Through this webinar I will show the workflow to integrate core and log data to generate Hydraulic Flow Units (HFU) using different methodologies; RQI/ FZI, Winland, and Pittman and implementing the Lorenz plot to define the HFU boundaries. Then, propagate those HFU in uncored intervals and wells. Finally, implement the results to construct Saturation Height Functions (SHF) from capillary pressure.

1. World Class Training Solutions

2. 2
Outline
• Brief Introduction to PetroTeach
• Introducing our Distinguished Instructor Professor Bahman Tohidi
• Introducing Course “ADVANCED PETROPHYSICS”
• Webinar Presentation (45 - 60 min.)
• Q&A (10 - 15 min.)

3. Introduction to PetroTeach
Reservoir............ 3
 Providing 150 training courses
 About 50 Distinguished Lecturers
 Online, Public and In-house Courses
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4. 4
Tuesday 1th – 16:00 GMT
Nightmare of Hydrate Blockage
Professor Bahman Tohidi
Wednesday 9th – 16:00 GMT
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Dr. Andrew Ross
Thursday 10th – 16:00 GMT
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Jerry Rusnak
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Professor Wumi Illedare
Thursday 3th – 16:00 GMT
Advanced Petrophysics
Mostafa Haggag

5. Advanced Petrophysics
Mostafa Haggag
2.09.2020
World Class Training Solutions
www.petro-teach.com

6. • B.Sc. in Geology, 1980, Very Good, Ain Shams University, Cairo, Egypt
• SPE Petroleum Professional Certificate, 2006
• MBA from Chifley Business School, Australia, 2013
• Has over thirty-eight years of experience in the oil industry, started with
Gulf of Suez Petroleum Co. as well site geologist and Petrophysics for
almost 15 years and at ADCO, UAE for 20 years as Professional
Petrophysicist.
• He successfully handle all Petrophysical activities for 20 years in a
professional manner; those activities include the operations and
interpretation on open hole logging for +/- 200 wells and production logs
(RST, PLT) for +/-100 wells.
• Handled and supervised many Petrophysical studies and published about
13 technical papers
• Mostafa was pointed as :
• ADCO’s Petrophysics Subject Matter Expert (SME),
• Petrophysics Career Ladder Committee Chairman for 6 years, and
• Petrophysics coach, and mentor, and verifier for new comers for more
than 15 years.
• ADCO’s focal point for data gathering cost optimization.
• SPWLA- A/D Chapter Technical Program Coordinator for more than
10 years
Mostafa Haggah
PetroTeach
Distingushed Instructor

7. Cased-Hole Logging with Approach to Logging Surveillance for EOR &
Productivity (online)
2 – 6 Nov. 2020
Register@petro-teach.com
train@petro-teach.com
• The five days course is a crucial for reservoir and petroleum engineers as well as Geologists and
Petrophysicits dealing with reservoir surveillance for reservoir development and enhancing oil
recovery projects. It will provide an overview on petrophysical knowledge to understand the EOR
process. It will tackle the log monitoring milestones before injection in context of ensuring well
integrity (cement and corrosion logs evaluation), and the strategy to select the logs for monitoring.
While injection; the logs ensuring injection efficiency (PLT) will be discussed, also the different
monitoring logs (Thermal decay time and porosity) evaluation and interpretation at well location
and between wells will be tackled. Case studies on logging surveillance for EOR will be presented
and discussed. The course will be conducted by group discussion, and Exercises and using of
Software.
• Learning Objectives
• Go through the petrophysical knowledge to understand the EOR process, and understand the strategy to
select the logs for monitoring.
• Tackle the log monitoring milestones before injection in context of ensuring well integrity (cement and
corrosion logs evaluation).
• Interpret the vertical and horizontal PLTs to evaluate the injection efficiency and zonal contribution.
• Evaluate and interpret the different monitoring logs (TDT and porosity) at well location and between wells.
• Demonstrate and discuss case studies on logging surveillance..
• Course price (Euro):
• Normal registration:1490+VAT
• 20% DISCOUNT for PhD students, Group (≥ 3 person) and early bird registrants (1 week before)
7

