Carr WVU Marcellus Logging

Log AnalysisLog AnalysisUsing Microsoft ExcelUsing Microsoft Excel®®

Focus on the MarcellusFocus on the MarcellusTim Carr

West Virginia University

West Virginia University, November 17, 2010

My Observations


BackgroundBackground Costs Are Becoming More Significant

High Land Costs More Moderate Commodity Price High Capital Costs

Horizontal Wells & Large Multi-Stage Fracture Stimulations

Key Reservoir Parameters Thickness Unit Definitions (Formation Bed) Lithology Thermal Maturity Total Organic Carbon (TOC) Gas Fraction (Adsorbed and Free) Permeability


AVERAGE WELL HEAD PRICEAVERAGE WELL HEAD PRICE

$2.95 per MMBtu 2002





$4.04 per MM Btu on 11/16/2010

EIA (http://www.eia.gov )


Recent Growth in Natural Gas Production, Lower 48 States, Attributed Largely to Unconventional Gas

(EIA, 2010)

40

45

50

55

60

65

Billi

on C

ubic

Fee

t per

Day

Date

Katrina & Rita

9 % Growth Rate

0 % Growth Rate

40

45

50

55

60

65

Billi

on C

ubic

Fee

t per

Day

Date

Katrina & Rita

9 % Growth Rate

0 % Growth Rate


Natural Gas Supply by source, 1990-2030 (trillion cubic feet)

0

5

10

15

20

25

30

1990 1995 2000 2005 2010 2015 2020 2025 2030

Source: Energy Information Administration, Annual Energy Outlook 2009

Unconventional

AlaskaNet imports

Associated-dissolved

Non-associated offshore

Non-associated conventional

History Projection


Marcellus Shale Resource

1 Potential Gas Committee, June 18, 20092 U.S. Energy Information Administration3 Marcellus Proved Reserves < 1 Tcf

500

256

220

0

100

200

300

400

500

600

Tc

f

Ch

esap

eake

En

erg

y

Po

ten

tia

l G

as C

om

mit

tee

Ran

ge

Res

ou

rces

Marcellus Resource

U.S. Resources1

2,080 Tcf

U.S. Proved Reserves2

244 TcfMarcellus Shale Resource3

256 Tcf

Annual U.S. Consumption23 Tcf


Marcellus Shale Production Forecasts

Sources: “An Emerging Giant: Prospects and Economic Impacts of Developing the Marcellus Shale Natural Gas Play.” T. Considine, R. Watson, R. Entler, J. Sparks, The Pennsylvania State University, College of Earth & Mineral Sciences, Department of Energy and Mineral Engineering. July 24, 2009.

Integrated Resource Plan for Connecticut. The Brattle Group. January 1, 2010. (Wood Mackenzie)

0.5

2.1

4.5

0.5

2.9

3.9

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

2010 2015 2020

Bc

f/d

Wood Mackenzie

Penn State


Marcellus Shale Production Outlook

Source: Williams Partners L.P.

0

2

4

6

8

10

12

14

16

18

20

10

20

12

20

14

20

16

20

18

20

20

20

22

20

24

20

26

20

28

20

30

20

32

20

34

20

36

20

38

Bc

f /

d


Unconventional Resource ProductionTechnology, Economies of Scale, Integration


Unconventional Resource ProductionTechnology, Economies of Scale, Integration


Gas Shale CharacteristicsGas Shale Characteristics Very High Gamma Ray Activity (Kerogen Content)

High Uranium High Resistivity – Low Water Saturation Relatively Low Clay Content

Smectite to Illite Transition Low Bulk Density (Kerogen Content) Kerogen - Petrophysical Characteristics

Bulk Density 1.0 to 1.2 g/cm3 U 0.18 to 0.24 Neutron Porosity 50 to 65 p.u. Gamma Ray Activity 500 to 4000 API Sonic Slowness 160 µs/ft


Three ApproachesThree Approaches Logs to be used

Bulk Density g/cm3 Density Porosity Percent or Decimal Neutron Porosity Percent or Decimal Photo-Electric Barns Gamma Ray API Units

Clay Typing – Related to Deposition & Diagensis Spectral Gamma Ray Logs

Uranium (PPM), Thorium (PPM) and Potassium (Percent) Compositional Lithology Logs

Rhomaa-Umaa Computational Analysis (Linear)


SpreadsheetsSpreadsheets Ubiquitous and Low Cost Provide Some Hands-On Understanding of the Process Allow Easy Export to Higher End Packages Use Basic Logs Clay Typing

