An overview of the basic well log interpretation methods

1. WELL LOG INTERPRETATION
Presented by: ADEMOLA SORUNGBE
October 22nd, 2019

2. PRESENTATION OUTLINE
 What is Well Log Interpretation?
 Historical Background
 Origin of the term “Petrophysics”
 Importance of Well Log Interpretation
 Well Log Interpretation
 Case Study 1
 Case Study 2
 Conclusion 2

3. WHAT IS WELL LOG
INTERPRETATION?
3

4.  Well log interpretation is the use of well log
data to estimate various reservoir properties
 Interpretation of well logs will reveal both
the mineralogical and proportion of solid
constituents of the rock (i.e. grains, matrix
and cement), and the nature and
proportions (porosity, saturations) of the
interstitial fluids (O. Serra, 1984)
 They are also key instruments in well
productivity assessment
4

5. Source: A Numerical Model of the Kizildere Geothermal Field, Turkey
(S.K. Garg et. Al, 2015)
Kizildere Oil Field, Denizli
Province, Turkey
5
 Well log interpretation is primarily aimed at
quantitative characterization of subsurface
reservoirs
Injectors
Producers

6. 6
 Well log analysts are the
investigators
 Well logs are the evidence

7. HISTORICAL
BACKGROUND OF
WELL LOG
INTERPRETATION
7

8. 8
The original geophysical
logging equipment used by
the Schlumberger brothers
in the late 1920's
Source: Schlumberger, 2000
 In 1927 the first electrical resistivity well log was acquired in France
 At this time well logs were only qualitative indicators of hydrocarbon presence

9.  Well log interpretation is as old as the
research of the “father of petrophysics” –
Gustavus Archie
 BS Mining Engineering (1931), Combined
MS in Mining Engineering and Geology
(1933)
 Joined SHELL (Kansas) in 1934
 His research focused on transforming the
then Schlumberger resistivity log to a
quantitative tool 9

10.  Young Archie was assigned the task of examining cuttings and electric
cores, before being transferred to the Texas Gulf area in 1938
 Archie undertook a systematic investigation of every existing Shell
Texas Gulf area electric log together with its companion core analysis,
mud log, and test data (E.C. Thomas, 2018)
 Archie’s work was aimed at solving one of the most serious problems
of the early 1940’s, that of obtaining porosity, permeability and
hydrocarbon saturation from electric log responses correlated and
calibrated to core measurements (E.C. Thomas, 2018)
10

11.  He played the key role in identification of producible horizons at the
giant Elk City Field in Oklahoma (1947)
 An episode which dramatically demonstrated for the first time the role
that well log measurements could play in identifying pay zones
(www.wiki.seg.com)
11
Archie’s Breakthrough

12.  SHELL was drilling a deep well (Walter 1) targeting the Springer sands
at 12000ft
 Cuttings and electric log indicated no hydrocarbon
 The shallower Granite Wash zone was cased and drillstem tests
showed no producible hydrocarbon in the deeper Springer sands
 Tulsa office requested permission from Houston to plug and
abandon, but met resistance from the then VP of Shell
12
Archie’s Breakthrough

13.  Archie had been analyzing the electric logs at the Tulsa office where
he plotted the RT and SP logs, observing a consistent trend except for
one zone (i.e. the Granite Wash zone)
 He convinced the VP against the wish of his colleagues at Tulsa to
test the Granite Wash zone, arguing that light hydrocarbon may not
show noticeable fluorescence
 Archie was right, but he lost his hat; a small price to pay for
discovering the 110-million BOE Elk City Field, which later
supported a 20-rig drilling program (E.R. Shorey, Jr.,1992) 13
Archie’s Breakthrough

14.  In September 1949, Gus presented before the Houston Geological
Society, and later published in the Bulletin of the American
Association of Petroleum Geologists, the paper which forever married
geology and physics: “Introduction to Petrophysics of Reservoir
Rocks” (Archie, 1950)
 In this seminal work, he introduced the term petrophysics to express
the physics of rocks
 The word itself had been coined earlier in discussions about the
subject with Gus’ counterpart with SHELL, J.H.M.A. Thomeer
14
Origin of the term “PETROPHYSICS”

15. 15
Well Log Interpretation: An interdisciplinary tool
Geomechanics
Petrophysics
Reservoir
Engr.
Production
Engr.
Drilling
Engr.
Geology Geophysics
RESERVOIR
CHARACTERIZATION

16. 16
 Reservoir characterization is the process of preparing a quantitative
representation of a reservoir using data from a variety of sources
and disciplines (www.sciencedirect.com)
 It includes:
 Reservoir mapping (seismic and lithostratigraphic)
 Fluid typing/contact delineation
 Rock property determination (e.g. porosity, permeability, clay volume)
 Fluid property analysis (e.g. fluid viscosity, formation volume factor)
 Pressure estimation
 Etc.

