| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Estadual Campinas, Ctr Petr Studies, BR-13083872 Campinas, SP - Brazil
[2] Univ Estadual Campinas, Geosci Inst, Campinas, SP - Brazil
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | PETROLEUM SCIENCE AND TECHNOLOGY; JAN 2022. |
| Web of Science Citations: | 0 |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 17/15736-3 - Engineering Research Centre in Reservoir and Production Management |
| Grantee: | Denis José Schiozer |
| Support Opportunities: | Research Grants - Research Centers in Engineering Program |