Shale Gas Plays Screening Criteria

A Sweet Spot Evaluation Methodology

A. Algarhy, M. Soliman, R. Bateman, and G. Asquith

Prepared to submitted to Fracturing Impacts and Technologies Conference

Texas Tech University, Lubbock, TX, USA

Sept. 2014

Ahmed Alzahabi, PhD Candidate

Bob L. Herd Department of Petroleum Engineering

Well Placement and Fracturing Optimization Research Team, TTU

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Agenda

Introduction

Objectives

Shale Success Factor

Building database for major shale plays

Shale Expert System( Toolbox, Benchmark)

Application of Shale Expert System

Conclusions & Recommendations 2

Introduction

No. Shale play

1 Barnett

2 Ohio

3 Antrim

4 New Albany

5 Lewis

6 Fayetteville

7 Haynesville

8 Eagle Ford

9 Marcellus

10 Woodford

11 Bakken

12 Horn River

> 70 shale-gas-plays 3

Objectives

Develop a candidate evaluation algorithm.

Develop an algorithm that considers geomechanical, petrophysical and

geochemical parameters of a newly discovered shale.

Provide a guiding database for major productive shale plays in North

America and list all possible potential

Develop guidelines to identify the sweet spots in unconventional resources.

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Building Success Factor

AlgorithmStatistics

Database Structure

Shale Success Factor

[0-100 %]

Candidate

Evaluation

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Data Structure

1. Shale Plays Spider Plot

2. Completion Strategies

3. Mineralogy Comparison

4. Mechanical Properties

5. Shale Plays Characteristics

6. Shale Gas Production Indicators

7. Sweet Spot Identifier

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Data Structure, Common shale plays spider plot

0102030405060708090

100TOC

RO

Total Porosity

Net ThicknessAdsorbed Gas

GasContent

Depth

Spider Plot

Barnett

Ohio

Antrim

New Albany

Lewis

Fayettevillle

Haynesville

Eagle Ford

Woodford

Bakken

Horn River

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Data Structure Completion Strategies:

No. Shale play Average Frac Stage Count. Average later length, ft.

1 Barnett 10-20 3500

2 Ohio n/a n/a

3 Antrim n/a n/a

4 New Albany n/a n/a

5 Lewis n/a n/a

6 Fayetteville 5 4000

7 Haynesville 10 4000-7000

8 Eagle Ford 10 2500

9 Marcellus 8 2900

10 Woodford n/a n/a

11 Bakken 14 9250

12 Horn River 11 4500

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Data Structure Mineralogy Comparison of shale gas plays:

No. Shale play Quartz,% Feldspar,% Clay,% Pyrite,% Carbonate,% Kerogen, %

1 Barnett 35-50 6-7 10-50 5-9 0-30 4.0

2 Ohio n/a n/a 15-57 n/a 7-80 n/a

3 Antrim 40-60% n/a n/a n/a 0-5% n/a

4 New Albany 28-47 % 2.1-5.1 11-23 3-9 0.5-2.5 n/a

5 Lewis 56 n/a 25 n/a n/a n/a

6 Fayetteville 45-50 n/a 5-25 n/a 5-10 n/a

7 Haynesville 23-35 0-3 20-39 n/a 20-53 4-8

8 Eagle Ford 11-50 n/a 20 n/a 46-78 4-11

9 Marcellus 10-60 0-4 10-35 5-13 3-50 5.1

10 Woodford 48-74 3-10 7-25 0-10 0-5 7-16

11 Bakken 40-90 15-25 2-18 5-40 8-16

12 Horn River 9-60 0-3 28-78 4-10 0-9 n/a

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Wt %

Data Structure Mechanical Properties of Shale Gas Plays:

No. Shale play E

1 Barnett 3.5 E+06 0.2

2 Ohio n/a n/a

3 Antrim n/a n/a

4 New Albany n/a n/a

5 Lewis n/a n/a

6 Fayetteville 2.75 E+06 0.22

7 Haynesville 2.00 E+06 0.27

8 Eagle Ford 1.00:4.00 E+06 019:0.27

9 Marcellus 2.00 E+06 0.26

10 Woodford 5.00 E+06 0.18

11 Bakken 6.00 E+06 0.22

Horn River 3.64 E+06 0.23

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Data Structure Shale Plays Characteristics

Some parts from Curtis 2002.

