Evaluating Fracture-Fluid Flowback in Marcellus Using Data-Mining Technologies
- Qiumei Zhou (Pennsylvania State University) | Robert Dilmore (National Energy Technology Laboratory) | Andrew Kleit (Pennsylvania State University) | John Yilin Wang (Pennsylvania State University)
- Document ID
- Society of Petroleum Engineers
- SPE Production & Operations
- Publication Date
- May 2016
- Document Type
- Journal Paper
- 133 - 146
- 2016.Society of Petroleum Engineers
- marcellus shale, flowback recovery, stimulation effectiveness, data mining
- 6 in the last 30 days
- 754 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 5.00|
|SPE Non-Member Price:||USD 35.00|
Natural-gas recovery from low-permeability unconventional reservoirs--enabled by advanced horizontal drilling and multistage hydraulic-fracture treatment--has become a very important energy resource in the past decade. While evaluation of early gas-production data to assess likely rate decline and ultimate gas recovery has been reported in literature, flowback-water recovery has been given little consideration. Fracture-fluid flowback is defined herein as aqueous phase produced within 3 weeks following a fracture treatment (exclusive of well shut-in time). Field data from Marcellus shale wells in northeastern West Virginia indicated approximately 2 to 26% of the fracture fluid is recovered during flowback. However, stimulation of gas shale is a complex engineering process, and the factors that control the volumetric flowback performance are not well-understood.
The objective of this paper is to use post hoc analysis to identify correlations between fracture-fluid flowback and attributes of well completion and geological setting, and to identify those factors that are most important in predicting flowback performances. To accomplish this objective we selected a representative subset of 187 wells for which complete data are available (from a full set of 631 wells), including well location, completion data, hydraulic-fracture-treatment data, and production data. The wells were classified into four groups on the basis of geological settings. For each geological group, engineering and statistical analyses were applied to study the correlation between flowback data and well completion through traditional regression methods. Important factors considered to affect flowback-water recovery efficiency include the number of hydraulic-fracture stages, lateral length, vertical depth, proppant mass applied, proppant size, fracture-fluid volume applied, treatment rate, and shut-in time. The total proppant mass, proppant size, and shut-in time have a relatively large influence on volumetric flowback performance.
The new results enable one to estimate flowback volume in a spatial domain on the basis of the known geological conditions and completion parameters, and lead to a better understanding of flowback behaviors in Marcellus shale. This also helps industry manage flowback water and optimize production operations.
|File Size||1 MB||Number of Pages||14|
Ahn, C. H., Chang, O. C., Dilmore, R. et al. 2014. A Hydraulic Fracture Network Propagation Model in Shale Gas Reservoirs: Parametric Studies to Enhance the Effectiveness of Stimulation. Presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Denver, 25–27 August. SPE-2014-1922580-MS. http://dx.doi.org/10.15530/urtec-2014-1922580.
Alfi, M., Yan, B., Cao, Y. et al. 2014. Three-Phase Flow Simulation in Ultra-Low Permeability Organic Shale via a Multiple Permeability Approach. Presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Denver, 25–27 August. SPE-2014-1895733-MS. http://dx.doi.org/10.15530/urtec-2014-1895733.
Alkouh, A., McKetta, S., and Wattenbarger, R. A. 2014. Estimation of Effective-Fracture Volume Using Water-Flowback and Production Data for Shale-Gas Wells. J Can Pet Technol 53 (5): 290–303. SPE-166279-PA. http://dx.doi.org/10.2118/166279-PA.
Barbot, E., Vidic, N. S., Gregory, K. B. et al. 2013. Spatial and Temporal Correlation of Water Quality Parameters of Produced Waters from Devonian-Age Shale following Hydraulic Fracturing. Environ. Sci. Technol. 47 (6): 2562–2569. http://dx.doi.org/10.1021/es304638h.
Bertoncello, A., Wallace, J., Blyton, C. et al. 2014. Imbibition and Water Blockage in Unconventional Reservoirs: Well-Management Implications Duing Flowback and Eartly Production. SPE Res Eval & Eng 17 (4): 497–506. SPE-167698-PA. http://dx.doi.org/10.2118/167698-PA.
