Seismic reservoir characterisation

Seismic reservoir characterisation Unconventional reservoir (shale gas) Robert Porjesz 1 2014 B A K E R H U G H E S I N C O R P O R A TED. A LL R I G H TS R E S E R V E D. TERMS A N D C O N D I TI O N S O F U S E : B Y A C C E P TI N G THIS DOCUMENT, THE RECIPIENT A G R E E S THAT THE DOCUMENT TOGETHER W I TH A LL I N FORMATI O N I N C LUDED THEREIN I S THE C O N FI D E N TI A L A N D P R O P R I E TARY PROPERTY OF B A K E R H U G H E S I N C O R P O R A TED AND INCLUDES VALUABLE TRADE SECRETS AND/OR PROPRIETARY INF ORMA TI O N O F B A K E R H U G H E S (C O LLECTI V E LY "I N FORMATI O N "). B A K E R H U G H E S R E TAINS A LL R I G H TS U N D E R C O P Y R I G H T LAW S A N D TRADE SECRET LAW S O F THE U N I TED STATES OF A M E R I C A A N D O THER COUNTRIES. THE RECIPIENT FURTHER A G R E E S TH A T THE D O C U M E N T M A Y N O T B E D I S TRIBUTED, TRANSMITTED, C O P I E D O R R E P R O D U C E D I N W H O LE O R I N P A R T B Y A N Y M E A N S, E LECTRONIC, MECHANICAL, O R O THERWISE, W I THOUT THE EXPRESS PRIOR WRITTEN C O N S E N T O F B A K E R H U G H E S, A N D M A Y N O T B E U S E D D I R E C TLY O R I N D I R E C TLY IN A N Y W A Y D E TRIMENTAL TO BAKER HUGHES INTEREST.

Overview Introduction Conventional seismic modelling Reservoir rock physics Seismic data for sweet spots Sample case history Unconventional (azimuthal) seismic modelling Reservoir rock physics Seismic data for sweet spots Sample case history Integration with Quantitative mineralogy Microseismic Financial impact/concluding thoughts 2

Introduction Reservoir modelling/drilling plan, operational decision making Without seismic Actual data <25% Model data 4

Introduction / objectives Reservoir modelling/drilling plan, operational decision making With seismic Actual data <5% Model data 5

Introduction Seismic reservoir characterisation No seismic With seismic 6 But not only seismic

Shale Gas in the US Extensive drilling Since 1997, more than 13,500 gas wells completed in the Barnett shale 7

Maximum gas 6 mo. production (MCF) The shale learning curve 400,000 Horizontal Vertical Directional 350,000 300,000 250,000 200,000 150,000 100,000 Multistage Completions 50,000 8 0 Jan-81 Jan-83 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Jan-07 Jan-09 Jan-11 Jan-13 Date Barnett Shale Development

Uncertainty 70% of unconventional wells in the U.S. do not reach their production targets.* 60% of all fracture stages are ineffective.** 73% of operators say they do not know enough about the subsurface* 9 *Source: Welling & Company, 2012 **Source: Hart s E&P, 2012 9

The need for Shale Science Shale resources are not homogenous Shale development is capital-intensive Lateral wells with multi-stage completions are expensive Economic success rates are low with current approaches Stimulation requires a significant amount of water We can t drill everywhere DRILL / COMPLETE SMART 10

Bulk Density (GRI), g/cc Shale Play Seismic Characterization in a nutshell Is the rock brittle? Brittleness Reservoir Quality 3.0 2.8 2.6 2.4 2.2 Gas in Place Porosity Saturation TOC BASIC ROCK PROPERTIES (GRI Method) 585 Samples y = -0.061x + 2.6671 R² = 0.6511 2.0 1.8 1.6 All JIP Wells Johnson Trust 1 #2 (Bossier) Johnson Trust 1 #2 (Haynesville) Johnson Trust 1 #2 (Haynesville Lime) 0 2 4 6 8 10 12 14 Total Organic Carbon (TOC), wt % Stress How will the fractures propagate? Shale Characterization 11

