This document discusses using artificial neural networks to predict the rate of penetration during drilling. It compares using an artificial neural network method called Polynomial Neural Network BIC on three different data sets to the traditional Bourgoyne and Young mathematical model. The results show that using Polynomial Neural Network BIC on the data set with the most parameters (7 parameters) provided the most accurate predictions of rate of penetration at 76%, compared to 20% for the Bourgoyne and Young model. Therefore, the study concludes that the Polynomial Neural Network BIC method using the full available parameter data set provides the best approach for predicting rate of penetration during drilling.

1. Abstract--In the drilling industry, the rate of penetration (ROP),
is the speed at which a drill bit breaks the rock under it to deepen
the borehole, also known as penetration rate or drill rate. It is
normally measured in feet per minute or meters per hour, but
sometimes it is expressed in minutes per foot. In any engineering
study of rotary drilling it is convenient to divide the factors which
affect the rate of penetration into bit type, weight on bit, rotary
speed, drilling fluid properties, bit hydraulics and formation
properties. A real time raw field data is obtained from the field
Kinabalu East-1well [Kinabalu East-1, 1990] where the rate of
penetration has been predicted by petroleum engineers [Sonny
Irawan, Adib Mahfuz, Saleem Qadir Tunio 2012] using
Bourgoyne and Young’s theory. In this project, Artificial Neural
Network is used to obtain the equations which can be used to
predict rate of penetration. Group data handling method which
uses the raw functions of artificial neural network in its software
is used. The final result outcome shows that Bourgoyne and
Young Mathematical Model gives 20% of accuracy of predicted
rate of penetration when compared with the actual rate of
penetration. On the other hand, Polynomial Neural Network BIC
using Data3 gives 76% of accuracy of predicted rate of
penetration when compared with the actual rate of penetration.
This shows that Polynomial Neural Network BIC using Data3
which consist of seven parameters gives the most accurate and
reliable prediction of rate of penetration. The system has
predicted the rate of penetration by using the equation which was
created using the seven parameters with the existing value from
the data. This means, the equation computed by the system for
Polynomial Neural Network BIC using Data3 can be used to
predict rate of penetration for other field data by inserting the
desired values of the seven parameters.
I. NOMENCLATURE
Wb = Bit Weight (1000lbs.inch)
H = Tooth Wear
Gp = Pore Gradient
D = Depth
Ppnn = Penetration (Polynomial Neural Network)
ROP = Rate of Penetration
II. INTRODUCTION
EVELOPEMENT of oilfield is focused to drill in cost
efficient manners (Tuna, 2010). For that reason oilfield
drilling operations will face hurdles to reduce overall
costs, increase performances and reduce the probability of
encountering problems. In oil well drilling drillability of rock
is noticed to decrease with increasing depth of the hole
(Garnier and van Lingen, 1959). The increase in complexity
for drilling operation has increase many problems thus results
in serious cost consideration (Saleem et al., 2011) as the
drilling rate is affected significantly by changes in differential
pressure (Vidrine and Benit, 1968). Different methods from
different disciplines are being used nowadays in drilling
activities in order to obtain a safe, environmental friendly and
cost effective well construction (Tuna, 2010). The effective
cost can be predicted and minimized by having efficient rate of
penetration during drilling. The higher the rate of penetration
is, the less cost it is as it consumes less trip time and more
production time. This document provides the best method to
be used to predict the rate of penetration in drilling program.
