Author details

scenarios. It is therefore recommended that the HMSE variables should always be included in

the data attributes in the prediction of ROP as they are good predictors.

Nomenclature

126 Drilling

AI artificial intelligence

ANN artificial neural network BHA bottom hole assembly

CART classification regression trees

CPU computer processing unit

DSE drilling specific energy

ELM extreme learning machine

GLC generalized linear classifiers

HMSE hydraulic mechanical specific energy

LSSVR least square support vector regression

IADC international association of drilling contractors

FD footage drilled by bit, ft GHI grit hot-pressed inserts

GPM gallon per minute

LWD logging while drilling

MWD measurement while drilling

MATLAB matrix laboratory MD measured depth

NPT non-productive time

Δpb pressure loss at bit in psi

PDA predictive data-driven analysis

PDC polycrystalline diamond compact

DEO drilling efficiency optimization

db diameter of bit

CIT computational intelligence techniques

AIAI artificial intelligence applications institute

Omogbolahan Ahmed1 \*, Ahmed Adeniran<sup>2</sup> and Ariffin Samsuri<sup>1</sup>

\*Address all correspondence to: saomogbolahan2@live.utm.my

1 Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia

2 Department of Systems Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