Unconventional reservoir rock physics What the (seismic driven) rock physics is? What are the benefits/objectives of applying rock physics analysis for reservoir modelling? 13

Unconventional reservoir rock physics What the (seismic driven) rock physics is? Kimmeridge Oil Shale Photo: Dr. Ramues Gallois (2011) 14

Unconventional reservoir rock physics What are the benefits/objectives of applying rock physics analysis for reservoir modelling? Kimmeridge Oil Shale Photo: Dr. Ramues Gallois (2011) 15

Unconventional reservoir rock physics What the (seismic driven) rock physics is? Pores / Fluid Rock Matrix 16 http://www.kgs.ku.edu/publications/oil/primer03.html Rock frame bulk modulus Porosity Fluid saturation Temperature Pressure Share modulus Density.

Unconventional reservoir rock physics What the (seismic driven) rock physics is? 17 v p, v s, r Rock physics Rock frame bulk modulus Porosity Fluid saturation Temperature Pressure Share modulus Density.

Unconventional reservoir rock physics What the (seismic driven) rock physics is? 18

Unconventional reservoir rock physics 19

Pre-stack Inversion Workflow Seismic Data Conditioning Seismic Interpretation Well Seismic Ties Extract Wavelets Model Building Inversion V P V s 2 r r V V E Dyn P S 2 3K(1 2 ) 2 2 2 k E E Dyn Stat 21

Pay Probability Map Haynesville Shale Gas 22

Multi Attribute - Production map HIGH HIGH WPTOC (mean) LOW Lambda Rho (min) LOW HIGH HIGH 23 BRITINDX (max) LOW S-impedance (mean) LOW

Multi Attribute - Production map WPTOC (mean) Lambda Rho (min) BRITINDX (max) S-impedance (mean) 24

Interpolation of Production Values ECONOMIC NON-ECONOMIC 25 2 5

Calibrated Production Map ECONOMIC NON-ECONOMIC 26

Impedance P-wave Azimuthal Anisotropy Azimuthal Impedance 15000 14800 14600 14400 14200 14000 13800 13600 0 45 90 135 180 Azimuth Azimuth F High stress 29

Fractured media characterisation Slide 30 AVAz methods: The near offset Rüger equation Azimuthal Fourier coefficients Simultaneous elastic inversion of Fourier Coefficients 30

Fractured media characterisation Slide 31 AVAz methods: The near offset Rüger equation Azimuthal Fourier coefficients Simultaneous elastic inversion of Fourier Coefficients 31

The near offset Rüger equation Slide 32 A popular method to perform azimuthal AVO is the near offset approximation (Rüger and Tsvankin, 1997) 2 R(, f) A [ B iso B ani sin iso 2 f f ]sin Where R(,f): Data for a given angle of incidence and azimuth f A: Intercept B iso : Isotropic gradient B ani : Anisotropic gradient f iso : Azimuth of isotropy plane B ani is often associated to the crack density (Hudson et al., 1981) and f iso is the fracture orientation. 32

Fractured media characterisation Slide 33 AVAz methods: The near offset Rüger equation Azimuthal Fourier coefficients Simultaneous elastic inversion of Fourier Coefficients 33

Slide 34 (Downton, SEG 2011) Azimuthally invariant part contains both isotropic and fracture properties 2 nd order Fourier coefficients directly related to the anisotropic gradient B ani 4 th order Fourier coefficients provide additional fracture information Combination of Fourier coefficients provide fracture properties (e.g. weaknesses, compliances) and unambiguous fracture strike R pp ( f, ) r r2 cos(2( f fsym)) r4 cos(4( f f 0 sym )) 34