Mathematical model of Bourgoyne and Young Model is
compared with simulation model of Artificial Neural Network
which is used in Gathering Method of Data Handling from
VariReg 0.10.1 software. By computing the parameters
available from a real field data, the Artificial Neural Network
will derive equations to predict the rate of penetration. The
methods and steps can be used to derive new equations with
the desired parameters from a real field data. Satisfactory
results have been achieved from this research and the best
model was obtained. The objective and aim of this study were
to find out the parameters effecting ROP. Secondly, is to study
the effect of ROP in drilling program. Lastly, to explore the
applicability of Artificial Neural Network and its models in
predicting and estimating rate of penetration
A. Artificial Neural Network
Artificial neural networks have the ability to recognize
complex patterns quickly with a high degree of accuracy, it
makes no assumptions about the nature and distribution of the
data and they are not biased in their analysis. In addition,
artificial neural networks have non-linear tools and as such are
good at predicting non-linear behaviors. Neural networks form
a broad category of computer algorithms that solve several
types of problems, including pattern classification, functions
approximation, pattern completion, pattern association,
filtering, optimization and automatic control [Mohaghegh,
2000]. Two primary elements make up neural networks:
processing elements, which process information, and
interconnections or links between the processing elements. The
structure of the neural network is defined by the
interconnection architecture between the processing elements.
Information processing within an artificial neural network
occurs in the processing elements that are called neurons
The Effect of Rate of Penetration in Drilling
Program
Hema Gayathiry Gunasagaran, Asst. Prof Aung Kyaw, Elhassan Mostaffa, Faculty of Engineering,
UCSI University, 2014
D

2. which are grouped into layers. Signals are passed between
neurons over the connecting links.
B. Gathering Method of Data Handling type Neural Network
There are many different ways to choose an order for partial
models consideration. The very first consideration order used
in GMDH and originally called multilayered inductive
procedure is the most popular one. It is a sorting-out of
gradually complicated models generated from Kolmogorov-
Gabor polynomial. The best model is indicated by the
minimum of the external criterion characteristic. Multilayered
procedure is equivalent to the Artificial Neural Network with
polynomial activation function of neurons. Therefore the
algorithm with such an approach usually referred as GMDH-
type Neural Network or Polynomial Neural Network.
III. METHODOLOGY
A. Case study
Field data shown in table 1 is taken from Kinabalu East – 1
Well (Kinabalu East – 1 Well, 1990). This case was used in
the study on predicting the rate of penetration for Bourgoyne
and Youngs Model (Sonny Irawan, Adib Mahfuz, Saleem
Qadir 2012). The prediction results of rate of penetration
obtained using Bourgoyne and Young Model is compared with
the predictions obtained by using Gathering Method of Data
Handling (Artificial Neural Network). The drilling rate of
penetration model adapted for this study is a function of
independent variables. Examples of the independent variables
are parameters like; compaction effect, bit diameter, weight
and rotary speed effect, tooth wear and bit hydraulics. All of
the drilling parameters is a real field data collected from
existing field. It is considered that the methodology is going to
function even if the mud type, the drilling depths, and bit types
are different, because all such parameters are taken into
account within the model itself.
TABLE I
FIELD DATA FROM KINABALU EAST – 1 WELL (KINABALU EAST – 1 WELL, 1990)
B. Methods
Methods used are Polynomial Neural Network AICC,
Polynomial Neural Network BIC, Polynomial AICC and
Polynomial BIC. The parameters in the data are split into
three data files according to the number of parameters which
has been used as the X variables, Data1, Data2 and Data3.
The data is split into three data files as Data1 contains three
parameters, Data2 contains five parameters and Data3 contains
seven parameters. Each model has all three data to be
simulated to determine the best data which gives accurate
prediction of ROP. This is done for the purpose of comparison
and the influence of the number of parameters on the
predictions outcome of rate of penetration.
Step one, is where the VariReg software is initiated. Step
two, the data saved in note pad format is loaded from load
training data. Step three, data1 is then loaded from file. Step
four, the polynomial neural network AICC is chosen. Step
five, the process is started by clicking the start process. The
software gives the equation for Data1 using three parameters,
predictions are saved. Predictions for Data1 PNN AICC are
obtained. Finally, the result for predicted rate of penetration is
used to plot comparison graphs with the actual rate of
penetration. Step four is repeated for each model and each data
entry and prediction results are obtained for comparison.
