Fast automatic detection of geological boundaries from multivariate log data using recurrence

Ayham Zaitouny, Michael Small, June Hill, Irina Emelyanova, M. Ben Clennell

Research output: Contribution to journalArticle

Abstract

Manual interpretation of data collected from drill holes for mineral or oil and gas exploration is time-consuming and subjective. Identification of geological boundaries and distinctive rock physical property domains is the first step of interpretation. We introduce a multivariate technique, that can identify geological boundaries from petrophysical or geochemical data. The method is based on time-series techniques that have been adapted to be applicable for detecting transitions in geological spatial data. This method allows for the use of multiple variables in detecting different lithological layers. Additionally, it reconstructs the phase space of a single drill-hole or well to be applicable for further investigations across other holes or wells. The computationally cheap method shows efficiency and accuracy in detecting boundaries between lithological layers, which we demonstrate using examples from mineral exploration boreholes and an offshore gas exploration well.
Original languageEnglish
Article number104362
JournalComputers & Geosciences
Volume135
DOIs
Publication statusPublished - Feb 2020

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Mineral exploration
Boreholes
Gases
well
Time series
Minerals
Physical properties
mineral exploration
Rocks
gas
spatial data
borehole
physical property
time series
oil
mineral
rock
method
automatic detection
Oils

Cite this

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title = "Fast automatic detection of geological boundaries from multivariate log data using recurrence",
abstract = "Manual interpretation of data collected from drill holes for mineral or oil and gas exploration is time-consuming and subjective. Identification of geological boundaries and distinctive rock physical property domains is the first step of interpretation. We introduce a multivariate technique, that can identify geological boundaries from petrophysical or geochemical data. The method is based on time-series techniques that have been adapted to be applicable for detecting transitions in geological spatial data. This method allows for the use of multiple variables in detecting different lithological layers. Additionally, it reconstructs the phase space of a single drill-hole or well to be applicable for further investigations across other holes or wells. The computationally cheap method shows efficiency and accuracy in detecting boundaries between lithological layers, which we demonstrate using examples from mineral exploration boreholes and an offshore gas exploration well.",
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Fast automatic detection of geological boundaries from multivariate log data using recurrence. / Zaitouny, Ayham; Small, Michael; Hill, June; Emelyanova, Irina; Clennell, M. Ben.

In: Computers & Geosciences, Vol. 135, 104362, 02.2020.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Fast automatic detection of geological boundaries from multivariate log data using recurrence

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AU - Small, Michael

AU - Hill, June

AU - Emelyanova, Irina

AU - Clennell, M. Ben

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