Jornadas de Producción, Tratamiento y Transporte de Gas El Desafío del Gas no Convencional Shale Engineering George Vassilellis, Carlos Sanz September 1, 2011 Gaffney, Cline & Associates 1
Agenda Objectives Economic importance (Global and Local) The problem and approaches Shale engineering concepts Models description Model A Near Wellbore (One frac stage) Model B Frac Stages (well construction model) Comparison with historical performance Well spacing field development concepts Conclusions 2
Objectives Provide a methodology to describe the formation and the reservoir created by the well construction (SRV) Predict production, but also a Practical platform for field development design and optimization Contrary to the traditional exploration shale reservoirs are there, we know that and do not require the discovery but the conceptual proof 3
Why Is This Important? More than 300 bn US$ in transactions since 2008 Horn River: 500 Tcf Montney*: 500 Tcf Montney West Coast: 41 Tcf Northeast: 473 Tcf Rocky Mountain: 58 Tcf Midcontinent: 63 Tcf Southwest: 87 Tcf Lower 48 Total: 827 Tcf Gulf Coast: 105 Tcf *Note: The Canadian Society for Unconventional Gas categorizes the Montneyplay as tight sands rather than shale gas. All Other Canadian Shale: 611 Tcf Map Source: Ziff Energy Group US Shale Resources Estimates: EIA, 2011 Canadian Shale Resource Estimates: Canadian Society for Unconventional Gas, 2010 Date Acquirer Acquirer Country Shale Play Target Company Acquisition Cost (US$MM) 12/23/2010 ExxonMobil US Fayetteville Petrohawk 575 12/20/2010 Sasol S. Africa Montney Talisman 1,050 12/10/2010 Occidental US Bakken Unspecified 1,400 11/22/2010 Hess US Bakken TRZ Energy 1,050 11/15/2010 Williams US Bakken Unspecified 925 11/9/2010 Chevron US Marcellus, Utica Atlas 4,300 11/3/2010 Chesapeake US Marcellus Anschutz Exploration 850 10/10/2010 Statoil Norway Eagle Ford Enduring Resources 663 10/10/2010 CNOOC China Eagle Ford Chesapeake 2,160 6/23/2010 Reliance Industries India Eagle Ford Pioneer 1,145 5/28/2010 Shell Netherlands Marcellus East Resources 4,700 5/10/2010 BG UK Marcellus EXCO 985 4/13/2010 Kinder Morgan US Haynesville-Bossier Petrohawk 921 4/9/2010 Reliance Industries India Marcellus Atlas 1,700 3/15/2010 CONSOL US Marcellus Dominion 3,475 3/1/2010 Korea Gas South Korea Montney EnCana 565 2/16/2010 Mitsui Japan Marcellus Anadarko 560 2/16/2010 Mitsui Japan Marcellus Anadarko 840 1/4/2010 Total France Barnett Chesapeake 2,250 12/14/2009 ExxonMobil US Marcellus, Haynesville XTO 41,200 6/30/2009 BG UK Haynesville-Bossier EXCO 1,127 5/18/2009 ENI Italy Barnett Quicksilver 280 11/10/2008 Statoil Norway Marcellus Chesapeake 3,375 9/2/2008 BP UK Fayetteville Chesapeake 1,900 4
Regional Importance 5
The Problem Shale reservoir performance prediction is more complex than more traditional formations Does a good fit imply a meaningful extrapolation? Type curves are widely used and, potentially, abused What other options are there? 6
Common Approach: Analogy and Statistics In-Place Description Assimilation of historical data and building of type curves has drawbacks: Does not involve natural constraints Difficult to explain well design effects Optimization is based on trial and error Large investments for know-how Geo-mechanical effects are ignored Long term predictions are questionable Statistical Analysis and Type Wells Performance Known area with history Type Well Most critical assumption is often over-simplified Not all shale formations are the same! Source - 2010 Responsible Drilling Alliance 7
Shale Engineering Advanced Reservoir Engineering for Shales In-Place Description Statistical Analysis and Type Wells Performance Shale Engineering Modeling Based on geo-mechanics Implements physical shale properties Describes properties of the induced reservoir after stimulation Matches observed performance Interprets and implements micro-seismic Optimizes well design and field development Provides early predictions of long term production behavior 8
Performance prediction challenges Formation description requires special attention Regional and local trends need to be addressed Ro, rock mechanics, stresses, TOC, are important to know Matrix properties are difficult to measure in logs and even in cores Natural fractures are difficult to quantify Conventional reservoir engineering tools may not apply Relation to in-place volumes is questionable Material balance approach does not apply Flow is not directly related to pressure difference The effect of natural fractures is often misunderstood DCA is impractical and uncertain Massive hydraulic stimulation creates an artificial reservoir; the bounds and quality need to be understood 9
