Development of a Powerful Data-Analysis Tool Using Nonparametric Smoothing Models To Identify Drillsites in Tight Shale Reservoirs With High Economic Potential
- Quan Cai (Texas A&M University) | Wei Yu (Texas A&M University) | Hwa Chi Liang (Texas A&M University) | Jenn-Tai Liang (Texas A&M University) | Suojin Wang (Texas A&M University) | Kan Wu (Texas A&M University)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- June 2018
- Document Type
- Journal Paper
- 719 - 736
- 2018.Society of Petroleum Engineers
- Data analysis, Big data, Nonparametric Smoothing Models, Unconventional oil and gas reservoirs, Multiple linear regression models
- 7 in the last 30 days
- 348 since 2007
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The oil-and-gas industry is entering an era of “big data” because of the huge number of wells drilled with the rapid development of unconventional oil-and-gas reservoirs during the past decade. The massive amount of data generated presents a great opportunity for the industry to use data-analysis tools to help make informed decisions. The main challenge is the lack of the application of effective and efficient data-analysis tools to analyze and extract useful information for the decision-making process from the enormous amount of data available. In developing tight shale reservoirs, it is critical to have an optimal drilling strategy, thereby minimizing the risk of drilling in areas that would result in low-yield wells. The objective of this study is to develop an effective data-analysis tool capable of dealing with big and complicated data sets to identify hot zones in tight shale reservoirs with the potential to yield highly productive wells. The proposed tool is developed on the basis of nonparametric smoothing models, which are superior to the traditional multiple-linear-regression (MLR) models in both the predictive power and the ability to deal with nonlinear, higher-order variable interactions. This data-analysis tool is capable of handling one response variable and multiple predictor variables. To validate our tool, we used two real data sets—one with 249 tight oil horizontal wells from the Middle Bakken and the other with 2,064 shale gas horizontal wells from the Marcellus Shale. Results from the two case studies revealed that our tool not only can achieve much better predictive power than the traditional MLR models on identifying hot zones in the tight shale reservoirs but also can provide guidance on developing the optimal drilling and completion strategies (e.g., well length and depth, amount of proppant and water injected). By comparing results from the two data sets, we found that our tool can achieve model performance with the big data set (2,064 Marcellus wells) with only four predictor variables that is similar to that with the small data set (249 Bakken wells) with six predictor variables. This implies that, for big data sets, even with a limited number of available predictor variables, our tool can still be very effective in identifying hot zones that would yield highly productive wells. The data sets that we have access to in this study contain very limited completion, geological, and petrophysical information. Results from this study clearly demonstrated that the data-analysis tool is certainly powerful and flexible enough to take advantage of any additional engineering and geology data to allow the operators to gain insights on the impact of these factors on well performance.
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Cook, E. R. and Peters, K. 1981. The Smoothing Spline: A New Approach to Standardizing Forest Interior Tree-Ring Width Series for Dendroclimatic Studies. Tree-Ring Bull. 41: 45–53.
Duchamp, T. and Werner, S. 2003. Spline Smoothing on Surfaces. Journal of Computational and Graphical Statistics 12 (2): 354–381. https://doi.org/10.1198/1061860031743.
Eburi, S., Jones, S., Houston, T. et al. 2014. Analysis and Interpretation of Haynesville Shale Subsurface Properties, Completion Variables, and Production Performance Using Ordination, A Multivariate Statistical Analysis Technique. Presented at the SPE Annual Technical Conference and Exhibition, Amsterdam, 27–29 October. SPE-170834-MS. https://doi.org/10.2118/170834-MS.
Esmaili, S. and Mohaghegh, S. D. 2016. Full-Field Reservoir Modeling of Shale Assets Using Advanced Data-Driven Analytics. Geoscience Frontiers 7 (1): 11–20. https://doi.org/10.1016/j.gsf.2014.12.006.
Fan, J. 1993. Local Linear Regression Smoothers and Their Minimax Efficiencies. The Annals of Statistics 21 (1): 196–216.
Gao, C. and Gao, H. 2013. Evaluating Early-Time Eagle Ford Well Performance Using Multivariate Adaptive Regression Splines (MARS). Presented at the SPE Annual Technical Conference and Exhibition, New Orleans, 30 September–2 October. SPE-166462-MS. https://doi.org/10.2118/166462-MS.
Golub, G. H., Heath, M., and Wahba, G. 1979. Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter. Technometrics 21 (2): 215–223.
Grujic, O., Silva, C. D., and Caers, J. 2015. Functional Approach to Data Mining, Forecasting, and Uncertainty Quantification in Unconventional Reservoirs. Presented at the SPE Annual Technical Conference and Exhibition, Houston, 28–30 September. SPE-174849-MS. https://doi.org/10.2118/174849-MS.
Gupta, S., Fuehrer, F., and Jeyachandra, B. C. 2014. Production Forecasting in Unconventional Resources Using Data Mining and Time Series Analysis. Presented at the SPE/CSUR Unconventional Resources Conference, Calgary, 30 September–2 October. SPE-171588-MS. https://doi.org/10.2118/171588-MS.
Hastie, T., Tibshirani, R., and Friedman, J. 2005. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer: New York.
