New Methodology for Estimating Unconventional Production and Long Term Performance Ian Walton, Ph.D. Senior Research Scientist iwalton@egi.utah.edu Reserves Estimation Unconventionals Houston August 2016 Project I 01024 iwalton@egi.utah.edu Unconventional Production Production from multi-fractured long horizontal wells • Ultra-low permeability reservoir • Most gas resident in the pores • Presence of a complex network of natural fractures iwalton@egi.utah.edu Outline Quantifying well productivity • • • • • Short-term Medium-term Long-term Single-phase: gas, oil Multi-phase: gas/oil Relating productivity to key parameters • Reservoir properties • Completions parameters • Operational parameters Estimating long-term recovery from limited production data iwalton@egi.utah.edu Some Shale Gas Production Data 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 2 4 6 time (years) 1,600 20000 18000 16000 14000 12000 10000 8000 6000 4000 2000 0 water rate drawdown gas rate 1,400 1,200 1,000 800 600 400 200 0 0 50 100 150 time (days) Baihly et al (SPE 135555) iwalton@egi.utah.edu 200 250 gas prodcution (Mscf/day) 0 water production (bbl/day) production rate (bscf/year) 1.8 Conventional Production Data Analysis Techniques: Decline Curve Analysis daily production, Mscf/day daily production data 1500 "b=1.6" "b=2" 1000 500 1500 2 1.5 1000 1 500 0.5 0 0 0 50 100 150 200 time (days) 250 qi (1 bDi t ) 1 for 0 b 1 b 0 0 300 1095 2190 3285 4380 5475 6570 7665 8760 time (days) Conventional Decline Analysis—Arps q 2.5 data "b=1.6" "b=2" "b=1.6" "b=2" cumulative production, bscf 2000 2000 Advantages •Quick, easy to use, familiar Disadvantages •Limited basis in the physics •Not appropriate for transient flow •No insights into production drivers •Exponent b is assumed to be constant iwalton@egi.utah.edu Shale Gas Production Models Numerical, incorporating flow in matrix and fracture networks − discrete fracture network − continuum model (dual porosity/dual permeability) Semi-analytic, • continuum model (dual porosity/dual permeability) • perturbation solution appropriate for shale gas reservoirs iwalton@egi.utah.edu Quantifying Productivity: Production Coefficient Theory suggests that for a substantial period of time cumulative production and production rate can be approximated by Q C p t, q 1 2 Cp t where C p depends on – – – – Pressures (bhfp, pore or reservoir pressure) Reservoir quality/ GIP (permeability, porosity) Gas properties (viscosity, compressibility, equation of state) Productive fracture surface area Cp p A 2 r p w2 ps c m k m Tm iwalton@egi.utah.edu 2 c m L km 2 New Production Data Analysis Method OLD NEW 1200000 cumulative production (mscf) 1.8 production rate (bsc/yearf) 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 1 2 3 4 5 6 time (years) • • 800000 600000 400000 200000 0 0 • • • 1000000 0 0.5 1 1.5 sqrt(time) years^0.5 2 2.5 Plot cumulative production against the square-root of time: expect straight line Slope of the line is the best metric of well productivity: Production Coefficient Solution is valid for many years of production Production data analysis is efficient and effective: data from thousands of wells have been analyzed Provides a rational basis for evaluating the production drivers, quantifying “what makes a good well”, assessing play-by-play variations and evaluating the role of natural fractures. iwalton@egi.utah.edu Impact of Completions Parameters 160 y = 17.109x - 77.545 cumulative production 140 120 350 ft spacing 100 250 ft spacing 80 y = 11.976x - 40.681 60 40 20 0 0 5 sqrt (time) 10 15 Ratio of