Horizontal Shale Well EUR Determination Integrating Geology, Machine Learning, Pattern Recognition and MultiVariate Statistics Focused on the Permian Basin
- Abhishek Gaurav (Texas Standard Oil LLC)
- Document ID
- Society of Petroleum Engineers
- SPE Liquids-Rich Basins Conference - North America, 13-14 September, Midland, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2017. Society of Petroleum Engineers
- 7.6.6 Artificial Intelligence, 7 Management and Information, 7.6.4 Data Mining, 7.6 Information Management and Systems
- Shale Oil, Multi-variate Statistics, Horizontal shale wells, Pattern Recognition, Machine Learning
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- 64 since 2007
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The objective of this work is to accurately determine horizontal shale well EUR for an area integrating geology, machine learning, pattern recognition and statistical analysis using various parameters of nearby producing horizontal shale wells, as inputs.
This work utilizes local geological information followed by execution of machine learning to identify critical well parameters that lead to better production. Then a pattern recognition step is performed while making sure the number of wells in each category are statistically significant. This also serves as a quality control measure by not basing the conclusion solely on the results of machine learning. The conclusions are verified using available literature on correlation between well production and various well parameters. Wells with optimum (controllable) parameters are selected to obtain a type curve for the target zone(s) in the area of interest.
The above-mentioned methodology helped in making the type-curve/EUR determination process scientific, systematic and seamless. Machine learning helped in identifying the key well-parameters that correlate to better production. Visual pattern recognition strengthened the confidence in the relationships identified in the last step. Different parameters were shown to affect production in different areas/targets confirming that every shale asset requires a thorough research before reaching a reasonable conclusion. The type-curves were established for each Wolfcamp bench in the area of interest selecting wells with optimum completions. The optimum completions parameters were identified by the methodology prescribed in the paper. This assisted in identifying the area of interest's true economic potential with regards to horizontal shale well development.
This paper prescribes a novel scientific data-intensive methodology to systemically use well data in a step-wise manner to identify the type-curve and EUR/well for an area, thereby determining the area's true economic potential.
Along with the prescribed big-data mining methodology, the most important take away from this study is: for the optimum evaluation of shale assets it is critical to tie in the controllable well parameters to well production. Once this relationship is established, the type-curve determination and the EUR estimation can be done more accurately.
|File Size||2 MB||Number of Pages||19|
Alabboodi, M.J., Mohaghegh, S.D. and Vass, R.L. 2016. Conditioning the Estimating Ultimate Recovery of Shale Wells to Reservoir and Completion Parameters, Presented at the Eastern Regional Meeting, Canton, 13-15 September. SPE-184064- MS. https://doi.org/10.2118/184064-MS
Gaurav, A., Gibbon, E.J. and Roberson, T.M. 2017. Asset Evaluation Utilizing Multi-variate Statistics Integrating Data-mining, Completion Optimization, And Geology Focused on Multi-bench Shale Plays, Presented at the Unconventional Resources Conference, 15-16 February. SPE-185018-MS. https://doi.org/10.2118/185018-MS
Lolon, E., Hamidieh, K., Weijers, L., Mayerhofer, M., Melcher, H. and Oduba, O. 2016. Evaluating the Relationship Between Well Parameters and Production Using Multivariate Statistical Models: A Middle Bakken and Three Forks Case History, Presented at the Hydraulic Fracturing Technology Conference, The Woodlands, 9-11 February. SPE-179171-MS. https://doi.org/10.2118/179171-MS
Mohaghegh, S.D., Gaskari, R. and Maysami, M. 2017. Shale Analytics: Making Production and Operational Decisions Based on Facts: A Case Study in Marcellus Shale, Presented at the Hydraulic Fracturing Technology Conference, The Woodlands, 24-26 January. SPE-184822-MS. https://doi.org/10.2118/184822-MS
Schuetter, J., Mishra, S., Zhong, M. and LaFollette, R. 2015. Data Analytics for Production Optimization in Unconventional Reservoirs, Presented at the Unconventional Resources Technology Conference, San Antonio, 20-22 July. SPE-178653-MS. https://doi.org/10.15530/URTEC-2015-2167005
Somanchi, K., O’Brien, C., Huckabee, P. and Ugueto, G.2016. Insights and Observation into Limited-Entry Perforation Dynamics from Fiber-Optic Diagnostics. Presented at the Unconventional Resources Technology Conference, San Antonio, 12 August. URTeC-2458389. https://doi.org/10.15530/URTEC-2016-2458389
Zakhour, N., Shoemaker, M. and Lee, D. 2015. Integrated Workflow Using 2D Seismic and Geomechanical Properties with Microseismic and Stimulation Data to Optimize Completions Methodologies: Wolfcamp Shale-oil Play Case Study in the Midland Basin. Presented at the Unconventional Resources Technology Conference, San Antonio, 20-22 July. SPE-177298- MS. http://dx.doi.org/10.2118/177298-MS
Walls, D. and Morcote, A.2015. Quantifying Variability of Reservoir Properties from a Wolfcamp Formation Core. Presented at the Unconventional Resources Technology Conference, San Antonio, 20-22 July. SPE-178601-MS. http://dx.doi.org/10.2118/178601-MS
Wu, W. and Sharma, M.M.2015. Acid Fracturing Shales: Effect of Dilute Acid on Properties and Pore Structure of Shale. Presented at the Hydraulic Fracturing Technology Conference, The Woodlands, 3-5 February. SPE-173390-MS. https://doi.org/10.2118/173390-MS