King, V. M. and Wray, L. 2014. Completion Optimization Utilizing Multivariate Analysis in the Bakken and Three Forks Formations. Presented at the SPE Western North American and Rocky Mountain Joint Regional Meeting, Denver, 16–18 April. SPE-169534-MS. https://doi.org/10.2118/169534-MS.
LaFollette, R. F., Izadi, G., and Zhong, M. 2014. Application of Multivariate Statistical Modeling and Geographic Information Systems Pattern-Recognition Analysis to Production Results in the Eagle Ford Formation of South Texas. Presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, USA, 4–6 February. SPE-168628-MS. https://doi.org/10.2118/168628-MS.
Lolon, E., Hamidieh, K., Weijers, L. et al. 2016. Evaluating the Relationship Between Well Parameters and Production Using Multivariate Statistical Models: A Middle Bakken and Three Forks Case History. Presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, USA, 9–11 February. SPE-179171-MS. https://doi.org/10.2118/179171-MS.
Maucec, M., Singh, A. P., Bhattacharya, S. et al. 2015. Multivariate Analysis and Data Mining of Well-Stimulation Data by Use of Classification-and-Regression Tree With Enhanced Interpretation and Prediction Capabilities. SPE Econ & Mgmt 7 (2): 60–71. SPE-166472-PA. https://doi.org/10.2118/166472-PA.
Mohaghegh, S. D. 2016. Determining the Main Drivers in Hydrocarbon Production From Shale Using Advanced Data-Driven Analytics—A Case Study in Marcellus Shale. Journal of Unconventional Oil and Gas Resources 15: 146–157. https://doi.org/10.1016/j.juogr.2016.07.004.
Neter, J., Wasserman, W., and Kutner, M. 1989. Applied Linear Regression Model, second edition. Boston, Massachusetts: Richard D. Irwin, Inc.
Picton, P. 2000. Neural Networks. New York City: PALGRAVE.
Pham, T. D. and Liu, X. 1995. Neural Networks for Indentification, Prediction and Control. London: Springler-Verlag London Limited.
Schuetter, J., Mishra, S., Zhong, M. et al. 2015. Data Analytics for Production Optimization in Unconventional Reservoirs. Presented at the SPE/AAPG/SEC Unconventional Resource Technology Conference, San Antonio, Texas, USA, 20–22 July. URTEC-2167005-MS. https://doi.org/10.15530/URTEC-2015-2167005.
Singh, A. 2015. Root-Cause Identification and Production Diagnostic for Gas Wells With Plunger Lift. Presented at the SPE Reservoir Characterization and Simulation Conference and Exhibition, Abu Dhabi, 14–16 September. SPE-175564-MS. https://doi.org/10.2118/175564-MS.
Tibshirani, R. 1996. Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society. Series B (Statistical Methodology) 58 (1): 267–288.
US Department of Energy (DOE). 2013. Modern Shale Gas Development in the United States: An Update, http://www.netl.doe.gov/File%20Library/Research/Oil-Gas/shale-gas-primer-update-2013.pdf.
US Energy Information Administration 2013a. Technically Recoverable Shale Oil and Shale Gas Resources: An Assessment of 137 Shale Formations in 41 Countries Outside the United States. http://www.eia.gov/analysis/studies/worldshalegas/.
US Energy Information Administration. 2013b. Early Release Overview. http://www.eia.gov/forecasts/aeo/er/pdf/0383er%282013%29.pdf.
US Energy Information Administration 2014a. Annual Energy Outlook. http://www.eia.gov/forecasts/aeo/mt_naturalgas.cfm.
US Energy Information Administration. 2014b. Drilling Productivity Report, http://www.eia.gov/petroleum/drilling/#tabs-summary-1.
United States Geological Survey (USGS). 2013. USGS Releases New Oil and Gas Assessment for Bakken and Three Forks Formations. USGS Webpost, “<https://www2.usgs.gov/blogs/features/usgs_top_story/usgs-releases-new-oil-and-gas-assessment-for-bakken-and-three-forks-formations/>”.
Voneiff, G., Sadeghi, S., Bastian, P. et al. 2013. A Well-Performance Model Based on Multivariate Analysis of Completion and Production Data From Horizontal Well in the Montney Formation in British Columbia. Presented at the SPE Unconventional Resources Conference, Calgary, 5–7 November. SPE-167154-MS. https://doi.org/10.2118/167154-MS.
Voneiff, G., Sadeghi, S., Bastian, P. et al. 2014. Probabilistic Forecasting of Horizontal Well Performance in Unconventional Reservoirs Using Publicly-Available Completion Data. Presented at the SPE Unconventional Resources Conference, The Woodlands, Texas, USA, 1–3 April. SPE-168978-MS. https://doi.org/10.2118/168978-MS.
Willigers, B. J. A., Begg, S., and Bratvold, R. B. 2014. Combining Geostatistics With Bayesian Updating to Continually Optimize Drilling Strategy in Shale-Gas Plays. SPE Res Eval & Eng 17 (4): 507–519. SPE-164816-PA. https://doi.org/10.2118/164816-PA.
Wood, S. 2006. Generalized Additive Models: An Introduction With R. New York: CRC Press.
Zhong, M., Schuetter, J., Mishra, S. et al. 2015. Do Data Mining Methods Matter?: A Wolfcamp Shale Case Study. Presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, USA, 3–5 February. SPE-173334-MS. https://doi.org/10.2118/173334-MS.