a drilling tool inside a wellbore makes use of projection of optimal rate of penetration (ROP) and optimal controllable parameters such as weight-on-bit (WOB), and rotations-per-minute (RPM) for drilling operations. Optimum controllable parameters for drilling optimization can be predicted using a data generation model to produce synthesized data based on model physics, an ROP model, and stochastic optimization. The synthetic data can be combined with real-time data to extrapolate the data across the WOB and RPM space. The values for WOB an RPM can be controlled to steer a drilling tool. Examples of models used include a non-linear model, a linear model, a recurrent generative adversarial network (RGAN) model, and a deep neural network model.
    Type: Grant
    Filed: March 9, 2018
    Date of Patent: January 16, 2024
    Assignee: Landmark Graphics Corporation
    Inventors: Srinath Madasu, Nishant Raizada, Keshava Rangarajan, Robello Samuel
  • Patent number: 11868890
    Abstract: 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 b
    Type: Grant
    Filed: April 6, 2022
    Date of Patent: January 9, 2024
    Assignees: LANDMARK GRAPHICS CORPORATION, EMC IP HOLDING COMPANY LLC
    Inventors: Chandra Yeleshwarapu, Jonas F. Dias, Angelo Ciarlini, Romulo D. Pinho, Vinicius Gottin, Andre Maximo, Edward Pacheco, David Holmes, Keshava Rangarajan, Scott David Senften, Joseph Blake Winston, Xi Wang, Clifton Brent Walker, Ashwani Dev, Nagaraj Sirinivasan
  • Patent number: 11725489
    Abstract: Systems, methods, and computer-readable media are described for intelligent, real-time monitoring and managing of changes in oilfield equilibrium to optimize production of desired hydrocarbons and economic viability of the field. In some examples, a method can involve generating, based on a topology of a field of wells, a respective graph for the wells, each respective graph including computing devices coupled with one or more sensors and/or actuators. The method can involve collecting, via the computing devices, respective parameters associated with one or more computing devices, sensors, actuators, and/or models, and identifying a measured state associated with the computing devices, sensors, actuators, and/or models.
    Type: Grant
    Filed: April 27, 2017
    Date of Patent: August 15, 2023
    Assignee: Landmark Graphics Corporation
    Inventors: Joseph Blake Winston, Brent Charles Houchens, Feifei Zhang, Avinash Wesley, Andrew Shane Elsey, Jonathan Nguyen, Keshava Rangarajan, Olivier Germain
  • Publication number: 20230184080
    Abstract: 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.
    Type: Application
    Filed: February 7, 2023
    Publication date: June 15, 2023
    Applicant: Landmark Graphics Corporation
    Inventors: Keshava RANGARAJAN, Joseph Blake WINSTON, Anuj JAIN, Xi WANG
  • Patent number: 11668684
    Abstract: Methods and systems for solving inverse problems arising in systems described by a physics-based forward propagation model use a Bayesian approach to model the uncertainty in the realization of model parameters. A Generative Adversarial Network (“GAN”) architecture along with heuristics and statistical learning is used. This results in a more reliable point estimate of the desired model parameters. In some embodiments, the disclosed methodology may be applied to automatic inversion of physics-based modeling of pipelines.
    Type: Grant
    Filed: July 23, 2019
    Date of Patent: June 6, 2023
    Assignee: Landmark Graphics Corporation
    Inventors: Srinivasan Jagnnathan, Oluwatosin Ogundare, Srinath Madasu, Keshava Rangarajan
  • Publication number: 20230116456
    Abstract: Systems and methods for automated drilling control and optimization are disclosed. Training data, including values of drilling parameters, for a current stage of a drilling operation are acquired. A reinforcement learning model is trained to estimate values of the drilling parameters for a subsequent stage of the drilling operation to be performed, based on the acquired training data and a reward policy mapping inputs and outputs of the model. The subsequent stage of the drilling operation is performed based on the values of the drilling parameters estimated using the trained model. A difference between the estimated and actual values of the drilling parameters is calculated, based on real-time data acquired during the subsequent stage of the drilling operation. The reinforcement learning model is retrained to refine the reward policy, based on the calculated difference. At least one additional stage of the drilling operation is performed using the retrained model.
    Type: Application
    Filed: June 5, 2020
    Publication date: April 13, 2023
    Inventors: Yashas Malur Saidutta, Raja Vikram R Pandya, Srinath Madasu, Shashi Dande, Keshava Rangarajan
  • Patent number: 11598197
    Abstract: 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.