IV. RESULTS AND DISCUSSION
The result shows that for Polynomial Neural Network, Data3
gives the most accurate prediction of rate of penetration. For
Polynomial, Data2 gives the most accurate prediction of rate
of penetration. The difference between Data2 and Data3 is that
Data2 consist of five parameters as variables and Data3 consist
of seven parameters as variables. From here, these four models
are compared to determine which model consisting of which
data gives the most accurate and reliable prediction when
compared with the actual rate of penetration and among each
other. Graphs have been plotted using this models chosen data.
From the analysis, it is proven that Polynomial Neural
Network BIC Data3 gives 44% of accuracy with the actual rate
of penetration when compared with the other three models
(Table 2).
TABLE
COMPARISON RESULTS OF THE BEST DATA OF ALL FOUR MODELS
Model Accurately predicted
Data
Percentage
PNN AICC Data3 44%
PNN BIC Data3 60%
Polynomial AICC Data2 48%
Polynomial BIC Data2 52%
The difference between Data2 and Data3 is the number of
parameters involved. Data2 consist of five parameters and

3. Data3 consist of seven parameters. The result here which
shows Data3 (Figure 1) to be more accurate in predicting rate
of penetration proves that the more data computed into the
software model, the more accurate the system can predict the
rate of penetration. Therefore, the predicted rate of penetration
using Polynomial Neural Network BIC of Data3 is compared
with the predicted rate of penetration using Bourgoyne and
Young Model with the actual rate of penetration taken from
Kinabalu East-1 Well.
After all the interpretation and analysis, the final result
outcome shown in figure 2 proves that Bourgoyne and Young
Mathematical Model gives 20% of accuracy of predicted rate
of penetration when compared with the actual rate of
penetration.
Fig. 1: Actual ROP vs. PNN AICC data3vs. PNN BIC data3 vs. Polynomial
AICC data2 vs. Polynomial BIC data2.
On the other hand, Polynomial Neural Network BIC using
Data3 gives 76% of accuracy of predicted rate of penetration
when compared with the actual rate of penetration.
Fig. 2: Actual ROP vs. Bourgoyne and Young vs. Polynomial Neural Network
BIC Data3.
This shows that Polynomial Neural Network BIC using Data3
which consist of seven parameters gives the most accurate and
reliable prediction of rate of penetration. The system has
predicted the rate of penetration by using the equation which
was created using the seven parameters with the existing value
from the data. This means, the equation computed by the
system for Polynomial Neural Network BIC using Data3 can
be used to predict rate of penetration for other field data by
inserting the desired values of the seven parameters as shown
in equation (1) and equation (2).
The main equation to predict the rate of penetration using the
above parameters is shown in eq. (1):
PpnnPpnnDgpDE
gpDgpROP
]03.0003.01.22.22[))]2^(6(03.2[
]0002.0035.0[9.13382(2429
+++−−+
+−+−=
(1): Rate of Penetration
The sub equation to obtain Penetration Polynomial Neural
Network (Ppnn) is show in eq. (2):
))E(-6(D^2×1.118-0.0345h]D+0.0109Wb+[0.0042+
214]h-78.3Wb-[325-Wb]26.7Wb-[32.1-48.9=PPNN
(2): Penetration Polynomial Neural Network
V. CONCLUSION
By using VariReg software, the best model to predict the rate
of penetration using Artificial Neural Network has been
obtained. The parameters affecting rate of penetration were
analyzed. Satisfactory result has been achieved from this
research. Developed model shows that the Polynomial Neural
Network BIC induced by Gathering Method of Data Handling
using the highest number of parameters as variables, proved to
be the best method to predict the rate of penetration which
aided to achieve the first and second objective. The equation
model to predict the rate of penetration has been derived. This
model is extremely useful to predict the rate of penetration for
any field and drilling program by using the involved
parameters from the model which aided in achieving the third
objective. By predicting the rate of penetration, trip time of
drill bit can be calculated. By calculating the trip time, the
driller will know how to drill efficiently with least cost as time
is money. By this manner, drilling cost can be minimized
which is the main objective in every drilling program. It is
known that drilling with minimal cost with least expenses is
considered to be an effective and optimized drilling program.