8. Advanced Petrophysics (online)
6 – 10 Feb. 2021
Register@petro-teach.com
train@petro-teach.com
• This course five-day is essential for reservoir engineers, reservoir geologists, and
Petrophysicists dealing with reservoir modeling. The course will provide the different
techniques to establish hydraulic flow units from core data. Also it will discuss the
different methods to predict the defined HFU for uncored wells and intervals. Then define
the saturation height function (SHF) for different HFU either on capillary based or on log
based.
• Learning Objectives
• Provide the different techniques to establish hydraulic flow units from core data; RQI, Winland R35,
Pittman.
• o Discuss and practice the different methods to propagate the defined HFU for uncored wells and
intervals. NN, Fuzzy Logic, MRL, SOM, Cluster Analysis, PCA, Contingency Tables.
• o Establish the saturation height function (SHF) for different HFU either on capillary or log based with
reconciliation with logs.
• Course price (Euro):
• Normal registration:1490+VAT
• 20% DISCOUNT for PhD students, Group (≥ 3 person) and early bird registrants (1 week before)
8

9. Through this webinar I will show the workflow to integrate core and log data to generate
Hydraulic Flow Units (HFU) using different methodologies; RQI/ FZI, Winland, and Pittman and
implementing the Lorenz plot to define the HFU boundaries. Then, propagate those HFU in
uncored intervals and wells. Finally, implement the results to construct Saturation Height
Functions (SHF) from capillary pressure.
ADVANCED PETROPHYSICS
Integration of Core and Log Data for Generating Hydraulic Flow
Units (HFU) and Saturation Height Function (SHF)

10. ADVANCED PETROPHYSICS
Integration of Core and Log Data for Generating Hydraulic
Flow Units (HFU) and Saturation Height Function (SHF)
Mostafa Haggag
3.09.2020
World Class Training Solutions
www.petro-teach.com

11. Course Overview
• This course is essential for reservoir engineers, reservoir geologists, and Petrophysicists dealing with
reservoir modeling.
• The course will provide the different techniques to establish hydraulic flow units from core data.
• Also, it will discuss the different methods to predict the defined HFU for uncored wells and intervals.
• Then define the saturation height function (SHF) for different HFU either on capillary based or on
log based.
Learning Objectives
The learning objective of the top-most level content of the course are:
• Provide the different techniques to establish hydraulic flow units from core data; RQI, Winland R35,
Pittman.
• Discuss and practice the different methods to propagate the defined HFU for uncored wells and
intervals. NN, Fuzzy Logic, MRL, SOM, Cluster Analysis, PCA, Contingency Tables.
• Establish the saturation height function (SHF) for different HFU either on capillary or log based with
reconciliation with logs

12. Course Contents
• Data Preparation and QC
Data Available Loading
Log Data Editing and Create Flags
Pc Data QC and Corrections
Data Visualization
• Generate HFU
RQI
Winland R35
Pittman
……
• Predicate HFU & Permeability
Principle Component Analysis
Multiple Linear Regression
Fuzzy Logic
Cluster Analysis
Self-Organizing Maps
Neural Network
Contingency Table
• Generate Saturation Height Modeling
Fitting and Smoothing
Averaging
Reconciliation with Log Data
Course Theme
Group Discussion, Exercises and Using of Software.

13. Data Preparation and QC

14. Data Available
Deliverables
• At Wells Location : Continuous
 From Logs : SW, Φ
 Predicted : HFU, K
• Between Wells : Saturation Height Function SHF
• Logs + Full Core
• Logs + Partially Cored
• Logs Only
• Log Data
 Raw : Den- Neu -GR-Res-Sonic
 Inter: PHIE- Sw-Volumes
• Core Data
 K– Φ - MICP – Core Description
Inputs
Sw
Φ
HFU
K
• Modeling Geo)
 HFU
 Φ
 K
• SHF (PPT)
 Sw
• Modeling Geo)
 HFU
 Φ
 K
• SHF (PPT)
 Sw
Sw
Φ
HFU
K
Sw
Φ
HFU
K

15. Data Preparation
1- Create Flags
• Location Flag
 North : 1
 Center : 2
 South : 3
• Data Quality
 Good Data: 1
 Bad Data :0
• Zones Flag
3- RCA QC
• Log- Core Depth Match
• Comparison between core and log Φ
• X-Plot K-Φ
2- Log Editing
• Log Depth Matching
• HC Correction
• Logs Normalization