Blauch, M. E, Myers R. R., Moore, T. et al. 2009. Marcellus Shale Post-Frac Flowback Waters—Where is All the Salt Coming from and What are the lmplications? Presented at the SPE Eastern Regional Meeting, Charleston, West Virginia, USA, 23–25 September. SPE-125740-MS. http://dx.doi.org/10.2118/125740-MS.
Bruner, K. R. and Smosna, R. 2010. A Comparative Study of the Mississippian Barnett Shale, Fort Worth Basin, and Devonian Marcellus Shale, Appalachian Basin. Report, DOE/NETL-2011/1478, US Department of Energy/National Energy Technology Laboratory (April 2011).
Carter, K.M. 2007. Subsurface Rock Correlation Diagram, Oil and Gas Producing Regions of Pennsylvania. Open-file Report OFOG 07-01.1, Pennsylvania Geological Survey, Department of Conservation and Natural Resources, http://www.dcnr.state.pa.us/topogeo/publications/pgspub/openfile/drc/index.htm (accessed 15 October 2014).
Carter, K. E., Hakala, J. A., and Hammack, R. W. 2013. Hydraulic Fracturing and Organic Compounds—Uses, Disposal and Challenges. Presented at the SPE Eastern Regional Meeting, Pittsburgh, Pennsylvania, USA, 20–22 August. SPE-165692-MS. http://dx.doi.org/10.2118/165692-MS.
Centurion, S. 2011. Eagle Ford Shale: A Multi-Stage Hydraulic Fracturing, Completion Trends and Production Outcome Study Using Practical Data Mining Techniques. Presented at the SPE Eastern Regional Meeting, Columbus, Ohio, USA, 17–19 August. SPE-149258-MS. http://dx.doi.org/10.2118/149258-MS.
Centurion, S., Cade, R., and Luo, X. 2012. Eagle Ford Shale: Hydraulic Fracturing, Completion, and Production Trends: Part II. Presented at the SPE Annual Techinical Conference and Exhibition, San Antonio, Texas, USA, 8–10 Octorber. SPE-158501-MS. http://dx.doi.org/10.2118/158501-MS.
Centurion, S., Cade, R., and Luo, X. 2013. Eagle Ford Shale: Hydraulic Fracturing, Completion and Production Trends, Part III. Presented at the SPE Annual Techinical Conference and Exhibition, New Orleans, 30 September–2 Octorber. SPE-166494-MS. http://dx.doi.org/10.2118/166494-MS.
Clarkson C. R. and Williams-Kovacs, J. 2013. Modeling Two-Phase Flowback of Multifractured Horizontal Wells Completed in Shale. SPE J. 18 (4): 795–812. SPE-162593-PA. http://dx.doi.org/10.2118/162593-PA.
Cunningham, C. F., Cooley, L., Wozniak, G. et al. 2012. Using Multiple Linear Regression To Model EURs of Horizontal Marcellus Shale Wells. Presented at the SPE Eastern Regional Meeting, Lexington, Kentucky, USA, 3–5 October. SPE-161343-MS. http://dx.doi.org/10.2118/161343-MS.
Esmaili, S., Kalantari-Dahaghi, A., and Mohaghegh, S. D. 2012a. Modeling and History Matching of Hydrocarbon Production from Marcellus Shale using Data Mining and Pattern Recognition Technologies. Presented at the SPE Eastern Regional Meeting, Lexington, Kentucky, USA, 3–5 October. SPE-161184-MS. http://dx.doi.org/10.2118/161184-MS.
Esmaili, S., Kalantari-Dahaghi, A., and Mohaghegh, S. D. 2012b. Forecasting, Sensitivity and Economic Analysis of Hydrocarbon Production from Shale Plays Using Artificial Intelligence & Data Mining. Presented at the SPE Canadian Unconventional Resource Conference, Calgary, 30 October–1 November. SPE-162700-MS. http://dx.doi.org/10.2118/162700-MS.
Haluszczak, L. O., Rose, A. W., and Kump, L. R. 2013. Geochemical Evaluation of Flowback Brine from Marcellus Gas Wells in Pennsylvania, USA. Applied Geochemistry 28: 55–61. http://dx.doi.org/10.1016/j.apgeochem.2012.10.002.
