Achieving the highest Rate of Penetration (ROP) with the least possible
Bit Tooth Wear Rate (BTWR) is the aim of every drilling engineer when
selecting a drilling bit. Predicting the optimal ROP has become increasingly
important given the rise in expenses involved in drilling a well. This has meant
that oil companies engage in a perpetual struggle to predict the optimum rock
mechanical property parameters.
Predicting optimal rock mechanical property parameters, specifically
Rate of Penetration (ROP), has become increasingly important given the rise in
expenses involved in drilling a well. The prediction of ROP from the current
available data is an important criterion for reduction of drilling costs. ROP
represents rock bit interaction which relates rock compressive strength and bit
aggressivity. ROP prediction is complex because of the numerous variables
which lead to difficulties in evaluating drilling parameters. Several models and
methods have been published for predicting, and therefore potentially
optimizing rate of penetration. However, these models and methods have
limitations, too many variables are included, their input parameters are often
not readily available, and their relationships are complex and not easily
modeled. Therefore, the application of Neural Network is suggested in this
study.
A new methodology has been developed to predict the rate of
penetration using the Artificial Neural Network (ANN). Three case studies
representing different formations in Kuwait have been conducted to investigate
ROP prediction for various applications. These cases have investigated the
prediction of ROP for a specific heterogeneous formation (CASE I); a semihomogenous
formation (CASE II); a drilling section composed of a
heterogeneous formation and for a drilling section composed of a complex
heterogeneous set of formations (CASE III). Predicting ROP parameters is of
particular interest, therefore finding a new method to predict ROP for the cases
investigated in this study will be a valuable achievement. Application of the
new network models would then be used for selecting the best parameters for
an optimal drilling strategy based on field data.
In addition to the prediction of ROP, several runs were carried out to
predict Tooth Wear Rate (TWR) for a drilling section in case III. Rock bit
interactions in the field as a function of rock mechanical property parameters
was achieved by predicting ROP which relates to rock compressive strength
and bit aggressivity; as well as TWR which relates to rock abrasiveness and
wear resistance.
History of bit runs, mud logging data, geological information, offset
well bit records, drill bit characteristics, and wireline data all play an important
role in the prediction of rock bit interactions in this study. Based on field data,
the prediction of rock mechanical property parameters can be accomplished by
the use of a neural network as an alternative prediction and optimization
method. Neural network offers a new form of information processing that is
fundamentally different from a traditional processing system. The system uses
a knowledge base of various drilling parameters, to produce a “correlation”
description of the optimal Rate of Penetration.