    Type: Grant
    Filed: April 5, 2018
    Date of Patent: March 7, 2023
    Assignee: LANDMARK GRAPHICS CORPORATION
    Inventors: Keshava Rangarajan, Joseph Blake Winston, Anuj Jain, Xi Wang
  • Publication number: 20220300812
    Abstract: 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 b
    Type: Application
    Filed: April 6, 2022
    Publication date: September 22, 2022
    Applicants: Landmark Graphics Corporation, EMC IP Holding Company LLC
    Inventors: Chandra YELESHWARAPU, Jonas F. DIAS, Angelo CIARLINI, Romulo D. Pinho, Vinicius GOTTIN, Andre MAXIMO, Edward PACHECO, David HOLMES, Keshava RANGARAJAN, Scott David SENFTEN, Joseph Blake WINSTON, Xi WANG, Clifton Brent WALKER, Ashwani DEV, Nagaraj SIRINIVASAN
  • Patent number: 11441404
    Abstract: A method for fracturing a formation is provided. Real-time fracturing data is acquired from a well bore during fracturing operation. The real-time fracturing data is processed using a recurrent neural network trained using historical data from analogous wells. A real-time response variable prediction is determined using the processed real-time fracturing data. Fracturing parameters for the fracturing operation are adjusted in real-time based on the real-time response variable prediction. The fracturing operation is performed using the fracturing parameters that were adjusted based on the real-time response variable prediction.
    Type: Grant
    Filed: April 12, 2018
    Date of Patent: September 13, 2022
    Assignee: Landmark Graphics Corporation
    Inventors: Srinath Madasu, Yogendra Narayan Pandey, Keshava Rangarajan
  • Publication number: 20220236707
    Abstract: A system for autonomous operation and management of oil and gas fields includes at least one autonomous vehicle. The system also includes a processor communicatively couplable to the plurality of autonomous vehicles and a non-transitory memory device including instructions that are executable by the processor to cause the processor to perform operations. The operations include receiving field analytics data of an oil and gas field and producing at least one hydrocarbon field model based on the field analytics data. Additionally, the operations include deploying the at least one hydrocarbon field model to a sensor trap appliance using the at least one autonomous vehicles and collecting well sensor data from the sensor trap appliance. Further, the operations include detecting an anomaly using the at least one hydrocarbon field model and the well sensor data and triggering an operational process based on detecting the anomaly.
    Type: Application
    Filed: March 16, 2020
    Publication date: July 28, 2022
    Inventors: Keshava Rangarajan, Shashi Dande, Rohan Lewis, Siddhartha Kazuma Rangarajan, Aditya Chemudupaty
  • Patent number: 11346202
    Abstract: A drill bit subsystem can include a drill bit, a processor, and a non-transitory computer-readable medium for storing instructions and for being positioned downhole with the drill bit. The instructions of the non-transitory computer-readable medium can include a machine-teachable module and a control module that are executable by the processor. The machine-teachable module can receive depth data and rate of drill bit penetration from one or more sensors in a drilling operation, and determine an estimated lithology of a formation at which the drill bit subsystem is located. The control module can use the estimated lithology to determine an updated location of the drill bit subsystem, and control a direction of the drill bit using the updated location and a drill plan.
    Type: Grant
    Filed: June 27, 2018
    Date of Patent: May 31, 2022
    Assignee: Landmark Graphics Corporation
    Inventors: Greg Daniel Brumbaugh, Youpeng Huang, Janaki Vamaraju, Joseph Blake Winston, Aimee Jackson Taylor, Keshava Rangarajan, Avinash Wesley
  • Patent number: 11315014
    Abstract: 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 b
    Type: Grant
    Filed: August 16, 2018
    Date of Patent: April 26, 2022
    Assignee: EMC IP HOLDING COMPANY LLC
    Inventors: Jonas F. Dias, Angelo Ciarlini, Romulo D. Pinho, Vinicius Gottin, Andre Maximo, Edward Pacheco, David Holmes, Keshava Rangarajan, Scott David Senften, Joseph Blake Winston, Xi Wang, Clifton Brent Walker, Ashwani Dev, Chandra Yeleshwarapu, Nagaraj Srinivasan
  • Publication number: 20210324723
    Abstract: Systems and methods for controlling drilling operations are provided. A controller for a drilling system may provide drilling parameters such as weight-on-bit and rotation rate parameters to the drilling system, based on a machine-learned reward policy and a model-based prediction. The machine-learned reward policy may be generated during drilling operations and used to modify recommended values from the model-based prediction for subsequent drilling operations to achieve a desired rate-of-penetration.