production coefficients = 1.42 Cp p A 2 r p w2 ps c m k m Ratio of # fractures = 350/250 = 1.4 Impact on EUR? iwalton@egi.utah.edu Impact of Variable Drawdown Granite Wash Data (SPE 163820) iwalton@egi.utah.edu Early-time Transient Regime cumulative production 45000 40000 Barnett 35000 Fayettville 30000 Woodford 25000 Haynesville 20000 15000 10000 5000 0 0 1 2 sqrt (time) (sqrt(months)) iwalton@egi.utah.edu 3 4 water rate drawdown gas rate 1,600 1,400 1,200 1,000 800 600 400 200 0 0 50 100 150 200 20000 18000 16000 14000 12000 10000 8000 6000 4000 2000 0 gas prodcution (Mscf/day) water production (bbl/day) Identification of Flow Regimes 250 time (days) Transient regime I Transient regime II iwalton@egi.utah.edu Boundaryimpacted regime Decline rate and Arps exponent dq D dt q b Arps exponent: Linear Flow regime Boundary-impacted regime q 1 2 Cp t D 1 dD dt D 2 dt D Q Q (1 e D d1 t ) 1 2t b2 q Q e t b0 Tm iwalton@egi.utah.edu 2 1 4 Tm 2 c m L km 2 Variation of Arps b-exponent Transient regime I Transient regime II Boundaryimpacted regime Terminal decline rate: 12% b=?? b=2 b=0 iwalton@egi.utah.edu iwalton@egi.utah.edu New Estimates of Terminal Decline Rate and EUR •Terminal decline rate 2 1 D 4 Tm Tm 2 c m L 2 km • From production data, identify point at which linear flow ends: Q Qelf D Delf •From the semi-analytic solution transition to boundary dominated regime (exponential decline) begins (linear flow ends) at about t elf 0.15Tm Cumulative Oil (bbl) t t elf 900000 800000 700000 600000 500000 400000 300000 200000 100000 0 0 2 4 6 8 sqrt (time) (Months After Initial Production)^0.5 iwalton@egi.utah.edu New Estimates of Terminal Decline Rate and EUR D 2 1 4 Tm Tm 2 c m L 2 km t elf 0.15Tm • New estimate of terminal decline rate Delf 1 1 4 D 1.35D 2t elf 0.3Tm 0.3 2 Cumulative Oil (bbl) •New estimate of EUR 900000 800000 700000 600000 500000 400000 300000 200000 100000 0 0 2 4 6 8 sqrt (time) (Months After Initial Production)^0.5 iwalton@egi.utah.edu Impact of Terminal Decline Rate on EUR Qelf C p t elf C p t Delf D 2 1 4 Tm Q C p 1 4 t Delf Tm 3 16 2 c m L 2 km Cumulative Oil (bbl) Q Qelf Tm 1 D 900000 800000 700000 600000 500000 400000 300000 200000 100000 0 0 2 4 6 8 sqrt (time) (Months After Initial Production)^0.5 iwalton@egi.utah.edu Decline Rates: Summary • Shale gas wells produce in the infinite acting regime for many years. For instantaneous drawdown, decline rate ~ 1/2t, independent of the value of the production coefficient. • Variable drawdown in the first few months of production may reduce the early-time decline rate, and it is often negative. •Terminal decline rate is not arbitrary: it is related to the time at the end of linear flow. • Arps b-exponent varies in time in a rational manner. iwalton@egi.utah.edu Some key results Better characterized shale gas production through several stages of production including early-time transient, linear flow and boundary-dominated flow. Methodology of presenting and analyzing production data using an alternative metric for shale gas productivity—the Production Coefficient. Identified the major production drivers in terms of reservoir and completion parameters. Improved estimates of future production in comparison to conventional techniques. Demonstrated technique for estimating recovery from a limited amount of production data. iwalton@egi.utah.edu Thank You! Ian Walton, Ph.D. Senior Research Scientist iwalton@egi.utah.edu Reserves Estimation Unconventionals Houston August 2016 Project I 01024 iwalton@egi.utah.edu
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