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Measuring Wellbore
Tortuosity
Pradeep AshokRAPID, The University of Texas at Austin
RAPID is an interdisciplinary group of researchers and students from multiple engineering disciplines (petroleum, mechanical, and aerospace) with these objectives and goals:
Background: RAPID
• deliver automation solutions for any and every aspect of well construction (drilling, cementing, completion / stimulation, production?)
• reducing drilling/completion time and cost by > 50%
• reducing the number of individuals at the rig site by > 50%.
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RAPID Sponsors
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RAPID Management Team
Dr. Mitch
PryorDr. Pradeep
AshokDr. Behcet Acikmese
(UW)
Dr. Eric van
Oort
Dr. Dongmei “Maggie”
Chen
Dr. Ali Karimi
(QRI)
External Advisors & Collaborators
Dr. John Foster
Dr. Roman Shor
(UoC)
Dr. Adrian Ambrus
(IRIS)
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Graduate Student Group (2017-2018)
Can PehlivanturkMechanical Engineering
Directional Drilling
Melissa LeeMechanical Engineering
Intelligent Mechanization:
Snubbing Automation
John D’AngeloMechanical Engineering
Tortuosity Index
Sercan GulPetroleum Engineering
Automated Rheology
Measurement
Abhinav SinhaMechanical Engineering
NOVOS
Katy HansonPetroleum Engineering
Isogeometric Analysis
Gurtej SainiPetroleum Engineering
Twinning
Parham PournazariMechanical Engineering
Machine Learning: Automated
Event Detection
Tim ChanCivil Engineering
HIL Simulator
Carolyn PowellPetroleum Engineering
Data Analytic: Drilling and
Completions
Qifan GuMechanical Engineering
Managed Pressure Drilling
Runqi HanPetroleum Engineering
Cuttings Sensor
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RAPID R&D Focus Areas
Automation control systems
Modeling, simulation, and
empirical validation in downhole
environments
(Real-Time) Monitoring,
data analytics, and “Big Data”
issues
Intelligent mechanization,
automation, and equipment
re-design
Undergraduate Programs
Drilling Automation Research
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Agenda
▪ Motivation
▪ Prior Work
▪ A New Tortuosity Metric
– Calculation Methodology
▪ Application
▪ Conclusions
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Agenda
▪ Motivation
▪ Prior Work
▪ A New Tortuosity Metric
– Calculation Methodology
▪ Application
▪ Conclusions
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Increased Well Cost Decreased Well Production
Motivation: Wellbore Quality Matters
Early detection and prevention of excess tortuosity is key.
▪ Significantly reduced hydrocarbon recovery
– Poor proppant displacement
– Inadequate fracture growth
– Fracture reorientation
▪ Stuck pipe /casing
▪ Poor zonal isolation
▪ Early tool failure
▪ Torque and drag
– Max. drillable length
$/bbl
$bbl
Agenda
▪ Motivation
▪ Prior Work
▪ A New Tortuosity Metric
– Calculation Methodology
▪ Application
▪ Conclusions
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Literature Review: Dogleg Severity
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DLS – is an estimate of the overall curvature of a well path between two consecutive survey stations
Literature Review: Friction Factor Back Calculation
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A method for quantifying wellbore tortuosity based on friction factors
𝑇𝐴 is the tortuosity of the as-drilled well, 𝑇𝑃 is the Planned Tortuosity of the well, 𝑇𝐿 is the Large-scale tortuosity of the as-drilled well, and 𝑇𝑀 is the Micro-Tortuosity of the as-drilled well
Gaynor et al. (2002)
Literature Review: Tortuosity Parameter
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Bang, et al., Analysis and Quantification of Wellbore Tortuosity, 2017
Where 𝑆 is the along-hole length of the wellbore section of interest, 𝐿 is the straight-line distance between the ends of the section
Effective Diameter is the maximum diameter of a straight cylinder that can be inserted into the section without distorting it (barely touching the inner walls)
Literature Review: Directional Difficulty Index
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Along Hole Displacement (AHD), Measured Depth (MD), and True Vertical Depth (TVD)
IADC/SPE 59196 THE DIRECTIONAL DIFFICULTY INDEX - A NEW APPROACH TO PERFORMANCE BENCHMARKING
Oag, Alistair W., and Mike Williams.
