Perspectives on the Interpretation of Flowback Data from Wells in Shale Reservoir Systems

Transcription

1 SPE Workshop Production and Reservoir Performance Through Pressure Management Perspectives on the Interpretation of Flowback Data from Wells in Shale Reservoir Systems Tom BLASINGAME Petroleum Engineering Texas A&M University College Station, TX (USA) Slide 1/30

2 Brief Biography: Blasingame Role: Robert L. Whiting Professor, Texas A&M U. B.S., M.S., and Ph.D. degrees from Texas A&M U. (PETE) Counts: (April 2015) 55 M.S. (thesis) and 31 M.Eng. (report, non-thesis) Graduates 12 Ph.D. Graduates Over 140 Technical Articles Recognition: SPE Distinguished Member (2000) SPE Distinguished Service Award (2005) SPE Distinguished Lecturer ( ) SPE Uren Award (2006) SPE Lucas Medal (2012) SPE DeGolyer Distinguished Service Medal (2013) SPE Distinguished Achievement Award for PETE Faculty (2014) Current Research Activities: (April 2015) Flow Phenomena in Ultra-Low Permeability Reservoir Systems Production Performance Analysis for Shale Systems Performance Behavior of Naturally Fractured Reservoir Systems Numerical Modeling of Ultra-Low Permeability Reservoir Systems Slide 2/30

3 SPE Workshop Production and Reservoir Performance Through Pressure Management Perspectives on the Interpretation of Flowback Data from Wells in Shale Reservoir Systems Orientation Time-Rate Analysis Tom BLASINGAME Petroleum Engineering Texas A&M University College Station, TX (USA) Slide 3/30

4 Rationale: Analysis of Well Performance "Well Performance Analysis" in Unconventional Reservoir Systems: To understand the characteristics which control performance. To evaluate completion/stimulation effectiveness. To forecast production and estimate reserves (EUR). Issues: Uncertainty/non-uniqueness Understanding of flow regimes Understanding of phase behavior p tf p wf conversion Understanding of stimulated volume Integration of geomechanics (data quality?) (flow mechanisms?) (near-critical fluids?) (water rates?) (drainage area?) (pressure dependencies?) Key Points for Analysis and Forecasting: Duration of data required for accurate estimates of EUR. Optimal well spacing/orientation. Reserves estimation method(s) must be "reasonably certain." Slide 4/30

5 Work Path: Analysis of Well Performance Slide 5/30

6 Guidance: Typical Flow Regimes in Unconventional Reservoir Systems Linear Flow: (fracture flow does not interfere) Required Model Parameters: Permeability (k) Fracture half-length (x f ) Fracture conductivity (F c ) Drainage area (A) Skin factor (s) Well length (L w ) Number of fractures (n f ) "SRV" Flow: ("depletion") (fracture flow does interfere) "Post-SRV" Flow: ("Compound Linear Flow") Slide 6/30

7 Time-Rate Behavior: (Formation) Linear Flow Theory Solution for a Single Fracture: (transient linear flow) p D t Dxf q q C A xf C Additive Fractures: (transient linear flow) 1 t ( p i p wf 1 ) B c 1 ( p t k A i xf q p wf tot tot 1 t 1 ) B C [ A c xf, 3 C ( A k t Note: These solutions are only valid for transient linear flow [i.e., the case of non-interfering pressure distributions (due to the fractures)]. q A xf,1 xf ) A xf, 4 tot A xf, A t xf, n ] 1 t Slide 7/30

8 Time-Rate Behavior: (Formation) Linear Flow (Synthetic Example) Formation Linear Flow Log-log diagnostic plot: log[q(t)] versus log[t ] (slope = -1:2) "qdb" (time-rate) plot: log[q(t)] log[d(t)] log[b(t)] versus log[t ] "Traditional" plot: q(t) versus 1/SQRT[t ] (straight-line portion) Extrapolation using a linear flow model will over-predict EUR NO clean-up/ flowback effects Region of overextrapolation NO clean-up/ flowback effects Slide 8/30