17. 17
 Well logs are also used in reservoir and well performance
monitoring
 Which includes:
 Identifying flow profiles
 Well diagnostics
 Assessing treatment effectiveness
 Time lapse assessment (contact movement, saturation change)
 Etc.

18. 18
WELL LOG
INTERPRETATION

19. BASIC
WORK
FLOW
19
Load data
Define log cutoffs
Generate a summary report
Fluid Typing
Calculate Volume of Shale Calculate Porosity
Calculate Saturation
Data Check
View/Edit Data

20. Data Check
20
 Review the logs available (soft and hardcopy). Soft copy logs are mainly
in .las and .ascii formats
 Look through well log header and take note of relevant information
 Digitize hardcopy logs in the event of missing digital logs
 Prepare log availability matrix table for all the wells to assess evaluability

21. Log and Data Availability table
21

22. 22
 Digital logs are loaded
into the available
interpretation software
package e.g. Techlog, IP,
Geolog, PowerBench etc.
 Some softwares are now
integrated and can do
more than just basic well
log interpretation
Source: https://www.academia.edu : Schlumberger Techlog Manual
Import
window
Project
window
Data Import

23. 23
 Depth Shifting
 Removal of End Effects
 Rescaling
 Splicing
 Fill Gaps
 Value Editing
 Patching
 Before any log interpretation,
detailed log QC should
always be done
 The main purpose of well
log editing is to prepare the
well data for interpretation
Well Log Editing

24. Depth Shifting
24
 It is the process of aligning a log to
a common depth with respect to a
reference log (usually GR or RES)
 2 Methods of Depth Shifting
 Bulk depth shift
 Multiple tie line depth shift
 Core PHI and K are also shifted
where needed
Source: Well Log Data Processing by Shoaib Aamir Fahim

25. Removal of End Effect
25
 In some cases, the
logging tool records data
from the casing shoe or
spikes associated with
the first or last tool
reading
 These spikes are not
associated with lithology
Source: Well Log Data Processing by Shoaib Aamir Fahim

26. Removal of End Effect
26
End Effects
False indication of
evaporite

27. Rescaling
27
 Allows for correction of improper calibration, missed scale changes
of digitized logs, neutron count conversion, linear to logarithmic
conversion, etc.
Patching
 The patch curves editing is used to remove unwanted data points
such as noise spikes, and to reshape curves
 Editing of sonic for cycle skipping and density for any borehole
washout

28. Splicing
28
 This is useful for merging curves from different logging runs into a
single composite curve
Fill Gaps
 Fill Gap is used to replace nulls with values interpolated between
valid data points
 Typically the gaps are not more than 2ft

29. Fluid Typing
30
GAS-OIL
CONTACT
OIL-WATER
CONTACT
LIGHT
HYDROCARBON
EFFECT

30. Fluid distribution plots
31
 Fluid distribution stick
plots are diagrammatic
representations of the
lateral and vertical spread
of the fluids seen by each
well
 Fluid contacts are
extremely important tools
for contact analysis

31. Fluid distribution plots
32
 Stick plots of pre-production wells are used in selecting contacts for
HIIP volumetric in each reservoir
 Used by production technologists when choosing re-perforation
opportunities in collaboration with RST logs
 A plot of cumulative production versus contacts is sometimes used
to predict contact movement with production

32. 33
ROCK
PROPERTY
ESTIMATION

33. 34
What are Shales?
Shale Volume Estimation

34. 35
What are Shales?  Shales are ROCKS!
Rock classification on the basis of particle size

35. 36
What are Shales?  Shales are ROCKS!
Rock classification on the basis of particle size
 70% clay sized particles and 30% silt sized particles
 Clay particles – Clay minerals and micas
 Silt particles – Quartz and feldspars

36. 38
Why are we
interested in Shales?

37. 39
 Our interest in shales is for the most part indirect
Why are we
interested in Shales?

38. 40
 Our interest in shales is for the most part indirect
 The effect of clay minerals on log readings and pore interconnectivity
is our main interest
Why are we
interested in Shales?

39. 41
 Our interest in shales is for the most part indirect
 The effect of clay minerals on log readings and pore interconnectivity
is our main interest
 Shale beds are also important to us in net sand count
Why are we
interested in Shales?

40. 42
What are we really
estimating? Shale Volume
or Clay Volume?