16. Capillary Pressure Introduction
• Capillary Pressure is defined as the difference in pressure across a curved interface between two immiscible
fluids.
• Capillary Pressure is balance of
1. Wettability force up
o interfacial tension (dyne/cm)
2. Gravity force down
o height of oil column
1
2
= H(ρw-ρo)/144
(H: ft, Dens: lb/ft)

17. • Factors affecting the capillary pressure :
 Fluids: IFT, Density
 Rock: Pore size distribution, Mineralogy
 Rock / fluid interaction: Wettability, Contact angle
FWL
• Capillary Curve Main Components
Displacement/Entry / Threshold Pressure
o The pressure at which non-wetting phase starts entering the pore
network.
o Extrapolated displacement pressure is the pressure at which the
extrapolated plateau and zero non-wetting phase saturation lines
intersect. It determines the difference in height between the
OWC/GWC/GOC and the FWL
o Threshold pressure is defined as the pressure at which mercury
forms a connected pathway across the sample. This is estimated
from the inflection point of a graph like that
Plateau or Seat
Steep Slope
Transition zone
Irreducible water saturation, Swir

18. MICP
• MICP is used extensively for Sw height and rock typing
• High Pressure Mercury Injection (HPMI up to ~ 60,000 psi Hg Air)
• On trims, cheap
Capillary Pressure Data QC
• Criteria for good MICP data set to be used:
 Samples are well distrusted over the zone of interest
?
 Match between the log response & geological
descriptions and the cap. curve shape ?
 Is there a match between entry pressure, Φ, K and
core description ?
 Check the representative of the sample by
comparing the Φ of parent plug and Φ chip samples
 If there is big difference ; the sample should be
excluded due to heterogeneity (exclude the
outliers).
• The “good for use” MICP curve should have :
1. Complete measurements
2. Regular pressure increment
3. Acceptable trend?

19. MICP Data Conversion and Correction
1. Conversion from Laboratory) to Reservoir
𝐏𝐜, 𝐫𝐞𝐬=(𝛔 𝐂𝐎𝐒 𝜽)𝒓𝒆𝒔/((𝛔 𝐂𝐎𝐒 𝛉)𝒍𝒂𝒃) 𝐏𝐜, 𝐥𝐚𝐛
2. Closure Correction
The closure correction is run on all valid MICP Data
3. Stress Correction
• 𝐏𝐜, 𝐬𝐭𝐫𝐞𝐬𝐬 = 𝐏𝐜, 𝐥𝐚𝐛
∅ 𝒓𝒆𝒔
∅ 𝒍𝒂𝒃
−𝟎.𝟓
• 𝐒𝐰, 𝐬𝐭𝐫𝐞𝐬𝐬 = 𝐒𝐰, 𝐥𝐚𝐛
∅ 𝒓𝒆𝒔
∅ 𝒍𝒂𝒃
4. CBW Correction (Hg – Air)
Accounts for clay CBW eliminated from air-mercury tests

20. Data Visualization
MICP/
K
MICP /Φ
Core data/ Log data Overview Cap. Pres. Data Visualization

21. Generate HFU

22. Reservoir Rock Type (RRT)
Archie, 1950; rock typing is classifying reservoir rocks into distinct units:
• Deposited under similar conditions, and similar diagenetic processes.
• Unique porosity-permeability relationship, and similar capillary pressure profile
• Same water saturation for a given height above the free water level for each rock type
Hydraulic Flow Unit (HFU)
• The concept has been developed to identify and characterize rock types, based on
geological and physical parameters at pore scale.
• Ebanks et al., 1992: The HFU is defined as a mappable portion of the total reservoir and
affect the flow of fluids are consistent and predictably different from the properties of the
other reservoir rock volume
• Bear (2013) defined the hydraulic flow unit as the representative elementary volume of the
total reservoir rock within which geological and petrophysical properties are the same.
These properties are similar in the same flow unit but differ from one unit to another.
• Porosity and permeability are two key parameters that influence the flow in the reservoir.
They can be measured directly by core analysis.

