Vol. 59, n.1, March 2018
Application of Machine Learning and Digital Music Technology to distinguish high from low gas-saturated reservoirs
P. Dell'Aversana, G. Carrasquero, G. Gabbriellini and A. Amendola
Received: July 4, 2017; accepted: December 20, 2017
In this paper, we discuss a novel approach of pattern recognition, clustering and
classification of seismic data based on commonly applied techniques in the domain
of digital music and in musical genre classification. Our workflow starts with accurate
conversion of seismic data from SEGY to Musical Instrument Digital Interface (MIDI)
format. Then, we extract MIDI features from the converted data. These can be singlevalued
attributes related to instantaneous frequency and/or to the signal amplitude.
Furthermore, we use multi-valued (or "high-level") MIDI attributes that have no
equivalent in the seismic domain. For instance, we use MIDI features related to
melodic, harmonic and rhythmic patterns in the data. We discuss an application to real
data. We apply a Machine Learning approach to the MIDI-converted seismic data set
with the purpose of accurate seismic facies classification. The final objective of the test
is to distinguish between geological formations prevalently formed by clay, from two
different gas-bearing sandy layers: one is a low gas-saturated reservoir and the other
one is a high gas-saturated reservoir. In this paper, we present encouraging results.
Considering the novelty of our approach, additional investigations are in progress on
larger data sets, for a complete understanding of the physical meaning of the new
"high-level" MIDI attributes.
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