41. 43
What are we really
estimating? Shale Volume
or Clay Volume?
 GR logs respond to clays

42. 44
What are we really
estimating? Shale Volume
or Clay Volume?
 GR logs respond to clays
 An increase in Density and
Neutron typically means an
increase in clay content

43. 45
What are we really
estimating? Shale Volume
or Clay Volume?
 GR logs respond to clays
 An increase in Density and
Neutron typically means an
increase in clay content
 Silt is fine grained QUARTZ and variations in log measurements
is caused by the occurrence of clay minerals and micas

44. 46
Shale/Clay
Occurrence
Pore filling
Pore lining
Pore bridging
Pore filling
kaolinite booklet
Clay laminae
Quartz grain
Filamentous illite
Sources: www.spec2000.net
www.webmineral.com

45. Computation (Neutron-Density)
49
Matrix
Parameter
Shale
Parameter

46. Computation (GR)
50
Assumed 100% shale
parameter GRsh
Assumed 0% shale
parameter GRclean

47. 51
Porosity Estimation
 Total Porosity (PHIT): Ratio of pore volume to bulk volume
i.e. Volume occupied by Free Fluid + Clay bound water + Capillary Bound
Water/Irreducible Water + Isolated pore fluids + micro-porosities in organic
matter
 Effective Porosity (PHIE): Portion of the total porosity available for
fluid flow
 Secondary Porosity: Porosities that developed after burial and
compaction e.g. fractures, vugs, etc.

48. 52
What exactly is
Total and Effective
Porosity?

49. 53
What exactly
is Total and
Effective
Porosity?

50. 54
What exactly
is Total and
Effective
Porosity?
Source: Development in Petroleum
Science Vol. 65: Physical Properties of
Rocks
Source: www.epgeology.com

51. 55
What exactly
is Total and
Effective
Porosity?
Source: Crain’s Petrophysics www.spec2000.net

52. 56
What exactly
is Total and
Effective
Porosity?
 Total and Effective porosity varies with the
“measuring instrument”
 PHIT from Neutron differs from that from
density log
 PHIT measurements from core require special
cleaning and drying techniques to avoid the
collapse of the clay crystals
 Proper core measurements is the ground truth,
but with minimal depth coverage

53. 57
What exactly
is Total and
Effective
Porosity?
 There is no final position on what constitutes
effective porosity
 This depends once again on the method of
measurement
 But, if residual hydrocarbon forms part of the
effective porosity, then Swir/Capillary bound
water should

54. 58
PHIE = PHIT * (1 – VSH)
PHIE = PHIT – VSH/PHITsh
PHIE = PHIT – VCL/PHITcl
i.e. PHIT - CBW
Computation (Density)
 Of all the basic logs, density log is the most accurate in estimating total
porosity
 Secondary porosity can be estimated by subtracting sonic porosity from
density porosity

55. 59
Computation (Sonic)

56. 60
 Permeability is a measure of a rocks’ ability to transmit fluid/gas
 It is dependent on a rock’s effective porosity and also on 2 facies
dependent variables (pore throat size and distribution)
 There are several porosity dependent empirical models that attempt
to predict permeability e.g. Coates, Timur, Wyllie and Rose, Morris-
Briggs, etc.
 These models do not account for the effect of pore throat sizes and
distribution
Permeability Prediction

57. 61
 Results from these models can be calibrated to accurate
measurements from core or well test
Log-based Permeability

58. 62
Facies-based Permeability (FZI)
 Proposed by Amaefule et al (1993). Core and Log data identify flow
units and predict permeability in uncored intervals
 FZI values determined from core analysis data (poro, perm) is
used to identify appropriate FZI values for each facies class
 FZI values are assigned to each defined facies class along the well

59. 63
Facies-based Permeability (FZI)

60. 64
Facies-based Permeability (Poro-Perm)

61. 65
Water Saturation Estimation
Archie’s Equation Where:
Rw = water resistivity
Rt = true resistivity deep
 = porosity
m = cementation exponent
n = cementation exponentRw – Pickett plot or produced water analysis
m, n – Core analysis report or empirical relationships

62. 66
Shaly Sand Empirical Models
 At the start few empirical
equations were developed
 These applied to specific regions
 They used effective porosity (φe)
 They used parameters for clay,
such as Vcl and Rcl

63. 67
Shaly Sand Excess Conductivity Models
 Three equations were
developed independently by
Shell and Schlumberger to
account for the conductivity
of the shale
 These equations are based
on theory and hence have
more universal applicationsSource: http://www.nexttraining.net

