eprintid: 20045
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/02/00/45
datestamp: 2024-06-04 14:19:47
lastmod: 2024-06-04 14:19:47
status_changed: 2024-06-04 14:16:28
type: article
metadata_visibility: show
creators_name: Hasan, M.L.
creators_name: M. Tóth, T.
title: Multiple linear regression and gene expression programming to predict fracture density from conventional well logs of basement metamorphic rocks
ispublished: pub
keywords: Forecasting; Fracture; Gamma rays; Gene expression; Metamorphic rocks; Petroleum reservoir evaluation; Petroleum reservoirs; Porosity; Seismology; Well logging, Analysis expressions; Conventional well logs; Fracture density; Fractured reservoir; Gene-expression programming; Image logs; Metamorphic basements; Multiple linear regressions; Principal-component analysis; Well log data, Multiple linear regression
note: cited By 0
abstract: Fracture identification and evaluation requires data from various resources, such as image logs, core samples, seismic data, and conventional well logs for a meaningful interpretation. However, several wells have some missing data; for instance, expensive cost run for image logs, cost concern for core samples, and occasionally unsuccessful core retrieving process. Thus, a majority of the current research is focused on predicting fracture based on conventional well log data. Interpreting fractures information is very important especially to develop reservoir model and to plan for drilling and field development. This study employed statistical methods such as multiple linear regression (MLR), principal component analysis (PCA), and gene expression programming (GEP) to predict fracture density from conventional well log data. This study explored three wells from a basement metamorphic rock with ten conventional logs of gamma rays, thorium, potassium, uranium, deep resistivity, flushed zone resistivity, bulk density, neutron porosity, sonic porosity, and photoelectric effect. Four different methods were used to predict the fracture density, and the results show that predicting fracture density is possible using MLR, PCA, and GEP. However, GEP predicted the best fracture density with R2 > 0.86 for all investigated wells, although it had limited use in predicting fracture density. All methods used highlighted that flushed zone resistivity and uranium content are the two most significant well log parameters to predict fracture density. GEP was efficient for use in metamorphic rocks as it works well for conventional well log data as the data is nonlinear, and GEP uses nonlinear algorithms. © The Author(s) 2024.
date: 2024
publisher: Springer Science and Business Media B.V.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191055252&doi=10.1007%2fs13202-024-01800-z&partnerID=40&md5=f28fdc219d9c29048e48b4d70be14808
id_number: 10.1007/s13202-024-01800-z
full_text_status: none
publication: Journal of Petroleum Exploration and Production
refereed: TRUE
issn: 21900558
citation:   Hasan, M.L. and M. Tóth, T.  (2024) Multiple linear regression and gene expression programming to predict fracture density from conventional well logs of basement metamorphic rocks.  Journal of Petroleum Exploration and Production.   ISSN 21900558