公开(公告)号：US20200149354A1

公开(公告)日：2020-05-14

申请号：US16611817

申请日：2018-08-31

Applicant: LANDMARK GRAPHICS CORPORATION

Inventor： Ajay Pratap Singh ,  Roxana Nielsen, Jr. ,  Satyam Priyadarshy ,  Ashwani Dev ,  Geetha Gopakumar Nair ,  Suresh Venugopal

IPC: E21B12/02 ,  G06N3/08 ,  E21B10/62

Abstract: The subject disclosure provides for a mechanism implemented with neural networks through machine learning to predict wear and relative performance metrics for performing repairs on drill bits in a next repair cycle, which can improve decision making by drill bit repair model engines, drill bit design, and help reduce the cost of drill bit repairs. The machine learning mechanism includes obtaining drill bit data from different data sources and integrating the drill bit data from each of the data sources into an integrated dataset. The integrated dataset is pre-processed to filter out outliers. The filtered dataset is applied to a neural network to build a machine learning based model and extract features that indicate significant parameters affecting wear. A repair type prediction is determined with the applied machine learning based model and is provided as a signal for facilitating a drill bit operation on a cutter of the drill bit.

公开(公告)号：US20220316328A1

公开(公告)日：2022-10-06

申请号：US17361964

申请日：2021-06-29

Applicant: Landmark Graphics Corporation

Inventor： Aman Srivastava ,  Geetha Gopakumar Nair

IPC: E21B49/00 ,  E21B44/00 ,  G01V5/12

Abstract: The disclosure provides a method for evaluating a worn-out condition of a drilling bit in real time, i.e., when the drilling bit is drilling in the borehole. The method disclosed herein incorporates both physics based as well as machine learning based aspects to provide existing and forecasted evaluations. In one example a method of evaluating properties of a drilling bit when in a borehole is disclosed that includes: (1) determining formation properties corresponding to a subterranean formation at a location of the drilling bit in the borehole, (2) calculating an existing bit wear condition of the drilling bit based on the formation properties, (3) providing a forecasted bit wear condition of the drilling bit based on the existing bit wear condition and real time parameters, and (4) evaluating performance of the drilling bit based on the forecasted bit wear condition.