Shale Model Description NATURAL Natural fractures Dual Permeability Fracture Anisotropy INDUCED MAJOR Proppant placement Matrix Stimulated Rock Volume (SRV) NATURAL INDUCED MINOR Non-Darcy flow Gas Desorption 10
Stimulation Sequence σ max Proppant Placement Shear Induced Fractures Pressure = σhmin σh HYDRAULIC DRILLING Production PRESSURE Stimulated Reservoir Volume (SRV) 11
From Natural Shale to the Artificial Reservoir Logs and core In situ stress determination Microseismic Re-processing Natural Fracture Permeability Analysis Benefits Enhancing reservoir understanding Exploiting modern technology 12
Modeling Approach Build Two Models Model Attributes Flow tied to geomechanics through dilation-compaction tables Compositional formulation Gas desorption Non-Darcy matrix flow A. Fracture propagation model Fine grid (models minutes) Stimulation and backflow timeframe Matches frac and microseismic surveillance Outputs stimulated rock volume (SRV) permeability Models fracture propagation B. Full well model Coarse grid (models decades) Uses SRV attributes from model A Adjusts frac stage contribution to PLT measurements Adjusted to historical production rates and pressures Provides long term production forecast 13
Model Objectives SRV description related to hydrofrac (model A) Long term well performance (model B) Model A Induced permeability Proppant conductivity Model B 14
Model A Gridding Stimulated Reservoir Volume (SRV) Proppant nodes Welbore node σ max σh Wellbore 15
Model A - Modeling Fracture Propagation Challenging well: foam frac, tight sand/shale/partially depleted Used detailed rock and performance description Matched stage development with surface measurements Matched post-frac production rates Shale Gas Reservoir I_ST7 EQT_Stage7_v12b.irf 200 2.50 8,000 SRV extent Gas Rate SC (MMcf/day) 150 100 50 Rate Match Surface Pressure Match 2.00 1.50 1.00 0.50 Cumulative Gas SC (MMcf) 6,000 4,000 2,000 Well Bottom-hole Pressure (psi) 0 0.00 2009-12-1 2009-12-1.02 2009-12-1.04 2009-12-1.06 2009-12-1.08 Time (Date) 0 Microseismic events Cumulative Gas SC EQT_Stage7_v12b.irf Gas Rate SC EQT_Stage7_v12b.irf Well Bottom-hole Pressure EQT_Stage7_v12b.irf Well Head Pressure EQT_Stage7_v12b.irf Well Head Pressure2.fhf Cumulative Gas SC EQT_Stage7_v10.irf Gas Rate SC EQT_Stage7_v10.irf Well Bottom-hole Pressure EQT_Stage7_v10.irf Well Head Pressure EQT_Stage7_v10.irf Gas Injection Rate.fhf 16
A Real Shale Engineering Application Rates measured by PLT five months later 9 8 7 6 5 4 3 2 1 Micro-seismic events do not relate to production response Do all micro-seismic events relate to fluid presence? Production seems dependent on natural fractures 17
Comparing Projections with Actual Results 300 Stage 7 6,000 Gas Injection Rate Well Head Pressure 250 5,000 Gas Rate SC (MMcf/d day) 200 150 100 4,000 3,000 2,000 (psi) Well Head Pressure ( 50 1,000 Difference in pre-existing natural fractures Stage 3 0 0 2009-11-30.78 2009-11-30.83 2009-11-30.88 2009-11-30.93 2009-11-30.98 2009-12-1.03 2009-12-1.08 Time (Date) 18
Model B - Shale Engineering Predictive Model Matched production history and production logging Frac stage contribution match Proppant placement match Well History match Pressure drop, psi 2,500 1,000 2,000 800 Gas Rate SC (MSCF/day) 1,500 1,000 Narrow Uncertainty 600 400 Well Head Pressure (psi) 500 200 0 0 2010-1 2010-7 2011-1 2011-7 2012-1 2012-7 Time (Date) 19
Comparison with Historical Performance 2,500 1,000 Tuned for: 2,000 800 Grid scaling Geomechanical effects Gas Rate SC (MSCF/day) 1,500 1,000 600 400 Well Head Pressure (psi) Matrix parameters 500 200 Initial pressure 0 0 2010-1 2010-7 2011-1 2011-7 2012-1 2012-7 Time (Date) Proppant properties Gas desorption Water saturation About 63 runs 20
Fitting Challenges Forecast Uncertainties Initial trends can be influenced by: Fluid unloading Surface pressure restrictions Interaction with other wells or formations through natural fractures Gas Rate SC (M MSCF/day) 2,500 2,250 2,000 1,750 1,500 1,250 1,000 750 500 Gas Rate SC, Historical Well Head Pressure, Historical Water Rate SC, Model Fluid unloading Gas Rate SC, Model Well Head Pressure, Model Line pressure restriction 500 400 300 200 100 Well Head Press sure (psi) 250 200 150 100 50 (bbl/day) Water Rate SC ( 250 0 2010-1 2010-4 2010-7 2010-10 2011-1 2011-4 2011-7 Time (Date) 0 0 21 21
