Fast automatic detection of geological boundaries from multivariate log data using recurrence
| Autor: | Irina Emelyanova, M. Ben Clennell, Ayham Zaitouny, June Hill, Michael Small |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Multivariate statistics
Drill 0208 environmental biotechnology Petrophysics Well logging Borehole FOS: Physical sciences Mineralogy 02 engineering and technology 010502 geochemistry & geophysics 01 natural sciences Geophysics (physics.geo-ph) 020801 environmental engineering Physics - Geophysics Mineral exploration Physics - Data Analysis Statistics and Probability Computers in Earth Sciences Recurrence plot Spatial analysis Data Analysis Statistics and Probability (physics.data-an) Geology 0105 earth and related environmental sciences Information Systems |
| Zdroj: | Computers & Geosciences. 135:104362 |
| ISSN: | 0098-3004 |
| DOI: | 10.1016/j.cageo.2019.104362 |
| Popis: | 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. 20 pages, 10 figures, submitted to Computers & Geosciences, 2019 |
| Databáze: | OpenAIRE |
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