公开(公告)号：US11959374B2

公开(公告)日：2024-04-16

申请号：US17256164

申请日：2020-02-03

Applicant: LANDMARK GRAPHICS CORPORATION

Inventor： Mahdi Parak ,  Srinath Madasu ,  Egidio Marotta ,  Dale McMullin ,  Nishant Raizada

IPC: E21B44/02 ,  G05B13/02 ,  G05B13/04 ,  G06N20/00

CPC classification number: E21B44/02 ,  G05B13/0265 ,  G05B13/042 ,  G05B13/048 ,  G06N20/00 ,  E21B2200/20 ,  E21B2200/22

Abstract: System and methods for event prediction during drilling operations are provided. Regression data associated with coefficients of a predictive model are retrieved for a downhole event during a drilling operation along a planned path of a wellbore. The regression data includes a record of changes in historical coefficient values associated with prior occurrences of the event. As the wellbore is drilled over different stages of the operation, a value of an operating variable is estimated based on values of the coefficients and real-time data acquired during each stage. A percentage change in coefficient values adjusted between successive stages of the operation is tracked. An occurrence of the downhole event is estimated, based on a correlation between the percentage change tracked for at least one coefficient and a corresponding change in the historical coefficient values. The path of the wellbore is adjusted, based on the estimated occurrence of the event.

公开(公告)号：US11795804B2

公开(公告)日：2023-10-24

申请号：US16764558

申请日：2019-07-12

Applicant: Landmark Graphics Corporation

Inventor： Yashas Malur Saidutta ,  Srinath Madasu ,  Shashi Dande ,  Keshava Prasad Rangarajan ,  Raja Vikram R. Pandya ,  Jeffrey M. Yarus ,  Robello Samuel

IPC: E21B44/00 ,  E21B7/04 ,  E21B41/00 ,  G06Q10/0631

CPC classification number: E21B44/00 ,  E21B7/04 ,  E21B41/00 ,  G06Q10/06313 ,  E21B2200/22

Abstract: A drilling device may use a concurrent path planning process to create a path from a starting location to a destination location within a subterranean environment. The drilling device can receive sensor data. A probability distribution can be generated from the sensor data indicating one or more likely materials compositions that make up each portion of the subterranean environment. The probability distribution can be sampled, and for each sample, a drill path trajectory and drill parameters for the trajectory can be generated. A trained neural network may evaluate each trajectory and drill parameters to identify the most ideal trajectory based on the sensor data. The drilling device may then initiate drilling operations for a predetermined distance along the ideal trajectory.

公开(公告)号：US11591895B2

公开(公告)日：2023-02-28

申请号：US16754850

申请日：2018-10-15

Applicant: LANDMARK GRAPHICS CORPORATION

Inventor： Srinath Madasu ,  Keshava Prasad Rangarajan

IPC: E21B44/00

Abstract: A system and method for controlling a drilling tool inside a wellbore makes use of simulated annealing and Bayesian optimization to determine optimum controllable drilling parameters. In some aspects, a computing device generates sampled exploration points using simulated annealing and runs a Bayesian optimization using a loss function and the exploration points to optimize at least one controllable drilling parameter to achieve a predicted value for a selected drilling parameter. In some examples, the selected drilling parameter is rate-of-penetration (ROP) and in some examples, the controllable drilling parameters include such parameters as rotational speed (RPM) and weight-on-bit (WOB). In some examples, the computing device applies the controllable drilling parameter(s) to the drilling tool to achieve the predicted value for the selected drilling parameter and provide real-time, closed-loop control and automation in drilling.

公开(公告)号：US20220253052A1

公开(公告)日：2022-08-11

申请号：US17626368

申请日：2020-01-16

Applicant: Landmark Graphics Corporation

Inventor： Aditya Chemudupaty ,  Srinath Madasu ,  Shashi Dande ,  Keshava Prasad Rangarajan ,  Rohan Lewis

IPC: G05B23/02

Abstract: A method for detecting anomalies in a piece of wellsite equipment. The method may include measuring data related to the piece of wellsite equipment. The method may also include encoding the measured data with a first autoencoder to produce a first set of encoded data. The method may further include performing a first Gaussian process regression (“GPR”) on the first set of encoded data to produce a first set of results that identifies a first anomaly in the measured data and that provides a first confidence interval for the first anomaly.

公开(公告)号：US20220228465A1

公开(公告)日：2022-07-21

申请号：US17613761

申请日：2019-07-02

Applicant: Landmark Graphics Corporation

Inventor： Srinath Madasu ,  Shashi Dande ,  Keshava Prasad Rangarajan

IPC: E21B43/12

Abstract: A system and method for controlling a gas supply to provide gas lift for wellbore(s) using Bayesian optimization. A computing device controls a gas supply to inject gas into wellbore(s). The computing device receives first reservoir data associated with a first subterranean reservoir and simulates production using the first reservoir data, using a model for the first subterranean reservoir. The production simulation provides first production data. The computing device receives second reservoir data associated with a subterranean reservoir and simulates production using the second reservoir data, using a model for the second subterranean reservoir. The production simulation provides second production data. A Bayesian optimization of an objective function of the first and second production data subject to any gas injection constraints can be performed to produce gas-lift parameters. The gas-lift parameters can be applied to the gas supply to control injection of gas into the wellbore(s).

