NEED FOR BETTER KNOWLEDGE OF IN-SITU

UNCONFINED COMPRESSIVE STRENGTH OF

ROCK (UCS) TO IMPROVE ROCK

DRILLABILITY PREDICTION

V.C. KELESSIDIS

Technical University of Crete

Mineral Resources Engineering

AMIREG 2009

Athens, September 7-9, 2009

Research aim

�Optimization of drilling rates

� Less expensive and safer drilling practices

�Hydrocarbon, geothermal, mining, water well

drilling

�Multitude of parameters affecting drilling

performance – Rock Strength

�Availability of data and proper modeling

software

�Optimum combination ���� better drilling rates

22

The problem

�Drilling allows for access to subsurface target areas

� Pythagoras saying ‘whoever digs, finds, he who never digs, will never find’

�Drilling is expensive

Optimum drilling practice arrive to target �Optimum drilling practice � arrive to target in the most economical way, but with safety

�Main monitoring parameter – Penetration Rate (m/h)

�Depends on two main groups�Formation

�Drilling parameters33

Main parameters

FORMATION

� Local stresses

�Rock compaction

�Mineralogical content

�Fluid pore pressure�Fluid pore pressure

DRILLING

�Weight on bit and torque

�Rpm

�Hydraulic parameters

�Bit condition44

Modeling drilling process – Teale (1965)

�Rock-bit interaction

�Energy to the bit

�Efficiency of energy transfer

( )( )( )ROP

AWOBDRPM

A

WOBSE

bitt

/8 µ+=ENERGY PER

UNIT VOLUME

55

ROPASE

bitt +=

ROCK-BIT MODEL tSEeff

UCS=

UNIT VOLUME

( ) ( )( )

Abit

WOB

eff

UCS

AbitWOBDRPMROP

−

=/)(8 µ

UCS - measurements

�Laboratory� standard procedures (ISRM 1978, ASTM 1984)

� expensive & time consuming

� need core� need core

�Our analysis, petroleum & mining

�Reported data� Extremely large variability & for different rocks

�Does the name of the rock IMPLY rock strength ? 66

Rock classification, Tanaino (2005)

77

Data set of 1000 rock samples

Rock Strength – Factors affecting it

�Weathering � how to account for this ?

�Weak and Strong rock� definitions ?

�FACTORS INFLUENCING MAIN ISSUE�FACTORS INFLUENCING� poor cementation

� weathering

� tectonic disturbance

� porosity

� mineral composition

� particle size

� …88

MAIN ISSUE

GET FAIR

ESTIMATE OF UCS

How about CCS ?

99

Indirect UCS estimation

�Measurements time consuming & expensive

�Require core data

VARIETY OF ESTIMATION METHODS

�From cuttings, fair successes, LAG TIME !

�Schmidt hammer test

� Point load test

� Impact strength test

�Multitude studies, R2 ~ 0.40 to 0.90

1010

Require sample

Less expensive than Less expensive than UCS measurment

Still time consuming

Estimation of UCS from sonic data

�Non destructive

�Sonic velocity - APPLIED ONSITE !

�Use of ultrasonic pulses

�Speed of sound depends onSpeed of sound depends on� rock density, stiffness, mineralogy

� grain size

� weathering

� stress levels

� water absorption, water content

� temperature1111

Khaksar et al., SPE 121972�2009 research, Oil Well drilling

�derive Apparent Strength from porosity logs

�Aim, Use the logs to estimate UCS for improving drilling parameter values, wellbore stability, sanding

�26 correlations for sandstones�26 correlations for sandstones

� 11 correlations for shales

�7 correlations for carbonates

� Poor estimates, can be improved with data an better analysis techniques (fuzzy logic, pattern recognition) 1212

UCS from sonic data

�Equations of the form b

pVaUCS ⋅=

( )_

1

K

KUCSA =

1313

( ) 240_

K

ctUCSA

−∆=

pVeUCS

/035.0000,143

−⋅= Mc Nally

99.1170642.0 −⋅= pVUCS

1987

Data versus correlations !

USA dataUCS versus Sonic

Travel Time

Oyler et al., 2008

1414

Data from 10 wellsApparent Rock Strength from log data

Andrew et al., 2007

Predictions, Simple versus Complex�Zhou et al., 2005

�Use of all available geophysical log data

�3 wells

�2 techniques for data processing

� Plus McNally Equation

1515

�R2 ~ 0.62 to 0.75

� Poor prediction !

�Data, site specific !

�MEASURE & CALIBRATE

UCS – Vp - Data analysis�Various sources, 187 data sets, different rocks

�Variations > +/- 100% !

250

300

350S-Papanacli M-Papanacli

C-Papanacli Sharma & Singh

Kahraman Vogiatzi

McNally L-S1, Moradian & Behnia

L-S2, Moradian & Behnia L-S3, Moradian & Behnia

L-S4, Moradian & Behnia

1616

0

50

100

150

200

0 2000 4000 6000 8000

UC

S (

MP

a)

Sonic velocity (m/s)

L-S4, Moradian & Behnia

What is the impact of UCS errors ?

�With use of fairly accurate oil-well drilling simulator

�Uses UCS as main input parameter

�Can be tuned with real drilling data

�Once ‘tuned’ � evaluate different scenarios�Once ‘tuned’ � evaluate different scenarios

�E.g. effect of a higher UCS than the one used

�Tested on several wells - Example case here

�Shale, soft sand, hard sand

�SCENARIO: Increase of UCS by 50% (+/-100%)

1717

Increase in UCS by 50%

UCS+50%

1818

Increase may range between 58

and 96%, giving an overall

increase in total drilling time for

the sections chosen for the

simulation of 82%

UCS

Conclusions

�Need good drilling rate models

�Related to rock drillability (RD)

� In their absence, RD ~ UCS

There are standard procedures for UCS �There are standard procedures for UCS

measurements

�Time consuming, expensive, need cores

� Indirect methods exist

1919

Conclusions

� In situ estimation � sonic velocity

measurement

�Multitude of correlations, UCS – Vp

�Correlation coefficients LOW!, 0.50 to 0.70�Correlation coefficients LOW!, 0.50 to 0.70

� Inaccuracies in UCS estimation impacts

strongly rock drillability prediction

�Example case: Increase (error) of UCS by

50% � decrease in ROP by 82%

2020

Conclusions

�NEED methodologies for

�Better UCS estimates

�ROCK – BIT INTERACTION�ROCK – BIT INTERACTION

�They will help greatly drilling industry

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