Paper Info
Title
Real-Time Data-Driven Detection of the Rock-Type Alteration During a Directional Drilling
Abstract
During directional drilling, a bit may sometimes go to a nonproductive rock layer due to the gap about 20 m between the bit and high-fidelity rock-type sensors. The only way to detect the lithotype changes in time is the usage of measurements while drilling (MWD). However, there are no general mathematical modeling approaches that both well reconstruct the rock type based on MWD data and correspond to specifics of the oil and gas industry. In this letter, we present a data-driven procedure that utilizes MWD data for quick detection of changes in rock types. We propose the approach that combines traditional machine learning (ML) based on the solution of the rock-type classification problem with change detection procedures rarely used before in oil and gas industry. The data come from a newly developed oilfield in the north of western Siberia. The results suggest that we can detect a significant part of changes in rock types, reducing the change detection delay from 20 to 1.8 m and the number of false-positive alarms from 43 to 6 per well.
Year
DOI
Venue
2020
10.1109/LGRS.2019.2959845
IEEE Geoscience and Remote Sensing Letters
Keywords
DocType
Volume
Change detection,classification,directional drilling,logging while drilling (LWD),machine learning (ML),measurements while drilling (MWD),rock type
Journal
17
Issue
ISSN
Citations 
11
1545-598X
0
PageRank 
References 
Authors
0.34
0
11
Name
Order
Citations
PageRank
Evgenya Romanenkova100.34
A. Zaytsev201.01
Nikita Klyuchnikov301.35
Arseniy Gruzdev400.34
Ksenia Antipova501.35
Leyla Ismailova600.34
Evgeny Burnaev711942.78
Artyom Semenikhin801.01
Vitaliy Koryabkin900.34
Igor Simon1001.35
Dmitry Koroteev1123.45