Well Placement and Fracturing Optimization Research Team, TTU Shale Gas Plays Screening Criteria A Sweet Spot Evaluation Methodology Ahmed Alzahabi, PhD Candidate A. Algarhy, M. Soliman, R. Bateman, and G. Asquith Bob L. Herd Department of Petroleum Engineering Sept. 2014 Texas Tech University, Lubbock, TX, USA Prepared to submitted to Fracturing Impacts and Technologies Conference 1
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
Shale Plays (from Google & EIA, 2014) Variables used as an input for the algorithm to help evaluating shale plays : Thickness, Depth, TOC, Maturity, Brittleness, Mineral Composition, Total porosity, Adsorbed gas, Gas content, and Geologic age 4
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. 5
Building Success Factor Candidate Evaluation Database Structure Shale Success Factor [0-100 %] Algorithm Statistics 6
Roadmap of Shale Expert System 4 1 Data Structure 2 Algorithm Design 3 Algorithm System Flow chart Application 7
Roadmap of Shale Expert System 1 Data Structure Algorithm Design Algorithm System Flow chart Application 8
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 9
Data Structure, Common shale plays spider plot Spider Plot Barnett Ohio Antrim New Albany Lewis Fayettevillle Haynesville Gas Content Depth TOC 100 90 80 70 60 50 40 30 20 10 0 RO Total Porosity Eagle Ford Woodford Bakken Adsorbed Gas Net Thickness Horn River 10
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 11
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 Wt % 12
Data Structure Mechanical Properties of Shale Gas Plays: No. Shale play E ν 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 13
Data Structure Shale Plays Characteristics parameters Shales TOC RO Total Porosity Net Thickness Adsorbed Gas Gas Content Depth Permeability, nd Geological 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 Devonian 3 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 145-206 Late 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 Some parts from Curtis 2002. 14
Completion Strategy for each shale play 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 15
Production Potential for each shale play parameters Shales Decline Historic Production area 1 Barnett Wise County, Texas 2 Ohio Pike County, Kentucky 3 Antrim Otsego, County, Michigan 4 New Albany Harrison County, Indiana 5 Lewis 6 Fayettevillle 7 Haynesville 8 Eagle Ford 9 Marcellus 10 Woodford 11 Bakken 12 Horn River Hyperbolic (5.6%) San Juan & Rio Arriba Counties, New Mexico. Shale Gas Reservoirs of Utah Steven Schamel Sept. 2005 16
Data Structure Shale Gas Production Indicators: Laterally extensive shale Thickness >30m Total organic carbon content >3% Thermal maturity in gas window (Ro= 1.1 to 1.4), Dry Gas-Ro>1.0, Wet Gas-Ro=0.5-1.0, Oil-Ro<0.5. Good gas content >100scf/ton Moderate clay content <40% Brittle composition Quartz The necessary elements for a shale gas play are (Curtis, 2009): 17
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% 18
Data Structure Assessing Shale Plays Potential Reserves Sweet Spot Identifier Parameters Brittleness Young modulus TOC Conditions > 45% (Rickman & Mullen criterion) + 3.5 10 ^6 psi (SPE125525) +1 wt.% Poisson ratio <0.2 Vitrine Reflectance >1.3% RO Kerogen Type Mineralogy Type I &II better gas yield than type III + 40 % Quartz-Calcite/ less Clay (Less clay/low Smectite <4wt%) DHSR Very low <40 % Fracturability Index >45% 19
Roadmap of Shale Expert System Data Structure 2 Algorithm Design Algorithm System Flow chart Application 20
Algorithm Design 1. The knowledge base would contain characteristics & strategies 2. Measure similarities based prioritized shale parameters. 3. The advice would be the likelihood of similarity then recommended future development approaches. 4. Recommend completion strategies 21
Roadmap of Shale Expert System Data Structure Algorithm Design 3 Algorithm System Flow chart Application 22
Algorithm System Flow chart 3 Application Data Structure 3.1 Algorithm Design 3.2 How it works Algorithm System Flow chart 3.3 Algorithm System Flow chart 3.4 Shale Expert System database Application 3.5 Maturity Check Microsoft Clustering Model 23
Shale Expert Algorithm System Flow chart: Enter input of E, ν, TOC and mineralogy values of the newly under test shale Check Maturity based on stored spider plots No It is not a potential shale play Yes Check similarity based on data clusters Rank all shale plays according to similarity Guide in development of the new shale play. Generate recommendation list 24
Microsoft Clustering Model Model Root Check Maturity Check similarity Clusters Rank Guide 25
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. 26
Shale Expert System 27
Shale Gas Reservoirs of Utah Steven Schamel Sept. 2005 28
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Roadmap of Shale Expert System 4 Data Structure Algorithm Design Algorithm System Flow chart Application 31
Application of Shale Expert System Case#1 North Africa Shale Input Parameters TOC, w% 2.3 RO 1.3 Total Porosity 10 Net Thickness 300 Adsorbed Gas N/A Gas Content N/A Depth range 13100=(12800-13400) E, PR N/A Mineralogy 32
World Shale Plays Potential http://www.gidynamics.nl/products/gas-processing/unconventional-gas 33
Application of Shale Expert System Case#1 North Africa Shale Input Parameters TOC, w% 2.3 RO 1.3 Total Porosity 10 Net Thickness 300 Adsorbed Gas N/A Gas Content N/A Depth range 13100=(12800-13400) E, PR N/A Mineralogy 11 12 13 TOC Clustering 14 12 10 8 6 4 2 0 1 2 3 4 Output of Expert System Eagle Ford 70% Barnett 20% 10 5 9 6 8 7 34
Data Structure Shale Plays Characteristics parameters Shales TOC RO Total Porosity Net Thickness Adsorbed Gas Gas Content Depth Permeability, nd Geological 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 Devonian 3 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 TOC Ro h 6 Fayettevillle 6.75 3.00 5.00 110.00 60 140 4000 n/a Mississippian Haynesville Eagle Ford Barnett 7 Haynesville 3 2.2 7.3 225 18 215 12000 10-650 Upper Jurassic Marcellus Marcellus Lewis 8 Eagle Ford 4.5 1.5 9.7 250 35 150 11500 1100-2500 Upper Cretaceous Horn River Woodford Eagle Ford 9 Marcellus 3.25 1.25 4.5 350 50 80 6250 n/a Devonion Ohaio Haynesville Late Devonian -Early 10 Woodford 7 1.4 6 150 n/a 250 8500 145-206 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 Some parts from Curtis 2002. 35
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. 36
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 system 37
Thank you Questions? 38