Predicting Shale Production with Integrated Multi-Variate Statistics

Murray Roth

Transform Software and Services, Inc.

Barnett Overview

Barnett Stresses

Max Stress

Source: Texas RRC/Humble Geochemical Services

Barnett West-East Cross-section

Pollastro USGS

Barnett Stratigraphy

Source: USGS

Barnett Details

Geologic Age Mississippian

Lithology Siliceous Mudstone

Total Area Size (sq mi) 50000

Total Gas (tcf) 327

GIP (bcf/sq mi) 150

Producable Gas (tcf) 50

Depth (feet) 7500

Thickness (feet) 300

Hor Well Cost ($M) 2.8

Average EUR 2.65

Pressure (psi) 4000

Temperature (F) 200

Ro 2

TOC (%) 4.5

Porosity (%) 6

Matrix Permeability (nD) 250

Pressure Gradient (psi/ft) 0.526

Clay Content (%) 45

Adsorbed Gas (%) 35

300’

Courtesy: Devon

Most and Least Crackable Rocks Young’s Modulus versus Poisson’s Ratio

Relative Well EUR Distribution

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10-fold range in EUR!

~Economic Threshold At $4/mcf

But how do you Optimize? • Horizontal well lengths – 900-3700 ft?

• Number of frac stages – 1 -7?

• Horizontal well azimuth?

• Fracture parameters • Slick water

• Fluid volume

• Fluid rate

• Pressure

• Proppant volume??

• Location • Porosity?

• Facies/Lithology?

• Thickness?

• Hazards?

Engineering Parameters

IP, EUR Pressure

EUR vrs Horizontal Well Length

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Horizontal well length has poor correlation with EUR

EUR vrs Horizontal Well Azimuth

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Optimal well azimuth ~130 degrees from North

EUR vrs # of Fracture Stages

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Best production with 1 or 2 fracture stages?

How do you Optimize?

How do you Optimize?

Optimize ->

Shorter Wells?

~130 Degrees? 1 Fracture Stage?

MV Statistics - Engineering

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Prediction correlation improves to 0.426 using

well length, azimuth and # stages

MV Statistics – Non-linear

Prediction correlation improves to 0.663 if a periodic transform is

applied to wellbore azimuth

MV Statistics – Non-linear

Horizontal well bore length and transformed wellbore azimuth are key engineering predictors

for EUR gas production

But how do you Optimize? • Horizontal well lengths – 900-3700 ft?

• Number of frac stages – 1 -7?

• Horizontal well azimuth?

• Fracture parameters • Slick water

• Fluid volume

• Fluid rate

• Pressure

• Proppant volume??

• Location • Porosity?

• Facies/Lithology?

• Thickness?

• Hazards?

Reservoir Thickness Facies/Lithology

Conventional characteristics….

Reservoir Depth Vertical Position in Reservoir

Reservoir Depth

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Seismic time horizons, converted seismic depth

horizons, gridded geologic tops

Vertical Position in Reservoir

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Distance beneath and between reservoir boundaries

Average/maximum well depth below top reservoir, percentage

position in reservoir

Reservoir Thickness

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Distance beneath and between reservoir boundaries

Isochores, isochrons, spectral decomposition

Facies/Lithology

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Distance beneath and between reservoir boundaries

Facies/lithology maps or volumes, seismic impedance, seismic amplitude, spectral

decomposition

Reservoir Thickness Facies/Lithology

Conventional characteristics….

Reservoir Depth Vertical Position in Reservoir

MV Statistics – Non-linear

Thickness and amplitude maps slightly improve prediction

correlation to 0.836

Hydrocarbon Potential Rock “Crackability”

Faults and Fractures Stress Anisotropy

Unconventional characteristics….

Hydrocarbon Potential

TOC, thermal maturity, kerogen percentage, thickness, porosity,

organic/matrix permeability

Rock “Crackability”

Elastic inversion, shear modulus, bulk modulus, Young’s modulus, Poisson’s

ratio (Vp/Vs), breakdown pressure

Young’s Modulus

Poisson’s Ratio

Faults and Fractures Incoherence, curvature, fault volume, velocity anisotropy

Source: Global Geophysical/Weinman

Stress Anisotropy

Velocity anisotropy, curvature, triaxial stress, stress maps

Rich and Ammerman 2008

Hydrocarbon Potential Rock “Crackability”

Faults and Fractures Stress Anisotropy

Unconventional characteristics….

Karst Collapse Chimneys

Karst Collapse Chimneys and Faulting

Courtesy: Devon

Karst Collapse Chimney Creation

Key to Production Variability

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Microseismic Illustrates Paths

MV Statistics + Fracture Attributes

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Fracture attributes have high sensitivity and improve

correlation prediction to 0.957

Why Non-Linear Relationships?

No collapse chimneys

Increasing collapse chimneys

Collapse chimneys dominate

Incoherence Curvature

Managing Information Overload

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Many maps are redundant, with high correlation with other maps

Managing Information Overload

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Removing redundant maps

Multi-Variate Analysis Workflow

2) Engineering

“Best Practices”

Optimize Well Placement

and Completions

Assign Properties

To Wellbore Zones

Load & QC

Engineering Data

Wellbore Paths, Perfs,

Stages, Pressures, Slurry

Flow, Proppant

Load & QC

Geo Data

Logs, Tops

Lithology/Facies logs

Geochemical data

Stress/strain data

Grid/Extract

Map Properties

Create

Wellbore Zones

1) Multi-Variate

Statistical Analysis Correlation of Engineering

Parameters with

Production, Correcting for

Spatial Geo Data Effects

3) Prospectivity

Maps

Correlation of Geo Data

with Production, Correcting

for Variability in

Engineering Parameters

1) Integrated GG&E Statistics at Wells

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Engineering (at wells)

Geo Data (between wells)

2) Extract Optimal Engineering Parameters

IP, EUR Pressure

3) Use Spatial Statistics to Create Prospectivity Map

Blue zones are to be avoided, red/orange zones have best

prospectivity – potential locations identified

Good location, but poor engineering - azimuth

Summary • Ten-fold well production variability is not

uncommon within shale plays

• Using crossplots to optimize engineering parameters is not feasible due to the number and subtle interactions

• Visual map correlation can mislead due to the power of human pattern matching

• We propose using non-linear, multi-variate statistics for integrated analysis of engineering and geo data – to create prospectivity maps and estimates of best engineering parameters

Predicting Shale Production with Integrated Multi-Variate Statistics

Murray Roth

Transform Software and Services, Inc.