23. HFU/RRTDetermination
Geological Based
Facies Analysis
Diagenesis
Sequence
Stratigraphy
RCA
Φ/ K/ Pc / TS
Petrophysical Based
Core data
Rock Fabric Number
RQI/ FZI
Winland/ Pittman
Plot
Graphic Methods
Stratigraphic Flow
Profile
Str.Mod. Lorenz
Mod. LorenzLog Data
Bulk Volume
Method
IntegrationofalltechniquestodefineRRTandflowunits
forstaticanddynamicmodel
By: Mostafa Haggag

24. Lorenz Plots
Graphical tools used to determine flow units are:
• These methods support an easy description of reservoir flow units established based on
storage capacity (ΦH), flow capacity (KH), the sorted data is then linearly accumulated
and normalized to a give a maximum value of 1.0.
• The main aim of understanding the flow unit’s characterizations is to identify the
barriers, speed zones and baffles.
• Used to define the boundaries of HFU with different techniques, FZI, R35……

25. Reservoir Quality Index (RQI) & Flow Zone Indicator (FZI) Concept
• Black dots show that the Φ parameter is the only factor
explains the permeability K (a=0 & m=1)
• Red and green data show a lot of scatter and some samples of
same porosity but different permeability values, i.e. porosity is
not the only parameter that can explain permeability variation.
• This can be attributed to the existence of more than one rock
type (HFU) in the reservoir, where each rock type has fluid flow
properties different from the other.
• So, grouping and identifying the rocks with similar fluid flow
properties will give better correlation, hence better reservoir
characterization and modeling
𝐥𝐨𝐠 𝑲 = 𝒂 + 𝐦Φ
LogK
Φ

26. • Kozeny (1927) and Carman (1937) developed an equation to estimate permeability:
𝑲 =
𝟏
𝑭 𝑺. 𝝉 𝟐. 𝑺 𝒗𝒈
𝟐
∗
𝚽 𝟑
(𝟏 − 𝚽) 𝟐
K : Permeability (µm2)
Fs : Pore shape factor
τ : Tortuosity of the flow path
Svg : Surface area/ unit grain volume
Φ : Effective porosity
• The term 𝑭 𝑺. 𝝉 𝟐
. 𝑺 𝒗𝒈
𝟐
is a function of the geological characteristics of porous media.it
varies with changes in pore geometry.
• The discrimination of this term is the basis of HFU classification technique.
• The term (𝑭 𝑺. 𝝉 𝟐
) is the Kozeny constant. It describes the shape and geometry of the
pore channels and varies between flow units, but is constant in a given unit.
Ref.: Application of hydraulic flow units’ approach for improving reservoir characterization and predicting permeability, Mostafa Khalid

27. • Amaefule et al. (1993) addressed the variability of the Kozeny constant by
dividing the equation by Φ and taking square root of both sides:
𝒌
𝚽
=
𝟏
𝑭 𝒔 ∗ 𝝉 ∗ 𝑺 𝑽𝒈𝒓
∗
𝚽
𝟏 − 𝚽
Ref.: Application of hydraulic flow units’ approach for improving reservoir characterization and predicting permeability, Mostafa Khalid
• Where Φz=
𝚽
𝟏−𝚽
: the ratio of pore volume to grain volume, normalized porosity
• If K is in mD, the reservoir quality index (RQI) parameter is:
RQI = π*10-2 *
𝒌
𝚽
(µm) = .0314
𝒌
𝚽
Where :
K: Air Perm.
Φ: Porosity
• The term
𝟏
𝑭 𝒔∗𝝉∗𝑺 𝑽𝒈𝒓
is the flow zone indicator ( FZI) which reveals the geological
attributes of texture and mineralogy in defining the HFU.
o Rocks with fine grains, poorly sorted, with clay (high surface area and high
tortuosity -----> low FZI
o Rocks with coarse grains, well sorted have lower surface area and lower
tortuosity ------> high FZI
RQI = FZI * Φz
K= 1014 * (FZI)2*
𝚽 𝟑
𝟏−𝚽 𝟐

28. RQI = FZI * Φz
• Log (RQI)= Log (Φz) + Log (FZI)
• A log/log plot will show the same flow unit on straight line with unit slope.
• Samples that have same FZI will be classified into the same Hydraulic Flow Unit
(HFU), the intercept with Φz =1 is the FZI value.
• Each unit has a similar pore geometry and rock textures (i.e. grain size, sorting,
diagenesis) which exhibiting a similar fluid flow characteristics