Amplitude Azimuthal angle stacks Azimuthal Fourier Coefficients n u 2 v 2 u 4 v 4 f 1 2 1 1 5 x 10-3 4 Amplitude vs. Azimuth (40 degrees) n 2 1 2 1 0-1 2 u 2 v 2 u 4 v 4 3-2 f 2-3 0 50 100 150 200 250 300 350 Azimuth (degrees) 2 1 u 2 v 2 u 4 v 4 n n 35 f N

Fractured media characterisation AVAz methods: The near offset Rüger equation Azimuthal Fourier coefficients Simultaneous elastic inversion of Fourier Coefficients 36

SEI of azimuthal angle stacks f 1 n 2 (Downton and Roure, 2010) n 2 1 1 f 2 f N n 2 1 TWT, Ip, Is, ρ δ T, δ N, Φ sym Inversion minimizes a three term cost function: Data misfit Prior model f 1 f 2 f N δ T : tangential weakness δ N : normal weakness Φ sym : symmetry axis Lateral continuity 37

SEI of Fourier coefficients u 2 v 2 u 4 v 4 1 (Roure and Downton, 2012) u 2 v 2 u 4 v 4 2 n u 2 v 2 u 4 v 4 TWT, g δ T, δ N, Φ sym Inversion minimizes a three term cost function: Data misfit Prior model θ 1 θ 2 θ N δ T : tangential weakness δ N : normal weakness Φ sym : symmetry axis g: (Vs/Vp)^2 Lateral continuity 38

Differential Horizontal Stress Ratio Interpretation Crossplot H- h H Static Young s Modulus 39 Zone 1: Ductile (RED) Zone 2: Aligned Fractures (YELLOW) Zone 3: Hydraulic Fractures (GREEN) Zone 4: Transition (GREY)

Combining Stress & Brittleness Seismic Predictions hmin = Closure Stress Pressure Hmax Hmax BRITTLE hmin Young s Modulus high low 40 low DHSR high H h Plate size: DHSR H Plate orientation: direction of H

Predicted average production calibrated to horizontal well length High 28 24 27 13 14 7 26 25 23 11 12 10 15 1 2 5 9 3 4 6 18 22 16 19 20 21 17 Low 41

Introduction Seismic reservoir characterisation No seismic Quantitative mineralogy With seismic 43 But not only seismic

Cuttings Based Spectral Gamma Curve 75 µm 75 µm 44

Well log and XRD lithology with up-scaled and normalized RoqSCAN carbonate data (X s) 45

RoqSCAN SGR Redox & organic proxies RoqFRAC Cuttings Based Spectral Gamma Curve Vertical Data Summary Log Imported logs Bulk minerals Siliciclastics & carbonates Heavy minerals Provenance & marine indicators Porosity data Comments 46

Input to seismic attribute mapping and completions characterization 47 1000 ft

RoqFRAC ( Brittleness Index) vs. Dynamic Young s Modulus 48

Introduction Seismic reservoir characterisation No seismic Microseismic With seismic 49 But not only seismic

SRV SRV Correlation between SRV and Seismic-Derived Attributes For each stage: 1) Compute SRV 2) Compute average of seismic attribute inside SRV R = 0.68 (76 stages) R = - 0.60 (76 stages) 54 Young s Modulus DHSR

Original 13 stages Frac design and calibrated production map 56

Targeted, 9 stages Frac design and calibrated production map Optimized locations for frac stages 57

Cumulative Cash Flow ($MM) Well Economics $10.0 $8.0 $6.0 $4.0 $2.0 $0.0 ($2.0) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 Month ($4.0) Base Case Test Case 1 Test Case 2 ($6.0) ($8.0) Original design 13 stages 9 stage design vs original 13 stages Optimized 9 stage design vs original 13 ($10.0) 58

Final comments The shale reservoir characterization workflow demonstrated utilizes a combination of detailed well analysis, pre-stack seismic inversion, and seismic anisotropy. There is no single silver bullet; multi-attribute analysis is required. Validation will help refine seismic processes, mineralogy and microseismic analysis show promising correlations. Sweet Spot maps and volumes are statistically derived. Judicious validation should be applied. 59

Thank you 60