Drilling Engineering Prepared by: Tan Nguyen Drilling Engineering - PE 311 Modeling of Drilling Drill Bits.

Slides:



Advertisements
Similar presentations
Modeling of Data. Basic Bayes theorem Bayes theorem relates the conditional probabilities of two events A, and B: A might be a hypothesis and B might.
Advertisements

ROLLING CUTTER BITS Rolling Cutter Bits
Lesson 10: Linear Regression and Correlation
LECTURE 26- MASS MOMENT OF INERTIA OF PULLEY SYSTEMS
Well Design - PE 413 Chapter 1: Formation Pressure
Drilling Engineering – PE 311 An Introduction to Drilling Drill Bits
Fundamentals of Cutting and Cutting-Tool Materials & Cutting Fluids Presented by: Rita Silvernail Tony Cordisco John Congdon Richard Gasbarra.
A User-Friendly, Two-Zone Heat Release and Emissions Model Jeremy Cuddihy Major Professor: Dr. Steve Beyerlein.
Experiment #5 Momentum Deficit Behind a Cylinder
General Concepts for Development of Thermal Instruments P M V Subbarao Professor Mechanical Engineering Department Scientific Methods for Construction.
Lesson 20 Abnormal Pressure
Drilling Engineering – PE 311 Drill Bit Optimization
Drilling Engineering - PE 311 Rock Failure Mechanisms
Chapter 21 THEORY OF METAL MACHINING
ATMATM PETE 406 UBD ATMATM ATMATMATMATM PETE Underbalanced Drilling, UBD Lesson 9 Benefits of Underbalanced Drilling UDM - Chapter 3.
Mitra’s short time expansion Outline -Mitra, who’s he? -The model, a dimensional argument -Evaluating the leading order correction term to the restricted.
1 MECH 221 FLUID MECHANICS (Fall 06/07) Tutorial 7.
Linear Regression MARE 250 Dr. Jason Turner.
Introduction to Convection: Flow and Thermal Considerations
FUNDAMENTALS OF METAL FORMING
MARE 250 Dr. Jason Turner Correlation & Linear Regression.
Drilling Engineering – PE311 Rotary System
Lesson 4 Drilling Cost & Drilling Rate
F. Cheung, A. Samarian, W. Tsang, B. James School of Physics, University of Sydney, NSW 2006, Australia.
Airball Demo Modeling —— Dimensional Analysis Method Based on Genetic Algorithm for Key Parameters Identification Name : Zhisheng Team Advisor : Zhang.
ME Manufacturing Systems Metal Machining Metal Machining.
Force Analysis – Spur Gears
Introduction to Convection: Flow and Thermal Considerations
Linear Regression Analysis Additional Reference: Applied Linear Regression Models – Neter, Kutner, Nachtsheim, Wasserman The lecture notes of Dr. Thomas.
Lecture # 7 THEORY OF METAL MACHINING
Biostatistics Unit 9 – Regression and Correlation.
PVT Behavior of Fluids and the Theorem of Corresponding States
Lecture No 111 Fundamentals of Metal removal processes Dr. Ramon E. Goforth Adjunct Professor of Mechanical Engineering Southern Methodist University.
NFR project /210 Presented in Stavanger 4 April 2003 Presented by
Gases The Ideal Gas Law.  Objectives  State the ideal gas law  Using the ideal gas law, calculate pressure, volume, temperature, or amount of gas when.
WELCOME TO THETOPPERSWAY.COM.
Turbomachinery Lecture 4a Pi Theorem Pipe Flow Similarity
© 2014 Carl Lund, all rights reserved A First Course on Kinetics and Reaction Engineering Class 13.
FUNDAMENTALS OF METAL FORMING
Chapter 6 Introduction to Forced Convection:
Maintenance Workload Forecasting
A graph represents the relationship between a pair of variables.
Machining Processes 1 (MDP 114) First Year, Mechanical Engineering Dept., Faculty of Engineering, Fayoum University Dr. Ahmed Salah Abou Taleb 1.
GASES.
MARE 250 Dr. Jason Turner Linear Regression. Linear regression investigates and models the linear relationship between a response (Y) and predictor(s)
© 2014 Carl Lund, all rights reserved A First Course on Kinetics and Reaction Engineering Class 13.
Curve Fitting Discovering Relationships. Purpose of Curve Fitting Effectively communicate (describe) information Effectively communicate (describe) information.
A "Reference Series" Method for Prediction of Properties of Long-Chain Substances Inga Paster and Mordechai Shacham Dept. Chem. Eng. Ben-Gurion University.
THEORY OF METAL CUTTING THEORY OF METAL MACHINING 1.Overview of Machining Technology 2.Theory of Chip Formation in Metal Machining 3.Force Relationships.
FUNDAMENTALS OF METAL FORMING
Experimental Determination of Molecular Speeds Stephen Luzader Frostburg State University Frostburg, MD.
Chapter 16 Bulk Forming Processes (Part 3) EIN 3390 Manufacturing Processes Spring 2011.
THEORY OF METAL MACHINING
Correlation of Rate of Penetration to Geometric Attributes AASPI Joseph Snyder* and Kurt J. Marfurt, University of Oklahoma Summary In this analysis, the.
ENM208 INTRODUCTION to MACHINING ANADOLU UNİVERSITY Industrial Engineering Department.
Lecture # 7 THEORY OF METAL MACHINING 1.Overview of Machining Technology 2.Theory of Chip Formation in Metal Machining 3.Force Relationships and the Merchant.
Fundamentals of Metal cutting and Machining Processes THEORY OF METAL MACHINING Akhtar Husain Ref: Kalpakjian & Groover.
Parul Institute of Engineering & Technology
16 Heat Capacity.
Date of download: 12/22/2017 Copyright © ASME. All rights reserved.
CALL/WHATSAPP
Off-design Performance of A Rotor
Graphing.
Metal cutting. Introduction Metal cutting or “Machining” is a process which removing unwanted materials from the work piece by the form of chips. The.
16 Heat Capacity.
Primary Machining Parameters
Principle of the process Design For Manufacturing (DFM)
 Overview of Machining Technology  Theory of Chip Formation in Metal Machining  Force Relationships and the Merchant Equation  Power and Energy Relationships.
Asst. Prof. Dr. Hayder Mohammad Jaffal
Finding the Optimum BHA through Data Analytics & Modeling
Presentation transcript:

Drilling Engineering Prepared by: Tan Nguyen Drilling Engineering - PE 311 Modeling of Drilling Drill Bits

Drilling Engineering Prepared by: Tan Nguyen Modeling of Drill Bits Rollercone bits have been used extensively in the drilling industry and among its various types; the three-cone rolling cutter bit is by far the most widely used all over the two Cunningham (1960) showed that the drilling rate of a roller-cone bit is proportional to the rotary speed of the bit at the atmospheric pressure for a wide range of RPM and WOB as expressed below: R = K*W a *N where R = drilling rate, ft/hr ; W = weight on bit, klbf ; N = rotary speed ; and K, a = constants of proportionality Modeling of Roller Cone Bit

Drilling Engineering Prepared by: Tan Nguyen Modeling of Drill Bits Cunningham’s model did not integrate the effect of other drilling parameters: bit diameter, rock strength, etc. Maurer (1962) proposed his model for the perfect hole cleaning condition Modeling of Roller Cone Bit

Drilling Engineering Prepared by: Tan Nguyen Modeling of Drill Bits Bingham (1965) suggested the correlation based on limited laboratory data K is the constant accounting for the rock strength a is the WOB exponent Modeling of Roller Cone Bit

Drilling Engineering Prepared by: Tan Nguyen Modeling of Drill Bits Bourgoyne and Young (1973) suggested a drilling rate model considering the effect of several drilling variables on rate of penetration. In this model, the effect of the parameters such as WOB, RPM, bit tooth wear and others were assumed to be independent of one another. Modeling of Roller Cone Bit

Drilling Engineering Prepared by: Tan Nguyen Modeling of Drill Bits Modeling of Roller Cone Bit

Drilling Engineering Prepared by: Tan Nguyen Modeling of Drill Bits Warren (1981) introduced an equation to calculate the ROP for roller cone bits that integrated the effects of the mechanical conditions such as RPM, WOB, rock strength, bit type and size through the verification with full-scale experimental data. He developed his model using dimensional analysis and generalized response curves for the best fit using laboratory data. The results have revealed that the generated rock volume by a single tooth is proportional to tooth force squared and inversely proportional to rock strength squared. His model was later modified by Hareland (1994) for taking into account the bit wear and chip hold down effects as presented below: Modeling of Roller Cone Bit

Drilling Engineering Prepared by: Tan Nguyen Modeling of Drill Bits Modeling of Roller Cone Bit

Drilling Engineering Prepared by: Tan Nguyen Modeling of Drill Bits In the above model, the chip hold down function characterizes the resultant force on the cuttings after they are generated by the bit and its integrated effect on ROP. Also, the wear function shows the effect of the bit wear on rate of penetration as presented below: ΔBG represents the bit wear as a function of WOB, RPM, confined rock strength and formation abrasiveness. The constant Cc which is called “bit wear coefficient” Modeling of Roller Cone Bit

Drilling Engineering Prepared by: Tan Nguyen Modeling of Drill Bits Warren and Sinor (1986) proposed a single PDC cutter model to predict some parameters such as cutter forces, cutter temperature and cutter wear. Their model was developed based on a thorough geometrical relationships which was tested and verified using different sets of 30 laboratory data. Modeling of PDC

Drilling Engineering Prepared by: Tan Nguyen Modeling of Drill Bits Another model was developed by Kuru and Wojtanowsicz (1988) for assessing the PDC bits’ performance using single cutter force analysis. The effect of the friction between the PDC cutters and the rock was considered in their study which made it different than the previous approaches and capable of predicting ROP, bit torque as well as the bit life. Modeling of PDC

Drilling Engineering Prepared by: Tan Nguyen Modeling of Drill Bits Hareland and Rampersad (1994) developed a model for predicting formation drillability of drag bits. It was derived based on the conservation of mass where the rate of cutting removal in front of the cutters is equivalent to the rate of penetration. The effect of the operational parameters was integrated on rate of penetration with proper consideration of geometrical relationship and rock failure criteria as shown below. Modeling of PDC

Drilling Engineering Prepared by: Tan Nguyen Modeling of Drill Bits Finally, the most recent ROP model for the PDC bits was developed by Motahari et al. (2008) which claimed to be working accurately in drilling performance Modeling of PDC

Drilling Engineering Prepared by: Tan Nguyen Modeling of Drill Bits Modeling of PDC