When Petrophysics Meets Big Data: What can Machine Do?
- Chicheng Xu (Aramco Services Company: Aramco Research Center) | Siddharth Misra (The University of Oklahoma) | Poorna Srinivasan (Aramco Services Company: Aramco Research Center) | Shouxiang Ma (Saudi Aramco)
- Document ID
- Society of Petroleum Engineers
- SPE Middle East Oil and Gas Show and Conference, 18-21 March, Manama, Bahrain
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- 5.1 Reservoir Characterisation, 3 Production and Well Operations, 7.6 Information Management and Systems, 7 Management and Information, 1.2.3 Rock properties, 1.12 Drilling Measurement, Data Acquisition and Automation, 7.6.6 Artificial Intelligence, 1.12.4 Sensor Technology, 0.2 Wellbore Design, 0.2.2 Geomechanics, 3.3 Well & Reservoir Surveillance and Monitoring, 5 Reservoir Desciption & Dynamics, 7.6.4 Data Mining
- Formation Evaluation, Geological Modeling, Reservoir Characterization, Machine Learning, Petrophysical Data-Driven Analytics
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Petrophysics is a pivotal discipline that bridges engineering and geosciences for reservoir characterization and development. New sensor technologies have enabled real-time streaming of large-volume, multi-scale, and high-dimensional petrophysical data into our databases. Petrophysical data types are extremely diverse, and include numeric curves, arrays, waveforms, images, maps, 3-D volumes, and texts. All data can be indexed with depth (continuous or discrete) or time. Petrophysical data exhibits all the "7V" characteristics of big data, i.e., volume, velocity, variety, variability, veracity, visualization, and value. This paper will give an overview of both theories and applications of machine learning methods as applicable to petrophysical big data analysis.
Recent publications indicate that petrophysical data-driven analytics (PDDA) has been emerging as an active sub-discipline of petrophysics. Field examples from the petrophysics literature will be used to illustrate the advantages of machine learning in the following technical areas: (1) Geological facies classification or petrophysical rock typing; (2) Seismic rock properties or rock physics modeling; (3) Petrophysical/geochemical/geomechanical properties prediction; (3) Fast physical modeling of logging tools; (4) Well and reservoir surveillance; (6) Automated data quality control; (7) Pseudo data generation; and (8) Logging or coring operation guidance.
The paper will also review the major challenges that need to be overcome before the potentially game-changing value of machine learning for petrophysics discipline can be realized. First, a robust theoretical foundation to support the application of machine leaning to petrophysical interpretation should be established; second, the utility of existing machine learning algorithms must be evaluated and tested in different petrophysical tasks with different data scenarios; third, procedures to control the quality of data used in machine leaning algorithms need to be implemented and the associated uncertainties need to be appropriately addressed. The paper will outlook the future opportunities of enabling advanced data analytics to solve challenging oilfield problems in the era of the 4th industrial revolution (IR4.0).
|File Size||1 MB||Number of Pages||25|
Aifa, T. (2014). Neural network applications to reservoirs: physics-based models and data models. Journal of Petroleum Science and Engineering, 123, 1-6. doi: 10.1016/j.petrol.2014.10.015.
Akande, K. O., Olatunji, S. O., Owolabi, T. O., and AbdulRaheem, A. (2015). Comparative analysis of feature selection-based machine learning techniques in reservoir characterization. Society of Petroleum Engineers. doi: 10.2118/178006-MS.
Akinnikawe, O., Lyne, S., and Roberts, J. (2018). Synthetic well log generation using machine learning techniques. Unconventional Resources Technology Conference. doi: 10.15530/URTEC-2018-2877021.
Al-Mudhhi, M. A., Ma, S. M., Al-Hajari, A. A., Lewis, K., Berberian, G., Butt, P. J., & Richter, P. (2005, January 1). Geo-Steering with Advanced LWD Technologies - Placement of Maximum Reservoir Contact Wells in a Thinly Layered Carbonate Reservoir. International Petroleum Technology Conference. doi: 10.2523/IPTC-10077-MS
Alqahtani, N., Armstrong, R. T., and Mostaghimi, P. (2018). Deep learning convolutional neural networks to predict porous media properties. Society of Petroleum Engineers. doi: 10.2118/191906-MS.
