Shale Gas Plays Screening Criteria A Sweet Spot Evaluation Methodology

Transcription

1 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 Texas Tech University, Lubbock, TX, USA Prepared to submitted to Fracturing Impacts and Technologies Conference 1

2 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

3 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

4 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

5 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

6 Building Success Factor Candidate Evaluation Database Structure Shale Success Factor [0-100 %] Algorithm Statistics 6

7 Roadmap of Shale Expert System 4 1 Data Structure 2 Algorithm Design 3 Algorithm System Flow chart Application 7

8 Roadmap of Shale Expert System 1 Data Structure Algorithm Design Algorithm System Flow chart Application 8

9 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

10 Data Structure, Common shale plays spider plot Spider Plot Barnett Ohio Antrim New Albany Lewis Fayettevillle Haynesville Gas Content Depth TOC RO Total Porosity Eagle Ford Woodford Bakken Adsorbed Gas Net Thickness Horn River 10

11 Data Structure Completion Strategies: No. Shale play Average Frac Stage Count. Average later length, ft. 1 Barnett 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 Haynesville Eagle Ford Marcellus Woodford n/a n/a 11 Bakken Horn River

12 Data Structure Mineralogy Comparison of shale gas plays: No. Shale play Quartz,% Feldspar,% Clay,% Pyrite,% Carbonate,% Kerogen, % 1 Barnett Ohio n/a n/a n/a 7-80 n/a 3 Antrim 40-60% n/a n/a n/a 0-5% n/a 4 New Albany % n/a 5 Lewis 56 n/a 25 n/a n/a n/a 6 Fayetteville n/a 5-25 n/a 5-10 n/a 7 Haynesville n/a Eagle Ford n/a 20 n/a Marcellus Woodford Bakken Horn River n/a Wt % 12

13 Data Structure Mechanical Properties of Shale Gas Plays: No. Shale play E ν 1 Barnett 3.5 E 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 Haynesville 2.00 E Eagle Ford 1.00:4.00 E : Marcellus 2.00 E Woodford 5.00 E Bakken 6.00 E Horn River 3.64 E

14 Data Structure Shale Plays Characteristics parameters Shales TOC RO Total Porosity Net Thickness Adsorbed Gas Gas Content Depth Permeability, nd Geological Age 1 Barnett Mississipian 2 Ohio n/a Devonian 3 Antrim n/a Upper Devonian 4 New Albany n/a Devonian and Mississippian 5 Lewis n/a Devonian and Mississippian 6 Fayettevillle n/a Mississippian 7 Haynesville Upper Jurassic 8 Eagle Ford Upper Cretaceous 9 Marcellus n/a Devonion 10 Woodford n/a Late Devonian -Early Mississippian) 11 Bakken n/a n/a n/a Uppper Devonion 12 Horn River n/a n/a Some parts from Curtis

15 Completion Strategy for each shale play No. Shale play Configuration of horizontal wells Completion Style Frac Design 1 Barnett % 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, Mbbls/stage 15

16 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

17 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= , 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

18 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

19 Data Structure Assessing Shale Plays Potential Reserves Sweet Spot Identifier Parameters Brittleness Young modulus TOC Conditions > 45% (Rickman & Mullen criterion) ^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

20 Roadmap of Shale Expert System Data Structure 2 Algorithm Design Algorithm System Flow chart Application 20

21 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

22 Roadmap of Shale Expert System Data Structure Algorithm Design 3 Algorithm System Flow chart Application 22

23 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

24 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

25 Microsoft Clustering Model Model Root Check Maturity Check similarity Clusters Rank Guide 25

26 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

27 Shale Expert System 27

28 Shale Gas Reservoirs of Utah Steven Schamel Sept

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31 Roadmap of Shale Expert System 4 Data Structure Algorithm Design Algorithm System Flow chart Application 31

32 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=( ) E, PR N/A Mineralogy 32

33 World Shale Plays Potential 33

34 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=( ) E, PR N/A Mineralogy TOC Clustering Output of Expert System Eagle Ford 70% Barnett 20%

35 Data Structure Shale Plays Characteristics parameters Shales TOC RO Total Porosity Net Thickness Adsorbed Gas Gas Content Depth Permeability, nd Geological Age 1 Barnett Mississipian 2 Ohio n/a Devonian 3 Antrim n/a Upper Devonian 4 New Albany n/a Devonian and Mississippian 5 Lewis n/a Devonian and Mississippian TOC Ro h 6 Fayettevillle n/a Mississippian Haynesville Eagle Ford Barnett 7 Haynesville Upper Jurassic Marcellus Marcellus Lewis 8 Eagle Ford Upper Cretaceous Horn River Woodford Eagle Ford 9 Marcellus n/a Devonion Ohaio Haynesville Late Devonian -Early 10 Woodford n/a Mississippian) 11 Bakken n/a n/a n/a Uppper Devonion 12 Horn River n/a n/a Some parts from Curtis

36 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

37 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

38 Thank you Questions? 38