公开(公告)号：US20230313664A1

公开(公告)日：2023-10-05

申请号：US18331269

申请日：2023-06-08

Applicant: Schlumberger Technology Corporation

Inventor： Yingwei Yu ,  Velizar Vesselinov ,  Richard Meehan ,  Qiuhua Liu ,  Wei Chen ,  Minh Trang Chau ,  Yuelin Shen ,  Sylvain Chambon

IPC: E21B44/00 ,  E21B47/024 ,  E21B7/04

CPC classification number: E21B44/00 ,  E21B47/024 ,  E21B7/04

Abstract: A system and method that include receiving sensor data during drilling of a portion of a borehole in a geologic environment. The system and method also include selecting a drilling mode from a plurality of drilling modes based at least on a portion of the sensor data. The system and method additionally include simulating drilling of the borehole using the selected drilling mode and generating a state of the borehole in the geologic environment based on the simulated drilling of the borehole. The system and method further include generating a reward using the state of the borehole and a planned borehole trajectory and using the reward through deep reinforcement learning to maximize future rewards for drilling actions.

公开(公告)号：US20230272705A1

公开(公告)日：2023-08-31

申请号：US18308881

申请日：2023-04-28

Applicant: Schlumberger Technology Corporation

Inventor： Yingwei Yu ,  Qiuhua Liu ,  Richard John Meehan ,  Sylvain Chambon ,  Mohammad Khairi Hamzah

IPC: E21B44/00 ,  E21B21/08 ,  G06N7/00 ,  G01V1/50 ,  E21B49/00 ,  G01V11/00 ,  G06F30/20 ,  G06F17/15 ,  G06F17/16 ,  G06F30/27 ,  G06N3/044 ,  G06N3/047 ,  G06N3/08

CPC classification number: E21B44/00 ,  E21B21/08 ,  G06N7/00 ,  G01V1/50 ,  E21B49/003 ,  G01V11/00 ,  G06F30/20 ,  G06F17/15 ,  G06F17/16 ,  G06F30/27 ,  G06N3/044 ,  G06N3/047 ,  G06N3/08 ,  G01V2200/14 ,  G01V2200/16 ,  G06T13/80

Abstract: A system and method that can include training a deep neural network using time series data that represents functions of a non-linear Kalman filter that represents a dynamic system of equipment and environment and models a pre-defined operational procedure as a temporal sequence. The system and method can also include receiving operation data from the equipment responsive to operation in the environment and outputting an actual operation as an actual sequence of operational actions by the deep neural network. The system and method can additionally include performing an operation-level comparison to evaluate the temporal sequence against the actual sequence using a distance function in a latent space of the deep neural network and outputting a score function that quantifies the distance function in the latent space. The system and method can further include controlling an electronic component to execute an electronic operation based on the score function.

公开(公告)号：US11603749B2

公开(公告)日：2023-03-14

申请号：US16192584

申请日：2018-11-15

Applicant: Schlumberger Technology Corporation

Inventor： Yingwei Yu ,  Sylvain Chambon ,  Qiuhua Liu

IPC: E21B44/00 ,  E21B21/08 ,  G06N7/00 ,  G01V1/50 ,  E21B49/00 ,  G01V11/00 ,  G06F30/20 ,  G06F17/15 ,  G06F17/16 ,  G06N3/04 ,  G06F30/27 ,  G06N3/08 ,  G06T13/80

Abstract: A method can include receiving multi-channel time series data of drilling operations; training a deep neural network (DNN) using the multi-channel time series data to generate a trained deep neural network as part of a computational simulator where the deep neural network includes at least one recurrent unit; simulating a drilling operation using the computational simulator to generate a simulation result; and rendering the simulation result to a display.

公开(公告)号：US20210166115A1

公开(公告)日：2021-06-03

申请号：US16636317

申请日：2018-11-15

Applicant: Schlumberger Technology Corporation

Inventor： Yingwei Yu ,  Qiuhua Liu ,  Richard John Meehan ,  Sylvain Chambon ,  Mohammad Khairi Hamzah

IPC: G06N3/08 ,  G06F17/15 ,  G06F17/16

Abstract: A method can include training a deep neural network to generate a trained deep neural network where the trained deep neural network represents functions of a nonlinear Kalman filter that represents a dynamic system of equipment and environment via an internal state vector of the dynamic system; generating a base internal state vector, that corresponds to a pre-defined operational procedure, using the trained deep neural network; receiving operation data from the equipment responsive to operation in the environment; generating an internal state vector using the operation data and the trained deep neural network; and comparing at least the internal state vector to at least the base internal state vector.

公开(公告)号：US12158555B2

公开(公告)日：2024-12-03

申请号：US17250701

申请日：2018-08-21

Applicant: Schlumberger Technology Corporation

Inventor： Sai Venkatakrishnan Sankaranarayanan ,  Oney Erge ,  Sylvain Chambon ,  Richard Meehan ,  Mohammad Khairi Hamzah ,  Darine Mansour ,  David Conn

IPC: G01V1/36 ,  E21B41/00 ,  G01V1/34 ,  G01V1/50 ,  G06N7/01

Abstract: A method can include acquiring data associated with a field operation in a geologic environment; processing the data by partitioning operationally and representing symbolically; formulating a symbolic query for an operating procedure specification; performing a search of the symbolically represented data utilizing the symbolic query and a probabilistic chain model; receiving a search result responsive to the search; assessing compliance with the operation procedure specification utilizing the search result; and issuing a control signal to field equipment utilizing the assessment of compliance.