Overall, the Artificial Neural Network which is used in
Gathering Method of Data Handling is highly recommended to
be used in predicting the rate of penetration in drilling
program. This ANN simulation helps to aid in calculating and
predicting the rate of penetration in a very simple and easy
manner by just computing the parameters into the system. It is
user friendly and gives results with minimal error. The
predictions from this simulation are very accurate to almost
70% of the actual rate of penetration from a real field data.
Instead of using mathematical model such as Bourgoyne and
Young Model, this Artificial Neural Network based Model can
be used to predict the rate of penetration in drilling program.

4. Therefore, the Polynomial Neural Network BIC developed
model proves to give the most accurate and reliable prediction.
Hence, with no doubt it can be used to predict the rate of
penetration for any existing oil and gas field drilling program.
VI. ACKNOWLEDGMENT
First and foremost, author would like to thank first
supervisor, Asst. Mr. Aung Kyaw for the valuable guidance
and advice given during my final year project. Next, author
would like to thank second supervisor Mr. Elhassan Mostafa
Abdallah Mohammed for his continuous support which he
gave throughout this final year project. His willingness to
motivate, contributed tremendously for the completion of final
year project. Author would also like to thank Mr. Sami
Abdelrahman Musa for his assistance in Artificial Neural
Network simulation. This thesis would not have been possible
to complete without their input and guidance. Also, an
honourable mention goes to author family and friends for their
understandings and support given to during the final year
project. The author gratefully acknowledges the contributions
of Sonny Irawan, Adib Mahfuz Abd Rahman and Saleem
Qadir Tunio for their work on the original version of the
prediction of rate of penetration using Bourgoyne and Young
Model document.
VII. REFERENCES
:
[1] Sonny Irawan, Adib Mahfuz Abd Rahman and Saleem Qadir Tunio,
2012. Optimization of Weight on Bit During Drilling Operation Based
on Rate of Penetration Model. Research Journal of Applied Sciences,
Engineering and Technology 4(12): 1690-1695, 2012 ISSN: 2040-7467.
[Retrieved 08 May 2014].
[2] Eren T., and Ozbayoglu E., “Real Time Optimization of Drilling
Parameters During Drilling Operations” SPE Paper129126, SPE Oil and
Gas India Conference and Exhibition, Mumbai, India, 20–22 January
2010. [Retrieved 08 May 2014].
[3] Eren T., Ozkale A., Ozer C., Kirbiyik S. A Case Study: Comparison of
the Theoretical and Actual Drilling HydraulicPressures for Wells in
Turkey. 17th International Petroleum and Natural Gas Congress and
Exhibition of Turkey, 29-31 May 2007, Turkey. [Retrieved 08 May
2014].
[4] Alum, M.A., Egbon, F. 2011. Semi-Analytical Models on the Effect of
Drilling Fluid Properties on Rate of Penetration (ROP). Paper SPE
150806 presented at the Nigeria Annual International Conference and
Exhibition, Abuja, Nigeria, 30 July - 3 August. DOI: 10.2118/150806-
MS. [Retrieved 08 May 2014].
[5] Asha Ramgulam, 2006. Utilization of artificial neural networks in the
optimization of history matching. Thesis. Pennsylvania. Petroleum and
Natural Gas Engineering. [Retrieved 09 May 2014].
[6] Real-time-optimization of drilling parameters during drilling operations
[Online], school of natural and applied sciences of Middle East
technical university. [Retrieved 09 May 2014].
[7] Bahari, M.H., Bahari,A., and Moharrami,F.N. et al. 2008. Determining
Bourgoyne and Young Model Coefficients Using Genetic Algorithm to
Predict Drilling Rate. Journal of Applied Sciences. [Retrieved 09 May
2014].
[8] Bahari, A., Baradaran, A. 2007. Trust-Region Approach To Find
Constants of Bourgoyne and Young Penetration Rate Model in
Khangiran Iranian Gas Field. Paper SPE 107520 presented at the SPE
Latin American and Carribean Petroleum Engineering Conference,
Buenos Aires, 15-18 April. [Retrieved 09 May 2014].
