measuring wellbore tortuosity - | iadd

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



▪ 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



▪ 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



▪ 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


𝐿𝑐𝑠𝑖


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


𝐿𝑐𝑠𝑖


𝐿𝑐 = Total curve length*

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TI: Calculation

𝑇𝐼𝐼𝑛𝑐𝑙/𝐴𝑧𝑚 =𝑛−1

𝑛

1


𝐿𝑐𝑠𝑖


𝑇𝐼3𝐷 = 𝑇𝐼𝐼𝑛𝑐𝑙2 + 𝑇𝐼𝐴𝑧𝑚

2

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Variant of the Index

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Preserving first curve turn:

Additional Tweak

𝑇𝐼 =𝑛−1

𝑛

1


𝐿𝑐𝑠𝑖

𝐿𝑥𝑠𝑖− 1) 𝑇𝐼 =

𝑛

𝑛+1

1


𝐿𝑐𝑠𝑖


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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



▪ 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



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Agenda

▪ Motivation

▪ Prior Work



▪ 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

measuring wellbore tortuosity - | iadd

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