公开(公告)号：US12078048B2

公开(公告)日：2024-09-03

申请号：US18308881

申请日：2023-04-28

Applicant: Schlumberger Technology Corporation

Inventor： Yingwei Yu ,  Qiuhua Liu ,  Richard John Meehan ,  Sylvain Chambon ,  Mohammad Khairi Hamzah

IPC: E21B44/00 ,  E21B21/08 ,  E21B49/00 ,  G01V1/50 ,  G01V11/00 ,  G06F17/15 ,  G06F17/16 ,  G06F30/20 ,  G06F30/27 ,  G06N3/044 ,  G06N3/047 ,  G06N3/08 ,  G06N7/00 ,  G06T13/80

CPC classification number: E21B44/00 ,  E21B49/003 ,  G01V1/50 ,  G01V11/00 ,  G06F17/15 ,  G06F17/16 ,  G06F30/20 ,  G06F30/27 ,  G06N3/044 ,  G06N3/047 ,  G06N3/08 ,  G06N7/00 ,  E21B21/08 ,  G01V2200/14 ,  G01V2200/16 ,  G06T13/80

Abstract: A system and method that can include training a deep neural network using time series data that represents functions of a non-linear Kalman filter that represents a dynamic system of equipment and environment and models a pre-defined operational procedure as a temporal sequence. The system and method can also include receiving operation data from the equipment responsive to operation in the environment and outputting an actual operation as an actual sequence of operational actions by the deep neural network. The system and method can additionally include performing an operation-level comparison to evaluate the temporal sequence against the actual sequence using a distance function in a latent space of the deep neural network and outputting a score function that quantifies the distance function in the latent space. The system and method can further include controlling an electronic component to execute an electronic operation based on the score function.

公开(公告)号：US11674375B2

公开(公告)日：2023-06-13

申请号：US16636317

申请日：2018-11-15

Applicant: Schlumberger Technology Corporation

Inventor： Yingwei Yu ,  Qiuhua Liu ,  Richard John Meehan ,  Sylvain Chambon ,  Mohammad Khairi Hamzah

IPC: E21B44/00 ,  E21B21/08 ,  G06N7/00 ,  G01V1/50 ,  E21B49/00 ,  G01V11/00 ,  G06F30/20 ,  G06F17/15 ,  G06F17/16 ,  G06F30/27 ,  G06N3/044 ,  G06N3/047 ,  G06N3/08 ,  G06T13/80

CPC classification number: E21B44/00 ,  E21B21/08 ,  E21B49/003 ,  G01V1/50 ,  G01V11/00 ,  G06F17/15 ,  G06F17/16 ,  G06F30/20 ,  G06F30/27 ,  G06N3/044 ,  G06N3/047 ,  G06N3/08 ,  G06N7/00 ,  G01V2200/14 ,  G01V2200/16 ,  G06T13/80

Abstract: A method can include training a deep neural network to generate a trained deep neural network where the trained deep neural network represents functions of a nonlinear Kalman filter that represents a dynamic system of equipment and environment via an internal state vector of the dynamic system; generating a base internal state vector, that corresponds to a pre-defined operational procedure, using the trained deep neural network; receiving operation data from the equipment responsive to operation in the environment; generating an internal state vector using the operation data and the trained deep neural network; and comparing at least the internal state vector to at least the base internal state vector.

公开(公告)号：US11542760B2

公开(公告)日：2023-01-03

申请号：US17247195

申请日：2020-12-03

Applicant: Schlumberger Technology Corporation

Inventor： Sylvain Chambon ,  Sai Venkatakrishnan Sankaranarayanan

IPC: E21B19/16 ,  E21B47/09 ,  E21B47/06 ,  E21B19/08 ,  E21B19/00 ,  E21B19/10

Abstract: A method can include, during drilling operations at a wellsite, receiving operational data, where the data include hookload data, surface rotation data and block position data; training a controller using the hookload data, the surface rotation data and the block position data for determination of one or more transition thresholds, where the transitions thresholds include an in-slips to out-of-slips transition threshold and an out-of-slips to in-slips transition threshold; during the drilling operations, receiving additional operational data that include additional hookload data; and storing at least a portion of the additional operational data in association with slips state as determined based at least in part on a comparison of at least a portion of the additional hookload data and at least one of the determined transition thresholds.

公开(公告)号：US11421523B2

公开(公告)日：2022-08-23

申请号：US16605799

申请日：2018-06-27

Applicant: Schlumberger Technology Corporation

Inventor： Sai Venkatakrishnan ,  Sylvain Chambon ,  James P. Belaskie ,  Yingwei Yu ,  Mohammad Khairi Hamzah

IPC: E21B44/00 ,  E21B44/10 ,  E21B47/12 ,  G06N7/00

Abstract: A method includes acquiring data during rig operations where the rig operations include operations that utilize a bit to drill rock and where the data include different types of data; analyzing the data utilizing a probabilistic mixture model for modes, a detection engine for trends and a network model for an inference based at least in part on at least one of a mode and a trend; and outputting information as to the inference where the inference characterizes a relationship between the bit and the rock.

公开(公告)号：US20220178248A1

公开(公告)日：2022-06-09

申请号：US17247195

申请日：2020-12-03

Applicant: Schlumberger Technology Corporation

Inventor： Sylvain Chambon ,  Saivenkatakrishn Venkatakrishnan

IPC: E21B47/09 ,  E21B47/06

Abstract: A method can include, during drilling operations at a wellsite, receiving operational data, where the data include hookload data, surface rotation data and block position data; training a controller using the hookload data, the surface rotation data and the block position data for determination of one or more transition thresholds, where the transitions thresholds include an in-slips to out-of-slips transition threshold and an out-of-slips to in-slips transition threshold; during the drilling operations, receiving additional operational data that include additional hookload data; and storing at least a portion of the additional operational data in association with slips state as determined based at least in part on a comparison of at least a portion of the additional hookload data and at least one of the determined transition thresholds.