公开(公告)号：US20240003246A1

公开(公告)日：2024-01-04

申请号：US18343870

申请日：2023-06-29

Applicant: Schlumberger Technology Corporation

Inventor： Chao Mu ,  Florian Le Blay ,  Tao Yu

IPC: E21B47/10 ,  G08B21/18

CPC classification number: E21B47/10 ,  G08B21/182

Abstract: A method of analyzing flowback of a downhole system includes generating active flowback data by monitoring an active flowback from a wellbore. Historic flowback data for historic flowback from the wellbore is used to determine a flowback cluster. The flowback cluster is selected based on comparing the active flowback data to the historic flowback data and determining one or more data instances of the historic flowback data that have features that are similar to that of the active flowback data. Based on the flowback cluster, one or more thresholds may be determined in order to generate an alert when the active flowback data exceeds the thresholds.

公开(公告)号：US20240183264A1

公开(公告)日：2024-06-06

申请号：US18526156

申请日：2023-12-01

Applicant: Schlumberger Technology Corporation

Inventor： Xin Li ,  Lei Zhu ,  Bo Bi ,  Chao Mu ,  Tao Yu ,  Kewen Sun

IPC: E21B44/00 ,  E21B41/00

CPC classification number: E21B44/00 ,  E21B41/00 ,  E21B21/08

Abstract: A method may include receiving a digital well plan that specifies planned drilling parameters for drilling a borehole, where each of the specified planned drilling parameters includes one or more of a minimum threshold and a maximum threshold; generating statistical distributions of actual drilling parameters in real-time responsive to receipt of real-time data acquired during a depth-based window for the drilling; generating comparisons in real-time, where each of the comparisons is between one of the statistical distributions and a corresponding one of the planned drilling parameters for the depth-based window; and, responsive to one or more of the comparisons indicating an unacceptable actual to planned parameter deviation, issuing a control signal.

公开(公告)号：US11499409B2

公开(公告)日：2022-11-15

申请号：US16408514

申请日：2019-05-10

Applicant: Schlumberger Technology Corporation

Inventor： Hendrik Suryadi ,  Paul Bolchover ,  Tao Yu ,  Ji Tang Liu ,  Chao Mu ,  Rongbing Chen

IPC: E21B44/00 ,  E21B43/12

Abstract: A method can include providing a trained drilling motor model trained via machine learning based at least in part on drilling motor simulation results; instantiating a motor engine component with an interface in a computational environment; and, responsive to receipt of a call via the interface, returning drilling motor information based at least in part on the trained drilling motor model.