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公开(公告)号：US12018555B2
公开(公告)日：2024-06-25
申请号：US17054629
申请日：2020-03-26
Applicant: Landmark Graphics Corporation
Inventor： Avinash Wesley , Robello Samuel , Manish K. Mittal
IPC: E21B44/00 , E21B47/007 , G06F30/20
CPC classification number: E21B44/00 , E21B47/007 , E21B2200/20 , G06F30/20
Abstract: Aspects and features of this disclosure relate to projecting physical drilling parameters to control a drilling operation. A computing system applies Bayesian optimization to a model incorporating the input data using varying values for an adverse drilling factor to produce a target function. The computing system determines a minimum value for the target function. The computing system provides a projected value for the physical drilling parameters based on the minimum value. The computing system generates an alert responsive to determining that the projected value for the physical drilling parameters exceeds a prescribed limit.
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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
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.
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公开(公告)号：US10436010B2
公开(公告)日：2019-10-08
申请号：US14889941
申请日：2014-11-05
Applicant: Landmark Graphics Corporation
Inventor： Avinash Wesley , Peter C. Yu
Abstract: Tight spots in movements of a drill string in an oil well are identified by comparing a large interval hookload moving average to a short interval hookload moving average, comparing a large interval bit depth moving average to a short interval bit depth moving average, and DBSCANing the tight spots to identify a fully-stuck event.
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公开(公告)号：US20200378236A1
公开(公告)日：2020-12-03
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
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.
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5.发明申请公开(公告)号：US20200320386A1
公开(公告)日：2020-10-08
申请号：US16642452
申请日：2017-12-26
Applicant: Landmark Graphics Corporation
Inventor： Andrey Filippov , Jianxin Lu , Avinash Wesley , Keshava P. Rangarajan , Srinath Madasu
Abstract: System and methods for training neural network models for real-time flow simulations are provided. Input data is acquired. The input data includes values for a plurality of input parameters associated with a multiphase fluid flow. The multiphase fluid flow is simulated using a complex fluid dynamics (CFD) model, based on the acquired input data. The CFD model represents a three-dimensional (3D) domain for the simulation. An area of interest is selected within the 3D domain represented by the CFD model. A two-dimensional (2D) mesh of the selected area of interest is generated. The 2D mesh represents results of the simulation for the selected area of interest. A neural network is then trained based on the simulation results represented by the generated 2D mesh.
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Patent Agency Ranking
公开(公告)号：US10060246B2
公开(公告)日：2018-08-28
申请号：US15025625
申请日：2014-12-29
Applicant: LANDMARK GRAPHICS CORPORATION
Inventor： Giulia Toti , Peter C. Yu , Avinash Wesley
Abstract: An example method includes receiving a data set containing combinations of drilling parameter values and operating condition values for a drilling system corresponding to each combination of drilling parameter values. At least one of a frequency and a duration of use may be determined for each of the combinations of drilling parameter values in the data set. For at least some of the combinations of drilling parameter values, a contour map identifying the combinations of drilling parameter values, the operating condition values corresponding to the combinations of drilling parameter values, and at least one of the frequency and the duration of use may be displayed for at least some of the combinations of drilling parameter values.
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公开(公告)号：US20230095708A1
公开(公告)日：2023-03-30
申请号：US17054629
申请日：2020-03-26
Applicant: Landmark Graphics Corporation
Inventor： Avinash Wesley , Robello Samuel , Manish K. Mittal
IPC: E21B44/00 , E21B47/007
Abstract: Aspects and features of this disclosure relate to projecting physical drilling parameters to control a drilling operation. A computing system applies Bayesian optimization to a model incorporating the input data using varying values for an adverse drilling factor to produce a target function. The computing system determines a minimum value for the target function. The computing system provides a projected value for the physical drilling parameters based on the minimum value. The computing system generates an alert responsive to determining that the projected value for the physical drilling parameters exceeds a prescribed limit.
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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
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.
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9.发明授权公开(公告)号：US12061980B2
公开(公告)日：2024-08-13
申请号：US16642452
申请日：2017-12-26
Applicant: Landmark Graphics Corporation
Inventor： Andrey Filippov , Jianxin Lu , Avinash Wesley , Keshava P. Rangarajan , Srinath Madasu
Abstract: System and methods for training neural network models for real-time flow simulations are provided. Input data is acquired. The input data includes values for a plurality of input parameters associated with a multiphase fluid flow. The multiphase fluid flow is simulated using a complex fluid dynamics (CFD) model, based on the acquired input data. The CFD model represents a three-dimensional (3D) domain for the simulation. An area of interest is selected within the 3D domain represented by the CFD model. A two-dimensional (2D) mesh of the selected area of interest is generated. The 2D mesh represents results of the simulation for the selected area of interest. A neural network is then trained based on the simulation results represented by the generated 2D mesh.
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公开(公告)号：US20220307366A1
公开(公告)日：2022-09-29
申请号：US17594842
申请日：2020-01-16
Applicant: Landmark Graphics Corporation
Inventor： Anandhan M. Selveindran , Avinash Wesley , Nitish Damodar Chaudhari , Helmut Andres Pirela
IPC: E21B47/00
Abstract: An automated offset well analytics engine generates offset well rankings for a prospect well. The engine aggregates data for offset wells and the prospect wells is across multiple disparate data sources corresponding to a user-specified scope. The engine generates features comparing the offset wells to the prospect well are using a combination of machine-learning based models and risk analysis. Offset wells are ranked by feature and further ranked across features using a weighted feature ranking map. Feature weights are iteratively trained using a reinforcement learning model in a feedback loops with a well expert. A prospect well casing schema and bottom hole assembly is designed using automatically generated offset well rankings.
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