eprintid: 13560
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/35/60
datestamp: 2023-11-10 03:28:07
lastmod: 2023-11-10 03:28:07
status_changed: 2023-11-10 01:51:28
type: conference_item
metadata_visibility: show
creators_name: Iferobia, C.C.
creators_name: Ahmad, M.
creators_name: Salim, A.M.A.
creators_name: Sambo, C.
creators_name: Michaels, I.H.
title: Acoustic data driven application of principal component multivariateregression analysis in the development of unconfined compressivestrength prediction models for shale gas reservoirs
ispublished: pub
keywords: Boreholes; Compressive strength; Energy resources; Forecasting; Gases; Multivariant analysis; Oil field equipment; Regression analysis; Shale gas; Well logging, Acoustic data; Acoustic parameters; Data-driven applications; Key technologies; Model development; Prediction modelling; Principal Components; Property; Shale gas reservoirs; Unconfined compressive strength, Principal component analysis
note: cited By 1; Conference of SPE Annual Technical Conference and Exhibition 2020, ATCE 2020 ; Conference Date: 26 October 2020 Through 29 October 2020; Conference Code:164382
abstract: Unconfined compressive strength (UCS) equally represented as geomechanical strength remains a criticalmechanical property in the successful implementation of key technologies for shale gas reservoirs'development and production. Attention has been less concentrated on prediction models' development forshale geomechanical strength evaluation. Majority of the existing shale geomechanical strength correlationsare dependent on single log input parameter, which is insufficient to account for the complex and non-linear behaviour of UCS across the entire reservoir interval of interest. The high relevance of UCShas therefore triggered the need for the application of an integrated system of principal component -multivariate regression analysis in driving UCS predictive models' development for shale gas reservoirs.Generated acoustic datasets of notable shale gas reservoirs (Marcellus, Montney, Longmaxi and Roseneath)in respective countries (United States of America (USA), Canada, China and Australia) were used. Statisticaltest analysis was conducted in validation for wider applications of the developed UCS prediction models. Models development were driven by 21,708 datapoints of acoustic parameters, models' accuracy ratingswere above 99, R-squared values had high degrees of closeness to unity, mean absolute percentage error(MAPE) values were at less than 10 and coefficient of variation (COV) at less than (1.0). UCS predictionmodels were all dependent on multiple direct log measured acoustic parameters in distinction to existingUCS empirical correlations; thus, a pure reflection of significant boost to the accuracy and reliability ofUCS measurements for shale gas reservoirs. The developed prediction models will promote geomechanicalstrength accountability and lead to creation of a robust base in minimization of wellbore instability problems,optimization of wellbore trajectory and containment of hydraulic fractures. This will significantly contributein putting gas resources of shale reservoirs with enormous potentials, at the forefront of quantitativelymeeting natural gas requirements in global energy demand. © 2020 Society of Petroleum Engineers (SPE). All rights reserved.
date: 2020
publisher: Society of Petroleum Engineers (SPE)
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139379202&doi=10.2118%2f201287-MS&partnerID=40&md5=fa2527b13dc72b393b07b49cf3c6c579
id_number: 10.2118/201287-MS
full_text_status: none
publication: Proceedings - SPE Annual Technical Conference and Exhibition
refereed: TRUE
issn: 26386712
citation:   Iferobia, C.C. and Ahmad, M. and Salim, A.M.A. and Sambo, C. and Michaels, I.H.  (2020) Acoustic data driven application of principal component multivariateregression analysis in the development of unconfined compressivestrength prediction models for shale gas reservoirs.  In: UNSPECIFIED.