Andrianova, A., Simonov, M., Perets, D., Margarit, A., Serebryakova, D., Bogdanov, Y., Bukharev, A. (2018). Application of machine learning for oilfield data quality improvement (Russian). Society of Petroleum Engineers. doi: 10.2118/191601-18RPTC-RU.
Anifowose, F. A., Abdulraheem, A., Al-Shuhail, A. A., and Schmitt, D. P. (2013). Improved permeability prediction from seismic and log data using artificial intelligence techniques. Society of Petroleum Engineers. doi: 10.2118/164465-MS.
Anifowose, F., Adeniye, S., Abdulraheem, A., and Al-Shuhail, A. (2016). Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models. Journal of Petroleum Science and Engineering, 145, 230-237.
Archie, G. E. (1942). The Electrical Resistivity Log as an Aid in Determining Some Reservoir Characteristics. Society of Petroleum Engineers. doi: 10.2118/942054-G.
Bergman, D. L., Henning, M. J., Sarma, P., and Hunt, I. (2017). Applying statistical learning to quantitative well log analysis. Society of Petroleum Engineers. doi: 10.2118/186044-MS.
Carpenter, C. (2016). Geology-driven estimated-ultimate-recovery prediction with deep learning. Society of Petroleum Engineers. doi: 10.2118/0516-0074-JPT.
Crnkovic-Friis, L., and Erlandson, M. (2015). Geology Driven EUR Prediction Using Deep Learning. Society of Petroleum Engineers. doi: 10.2118/174799-MS.
Cuddy, S. J., and Putnam, T. W. (1998). Litho-facies and permeability prediction from electrical logs using fuzzy logic. Society of Petroleum Engineers. doi: 10.2118/49470-MS.
Dashevskiy, D., Dubinsky, V., & Macpherson, J. D. (1999). Application of Neural Networks for Predictive Control in Drilling Dynamics. Society of Petroleum Engineers. doi: 10.2118/56442-MS.
IKTVA. (2017). Saudi Aramco Report. http://iktva.sa/.
Ismagilov, A., Sudakov, V., Nurgaliev, D., Murtazin, T., Usmanov, S., and Nugumanova, N. (2018). Machine learning approach to open hole interpretation and static modelling applied to a giant field. Society of Petroleum Engineers. doi: 10.2118/191595-18RPTC-MS.
Jain, V., Gzara, K., Makarychev, G., Minh, C. C., and Heliot, D. (2015). Maximizing information through data driven analytics in petrophysical evaluation of well logs. Society of Petroleum Engineers. doi: 10.2118/174735-MS.
Kazak, A., Chugunov, S., and Chashkov, A. (2018). Integration of large-area Scanning-Electron-Microscopy imaging and automated mineralogy/petrography data for selection of nanoscale pore-space characterization sites. Society of Petroleum Engineers. doi: 10.2118/191369-PA.
Khan, M. R., Tariq, Z., and Abdulraheem, A. (2018). Machine learning derived correlation to determine water saturation in complex lithologies. Society of Petroleum Engineers. doi: 10.2118/192307-MS.
Korjani, M. M., Popa, A. S., Grijalva, E., Cassidy, S., and Ershaghi, I. (2016). Reservoir characterization using fuzzy kriging and deep learning neural networks. Society of Petroleum Engineers. doi: 10.2118/181578-MS.
Koryabkin, V., Stishenko, S., Kolba, P., and Tatur, O. (2018). Application of the combined real-time petrophysical and geosteering model to increase drilling efficiency. Society of Petroleum Engineers. doi: 10.2118/191689-18RPTC-MS.
Ma, S. (Mark). (2018). Technology Focus: Formation Evaluation (August 2018). Society of Petroleum Engineers. doi: 10.2118/0818-0050-JPT.
Mahmoud, A. A., ElKatatny, S., Abdulraheem, A., Mahmoud, M., Omar Ibrahim, M., and Ali, A. (2017). New technique to determine the total organic carbon based on well logs using artificial neural betwork (White Box). Society of Petroleum Engineers. doi: 10.2118/188016-MS.