公开(公告)号：US11151454B2

公开(公告)日：2021-10-19

申请号：US16631429

申请日：2017-09-28

Applicant: LANDMARK GRAPHICS CORPORATION

Inventor： Srinath Madasu ,  Keshava Prasad Rangarajan

IPC: E21B47/00 ,  G06N3/08 ,  E21B43/26

Abstract: A system for multi-stage placement of material in a wellbore includes a recurrent neural network that can be configured based on data from a multi-stage, stimulated wellbore. A computing device in communication with a sensor and a pump is operable to implement the recurrent neural network, which may include a long short-term neural network model (LSTM). Surface data from the sensor at each observation time of a plurality of observation times is used by the recurrent neural network to produce a predicted value for a response variable at a future time, and the predicted value for the response variable is used to control a pump being used to place the material.

公开(公告)号：US20210270998A1

公开(公告)日：2021-09-02

申请号：US17260541

申请日：2018-08-30

Applicant: LANDMARK GRAPHICS CORPORATION

Inventor： Srinath Madasu ,  Keshava Prasad Rangarajan ,  Terry Wong

IPC: G01V99/00 ,  E21B43/16 ,  G06F30/20 ,  G06N7/00

Abstract: A history-matched oilfield model that facilitates well system operations for an oilfield is generated using a Bayesian optimization of adjustable parameters based on an entire production history. The Bayesian optimization process includes stochastic modifications to the adjustable parameters based on a prior probability distribution for each parameter and a model error generated using historical production measurement values and corresponding model prediction values for various sets of test parameters.

公开(公告)号：US20210062634A1

公开(公告)日：2021-03-04

申请号：US16957811

申请日：2018-05-07

Applicant: Landmark Graphics Corporation

Inventor： Srinath Madasu ,  Keshava Prasad Rangarajan

IPC: E21B44/02 ,  E21B45/00

Abstract: A system and method for controlling a drilling tool inside a wellbore makes use of Bayesian optimization with range constraints. A computing device samples observed values for controllable drilling parameters such as weight-on-bit (WOB) and drill bit rotational speed in RPM and evaluates a selected drilling parameter such a rate-of-penetration (ROP) for the observed values using an objective function. Range constraints can be continuously learned by the computing device as the range constraints change. A Bayesian optimization, subject to the range constraints and the observed values, can produce an optimized value for the controllable drilling parameter to achieve a predicted value for the selected drilling parameter. The system can then control the drilling tool using the optimized value to achieve the predicted value for the selected drilling parameter.

公开(公告)号：US20210058235A1

公开(公告)日：2021-02-25

申请号：US17000087

申请日：2020-08-21

Applicant: Landmark Graphics Corporation

Inventor： Keshava Prasad Rangarajan ,  Raja Vikram R. Pandya ,  Srinath Madasu ,  Shashi Dande

IPC: H04L9/06 ,  G06N20/00 ,  G06N7/00 ,  H04L9/00 ,  H04L9/32

Abstract: A system for managing well site operations comprising a well site operations module, a chain of blocks of a distributed network, and a sensor bank and control module. The operations module generates earth model variables using a physics model, well log variables or seismic variables, or both, and a trained AI/ML algorithmic model. The chain of blocks comprises a plurality of subsequent blocks. Each subsequent block comprises a well site entry and a hash value of a previous well site entry. A well site entry comprises transacted operation control variables. The well site operations module generates production operation control variables or development operation control variables from earth model variables. The well site entry can also include transacted earth model variables and sensor variables. The sensor bank and control module provides well log variables and the operations module couples control variables to the control module to control well site equipment.

公开(公告)号：US11639657B2

公开(公告)日：2023-05-02

申请号：US16899946

申请日：2020-06-12

Applicant: Landmark Graphics Corporation

Inventor： Srinath Madasu

IPC: G06F17/00 ,  G01V99/00 ,  G06N3/08 ,  E21B44/00 ,  G06F30/27 ,  E21B47/07 ,  G05B13/02 ,  G05B13/04 ,  G06N3/04

Abstract: A system includes equipment for at least one of formation of, stimulation of, or production from a wellbore, a processor, and a non-transitory memory device. The processor is communicatively coupled to the equipment. The non-transitory memory device contains instructions executable by the processor to cause the processor to perform operations comprising training a hybrid deep generative physics neural network (HDGPNN), iteratively computing a plurality of projected values for wellbore variables using the HDGPNN, comparing the projected values to measured values, adjusting the projected values using the HDGPNN until the projected values match the measured values within a convergence criteria to produce an output value for at least one controllable parameter, and controlling the equipment by applying the output value for the at least one controllable parameter.