Spacing Determination Biggest game changer, reducing spacing by 25% Reducing spacing by 30% does not add significant value Composite performance from a multi-squaremile development module 22
Multi-Point Validation Production history Pressures and rates Fluids Stage contribution Production logging Flow back data Stimulated rock volume Hydrofrac plots Microseismic shape 23
Conclusions Shale Engineering provides a new look at shale performance Includes rock mechanics Models mass transfer through diffusion Describes the induced reservoir Transverse fracture stages do not perform similarly Despite using similar design along a relative uniform formation Subtle differences in natural fractures and stress conditions may trigger differences in the induced reservoir (SRV) Micro-seismic events may reflect rock-to-rock interaction Overstate spacing assumptions Un-interpreted micro-seismic plots do not relate to the induced reservoir (SRV) 24
Conclusions (Continued) The hysteretic permeability models that are employed in numerical modeling can offer a description of the SRV and can be also used in addressing longer term geomechanical effects in a practical manner. The application of this methodology in a challenging shale play shows that differences in production by stage can be explained by observed differences in the natural fracture network. The information gathered during the initial months of the well s operation can be used to fine tune numerical modeling at many levels, such as at individual frac stages, in reconciling production logs and initial performance, and in offering a narrow band of uncertainty for long term predictions. 25
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Questions? 27
Shear Fracture Activation Model Pre-slip: Small open aperture (a o ) a o ao Shear slip closure σn closure σ n Permeab bili ty Post-slip: Larger open aperture (a o ) Effective normal stress 28 28
Theoretical Basis for Coupling 29 Fracture Conductivity ~ α
Tuning the empirical hysteresis curves Theoretical Laboratory Stress conditions Rock properties Special tests Frac Pressure Slope ~ Bulk Modulus Hysteresis measurement from special tests Slope ~ Pore Compressibility Critical Pressure inferred from in-situ stress Shape based on theoretical model 30
Test matching for fracture strength and flow properties Predicted flowback data 4 3 Fit to injection data 1 2 Model fit is controlled by: Pressure at knee is controlled by fracture strength Slopes controlled by pre and post-stimulation flow properties 31
Model A for different frac stages Stage 3 241,800 241,700 241,600 241,500 Gas Rate SC (MMcf/day) 400 350 Shale Gas Reservoir 1,756,700 1,756,800 1,756,900 1,757,000 1,757,100 1,757,200 Permeability I - Fracture (md) 2009-11-30.8638426 K layer: 27 300 0.00 70.00 140.00 feet 250 200 150 241,500 241,600 241,700 241,800 241,900 500.00 326.60 213.34 139.36 91.03 59.46 38.84 25.37 16.57 10.83 7.07 4.62 3.02 1.97 8,000 1.29 0.84 0.55 7,000 0.36 0.23 0.15 0.10 6,000 Stage 7 240,900 240,800 240,700 Shale Gas Reservoir 1,757,500 1,757,600 1,757,700 1,757,800 1,757,900 Permeability I - Fracture (md) 2009-12-01.0104167 K layer: 27 1,758,000 Natural Fracture distribution is the main Permeability, md differentiating factor for stage stimulation C2_S1 performance I_ST3 Gas Injection Rate Gas Injection Rate Stage3 Well Head Pressure Well Head Pressure Stage3 Few natural fractures High Treatment pressure 5,000 250 Smaller SRV Low productivity 1,756,700 1,756,800 1,756,900 1,757,000 1,757,100 1,757,200 4,000 3,000 Well Head Pressure (psi) 240,600 Gas Rate SC (MMcf/day) Fracture Permeability, md 1,000.00 683.83 467.62 319.78 218.67 149.53 102.26 C7_S7 C7_S8 C7_S9 69.93 I_ST7 47.82 32.70 22.36 15.29 10.46 400 8,000 7.15 4.89 Gas Injection Rate 3.34 350 Gas Injection Rate Stage7 2.297,000 Well Head Pressure Well Head Pressure Stage7 1.56 1.07 0.73 300 6,000 0.00 70.00 140.00 feet 0.50 200 150 More natural fractures Low Treatment pressure Larger SRV High productivity 1,757,500 1,757,600 1,757,700 1,757,800 1,757,900 1,758,000 5,000 4,000 3,000 240,700 240,800 240,900 240,600 Well Head Pressure (psi) 100 2,000 100 2,000 50 1,000 50 1,000 0 0 2009-11-30.84 2009-11-30.85 2009-11-30.86 2009-11-30.87 2009-11-30.88 Time (Date) 0 0 2009-11-30.99 2009-12-1 2009-12-1.01 2009-12-1.02 2009-12-1.03 Time (Date) 32