公开(公告)号：US11346202B2

公开(公告)日：2022-05-31

申请号：US16968705

申请日：2018-06-27

Applicant: LANDMARK GRAPHICS CORPORATION

Inventor： Greg Daniel Brumbaugh ,  Youpeng Huang ,  Janaki Vamaraju ,  Joseph Blake Winston ,  Aimee Jackson Taylor ,  Keshava Rangarajan ,  Avinash Wesley

IPC: E21B44/02 ,  E21B7/06 ,  E21B47/04 ,  E21B47/00 ,  E21B47/026 ,  E21B44/04

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.

公开(公告)号：US12189354B2

公开(公告)日：2025-01-07

申请号：US17619341

申请日：2020-03-16

Applicant: Landmark Graphics Corporation

Inventor： Keshava Rangarajan ,  Shashi Dande ,  Rohan Lewis ,  Siddhartha Kazuma Rangarajan ,  Aditya Chemudupaty

IPC: G05B19/042

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.

公开(公告)号：US11982171B2

公开(公告)日：2024-05-14

申请号：US17047109

申请日：2020-06-05

Applicant: Landmark Graphics Corporation

Inventor： Yashas Malur Saidutta ,  Raja Vikram R Pandya ,  Srinath Madasu ,  Shashi Dande ,  Keshava Rangarajan

IPC: E21B44/00 ,  G06N3/092

CPC classification number: E21B44/00 ,  G06N3/092 ,  E21B2200/20 ,  E21B2200/22

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.

公开(公告)号：US11959373B2

公开(公告)日：2024-04-16

申请号：US17047837

申请日：2018-08-02

Applicant: Landmark Graphics Corporation

Inventor： Keshava Rangarajan ,  Joseph Blake Winston ,  Srinath Madasu ,  Xi Wang ,  Yogendra Narayan Pandey ,  Wei Chiu ,  Jeffery Padgett ,  Aimee Jackson Taylor

IPC: E21B44/02 ,  E21B44/00 ,  E21B47/07 ,  E21B49/00 ,  G05B13/04 ,  G05B19/4155 ,  H04Q9/00 ,  E21B43/26

CPC classification number: E21B44/02 ,  E21B44/00 ,  E21B47/07 ,  E21B49/00 ,  G05B13/041 ,  G05B19/4155 ,  H04Q9/00 ,  E21B43/26 ,  E21B2200/22 ,  G05B2219/45129

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.

公开(公告)号：US11868890B2

公开(公告)日：2024-01-09

申请号：US17714247

申请日：2022-04-06

Applicant: Landmark Graphics Corporation ,  EMC IP Holding Company LLC

Inventor： 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

IPC: G06N3/08 ,  G06N3/06 ,  G06F9/50 ,  G06F9/48 ,  G06N3/04 ,  G06N3/02 ,  G06N3/086

CPC classification number: G06N3/08 ,  G06F9/48 ,  G06F9/4843 ,  G06F9/4881 ,  G06F9/50 ,  G06F9/5061 ,  G06F9/5066 ,  G06F9/5077 ,  G06F9/5083 ,  G06N3/02 ,  G06N3/04 ,  G06N3/086 ,  G06F2209/501 ,  G06F2209/5011 ,  G06F2209/5019

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 based on the deep neural network output.

公开(公告)号：US11725489B2

公开(公告)日：2023-08-15

申请号：US16338972

申请日：2017-04-27

Applicant: LANDMARK GRAPHICS CORPORATION

Inventor： Joseph Blake Winston ,  Brent Charles Houchens ,  Feifei Zhang ,  Avinash Wesley ,  Andrew Shane Elsey ,  Jonathan Nguyen ,  Keshava Rangarajan ,  Olivier Germain

IPC: G06Q10/06 ,  E21B43/12 ,  E21B43/30 ,  G05B13/00 ,  G06Q10/0631 ,  G06Q50/02

CPC classification number: E21B43/12 ,  E21B43/30 ,  G05B13/00 ,  G06Q10/06313 ,  G06Q50/02 ,  E21B2200/20 ,  E21B2200/22

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. Further, the method can involve automatically generating, based on the respective graph and respective parameters, a decision tree for the measured state, and determining, based on the decision tree, an automated adjustment for modifying production of hydrocarbons and/or an economic parameter of the hydrocarbon production.

公开(公告)号：US20230116456A1

公开(公告)日：2023-04-13

申请号：US17047109

申请日：2020-06-05

Applicant: Landmark Graphics Corporation

Inventor： Yashas Malur Saidutta ,  Raja Vikram R Pandya ,  Srinath Madasu ,  Shashi Dande ,  Keshava Rangarajan

IPC: E21B44/00 ,  G06N3/092

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.

公开(公告)号：US20210115778A1

公开(公告)日：2021-04-22

申请号：US17047837

申请日：2018-08-02

Applicant: Landmark Graphics Corporation

Inventor： Keshava Rangarajan ,  Joseph Blake Winston ,  Srinath Madasu ,  Xi Wang ,  Yogendra Narayan Pandey ,  Wei Chiu ,  Jeffery Padgett ,  Aimee Jackson Taylor

IPC: E21B44/02 ,  H04Q9/00 ,  E21B49/00 ,  E21B47/07 ,  G05B19/4155

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.

公开(公告)号：US11873707B2

公开(公告)日：2024-01-16

申请号：US16629231

申请日：2018-03-09

Applicant: LANDMARK GRAPHICS CORPORATION

Inventor： Srinath Madasu ,  Nishant Raizada ,  Keshava Rangarajan ,  Robello Samuel

IPC: E21B44/02 ,  G06N20/00 ,  E21B7/04 ,  E21B45/00

CPC classification number: E21B44/02 ,  E21B7/04 ,  E21B45/00 ,  G06N20/00

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.

公开(公告)号：US11668684B2

公开(公告)日：2023-06-06

申请号：US16956605

申请日：2019-07-23

Applicant: Landmark Graphics Corporation

Inventor： Srinivasan Jagnnathan ,  Oluwatosin Ogundare ,  Srinath Madasu ,  Keshava Rangarajan

IPC: G01N29/44 ,  G06N3/047

CPC classification number: G01N29/4472 ,  G01N29/4481 ,  G06N3/047 ,  G01N2291/2634

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.