Details

Title

Effect of Rock Properties on ROP Modeling Using Statistical and Intelligent Methods: A Case Study of an Oil Well in Southwest of Iran

Journal title

Archives of Mining Sciences

Yearbook

2017

Numer

No 1

Publication authors

Divisions of PAS

Nauki o Ziemi

Abstract

<jats:title>Abstract</jats:title><jats:p>Rate of penetration (ROP) is one of the key indicators of drilling operation performance. The estimation of ROP in drilling engineering is very important in terms of more accurate assessment of drilling time which affects operation costs. Hence, estimation of a ROP model using operational and environmental parameters is crucial. For this purpose, firstly physical and mechanical properties of rock were derived from well logs. Correlation between the pair data were determined to find influential parameters on ROP. A new ROP model has been developed in one of the Azadegan oil field wells in southwest of Iran. The model has been simulated using Multiple Nonlinear Regression (MNR) and Artificial Neural Network (ANN). By adding the rock properties, the estimation of the models were precisely improved. The results of simulation using MNR and ANN methods showed correlation coefficients of 0.62 and 0.87, respectively. It was concluded that the performance of ANN model in ROP prediction is fairly better than MNR method.</jats:p>

Publisher

Committee of Mining PAS

Date

2017

Identifier

ISSN 0860-7001

DOI

10.1515/amsc-2017-0010

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