Estimate Uranium Content from Full Spectrum Gamma-Ray Logs

Compositional Lithology Logs Rhomaa-Umaa Computational Analysis (Linear)

Organic Content (Next Time) Saturation (Next Time)

Heavily Modified Archie


Gamma-Ray Log AnalysisGamma-Ray Log Analysis

U

K

Th


Gamma-Ray SpectrumGamma-Ray Spectrum

Uranium

Thorium


Gamma-Ray SpectrumGamma-Ray Spectrum

SchlumbergerLog Interpretation Principles1989, Page 3-7


Black Shale Composite (BSC) Quinby-Hunt et al. (1989)

Th 11.6 ppm, U 15.2 ppm, K 2.99%

GR = 215.8 API units

North American Shale Composite (NASC) Gromet et al. (1984)

Th 12.3 ppm, U 2.66 ppm, K 3.2%

GR = 121.7 API units

Geochemists’ concept of typical Geochemists’ concept of typical shale and black shaleshale and black shale

API unit multipliers: Th ppm 4 : U ppm 8 : K% 16


Typical Spectral Gamma-Ray Typical Spectral Gamma-Ray Log Presentation FormatLog Presentation Format


Potassium-Thorium Crossplot Potassium-Thorium Crossplot with with

Generalized Mineral Fields (after Schlumberger)Generalized Mineral Fields (after Schlumberger)


Potassium-Thorium Crossplot Potassium-Thorium Crossplot with with

Generalized Mineral Fields (after Schlumberger)Generalized Mineral Fields (after Schlumberger)

Increasing Th/K Ratio


Thorium and Uranium ConcentrationThorium and Uranium Concentrationandand

Redox Potential Redox Potential

Adams and Weaver (1958)


Gamma-Ray and

Spectral Ratio LogsPermian – Cretaceous

Central Kansas


Photo-Electric and Spectral Gamma RayPhoto-Electric and Spectral Gamma Ray

Schlumberger, Log Interpretation Principles 1989, Page 6-4


Photo-Electric and Spectral Gamma RayPhoto-Electric and Spectral Gamma Ray

Schlumberger, Log Interpretation Principles 1989, Page 6-4


Outside Shale

Upper Limestone

Core Shale

Middle Limestone

Outside Shale

Highstand - Regressive systems tract

exposure surface with paleosol

Condensed section (highstand)

Transgressive systems tract

flooding surface

Lowstand deposits (terrestrial)

Idealized Kansas Pennsylvanian CyclothemIdealized Kansas Pennsylvanian Cyclothem


Depth (feet)

0 150API unitsGamma Ray

SGRCGR

1400

-10 30Uranium ppm0 40Thorium ppm 0 10Potassium %

Th U

K

Springhill Ls

Vilas Sh

Captain Creek

Eudora Sh

Stoner Ls

Rock Lake Sh

Hickory CreekMerriam

0

Conoco Harrison #1 C-SW-NW 33-11S-10E Wabaunsee County, Kansas

Spectral Gamma-Ray Log Lansing Group, Wabaunsee County, Kansas


Chestnut Drive SectionChestnut Drive SectionSpectral Gamma Ray ResponseSpectral Gamma Ray Response


Devonian Shale AnalysisDevonian Shale Analysis0 200

Gamma Ray

200 400

Gamma Ray

3 0

Bulk Density

0 80

Uranium

HARRELL

TULLY

MAHANTANGO

MARCELLUS

ONONDAGA

ONONDAGA_BASE

67

05

67

20

67

35

67

50

67

65

67

80

67

95

68

10

68

25

68

40

68

55

68

70

68

85

69

00

69

15

69

30

69

45

69

60

69

75

69

90

LogDepth(ft)

LogDepth(ft)

6685 6685

6700 6700

6715 6715

6730 6730

6745 6745

6760 6760

6775 6775

6790 6790

6805 6805

6820 6820

6835 6835

6850 6850

6865 6865

6880 6880

6895 6895

6910 6910

6925 6925

6940 6940

6955 6955

6970 6970

6985 6985

API

API

PPM

G/CC

HS=1

PETRA 10/20/2009 12:03:52 AM (Type log Template.CSP)

Track 1 Track

HarrellHarrell

MahantangoMahantango

TullyTully

MarcellusMarcellus

OnondagaOnondaga


Devonian Shale: Devonian Shale: Oxidizing and Reducing ConditionsOxidizing and Reducing Conditions