Literature Review: Strain Gauge DLS Index
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SPE -173039-MS
𝑅𝑐 =𝑀
𝐸𝐼
Where M is the bending moment associated with the curve, 𝐼 is the moment of inertia of the cylindrical pipe, and E is the pipe’s modulus of elasticity
𝑅𝑐
The dogleg severity of a curved well segment is a driving factor in the bending moment associated with the segment.
𝜃𝐷𝐿𝑆
Strain gauges measure bending moment at a particular location along the BHA. These measurements are used to approximate the DLS at that location. The difference between this approximation and the planned DLS provides a quantification of local tortuosity.
Chris Marland and Jeremy Greenwood
Literature Review: Elastic Energy Scaled Tortuosity Index
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SPE -151274
Scaled Tortuosity Index: The total elastic energy required to move the entire string to final depth
𝑑𝑙 is a small length of pipe, H() is Heaviside step function** applied to the derivative of bending moment 𝑀𝑙(𝐷𝑚) with respect to the measured depth from surface 𝐷𝑚. This is integrated from 0 to TD to determine the energy to move a particular segment from surface to TD, and integrated again to get this sum across all drill string segments.
**H(x) will equal x for x > 0, and 0 otherwise.
Sjoerd Brands and Ross Lowdon
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Literature Review Summary
Tortuosity MethodRequires Additional
Sensors
Local/Overall
Metric
Constant Curvature
Assumption
Requires High
Resolution Survey
Deviation From
Planned or Smooth
Path
Dogleg Severity No Overall Yes No Planned
Friction Factor Back
Calculation (FFBC)Yes Overall No No Planned
Tortuosity Parameter Yes Local No Yes Smooth
Elastic Energy and Scaled
Tortuosity IndexYes Overall No Yes Smooth
Strain Gauge DLS Index Yes Local No No Planned
Directional Difficulty Index No Overall Yes No N/A
Agenda
▪ Motivation
▪ Prior Work
▪ A New Tortuosity Metric
– Calculation Methodology
▪ Application
▪ Conclusions
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Desired Characteristics of a Tortuosity Metric
▪ Real time
▪ Three dimensional
– Delineate Azm. & Inc. tortuosity
▪ Holistic assessment of well path
▪ No additional sensors– Azm., Inc., and MD only
▪ Correlates with “Torque and Drag”
▪ Robust for varying survey intervals
▪ Planned versus unplanned tortuosity
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TI : Calculation
Grisan, E. et al., A Novel Method for Automatic Grading of Retinal Vessel Tortuosity, IEEE 2008
Hypertensive PatientHealthy Person
Veins in Human Eyeball Identifying Individual Turns in Tortuous Veins
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TI : Calculation
𝑛𝑖
𝑛𝑖+1
𝑇𝐼 =𝑛−1
𝑛
1
𝐿𝑐σ𝑖=1𝑛 (
𝐿𝑐𝑠𝑖
𝐿𝑥𝑠𝑖− 1)
𝑛 = Number of curve turns
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TI : Calculation
𝑇𝐼 =𝑛−1
𝑛
1
𝐿𝑐σ𝑖=1𝑛 (
𝐿𝑐𝑠𝑖
𝐿𝑥𝑠𝑖− 1)
𝐿𝑐𝑠𝑖 = Arc length of curve turn
𝐿𝑥𝑠𝑖 = Chord length of curve turn 𝐿𝑐𝑠𝑖
𝐿𝑥𝑠𝑖
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TI : Calculation
𝐿𝑐
𝑇𝐼 =𝑛−1
𝑛
1
𝐿𝑐σ𝑖=1𝑛 (
𝐿𝑐𝑠𝑖
𝐿𝑥𝑠𝑖− 1)
𝐿𝑐 = Total curve length*
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TI: Calculation
𝑇𝐼𝐼𝑛𝑐𝑙/𝐴𝑧𝑚 =𝑛−1
𝑛
1
𝐿𝑐σ𝑖=1𝑛 (
𝐿𝑐𝑠𝑖
𝐿𝑥𝑠𝑖− 1)
𝑇𝐼3𝐷 = 𝑇𝐼𝐼𝑛𝑐𝑙2 + 𝑇𝐼𝐴𝑧𝑚
2
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Variant of the Index
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Variant of the Index
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Variant of the Index
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Variant of the Index
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Preserving first curve turn:
Additional Tweak
𝑇𝐼 =𝑛−1
𝑛
1
𝐿𝑐σ𝑖=1𝑛 (
𝐿𝑐𝑠𝑖
𝐿𝑥𝑠𝑖− 1) 𝑇𝐼 =
𝑛
𝑛+1
1
𝐿𝑐σ𝑖=1𝑛 (
𝐿𝑐𝑠𝑖
𝐿𝑥𝑠𝑖− 1)
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Flowchart for Calculating 3D TI
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Start
Read directional file:Inc., Azm., M.D.
