公开(公告)号：US20200270972A1

公开(公告)日：2020-08-27

申请号：US16800838

申请日：2020-02-25

Applicant: Schlumberger Technology Corporation

Inventor： Ambalavanan Sachidanandam ,  Balasubramanian Durairajan ,  Chandsudan Thirugnanasambandam Vasudevan ,  James Masdea ,  Rahul Bijai ,  Elson Lim ,  Davide Fanti

IPC: E21B41/00 ,  G06F30/20

Abstract: Systems and methods discussed herein relate to applying models to downhole tool records to identify, sort, and display a subset of available downhole tool records with index information as defined by the models that have a desired relationship with a received input. The received input may be indicative of a particular device, device family, or set of features for a downhole tool. The methods may include identifying, from a set of design information stored in computer-storage media, one or more downhole tool records that correspond to the received input, applying one or more index models to the identified one or more downhole tool records, applying one or more local models to the identified one or more downhole tool records, and displaying at least some of the one or more downhole tool records, with index information as defined by the one or more index and local models.

公开(公告)号：US20240202407A1

公开(公告)日：2024-06-20

申请号：US18531797

申请日：2023-12-07

Applicant: Schlumberger Technology Corporation

Inventor： Riadh Boualleg ,  Andrew Poor ,  Ling Li ,  Chris Bogath ,  Telman Maghrebi ,  James Masdea

IPC: G06F30/27

CPC classification number: G06F30/27 ,  E21B49/00 ,  E21B2200/20

Abstract: A method for optimizing a drill bit for a drilling operation includes training a plurality of machine learning (ML) models with historical drilling data to obtain a corresponding plurality of trained ML models; obtaining drilling operation parameters for the drilling operation; generating drill bit parameters for each of a plurality of potential drill bit configurations; inputting the obtained drilling operation parameters and the generated drill bit parameters into the plurality of trained ML models to estimate a corresponding plurality of drill bit performance metrics; and selecting an optimum drill bit configuration from the set of potential drill bit configurations based on the estimated drill bit performance metrics obtained from plurality of trained ML models.

公开(公告)号：US20250003293A1

公开(公告)日：2025-01-02

申请号：US18346195

申请日：2023-06-30

Applicant: Schlumberger Technology Corporation

Inventor： James Masdea

IPC: E21B10/55 ,  E21B10/43

Abstract: This disclosure is directed to drilling tools and, more specifically, drilling bits. One such bit includes a bit body comprising a first gauge pad and a second gauge pad. The bit further includes a first blade comprising a leading portion and a trailing portion, wherein the leading portion is disposed on the first gauge pad with a negative helix angle and the trailing portion is disposed on the second gauge pad with a positive helix angle.