• Reducing Vs. Oxidizing conditions determined by Th/U

Oxidizing


Devonian Shale: Clay TypeDevonian Shale: Clay Type

• Clay type can be determined from Th/K

•Illite-Pink Smectite-Green

•Illite can increase porosity by 4%


Wells 1 & 3Wells 1 & 3


Wells 1 & 3Wells 1 & 3


Well 2Well 2


Project 1Project 1

Make sure you open an LAS File with Notepad Import a LAS File to EXCEL

Well 3.LAS

Open Spectral Gamma Ray TemplateWell 1.LAS

• Marcellus (7375’-7562’)

Well 2.LAS• Marcellus (7359’-7501’)

Create & Examine PlotsWhat is the difference in the two wells

http://www.geo.wvu.edu/~tcarr/PTTC_11_2010


Open with NotepadOpen with Notepad


Importing a LAS File to EXCELImporting a LAS File to EXCEL







Introduction to Porosity LogsIntroduction to Porosity Logs

Porosity Logs DO NOT Directly Measure PorosityAcoustic (Sonic) Logs Measure Wave Travel

TimeDensity Logs Measure Formation Bulk DensityNeutron Logs Measure Formation Hydrogen

Content


Neutron Log ApplicationsNeutron Log Applications

Porosity Lithology with Density and/or Sonic Gas Indicator Clay Content Correlation Cased Hole


Neutron Tool BackgroundNeutron Tool Background Outgrowth of Work by Italian Physicists (1935)

Slowing down and stopping of neutrons by a hydrogen rich material (e.g., water). Radioactive Source of High Energy Neutrons

Americium and Beryllium Fairly Shallow Zone of Investigation

~ 6 inches (Flushed Zone (Rxo) in most cases) Neutrons lose energy each time they collide with nuclei as they travel through

the formation Greatest loss in energy when neutrons collide with nuclei of a similar mass

• Hydrogen atoms As the neutrons slow they can be captured and emit a gamma ray.

Reduction in Neutron Flux (Increased Gamma Rays) is largely controlled by concentration of hydrogen in the formation. Water (Oil) Filled Porosity in Flushed Zone of Clean Units Clays

Lithology Effect Hydrocarbon Gas Effect

Depress apparent neutron porosity


The Neutron The Neutron Porosity ToolPorosity Tool


Historical Development of Neutron LoggingHistorical Development of Neutron Logging

Common Curve Mnemonics ΦN, PHIN, NPHI Usually Tracks 2 or 3 and dashed line.

Units Counts %, Decimal Fraction


Neutron Energy LosesNeutron Energy Loses


Density Log ApplicationsDensity Log Applications

Porosity Lithology with PE, Neutron and/or Sonic Gas Indicator Synthetic Seismograms with Sonic Rock Properties with Sonic

Poisson’s Ratio, Young’s Modulus

Clay Content Borehole Conditions (Size and Rugosity)


Density Tool BackgroundDensity Tool Background Source of High Energy Gamma Rays

Cesium 137 Shallow Zone of Investigation

<2 inches Gamma rays interact with the electron clouds of the atoms they encounter, with a

reduction in the gamma ray flux, which is measured by both a near and far detector. Higher Energy Range Affected by Compton Scattering Reduction is a function of the electron density of the formation Number of Electrons Matched by the Number of Protons In Most Cases Z/A = 0.5

• Z - Atomic Number• A – Atomic Mass

Two Density Values Bulk Density (RhoB or ρb) – Measured by Logging Tool – Solid + Fluid

• DEN, ZDEN Matrix Density (ρma) – Density of the Rock that has no Porosity

Hydrocarbon Gas Effect Enhances apparent density porosity


The The Formation Formation Density ToolDensity Tool


Density PorosityDensity Porosity

ΦD = (ρma – ρb) / (ρma – ρfluid) DPHI, PHID, DPOR

Sandstone 2.644 gm/cm3

Limestone 2.710 gm/cm3

Dolomite 2.877 gm/cm3

Anhydrite 2.960 gm/cm3

Halite 2.040 gm/cm3

Freshwater 1.0 gm/cm3

Saltwater ~1.15 gm/cm3


Question

Why does ΦN read much higher Than ΦD in the red boxed area?

What are the general lithologiesin this well?