Calculate del Inc., del Azm., del M.D.
Azm.: 2nd
inflection point?
Inc.: 2nd
inflection point?
T.I. Inc. = prev. value
T.I. Azm. = prev. value
Calculate new T.I.
Inc.
Calculate new T.I. 3D
Calculate new T.I.
Azm.
NoNo
End of one cycle
Yes Yes
Lcs1
Lcs1
Lxs1
Case 1
Lxs1
Lcs3
Lxs3
Lcs2
Lxs2
Case 2
Case 3
Lcs1 Lxs1
TI Increase Along the Path
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▪ Tortuosity Index gives us a way to quantify the overall tortuosity of a well
▪ Need a way of filtering out “intended” tortuosity to better determine the directional drilling performance
Unplanned Tortuosity
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Comparison with Planned Trajectory
Agenda
▪ Motivation
▪ Prior Work
▪ A New Tortuosity Metric
– Calculation Methodology
▪ Application
▪ Conclusions
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Well 2
Two instances of motor failures @ 18,611ft., @16,003ft.
Black: low risk Blue: moderate riskRed: high risk
Blue: AzmBlack: IncPurple: 3D
Well 1
No instance reported
Note: scales are not the same
Higher T.I. Corresponds to Early Equipment Failures
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Well 3
No reported instances
Black: low risk Blue: moderate riskRed: high risk
Blue: AzmBlack: IncPurple: 3D
Higher T.I. Corresponds to Early Equipment Failures
Well 4
LWD failure @17,923ft, @15,277ft, experienced LWDtrouble @ 20,463ft, @15,080ft, @14,890ft, @11,667ft.
Note: scales are not the same
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Preliminary Analysis Summary (18 Wells)
Index No. of Wells
Comments Failure Rate
TI > 20 53 of which has documented multiple equipment (MWD, motor, etc.) failures in lateral section
60%
TI b/t10~20
3 1 of them has reports of MWD failure in lateral section 33%
TI <10 102 of which has a single report of MWD failure in lateral section
20%
Higher rate of equipment failures during drilling appears to be directly associated with high tortuosity index.
Tortuosity Index Case Study (Student Internship)
▪ Analyzed tortuosity index with three case studies
– Tortuosity Index and Production
– Tortuosity Index and Rod Pump Reliability
– Tortuosity Index and Drilling Efficiency
▪ Utilized data from over 6000 wells in one region
38 Courtesy: Hess Corporation
Tortuosity Index Case Study (Student Internship)
▪ Overall Results:
– As Tortuosity Index increases, the average drilling cycle time per sectionincreases
– As Tortuosity Index increases, the average rod pump failures per well increases
– As Tortuosity Index increases, the average initial production decreases
▪ A Tortuosity Index model could be used through a well’s planning, execution, completion and production stages, linking different engineering disciplines
39 Courtesy: Hess Corporation
Well 1Well 3
Well 2
Comparison with Drag Model: Stiff String
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Comparison with Drag Model: Stiff String
— Well 1— Well 2— Well 3
— Well 1— Well 2— Well 3
— Well 1— Well 2— Well 3
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Agenda
▪ Motivation
▪ Prior Work
▪ A New Tortuosity Metric
– Calculation Methodology
▪ Application
▪ Conclusions
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Conclusions
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▪ Real time
▪ Three dimensional
– Delineate Azm. & Inc. tortuosity
▪ Holistic assessment of well path
▪ No additional sensors– Azm., Inc., and MD only
▪ Correlates with “Torque and Drag”
▪ Robust for varying survey intervals
▪ Planned versus unplanned tortuosity
Acknowledgement
▪ Thanks to John D’ Angelo, Alex Zhou, Dandan Zheng, and the RAPID research team.
▪ Thanks to RAPID sponsors: Hess, ConocoPhillips, Apache, Pioneer for providing us with data for the analysis
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Thank You