parameters

Shales TOC RO Total Porosity Net Thickness Adsorbed Gas Gas Content Depth

Permeability,

ndGeological Age

1 Barnett 4.50 2.00 4.50 350.00 25 325 6500 25-450 Mississipian

2 Ohio 2.35 0.85 4.70 65.00 50 80 3000 n/a Devonian3 Antrim 5.50 0.50 9.00 95.00 70 70 1400 n/a Upper Devonian

4 New Albany 12.50 0.60 12.00 75.00 50 60 1250 n/a Devonian and Mississippian

5 Lewis 0.45-1.59 1.74 4.25 250.00 72.5 29.5 4500 n/a Devonian and Mississippian

6 Fayettevillle 6.75 3.00 5.00 110.00 60 140 4000 n/a Mississippian

7 Haynesville 3 2.2 7.3 225 18 215 12000 10-650 Upper Jurassic

8 Eagle Ford 4.5 1.5 9.7 250 35 150 11500 1100-2500 Upper Cretaceous

9 Marcellus 3.25 1.25 4.5 350 50 80 6250 n/a Devonion

10 Woodford 7 1.4 6 150 n/a 250 8500 145-206Late Devonian -Early

Mississippian)

11 Bakken 10 0.9 5 100 n/a n/a 10000 n/a Uppper Devonion

12 Horn River 3 2.5 3 450 34 n/a 8800 150-450 n/a

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No. Shale play Configuration of horizontal wells Completion Style Frac Design

1 Barnett 10-12 % fracturing fluid as a pad 75-85% as

a sand laden slurry

2 Ohio

3 Antrim

4 New Albany

5 Lewis

6 Fayetteville

7 Haynesville

8 Eagle Ford

9 Marcellus

10 Woodford

11 Bakken Single Lateral, Multilateral Barefoot open hole

Non-isolated uncemented preperforated liner

Frac ports/ball activated sleeves

Plug and perf

Slick water/ gel

Plug and perf

Frac ports/ ball activated sleeves

100 mesh, 40/70,30/50, 20/40, 16/20, 12/18,

12 Horn River Plug and perf Slick water, 15 stages, 200 tonnes/ stage, 17.

6 Mbbls/stage

Completion Strategy for each shale play

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parameters

Shales Decline Historic Production area

1 Barnett Wise County, Texas2 Ohio Pike County, Kentucky 3 Antrim Otsego, County, Michigan

4 New Albany Harrison County, Indiana

5 LewisHyperbolic

(5.6%)

San Juan & Rio Arriba Counties,

New Mexico.

6 Fayettevillle

7 Haynesville

8 Eagle Ford

9 Marcellus

10 Woodford

11 Bakken

12 Horn River

Shale Gas Reservoirs of Utah Steven Schamel Sept. 2005

ProductionPotential for each shale play

Data Structure Average Shale Characteristics

Based on 10,000 shales (Yaalon, 1962), after Asquith Class

Clay Minerals(mostly Illite ) 59%Quartz and Chert 20%

Feldspar 8%Carbonate 7%Iron Oxides 3%

Organic Material 1%Others 2%

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Data Structure Assessing Shale Plays Potential ReservesSweet Spot Identifier

Parameters Conditions

Brittleness > 45% (Rickman & Mullen criterion)

Young modulus + 3.5 10 ^6 psi (SPE125525)

TOC +1 wt.%

Poisson ratio 1.3% RO

Kerogen Type Type I &II better gas yield than type III

Mineralogy + 40 % Quartz-Calcite/ less Clay (Less clay/low Smectite

Algorithm Used; How it works ?

Identifies relationships in a dataset in a form of Spider plot.

Generates a series of clusters based on those relationships.

The clusters group points on the spider plot and illustrate the relationships that the

algorithm identifies.

Calculates how well the cluster groups.

Tries to redefine the groupings to create clusters that better represent the data

The algorithm iterates through this process until it cannot improve the results more

by redefining the clusters.

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Shale Expert System

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Shale Gas Reservoirs of Utah Steven Schamel Sept. 2005

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World Shale Plays Potential

33http://www.gidynamics.nl/products/gas-processing/Unconventional-Gas

Recommendations

The current model is still in the early stages

Production data for large shale fields need to be considered in the future w

ork.

Decline curve parameters of each major play should be a part of data base.

Trends and patterns should be obtained for the 12 majors shale plays

Clustering similar regions within the same shale is a possible sweet spot

identifying tool, adding it to the expert system37

Conclusions

A new shale plays benchmark has been created

The output of this study has an important value to evaluate any shale play and to

suggest future development strategies.

The algorithm check maturity of newly discovered shale play.

The algorithm works as a guide for identifying Sweet Spot, identification operat

ionally approved method help increase the potentiality of existing shale natural

gas accumulations recovery.

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Thank youQuestions ?

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