Ketchen, D. J. and Shook, C. L. 1996. The Application of Cluster Analysis in Strategic Management Research: An Analysis and Critique. Strategic Management Journal 17 (6): 441–458. http://dx.doi.org/10.1002/(SICI)1097-0266(199606)17:6<441::AID-SMJ819>3.0.CO;2-G.
Kutner, M., Nachtsheim, C., Neter, J. et al. 2004. Applied Linear Statistical Model, fifth edition. McGraw-Hill/lrwin.
LaFollette, R. F. and Holcomb, W. D. 2011. Practical Data Mining: Lessons Learned from the Barnett Shale of North Texas. Presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, USA, 24–26 January. SPE-140524-MS. http://dx.doi.org/10.2118/140524-MS.
LaFollette, R. F., Holcomb, W. D., and Aragon, J. 2012. Practical Data Mining: Analysis of Barnett Shale Production Results with Emphasis on Well Completion and Fracture Stimulation. Presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, USA, 6–8 February. SPE-152531-MS. http://dx.doi.org/10.2118/152531-MS.
Lan, Q., Ghanbari, E., Dehghanpour H. et al. 2014. Water Loss Versus Soaking Time: Spontaneous Imbibition in Tight Rocks. Energy Technology 2 (12): 1033–1039. http://dx.doi.org/10.1002/ente.201402039.
Lee, D. S., Herman, J. D., and Elsworth, D. 2010. A Critical Evaluation of Unconventional Gas Recovery From the Marcellus Shale, Northeastern United States. Presented at the 44th US Rock Mechanics Symposium and 5th US-Canada Rock Mechanics Symposium, Salt Lake City, Utah, USA, 27–30 June. ARMA-10-440.
Lindeman, R. H., Merenda, P. F., and Gold, R. Z. 1980. Introduction to Bivariate and Multivariate Analysis, first edition. Dallas, Texas: Scott, Foresman.
Mahalanobis, P. C. 1936. On the generalised distance in statistics. Proceedings of the National Institute of Sciences of India 2 (1): 49–55.
Rousseeuw, P. J. 1987. Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis. Journal of Computational and Applied Mathematics 20: 53–65. http://dx.doi.org/10.1016/0377-0427(87)90125-7.
Seales, M. 2015. Analysis of Fracture Fluid Cleanup and Long-Term Recovery in Shale Gas Reservoirs. PhD dissertation, Penn State University, State College, Pennsylvania (August 2015).
Shelley, B., Grieser, B., Johnson, B. J. et al. 2008. Data Analysis of Barnett Shale Completions. SPE J. 13 (3): 366–374. SPE-100674-PA. http://dx.doi.org/10.2118/100674-PA.
Soeder, D. J. 1988. Porosity and Permeability of Eastern Devonian Gas Shale. SPE Form Eval 3 (1): 116–124. SPE-15213-PA. http://dx.doi.org/10.2118/15213-PA.
Sweeney, J., Filer, J., Patchen, D. et al. 1986. Stratigraphy and Petroleum Production of Middle and Upper Devonian Shales, Northwestern West Virginia. Presented at the SPE Unconventional Gas Technology Symposium, Louisville, Kentucky, USA, 18–21 May. SPE-15222-MS. http://dx.doi.org/10.2118/15222-MS.
Ward, J. A. 2010. Kerogen Density in the Marcellus Shale. Presented at the SPE Unconventional Gas Conference, Pittsburgh, Pennsylvania, USA, 23–25 February. SPE-131767-MS. http://dx.doi.org/10.2118/131767-MS.
Zammerilli, A. M. 2010. Projecting the Economic Impact of Marcellus Shale Gas Development inWest Virginia: A Preliminary Analysis Using Public Available Data. Report, DOE/NETL-402/033110, US Department of Energy/National Energy Technology Laboratory (31March 2010).
Zhou, Q., Dilmore, R., Kleit, A. et al. 2014. Evaluating Gas Production Performances in Marcellus Using Data Mining Technologies. Journal of Natural Gas Science and Engineering 20: 109–120. http://dx.doi.org/10.1016/j.jngse.2014.06.014.