[9] Bielstein, W.J, Cannon, G.E. 1950. Factors Affecting the Rate of
Penetration of Rock Bits. Paper API presented at the spring meeting,
South-western District, Division of Production, Dallas, USA, March.
[Retrieved 09 May 2014].
[10] Bourgoyne, A.T.,Young, F.S. 1974. A Multiple Regression Approach to
Optimal Drilling and Abnormal Pressure Detection. J. SPE 14(4).
[Retrieved 09 May 2014].
[11] Ali, J.K., Neural Networks: A New Tool for the Petroleum Industry.
Society of Petroleum Engineers, Paper No. 27561, 1994. [Retrieved 09
May 2014].
[12] Al-Fattah, S.M., Startzman, R.A., Predicting Natural Gas Production
Using Artificial Neural Network. Society of Petroleum Engineers, Paper
No. 68593, 2001. [Retrieved 09 May 2014].
[13] Centilmen, A., Applications of Neural-networks in Multi-well Field
Development. The Pennsylvania State University, Department of
Mineral Engineering, 1999. [Retrieved 09 May 2014].
[14] Doraisamy, H. Methods of Neuro-Simulation for Field Development –
A Thesis in Petroleum and Natural Gas Engineering. The Pennsylvania
State University, Department of Mineral Engineering, 1998. [Retrieved
09 May 2014].
[15] Dye, L.W., Horne, R.N., Aziz, K. A New Method for Automated History
Matching of Reservoir Simulators. Society of Petroleum Engineers,
Paper No. 15137, 1986. [Retrieved 09 May 2014].
[16] Ertekin, T., Abou-Kassam, J. H., King, G. Basic Applied Reservoir
Simulation. Henry L. Doherty Memorial Fund of AIME, Society of
Petroleum Engineers, Richardson, Texas, 2001. [Retrieved 09 May
2014].
[17] Hagan, M.T., Demuth, H.B., Beale, M. Neural Network Design, PWS
Publishing Company, 1996. Mohaghegh, S. Virtual-Intelligence
Applications in Petroleum Engineering, Parts 1-3. Society of Petroleum
Engineers, Paper Nos. 58046, 61926 & 62415, 2000. [Retrieved 09 May
2014].
[18] Schiozer, D.J., Use of Reservoir Simulation, Parallel Computing and
Optimization Techniques to Accelerate History Matching and Reservoir
Management Decisions, Society of Petroleum Engineers, Paper No.
53979, 1999. [Retrieved 09 May 2014].
[19] Altmis, U., “Estimation of Drilling Parameters Using Neural Networks.”
MS Thesis, West Virginia University, Morgantown, WV, 1996.
[Retrieved 09 May 2014].
[20] Azar, J., “Drilling Engineering Mechanics.” U Tulsa; Tulsa, 1995.
[Retrieved 09 May 2014].
[21] BakerHughes.“DrillingBits.”1996<www.bakerhughes.com/bakerhughes
/bhidefil.htm>. [Retrieved 09 May 2014].
[22] Bilgesu, H.I., Tetrick, L.T., Altmis, U., Mohaghegh, S., Ameri, S., A
New Approach for Prediction of Rate of Penetration (ROP) Values.
paper SPE 39231, presented at the SPE Eastern Regional Meeting,
Lexington, KY, Oct 1997. [Retrieved 09 May 2014].
[23] Bilgesu, H.I., Altmis, U., Mohaghegh, S., Ameri, S., and Aminian, K., A
New Approach to Predict Bit Life based on Tooth and Bearing Failure.
paper SPE 51082, presented at the SPE Eastern Regional Meeting,
Pittsburgh, PA, November 9-21, 1998. [Retrieved 09 May 2014].
[24] Bourgoyne, A.T.[et al.].,”Applied Drilling Engineering.” Society of
Petroleum Engineering, Richardson, TX, 1986. [Retrieved 09 May
2014].
___________________________________