9 Time-Rate Behavior: Flow Regimes for a Multi-Fracture Horizontal Well Logarithm of Production Rate 1:2 Slope (high F cd ) q( t) 1:4 Slope (low F cd ) Bilinear Flow Regime Early-Time Regimes are HYPERBOLIC? q /[(1 bd i qlf ( t) alf [1/ t ] q ( ) [1/ 4 BLF t a BLF t ] i t) Linear Flow Regime ( 1/ b) ] Compound Linear Flow Regime Transition Regime Logarithm of Production Time 1:1 Slope Depletion (SRV?) Elliptical Flow Regime For Shales: days weeks months years decades Slide 9/30

10 Time-Rate Behavior: Power-Law Exponential Rate Relation Clean-up/ flowback effects are not significant for this case PLE Rate Relation: q( t) Decline Function: D(t) D( t) qˆ Hyperbolic Function: b(t) b( t) i 1 q D d dt exp[ D [ ndˆ dq dt D( t) i 1 ndˆ nd ˆ i t i D t Dˆ t (1 n) t i (1 n) n (1 n) ] ] 2 t n Ilk, D., Rushing, J.A., Perego, A.D., and Blasingame, T.A.,: "Exponential vs. Hyperbolic Decline in Tight Gas Sands Understanding the Origin and Implications for Reserve Estimates Using Arps' Decline Curves," paper SPE presented at the 2008 Annual SPE Technical Conference and Exhibition, Denver, CO, USA, September Slide 10/30

11 Rate-Time Analysis: Calibration Linear Flow (Gas Shales) [1/2] Data taken from publicly available sources Horizontal Shale (Dry) Gas Wells ONLY Discussion: START of "Linear Flow" (~3-6 months). END of "Linear Flow" (~9-36 months). "Linear Flow" is represented by b = 2. EUR requires at least 20+ months (except Haynesville ~1 year; and Barnett ~3 years). Heckman, T.L., et al (2013): Best Practices for Reserves Estimation in Unconventional Reservoirs Present and Future Considerations, Keynote presentation presented at the 2013 SPE Unconventional Resources Conference, The Woodlands, TX (USA), April Slide 11/30

12 Rate-Time Analysis: Calibration Linear Flow (Gas Shales) [2/2] Data taken from publicly available sources Horizontal Shale (Dry) Gas Wells ONLY Discussion: START of "Linear Flow" (~3-6 months). END of "Linear Flow" (~9-36 months). Heckman, T.L., et al (2013): Best Practices for Reserves Estimation in Unconventional Reservoirs Present and Future Considerations, Keynote presentation presented at the 2013 SPE Unconventional Resources Conference, The Woodlands, TX (USA), April "Linear Flow" is represented by linear trends on these plots. Square root time plot used to show linear portion of trend (G p (t) vs. SQRT(t) best view). Slide 12/30

13 SPE Workshop Production and Reservoir Performance Through Pressure Management Perspectives on the Interpretation of Flowback Data from Wells in Shale Reservoir Systems Production Diagnostics Tom BLASINGAME Petroleum Engineering Texas A&M University College Station, TX (USA) Slide 13/30

14 Production Diagnostics: Flow Regimes (1:4) (1:2) (1:2 Slope Linear flow/high fracture conductivity) 1 3 (1:4 Slope Low fracture conductivity) 2 (1:1) Pseudo-elliptical flow regime (flow from matrix to collection of fractures) might exist after fracture interference. EUR LF (VERY OPTIMISTIC) (1:1 Slope Fracture interference/depletion (SRV?)) EUR Dep (CONSERVATIVE???) Flow Regimes: (Barnett Shale Example) Schematic illustrates flow regimes exhibited by time-rate-pressure data. Duration/existence of flow regimes is DIFFERENT for each play. Slide 14/30

