eprintid: 16331
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/63/31
datestamp: 2023-12-19 03:22:52
lastmod: 2023-12-19 03:22:52
status_changed: 2023-12-19 03:06:03
type: article
metadata_visibility: show
creators_name: Babikir, I.
creators_name: Elsaadany, M.
creators_name: Sajid, M.
creators_name: Laudon, C.
title: Evaluation of principal component analysis for reducing seismic attributes dimensions: Implication for supervised seismic facies classification of a fluvial reservoir from the Malay Basin, offshore Malaysia
ispublished: pub
keywords: Data Analytics; Decision trees; Feature Selection; Matrix algebra; Offshore oil well production; Seismology; Support vector machines, Dimensionality reduction; Fluvial reservoirs; Machine learning models; Malaysia; Offshores; Principal-component analysis; Seismic attributes; Seismic facies; Seismic facies classification; Supervised machine learning, Principal component analysis, artificial neural network; machine learning; principal component analysis; seismic data; support vector machine, Malay Basin; Pacific Ocean; South China Sea
note: cited By 7
abstract: Because of their effectiveness in identifying geologic features, seismic attributes are usually used as input to machine learning (ML) models for facies classification. Typically, too many attributes are computed for facies classification, making a predictive modeling task more challenging. Principal component analysis (PCA), a popular dimensionality reduction tool, is widely applied in unsupervised learning. This study investigates the use of PCA to reduce the number of attributes before supervised learning. Our motivation is to maximize the use of seismic attributes, data analytics, and ML to effectively classify the geomorphologic seismic facies of the I-X reservoir of A Field, Offshore Malaysia. A systematic approach is presented, including attribute extraction, dimensionality reduction, feature selection, performance measure, and prediction ability for different classes. We extract 31 attributes that belong to amplitude, Gray-Level Co-Occurrence Matrix (GLCM), instantaneous, geometric, and spectral families. PCA analysis is then carried out to reduce the attribute set of each group into fewer principal components (PCs). We label three classes that combine and represent all the seismic/lithologic facies in the interval. Correlation coefficients, including Pearson, Rank, and Mutual Information (MI) that map the relationship between the input features and the 3-classes output, are calculated to select the optimal subset of features. We train and test support vector machine (SVM), random forest (RF), and neural network (NN) algorithms that are widely used in seismic facies classification. Among the computed seismic attributes, we find that the amplitude, Gray-Level Co-Occurrence Matrix (GLCM), and the spectral group of attributes are the best predictors for the fluvial seismic facies. The trained ML models perform slightly better with seismic attributes than PCs, and only minor differences are observed in the classification results. We find that attribute-to-attribute crossplots and correlation heatmaps effectively facilitate feature selection by improving our understanding of the data redundancy and relevance. © 2022 Elsevier B.V.
date: 2022
publisher: Elsevier B.V.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135807526&doi=10.1016%2fj.petrol.2022.110911&partnerID=40&md5=399cd2e5cc40638009dd695bf8c57314
id_number: 10.1016/j.petrol.2022.110911
full_text_status: none
publication: Journal of Petroleum Science and Engineering
volume: 217
refereed: TRUE
issn: 09204105
citation:   Babikir, I. and Elsaadany, M. and Sajid, M. and Laudon, C.  (2022) Evaluation of principal component analysis for reducing seismic attributes dimensions: Implication for supervised seismic facies classification of a fluvial reservoir from the Malay Basin, offshore Malaysia.  Journal of Petroleum Science and Engineering, 217.   ISSN 09204105