Transcript Unconventional Petrophysical Analysis in Unconventional

Unconventional Petrophysical
Analysis in Unconventional
Reservoirs
Putting the Puzzle Together in Gas Shales
Lee Utley
“Intuitively, it is my belief that this
magnitude of money could be better spent
on other projects.”
Executive with Mitchell Energy in his
recommendation for attempting the
first completion in the Barnett Shale
‘discovery’ well (Slay #1) - 1982
“Why are we spending all this money to
find out how much gas is in the Barnett?
If we really want to know what will
happen in Johnson County, we just need to
drill some damn wells!
Engineering executive with Mitchell
Energy upon finding out the
magnitude of our planned spending
on coring and analysis to reevaluate
the gas content of the Barnett - 1999
Introduction
Has this happened to you?
Somebody just dumped some stuff in your office
Large stack of logs
Several CDs/DVDs of digital data
Core reports
Several maps and cross-sections
You are told that your company wants to get into this
Barnett Shale play everyone is talking about so
you need to figure this out.
Problems
General Goals
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Areal extent
Thickness
Type of hydrocarbon
Possible production mechanisms
Barriers to economic production
Evaluate the resource
Specific Goals to Achieve Using Log
Analysis
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Gas Content
Analysis of ‘conventional’ formations
Maturity
Total Organic Content
Porosity
Water saturation
Lithology
Rock Properties
Fracture types
Why is this so hard to do?
• Old logs with limited information
• Little or no core data
• Complex lithologies cause problems with
typical methods
• TOC calculation is difficult at best
• Porosity determination is complicated by
presence of TOC
Useful Core Data
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Geochemical analysis (Ro, TOC, etc…)
Porosity
Water saturation
Gas content (including adsorption isotherm
information)
• Mechanical properties
Gas Content
Gas Storage Sites
• Sorption – TOC
• Pore space
• Open natural fractures
Most gas is stored in the pore space and the
TOC. Fracture storage is usually minimal and
probably can’t be quantified.
Calculation of Gas Content
• For sorption, relate TOC to gas content –
usually through Langmuir parameters.
• Don’t forget about non-methane adsorption
• For pore space, use conventional gas-inplace equations.
TOC and porosity are two of the biggest keys
in looking at gas shales.
‘Conventional’ Analysis
Why look at ‘conventional’ areas
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Production pathways
‘Unfavorable’ porosity
Stimulation barriers
Uphole ‘bail-out’ zones
Maturity
Log Indicators of Maturity
• Resistivity
• Density – Neutron Separation
Use averages of these values in very well defined
geologically correlative areas to compare to core
vitrinite reflectance data.
Use resistivity as a predictor
(OGJ – Morel – 1999)
Use Old Resistivity Logs Too
• Use resistivity inversion modeling to get old ES
logs and induction logs up to modern standards –
compare apples to apples
1940’s
1980’s
Modern
Density – Neutron Separation
GasVitrinite
Shale Well
One
Lower
Reflectance
GasVitrinite
Shale Well
Two
Higher
Reflectance
TOC
Four main methods
• Use average TOC from published
accounts and apply it to every well
• Density log regression
• Delta log R
• Passey, et al – AAPG 1990
• Neural Networks
Porosity
Standard Porosity Transform
 matrix   log

 matrix   fluid
• Core matrix numbers exclude organic material.
• Normal log presentations show very high apparent
porosities. These porosities are closer to the
volume of pore space and organic material
combined.
Basic Porosity Equation
log   matrix1      fluid
Porosity Equation with TOC
log   matrix1    VTOC    fluid  TOCVTOC
Solved for Porosity
  matrixTOC
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 matrix   log 
 TOC  1
TOC
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 matrix   fluid
 matrix  2.60 to 2.68 gm / cc
TOC  1.3 to 1.4 gm / cc
 fluid  0.4 to 1.0 gm / cc
Water Saturation
What are the correct parameters?
Sw 
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n
aR w
m
 Rt
Pickett Plot
Calculate Water Saturation
Lithology
Two most common methods
• Probabilistic methodology
• Integrated neural network solution
Neural Network Solution
Rock Properties
Standard Rock Mechanic Equations
Use Lithology to Correlate with
Rock Properties
Rock Properties Computed
Young’s Modulus
Neural Network of Young’s Modulus in Two Permian Basin wells using
a Fort Worth Basin Model
Neural Network Computed Young’s Modulus
Fractures
Imaging Logs
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Fracture Size
Direction(s)
Complexity
Open/Closed
Induced fracture direction (stress field)
Barnett Shale Case Study
Core Data Acquired
Conventional and pressure cores – Extensive data
suite
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Porosity
Water Saturation
TOC
XRD
Canister desorption
Adsorption isotherms
Capillary pressures
CEC
Integrate Core Data
Quartz Plagioclase Calcite Dolomite Apatite Pyrite
Total
Total Organic Porosity
Clays
Carbon
Water
Bulk Volume Bulk Volume
Saturation
Water
Hydrocarbon
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1
10
6
0
0
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6
8
54
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37
4
13
3
1
0
29
5
7
46
3
4
32
2
20
3
1
0
29
5
8
50
4
4
23
2
46
4
1
1
22
1
0
76
0
0
13
1
41
20
0
0
18
3
4
37
1
2
12
2
61
17
1
1
4
1
2
32
1
1
23
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33
4
0
0
30
3
4
56
2
2
10
1
74
10
0
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3
0
1
67
1
0
30
0
26
8
0
0
33
1
3
89
2
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16
1
23
13
0
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40
2
5
75
3
1
31
3
11
3
1
0
42
4
5
70
3
1
34
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17
12
1
1
23
4
5
74
4
1
15
1
15
41
1
1
24
1
1
79
1
0
35
5
8
4
1
1
39
5
2
100
2
0
Train a Volumetric Neural Network
Apply integrated solution to all wells
Fort Worth Model Applied to
Permian Basin Well
Comparison
Conclusions
Gas shales can be effectively
analyzed
• Maturity, TOC, and porosity are some of
the keys to gas shale analysis and can be
determined from logs.
• Even without extensive core data, gas
shales can still be analyzed, at least in a
relative sense.
• Other gas shales can be evaluated from log
data and core data using these techniques.
An integrated study is required for full
evaluation.
Unconventional Petrophysical
Analysis in Unconventional
Reservoirs
Putting the Puzzle Together in Gas Shales
Lee Utley