29. HFU by RQI/ FZI
X-Plot-----> Clustering
Lorenz-----> Boundaries
FZI-----> Value @ Φz=1
HFU

30. HFU by Winland Plot
• Winland tested 312 samples with 82 Carb. and SS with low K, he found that the effective por
system that dominants flow through a rock corresponds to mercury saturation of 35% .
• That pore system has pore throat radii (called port size, or R35, so the 35th percentile wa
taken to approximate the model class of pore throat size where the pore network become
interconnected forming a continuous fluid path through the sample i.e. effectively contribut
…. the rest of pores contribute in storage not in flow.
• log R35 = 0.732 + 0.588 log K air – 0.864 log Φ
R35 = 10 0.732 + 0.588 log K air– 0.864 log Φ

31. HFU by Pittman Plot
The Pittman method has 14 different equations. The user must first select the appropriate equation
for the rock type. The appropriate equation can be determined from ‘Apex’ plots of mercury injection
capillary pressure measurements
Log(R10) = 0.459 + 0.500 Log(K) - 0.385 Log (Φ)
Log(R15) = 0.333 + 0.509 Log(K) - 0.344 Log (Φ)
Log(R20) = 0.218 + 0.519 Log(K) - 0.303 Log (Φ)
Log(R25) = 0.204 + 0.531 Log(K) - 0.350 Log (Φ)
Log(R30) = 0.215 + 0.547 Log(K) - 0.420 Log (Φ)
Log(R35) = 0.255 + 0.565 Log(K) - 0.523 Log (Φ)
Log(R40) = 0.360 + 0.582 Log(K) - 0.680 Log (Φ)
Log(R45) = 0.609 + 0.608 Log(K) - 0.974 Log (Φ)
Log(R50) = 0.778 + 0.626 Log(K) - 1.205 Log (Φ)
Log(R55) = 0.948 + 0.632 Log(K) - 1.426 Log (Φ)
Log(R60) = 1.096 + 0.648 Log(K) - 1.666 Log (Φ)
Log(R65) = 1.372 + 0.643 Log(K) - 1.979 Log (Φ)
Log(R70) = 1.664 + 0.627 Log(K) - 2.314 Log (Φ)
Log(R75) = 1.880 + 0.609 Log(K) - 2.626 Log (Φ)
Avg. Apex @ Sw=40%

32. Lucia Rock Classes (RC)
• In carbonate
• Rock-Fabric Numbers (RFN)
• Classify the rocks into
 Class 1: Grainstone
 Class 2: Grain dominant
 Class 3: Mud dominant
𝐋𝐨𝐠 𝐊 = 𝟗. 𝟕𝟗𝟖𝟐 − 𝟏𝟐. 𝟎𝟖𝟑𝟖𝐋𝐨𝐠 𝐑𝐅𝐍 + (𝟖. 𝟔𝟕𝟏𝟏 − 𝟖. 𝟐𝟗𝟔𝟓 𝐥𝐨𝐠 𝐑𝐅𝐍 𝐋𝐨𝐠 (Φip))
K : Permeability Φip: Interparticle porosity RFN: Rock Fabric Number

33. HFU and K Prediction

34. Introduction
• Logs+ K&Φ + HFU from cored intervals ------> HFU & K @ uncored intervals
and wells .
• Use Statistical methods
o Multiple Linear Regression for Permeability
o Fuzzy Logic
o Cluster Analysis
o Neural Net Work
o Self Organizing Map
• The results should verified with contingency table for HFU and blind test

35. Principal Component Analysis
• This technique is useful in Petrophysics and Geology as a preliminary method of
combining multiple logs into a single or two logs without losing information. The PC
curves then can be used for various tasks like Multi-Well tops correlation and regression
analysis
• In example; 6 curves were input, the results show that only 2 curves PC1 and PC2 have
56.1 % and 24.6% of the total data variability. So, the 6 curves are reduced to 3 curves
without any loss of information.