Photo Electric PPhoto Electric Pee Tool Tool Lithology with Density, Neutron and/or Sonic Supplementary Measurement of the Density Tool

1970’s Onward Lower Energy Range Gamma Rays Affected by Photoelectric

EffectLogged Value is a function of Z - Atomic Number

• Pe = (Z/10)3.6

• Barns per electron

Only mild affect of Pore Volume and Fluid/Gas ContentQuartz = 1.81 BarnsDolomite = 3.14 BarnsCalcite = 5.08 Barns

Pe, PE, PEF


Photoelectric factor logPhotoelectric factor log

0 5 10

10%

20%

30% porosity

coal

anhydrite

quartz dolomite calcite

kaolinitesmectite

illite chloritePe

barns/electron


Compositional AnalysisCompositional Analysis

Combing More Than Two Logs


Compositional AnalysisCompositional Analysis

Determine LithologyGraphic PlotsComputation

Identification and Semi-Quantitative Estimates


Porosity Log CombinationsPorosity Log Combinations

Single Porosity MeasurementLithology is Specified for Correct Porosity

• Choice of Matrix Value

Two Porosity Measurements Two Lithologies can be Predicted along with Porosity

Three Porosity Measurements Three Lithologies can be Predicted along with Porosity

Greater the number of Measurements the Greater the Complexity of the Lithology that can be Estimated


2 Logs

2 Minerals


Dolomitic-Limestone System


Three-Measurement Cross-Plot

Three Mineral Matrix Can Be Determined Usually Reduce From 3-D to 2-D

Collapse the 3 measurements to two axes with common denominator

M-N Plots Axis 1 – Sonic and DensityAxis 2 – Neutron and DensityProblem of Density and Sonic being Correlated

Addition of Pe in Newer Methods


M-N Cross Plot


M – N CrossplotM – N Crossplot Remove the effect of pore fluid

Usually drilling fluid

Combine Sonic and Density Logs (M)

M = (∆tfluid – ∆tmatrix) / (ρmatrix – ρfluid)

Combine Neutron and Density

N = (Φnfluid – Φn matrix) / (ρmatrix – ρfluid)


M-N Cross PlotM-N Cross Plot


RHOmaa – Umaa CrossplotRHOmaa – Umaa Crossplot

Mineral Identification (MID) Plots Apparent Matrix Density RHOmaa

Density and Neutron

Apparent Matrix Photoelectric Cross Section UmaaDensity, Neutron and Photoelectric Effect


Apparent Matrix Density Apparent Matrix Density RHOmaaRHOmaa


Photoelectric (PE) FactorPhotoelectric (PE) Factor

0 5 10

10%

20%

30% porosity

coal

anhydrite

quartz dolomite calcite

kaolinitesmectite

illite chloritePe

barns/electron


Volumetric Photoelectric Absorption Volumetric Photoelectric Absorption U/cmU/cm33

The photoelectric absorption index (Pe) is measured in units of barns per electron. In order to linearize its relation with composition, the variable must be converted to a volumetric photoelectric absorption index (U) with units of barns per cc

and is approximated by:


Volumetric Photoelectric Absorption Volumetric Photoelectric Absorption U of the matrixU of the matrix

This is the volumetric photoelectric absorption coefficient of the zone (matrix plus fluid). The hypothetical volumetric photoelectric absorption coefficient of the matrix is UMAA.

or approximated by


Umaa Values (Apparent Umaa Values (Apparent )𝜙)𝜙


RHOmaaRHOmaa

UmaaUmaa

PlotPlot

Pyrite


Shale CharacterizationShale Characterization


2 Logs

2 Minerals

Computational AnalysisComputational Analysis


Computational AnalysisComputational Analysis

CV=L

V = C-1L

C - matrix of the log responses of the components V - vector of the component proportions L - vector of the log readings

To Solve for V need the inverse of the componentmatrix


Log response equations:

Rewritten as matrices:


The compositional solution vector is then given by pre-multiplying the log response vector by the inverse of the coefficient matrix

We are Saved - Easily computed in Excel



Compositional Analysis


Project 2Project 2

Use Parameters From Appendix B Open Compositional Template

Load in Separate Template Well 1.LAS

• Marcellus (7375’-7562’)

• Onondaga (7562.5’ 7578’)

• Why are data points outside the Rhomaa-Umaa TriangleLoad in Separate Template Well 2.LAS

• Marcellus (7359’-7501’)

• Onondaga (7501.5’ 7516’)

• Why are data points outside the Rhomaa-Umaa Triangle Create Computational Plots

What is the difference in the two wells

http://www.geo.wvu.edu/~tcarr/PTTC_11_2010



My Observations



Tim CarrPhone: 304.293.9660Email: [email protected]

Documents

Carr WVU Marcellus Logging