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MASTER THESIS

A SURVEY ON THE METHODS TO PREDICT RATE OF PENETRATION IN DRILLING PROJECT

ILIOPOULOS PANAGIOTISSUPERVISOR: VASILEIOS GAGANIS

Contents

Introduction Literature overviewCommon optimization model theoryAdvanced optimization methods

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Keywords

• Mathematical studies• Multiple regression• Real time methods• Simulation models

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Objective of this study

•Presentation of the results from laboratory drilling experiments•Determination of parameters which influence rate of penetration•Presentation of the drilling rate equations•Analysis of mathematical models constants•Comparison between the most common ROP prediction models•Presentation of the optimum drilling conditions which minimize the drilling cost•Introduction of new advanced techniques

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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History of drilling optimization

Scientific period1950 – Expansion of drilling research – Beginning of drilling optimization1952 – Jet type of roller cone bits1959 – First drilling optimization model by Graham and Muench1963 – Galle and Woods model

Automation period1970 – Beginning of automation period1974 – Multiple regression model by Bourgoyne1986 – Real time drilling optimization at Chevron rig site1999 – Real time drilling monitoring 2003 – Real time operation centers Shell and Halliburton2005 – Real time monitoring at ExxonMobil rig site2006 – Real time transfer centers by Statoil

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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Factors affecting ROPIntroduction

Literature overview

Common optimization model theory

Advanced optimization methods

CONTROLLABLE FACTORS

OTHER ADDITIANAL MEASURABLE

FACTORS

UNCONTROLLABLE FACTORS

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Drilling activity parameters

Controllable parametersWOB, fluid properties, rotary speed, pump pressure, bit energyUncontrollable parametersgeological structure, formation properties Measurable parameterstorque, drillstring properties, vibration, bit balling, formation strength, downhole pressure, temperature

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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Drilling optimization research

ROP optimization studies•Most of these studies indicate the optimum combination between drilling parameters.•Mathematical equations and constants describe the parameters relationship.•As primary objective has to minimize the time and drilling cost.

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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Drilling optimization research

ROP simulation modelsFull scale simulation test, drilling software, program which analyzed different ROP equations, ROP isomeric maps as well as 3D graphs used in order to find the optimum drilling conditions.

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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Drilling optimization research

Studies of real time drilling optimization• New advanced data transmission system (IWW)•Advanced monitor system (NAVO) •Innovative system for drilling automation and simulation •Establishment of real time operation centers (RTOC)

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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Four ROP prediction models

I. Maurers Perfect Cleaning Theory.II. Warren Cutting Removal Model.III. Galle And Woods Best Constant Weight

And Rotary Speed.IV. Bourgoyne And Youngs Multiple

Regression Model.

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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I. Maurers perfect cleaning theory

This formula for roller cone bits is derived from rock crater mechanism.

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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I. Maurers perfect cleaning theory

Should be mention that•Tool angle ranking 30-90 degrees has the more effective point while tool angle greater than 90 degrees the effectiveness decrease.•When the weight on the bit has very high value the R/N ratio decrease very rapidly as the rotary speed increases due to cleaning problem which occurred from high drilling rates

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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II. Warren cutting removal model

Warren created a model making an effort to represent all the parameters of the physical process in one equation.He supported that under steady state drilling condition the cutting removal is equal to the rate which new chips are formed.

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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II. Warren cutting removal model

The first fraction is based on the assumption that the WOB is supported by a fixed number of teeth and is independent from the teeth depthThe second fraction describes the WOB distribution for more teeth as the WOB is increased and the teeth penetrate deeper into the rock. The third fraction indicates that when the cutting size is increased, an increase from the impact force is required, to maintain a particular level of cutting removal.

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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III. Galle and Woods model

They study is focused on the best selection effect of weight on the bit and rotary speed.•The best combination of constant weight and rotary speed•The best constant weight for any given rotary speed•The best constant rotary speed for any given weight

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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III. Galle and Woods model

Three fundamentals equationsIntroduction

Literature overview

Common optimization model theory

Advanced optimization methods

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IV. Bourgoyne and Youngs model

It is a linear penetration model, which is consisted from controllable and uncontrollable drilling variables.

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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IV. Bourgoyne and Youngs model

Term a1……a8 describe the variablesa1=Formation strength functiona2=Normal compaction a3=Under compactiona4=Differential pressure functiona5=Bit diameter and weight functiona6=Rotary speed functiona7=Tooth wear functiona8=Hydraulic function

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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IV. Bourgoyne and Youngs model

This method is considered to be the most suitable method for real time drilling optimization.

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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Real time prediction technologies

I. Transmission of real time data II. Real time bit wearIII. ROP prediction using K meansIV. ROP prediction based on ANN

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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I. Real time data

Measure while drilling pipingThere are three ways to transmit data from the downhole to the surface.

•Mud pulse telemetry (MPT)•Electromagnetic telemetry (EM)•Wired drill pipes

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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I. Real time data

Real time centers•Drilling reports are typically created and transferred from the rig site on a daily basis. •Data are stored and the optimization process start.•The software matches the historical data from other wells.•When data have been analyzed, regression coefficients should be determined in order to find the optimum drilling parameters.

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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II. Real time bit wear

Formation drillability

Mechanical specific energy The mechanical specific energy method is defined as the work needed to destroy a given volume of the rock.

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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II. Real time bit wear

If we combine the formation drillability and MSE have the following relationship.

Bit wear fraction can be obtained using the following equation for roller cone and PDC bits:

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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III. ROP prediction using K means

Fuzzy simulated annealing algorithm provides an estimator f(x) to approximate g(x) while predict undetermined parameters with minimum error.

•Clustering the training data•Setting up a typical Fuzzy system•Determine the parameters of Fuzzy system using simulated annealing

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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III. ROP prediction using K meansIntroduction

Literature overview

Common optimization model theory

Advanced optimization methods

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IV. ROP prediction based on ANN

The ANN use the previous data from offset wells while run to find the expected ROP, including any changed drilling conditions as input.

The system provides two outputs, which are

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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IV. ROP prediction based on ANN

. A multiple neural network (MNN) consists from

Introduction

Literature overview

Common optimization model theory

Advanced optimization methods

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Conclusions Topics discussed

• The ways in which to predict the penetration rate throughout the chronological duration of drilling activities

• Analysis of the ROP parameters• Application in the petroleum industry• Advance knowledge about modern prediction methods

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Conclusions

General consideration (1)• There are many parameters which influence the penetration

rate.• The constants of the penetration model improve the accuracy.• The combinations between the drilling parameters are unlimited

and the drilling measurement is always different.

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Conclusions

General consideration (2)• A Monitoring system needs to be used in order to have reliable

data.• To increase the accuracy of a model, it is necessary to use data

from more than a single well.• Simulators improve the evaluation of field data and find the

optimum drilling parameters.

Thank you for

your attention!