Data Driven Modeling and Prediction for Reservoir Characterization Using Seismic Attribute Analyses and Big Data Analytics
- Xu Zhou (Louisiana State Unviersity) | Mayank Tyagi (Louisiana State Unviersity) | Guoyin Zhang (China University of Petroleum - Beijing) | Hao Yu (Southwest Petroleum University) | Yangkang Chen (Zhejiang University)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 30 September - 2 October, Calgary, Alberta, Canada
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Big Data Analytics, Petrophysics, Reservoir Modeling, Seismic Reservoir Characterization, Seismic Attributes
- 9 in the last 30 days
- 249 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 9.50|
|SPE Non-Member Price:||USD 28.00|
With recent developments in data acquisition and storage techniques, there exists huge amount of available data for data-driven decision making in the Oil & Gas industry. This study explores an application of using Big Data Analytics to establish the statistical relationships between seismic attribute values from a 3D seismic survey and petrophysical properties from well logs. Such relationships and models can be further used for the optimization of exploration and production operations.
3D seismic data can be used to extract various seismic attribute values at all locations within the seismic survey. Well logs provide accurate constraints on the petrophysical values along the wellbore. Big Data Analytics methods are utilized to establish the statistical relationships between seismic attributes and petrophysical data. Since seismic data are at the reservoir scale and are available at every sample cell of the seismic survey, these relationships can be used to estimate the petrophysical properties at all locations inside the seismic survey.
In this study, the Teapot dome 3D seismic survey is selected to extract seismic attribute values. A set of instantaneous seismic attributes, including curvature, instantaneous phase, and trace envelope, are extracted from the 3D seismic volume. Deep Learning Neural Network models are created to establish the relationships between the input seismic attribute values from the seismic survey and petrophysical properties from well logs. Results show that a Deep Learning Neural Network model with multi-hidden layers is capable of predicting porosity values using extracted seismic attribute values from 3D seismic volumes. Ultilization of a subset of seismic attributes improves the model performance in predicting porosity values from seismic data.
|File Size||1 MB||Number of Pages||12|
Khair, Abul, H., Cooke, D., Backe G., King R.Hand, M., Tingay, M., And Holford S., 2012, Subsurface Mapping of Natural Fracture Networks; A Major Challenge to be solved. Case Study from the Shale intervals in the Cooper Basin, South Australia: Proceedings, Thirty-Seventh Workshop on Geothermal Reservoir Engineering, Stanford, California.
Li, F., 2019, Convolutional Neural Networks for Visual Recognition, lecture notes, Stanford University, Accessed from http://cs231n.stanford.edu/ on 05/30/2019.
Qu, S., Guan, Z., Verschuur, E., and Chen Y., 2019, Automatic high-resolution microseismic event detection via supervised machine learning, Geophysical Journal International, https://doi.org/10.1093/gji/ggz273
Schuetter, J., Mishra, S., Zhong, M., & LaFollette, R. (2015, July 20). Data Analytics for Production Optimization in Unconventional Reservoirs. Unconventional Resources Technology Conference. doi:10.15530/URTEC-2015-2167005
Udegbe, E., Morgan, E., & Srinivasan, S. (2018, September 24). Big Data Analytics for Seismic Fracture Identification, Using Amplitude-Based Statistics. Society of Petroleum Engineers. doi:10.2118/191668-MS
Wang, S., & Chen, S. (2016, August 24). A Comprehensive Evaluation of Well Completion and Production Performance in Bakken Shale Using Data-Driven Approaches. Society of Petroleum Engineers. doi:10.2118/181803-MS
Wang, Y., & Salehi, S. (2015, March 3). Drilling Hydraulics Optimization Using Neural Networks. Society of Petroleum Engineers. doi:10.2118/173420-MS
Wang, B., Feng, Y., Pieraccini, S., Scialo, S., and Fidelibus, C., (2018), Iterative coupling of boundary element method with domain decomposition, IJNME. doi: 10.1002/nme.5943.
Wang, B., Feng, Y., Du, J., Wang, Y., Wang, S., Yang, R. (2018, April 1). An Embedded Grid-Free Approach for NearWellbore Streamline Simulation. Society of Petroleum Engineers. doi:10.2118/182614-PA
Zhou, X., Taleghani, A. D., & Choi, J. W. (2017, July 24). Imaging Three-Dimensional Hydraulic Fractures in Horizontal Wells Using Functionally-Graded Electromagnetic Contrasting Proppants. Unconventional Resources Technology Conference. doi:10.15530/URTEC-2017-2697636.