MacAllister, D. J., Day, R., & McCarmack, M. D. (1996). Expert Systems: A Five-Year Perspective. Society of Petroleum Engineers. doi: 10.2118/26247-PA.
Mehta, A. (2016). Tapping the value from big data analytics. Journal of Petroleum Technology, 68(12). doi: 10.2118/1216-0040-JPT.
Negara, A., Jin, G., and Agrawal, G. (2016). Enhancing rock property prediction from conventional well logs using machine learning technique - case studies of conventional and unconventional reservoirs. Society of Petroleum Engineers. doi: 10.2118/183106-MS.
Noshi, C. I., Assem, A. I., and Schubert, J. J. (2018). The role of big data analytics in exploration and production: A review of benefits and applications. Society of Petroleum Engineers. doi: 10.2118/193776-MS.
Odi, U. and Nguyen, T. (2018). Geological facies prediction using computed tomography in a machine learning and deep learning environment. Unconventional Resources Technology Conference. doi: 10.15530/URTEC-2018-2901881.
Pineda, W., Soza, E., Bergeron, J., Williams, J., & Gonzalez, D. (2018). Wireline Formation Fluid Sampling: From Making the Value Case, To Applying the Lessons Learned. A Guide To Improve Rate of Success While Taking Fluid Samples in the Lower for Longer Oil Price Environment. Society of Petrophysicists and Well-Log Analysts.
Rivera, V. P. (1994). Fuzzy Logic Controls Pressure In Fracturing Fluid Characterization Facility. Society of Petroleum Engineers. doi: 10.2118/28239-MS.
Saad, B., Negara, A., and Syed Ali, S. (2018). Digital rock physics combined with machine learning for rock mechanical properties characterization. Society of Petroleum Engineers. doi: 10.2118/193269-MS.
Schlanser, K., Grana, D., and Campbell, Erin. (2014). Petro-elastic facies classification in the Marcellus Shale by applying expectation maximization to measured well logs. 659-663. 10.1190/segam2014-0939.1.
Serag El Din, S., Clerke, E., and Badawi, A. (2018). Selected results from big carbonate pore system database: A case study from Saudi Aramco. Society of Petroleum Engineers. doi: 10.2118/193163-MS.
Shabab, M., Jin, G., Negara, A., and Agrawal, G. (2016). New data-driven method for predicting formation permeability using conventional well logs and limited core data. Society of Petroleum Engineers. doi: 10.2118/182826-MS.
Sidahmed, M., Roy, A., and Sayed, A. (2017). Streamline rock facies classification with deep learning cognitive process. Society of Petroleum Engineers. doi: 10.2118/187436-MS.
Torlov, V., Bonavides, C., & Belowi, A. (2017). Data Driven Assessment of Rotary Sidewall Coring Performance. Society of Petroleum Engineers. doi: 10.2118/187107-MS.
Voleti, D. K., Kundu, A., and Singh, M. (2017). Normalized depths as key input and detailed QC steps for improved permeability predictions using existing machine learning techniques. Society of Petroleum Engineers. doi: 10.2118/188493-MS.
Wang, G., Ju, Y., Carr, T. R., Li, C., and Cheng, G. (2014). Application of artificial intelligence on black shale lithofacies prediction in Marcellus Shale, Appalachian Basin. Unconventional Resources Technology Conference. doi: 10.15530/URTEC-2014-1935021
Wikipedia. (2018). Outline of machine learning. (30 November 2018 revision), https://en.wikipedia.org/wiki/Outline_of_machine_learning#Deep_learning (accessed 20 December 2018).
Worthington, P. F. (1997). Recognition and Development of Low-Resistivity Pay. Society of Petroleum Engineers. doi: 10.2118/38035-MS.
Yang, L., Bale, D. S., Yang, D., Raum, M., Bello, O., Failla, R., Ye, S. (2018). Enabling Real-Time Asset Analytics for a Cloud-Based Fiber-Optic Data Management System. Society of Petroleum Engineers. doi: 10.2118/191592-MS.