    Type: Application
    Filed: August 30, 2018
    Publication date: October 21, 2021
    Inventors: Srinath Madasu, Keshava Rangarajan
  • Publication number: 20210285309
    Abstract: Systems, methods, and computer-readable media are described for intelligent, real-time monitoring and managing of changes in oilfield equilibrium to optimize production of desired hydrocarbons and economic viability of the field. In some examples, a method can involve generating, based on a topology of a field of wells, a respective graph for the wells, each respective graph including computing devices coupled with one or more sensors and/or actuators. The method can involve collecting, via the computing devices, respective parameters associated with one or more computing devices, sensors, actuators, and/or models, and identifying a measured state associated with the computing devices, sensors, actuators, and/or models.
    Type: Application
    Filed: April 27, 2017
    Publication date: September 16, 2021
    Applicant: LANDMARK GRAPHICS CORPORATION
    Inventors: Joseph Blake WINSTON, Brent Charles HOUCHENS, Feifei ZHANG, Avinash WESLEY, Andrew Shane ELSEY, Jonathan NGUYEN, Keshava RANGARAJAN, Olivier GERMAIN
  • Publication number: 20210164944
    Abstract: Methods and systems for solving inverse problems arising in systems described by a physics-based forward propagation model use a Bayesian approach to model the uncertainty in the realization of model parameters. A Generative Adversarial Network (“GAN”) architecture along with heuristics and statistical learning is used. This results in a more reliable point estimate of the desired model parameters. In some embodiments, the disclosed methodology may be applied to automatic inversion of physics-based modeling of pipelines.
    Type: Application
    Filed: July 23, 2019
    Publication date: June 3, 2021
    Inventors: Srinivasan JAGNNATHAN, Oluwatosin OGUNDARE, Srinath MADASU, Keshava RANGARAJAN
  • Publication number: 20210115778
    Abstract: 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.
    Type: Application
    Filed: August 2, 2018
    Publication date: April 22, 2021
    Inventors: Keshava Rangarajan, Joseph Blake Winston, Srinath Madasu, Xi Wang, Yogendra Narayan Pandey, Wei Chiu, Jeffery Padgett, Aimee Jackson Taylor
  • Publication number: 20210017845
    Abstract: A method for fracturing a formation is provided. Real-time fracturing data is acquired from a well bore during fracturing operation. The real-time fracturing data is processed using a recurrent neural network trained using historical data from analogous wells. A real-time response variable prediction is determined using the processed real-time fracturing data. Fracturing parameters for the fracturing operation are adjusted in real-time based on the real-time response variable prediction. The fracturing operation is performed using the fracturing parameters that were adjusted based on the real-time response variable prediction.
    Type: Application
    Filed: April 12, 2018
    Publication date: January 21, 2021
    Inventors: Srinath MADASU, Yogendra Narayan PANDEY, Keshava RANGARAJAN
  • Publication number: 20200378236
    Abstract: A drill bit subsystem can include a drill bit, a processor, and a non-transitory computer-readable medium for storing instructions and for being positioned downhole with the drill bit. The instructions of the non-transitory computer-readable medium can include a machine-teachable module and a control module that are executable by the processor. The machine-teachable module can receive depth data and rate of drill bit penetration from one or more sensors in a drilling operation, and determine an estimated lithology of a formation at which the drill bit subsystem is located. The control module can use the estimated lithology to determine an updated location of the drill bit subsystem, and control a direction of the drill bit using the updated location and a drill plan.
    Type: Application
    Filed: June 27, 2018
    Publication date: December 3, 2020
    Inventors: Greg Daniel Brumbaugh, Youpeng Huang, Janaki Vamaraju, Joseph Blake Winston, Aimee Jackson Taylor, Keshava Rangarajan, Avinash Wesley
  • Publication number: 20200190957
    Abstract: A system and method for controlling a drilling tool inside a wellbore makes use of projection of optimal rate of penetration (ROP) and optimal controllable parameters such as weight-on-bit (WOB), and rotations-per-minute (RPM) for drilling operations. Optimum controllable parameters for drilling optimization can be predicted using a data generation model to produced synthesized data based on model physics, an ROP model, and stochastic optimization. The synthetic data can be combined with real-time data to extrapolate the data across the WOB and RPM space. The values for WOB an RPM can be controlled to steer a drilling tool. Examples of models used include a non-linear model, a linear model, a recurrent generative adversarial network (RGAN) model, and a deep neural network model.
    Type: Application
    Filed: March 9, 2018
    Publication date: June 18, 2020
    Inventors: Srinath MADASU, Nishant RAIZADA, Keshava RANGARAJAN, Robello SAMUEL
  • Publication number: 20200182036
    Abstract: 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.
    Type: Application
    Filed: April 5, 2018
    Publication date: June 11, 2020
    Inventors: Keshava RANGARAJAN, Joseph Blake WINSTON, Anuj JIAN, Xi WANG