15 Production Diagnostics: Haynesville Examples Productivity Index vs. Production Time Reciprocal Productivity Index vs. Square Root of Production Time (1:1) Some clean-up/ flowback effects are evident for these cases. No clear straightline trend no linear flow? Discussion: Diagnosis of the performance of 9 wells producing in the same area. Performance comparison of multiple wells to identify characteristics. Differences in the productivity = f(completion and operational issues). Slide 15/30

16 Production Diagnostics: Eagle Ford Examples Linear flow apparent for first year (approximately). Some clean-up/ flowback effects are evident for these cases. (1:2) POSSIBLE depletion effects (could be liquid-loading). (1:1) Discussion: PLOT: Oil Productivity Index versus Oil Material Balance Time OBJECTIVE: Identify flow regimes/behavior exhibited by production data. Slide 16/30

17 Production Diagnostics: Eagle Ford Examples This is more of a "consistency/correlation" plot trends arise (indirectly) from the power-law exponential time-rate relation Diagnostics: PLOT: Oil Productivity Index versus Cumulative Oil Production. OBJECTIVE: (Empirically) project recovery based on flow behavior. Slide 17/30

18 SPE Workshop Production and Reservoir Performance Through Pressure Management Perspectives on the Interpretation of Flowback Data from Wells in Shale Reservoir Systems Perspectives on Flowback Tom BLASINGAME Petroleum Engineering Texas A&M University College Station, TX (USA) Slide 18/30

19 Flowback: Purpose and Process Objectives of flowback data analysis: Provide a comprehensive workflow for early-time flowback data. Provide a unique visualization of flowback data. Provide a correlative and integrated analysis of these data. Provide an interpretation of specific data features. Provide guidelines for flowback testing and optimal recovery. Process: Collection and quality control of well performance/completion data. Construct/calibrate a base well/reservoir model. Construct specialized plots to identify features (i.e., unloading). Correlate flowback data by empirical and non-parametric models. Implement an assessment loop to guide future practices. Slide 19/30

20 Flowback: Assortment of Plots Rationale for Plots: Base Plots: "Historical" data plots. Single Well Plots: Comparison of data functions on a per-well basis. Multi-Well Plots: Comparison of data functions across several wells. Slide 20/30

21 Flowback: Summary of Wells SPE Slide 21/30

22 Flowback: Choke Setting versus Production Time SPE Slide 22/30

23 Flowback: Gas-Water-Ratio Versus Cumulative Gas SPE Slide 23/30

24 Flowback: Reciprocal Gas PI versus Cumulative Gas SPE Slide 24/30

25 Flowback: Casing Pressure versus Gas Flowrate SPE Slide 25/30

26 Slide 26/30 Flowback: Correlations for Well B Flowback Data SPE

27 Flowback: p cf vs. Cumulative Gas Production SPE p cf a exp[ b Gp ] (?) Slide 27/30

28 Flowback: Work in SPE Summary: Correlation of flowback behavior using a q g predictive relation. Sequence of single-well and multi-well "dashboard" plots. Demonstrated analysis processes on a 5-well field example. Conclusions: (Demonstration of ) A parametric correlation of q g with q w, p tf, and choke history. A "dashboard" diagnostic plotting approach. Diagnostic value of accurate time-pressure-rate (TPR) data. Recommendations: "Optimal drawdown" practices for individual well/field cases. Analytical/semi-empirical models for the "water unloading" phase. Acquisition/integration of p wf and T wf (bottomhole measurements). Slide 28/30

29 Flowback: Lagniappe Slide 29/30

30 SPE Workshop Production and Reservoir Performance Through Pressure Management Perspectives on the Interpretation of Flowback Data from Wells in Shale Reservoir Systems End of Presentation Tom BLASINGAME Petroleum Engineering Texas A&M University College Station, TX (USA) Slide 30/30