64. 68
Petrophysical Property Summation
Cutoff Sensitivity Analysis

65. 69
Cutoff Logplot

66. 70
Summary Table

67. 71
CASE STUDY 1

68. 72
Analyzing Well Logs From the Montoya Lime Using a New Carbonate
Well Log Interpretation Procedure by Walsh, Brown, and Asquith
 In this study, they tried to account for the effect on pore type
(intercrystalline, bimodal, fracture, or vug) on cementation factor (m)
 Crossplots of well logs were used to determine the dominant porosity
types along a reservoir of interest (60ft low porosity Montoya Lime)
 The type of porosity determined their choice of variable m equation
that was used for Sw calculation
 @m=2 >>SwAR 80-100%

69. 73
Variable “m” Empirical Relationships
Intergranular/intercrystalline porosity
Vugs
Fractures
Bimodal porosity

70. 74
Pore Type Crossplots
PHIT_S vs PHIT_D
PHIT_Rs vs PHIT_D
M vs N
Rs/Rz vs Rt/Rw
SW_AR vs SW_Ratio
 Depending on where the points cluster, each
interval analyzed can be classified on the
basis on its predominant porosity type
 The final choice is based on results from all
the plots
 The authors noted that in carbonates, there is
a tendency for pore type to change vertically
along the same reservoir

71. 75
Montoya Lime Example
 Lithology: Limestone (confirmed by N/D and M-N crossplot)
 Porosity: 2 to 4% (from N/D)
 SwT: 80 – 100% (assuming constant m value of 2)
 Pore Type: Intercrystalline (only SWT_arch vs SWT_ratio indicated
fracture porosity)
 SwT: 40 – 65% (using pore type dependent variable m). A productive
zone would have been by-passed

72. 76
CASE STUDY 2

73. 77
Hydrocarbon Effect Correction on Porosity Calculation from Density
Neutron Logs using Volume of Shale in Niger Delta by Anyaehie and
Olanrewaju (SPDC Nigeria)
 The authors developed a method for the estimation of porosity that
does not depend on fluid density
 Fluid density depends on fluid composition, mud property, and
invasion profile
 The proposed method is a modification of the well known 1/3, 2/3
method

74. 78
Background
 In a clean water bearing zone, NPHI should read same as PHI_D
since NPHI is calibrated to water
 In a clean hydrocarbon bearing interval, the fluid alone is responsible
for a deviation from the above scenario
 This deviation from norm does not always occur in the same
proportion; especially in light hydrocarbon sands
 Therefore, it is possible to correct for this effect if the right
proportion can be established

75. 80
By combining the NPHI and
DPHI at a 50:50 proportion,
they arrived at the same average
porosity value of the core
acquired within this interval
 The authors tested
this “proportion
idea” in an oil
bearing zone with
core porosity for
reference
PHIToil = 0.5(0.16) + 0.5(0.29)

76. 81
 50:50 proportion was observed to work fine for clean oil zones
 However, the proportion had to be adjusted for the light
hydrocarbon zones due to the variability in the effect of gas/light oil
on the N/D logs compared to oil
PHITcorr = 1/3((PHIT_D)(2+VSH)+NPHI(1-VSH)) – For Gas Zones
PHITcorr = 0.5((PHIT_D)(1+VSH)+NPHI(1-VSH)) - For Oil Zones
 The 1/3:2/3 method does not give good results in shaly zones,
therefore the authors introduced the shale correction factor

77. 82
Comparison of proposed method with existing methods
Proposed method giving the best
result

78. 83
CONCLUSION

79. 84
In Conclusion
 Well log interpretation is the basis of any formation evaluation and
reservoir characterization exercise
 The use of well logs as an interpretation tool cuts across several
disciplines
 A good understanding of the operating principles of well logging
tools and the geologic interpretation of well logs is important for
proper well log interpretation

80. References
1. Development in Petroleum Science 15A: Fundamentals of well logging interpretation by O.
Serra 1984
2. SPWLA Today Newsletter. Issue 4. Vol. 1. September 2018
3. Enhanced Reservoir Description: Using Core and Log Data to Identify Hydraulic (Flow) Units
and Predict Permeability in Uncored Intervals/Wells
4. www.wiki.seg.com
5. https://www.academia.edu : Schlumberger Techlog Manual
6. Well Log Data Processing by Shoaib Aamir Fahim
7. Pettijohn, F.J. (1975) Sedimentary Rocks. 2nd Edition, Harper and Row Publishers, New York,
628 p.
8. http://www.nexttraining.net
9. Analyzing Well Logs From the Montoya Lime Using a New Carbonate Well Log Interpretation
Procedure by J.W. Walsh and S.L. Brown, The Logic Group, and G.B. Asquith, Texas Tech U.
(1994)
10. Hydrocarbon Effect Correction on Porosity Calculation from Density Neutron Logs using
Volume of Shale in Niger Delta by Anyaehie and Olanrewaju (SPDC Nigeria) (2010) 85

81. 86