Video: 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
- Publication Date
- Document Type
- 2019. Copyright is retained by the author. This presentation is distributed by SPE with the permission of the author. Contact the author for permission to use material from this video.
- Reservoir Modeling, Seismic Reservoir Characterization, Big Data Analytics, Seismic Attributes, Petrophysics
- 0 in the last 30 days
- 0 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 5.00|
|SPE Non-Member Price:||USD 12.00|
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.