36. Contingency Table
Table of the comparison between the input calibration curves data and the
output curves.

37. Permeability Prediction Work flow
R2
Transform +
Log Φ
Estimated K
Check with core K
OK
Est. K
Statistical methods
using available log &
core data
MLR
Fuzzy Logic
Neural Network
Others
Estimated KCheck with core K
K
Bad
Good
No
Yes
Yes
No
Empirical
Equations
K
Check with core K
Yes
Compute K
No
Logs
Core Data
By: Mostafa Haggag
1
2

38. 38
Permeability Estimation by Empirical Methods
• Core measurement is the only direct measurement for the permeability, any other
permeability value is just “estimated” and should be calibrated with core
measurements.
• Many techniques are used for permeability estimation :
 Porosity/ Permeability X-plot(equation)
 Empirical Equations From Logs ( for specific reservoirs)
o Wyllie and Rose (1950)
o Timur (1968)
o Coates and Dumanoir (1973)
 Permeability from NMR
o SDR (Schlumberger Doll Research)
o Timur/Coats
 Permeability from Formation Tester from mobility

39. Multiple Linear Regression - Permeability
Allows to predict a result curve from a number of input curves, using a least squares
regression routine, which will try and find the best fit to the input data.
• Create Regression Model to determine Formula coefficients
• Run Model to apply Formula to all wells selected
??

40. Cluster Analysis
The module works in two stages.
1- K-Mean Clustering
2- Cluster Consolidation
INPUTS
Cluster Means
Consolidation
Calibration
Results
Validation
• Contingency Table
• Blind test

41. Fuzzy Logic
• Fuzzy logic is the logic of partial truths
• Predict: Facies ,Permeability , Logs ..
• Use: Raw logs, Petrophysical results, Core results
• Two basic modes of prediction depending on input data
• Reproduces the dynamic range better than regression
• The Most Likely and 2nd Most Likely curves are ‘bins’ i.e.
they are stepped curves
• The weighted average is a smooth curve
INPUTS
Validation
Blind test
Model Build
Results

42. Neural Network
• Usually use several small intervals
• Training zones graphically selected
• Discrete data such as core data may require to
use longer intervals
Validation
Blind test
INPUTS

43. Train & Calibrate
Run Model
Self Organizing Maps (SOM)
• Uses a mathematical technique to enable data to be organized into groups
to produce a map. It is a form of neural network but are self trained
• The SOM is calibrated so it can be used to output either a facies type curve
(similar to the Cluster Analysis module) or to predict a continuous varying
curve like permeability.
Validation
• Blind test
• Contingency Table
INPUTS

44. Saturation Height Function

45. Capillary Pressure Implementation Workflow
1. Measurements
 QC
2. Corrections and conversions
 Lab to reservoir fluids
 Closure
 Stress
 Clay
3. Curve Fitting and Smoothing
4. Grouping and Averaging
5. Reconciliation with logs

46. Curve Fitting and Smoothing
• To produce a continuous curve from the
measured capillary pressure data some kind
of curve interpolation is necessary.
• Lambda is the first choice
• 𝐒𝐰 𝐰𝐞𝐭 = 𝐚. 𝐏𝐜−𝛌 +𝐛
Where:
a, b and λ are all regression constants
• The Lambda Function has been used to fit
curves through the 4 capillary pressure
datasets.
• The fit is excellent

47. Pc Grouping and Averaging
• The data is reduced by deriving average
cap. curves or saturation-height functions
for each RRT.
• There are a number of techniques for
averaging capillary curves data available
suitable for input to geological and
reservoir models.
• The comparison with the original data is
the real test of a saturation-height
function.
• If the comparison is excellent, then use
that function.

48. Range Method
• It is most suitable for reservoirs where no Φ/K trend can be
determined.
• The Range method defines the “likely”, "best" and "worst"
cap. curves from a set of cap. curves which represent the
reservoir of interest.
• On a capillary pressure versus saturation plot, all cap. curves
from the reservoir would plot between these two curves.
• Thus, they define the maximum and minimum saturations
(the range) at each pressure.
• The limiting curves can be done graphically by plotting all
cap. curves and selecting points at the boundaries.

49. Reconciliation with Logs

50. Reconciliation with Log Data

51. 51
• The Petrophysical parameters (m and n) were used for interpretation could be uncertain.
However, there is good match over most of the intervals in oil pool; hence this factor has
more effect on the intervals with higher water saturation and the transition zone.
***Conducted uncertainty analysis on Sw computation using Monte Carlo Technique
Parameter Used Value Uncertainty
a 1 +/- .1
m 2 +/- .2
n 2 +/- .2
Rw .015 +/- .03
RT +/- 10%
Φ +/- 10%

52. Thank You
Any Questions ?

53. 53
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