期刊
PETROLEUM SCIENCE AND TECHNOLOGY
卷 40, 期 12, 页码 1492-1511出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/10916466.2021.2025072
关键词
Fracture density map; seismic-based discontinuity attribute; dip and azimuth attributes; stress regime analysis; pre-salt carbonate rocks
资金
- EPIC - Energy Production Innovation Center
- FAPESP - Sao Paulo Research Foundation [2017/15736-3]
- Equinor Brazil
- ANP (Brazil's National Oil, Natural Gas and Biofuels Agency)
This study focuses on predicting fracture density distribution by integrating well and seismic data. Through a workflow of machine learning approaches, including data conditioning, attribute creation, and geostatistical methods, the fracture density map is successfully predicted.
Fractures play a significant role in the development and production phases of carbonate reservoirs. Quantitative interpretation of fractures not only enhances reservoir models but also reduces the drilling risk and optimizes well design. In this study, we attempt to predict the fracture density map by integrating well and seismic data along with maximum horizontal stress identification. To this end, we propose a workflow with a set of machine learning approaches. First, 3D seismic data is conditioned after the migration processing sequence and the main faults and horizons are interpreted. Next, a number of curvature and coherence attributes are created for a supervised neural network technique to generate new seismic-based discontinuity attribute. Using a geostatistical method to incorporate the interpreted dip and azimuth attributes from well image logs and 3D seismic discontinuity attribute, the fracture density map is predicted and the results validated with a blind well. Finally, we evaluate the strike azimuth of possible open fractures based on the stress regime analysis, from which two distinctive zones are identified. There are, however, some limitations in this study. The predicted fracture density map can be employed to build a discrete fracture network, update dual porosity and permeability estimation, and identify sweet spots.
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