Abstract
Time-lapse (4D) seismic processing is routinely used to monitor hydrocarbon reservoir production. Seismic reflections are sensitive to formation pressure and fluid content. This means that repeated seismic surveys can theoretically detect pressure changes and fluid changes associated with field production. These measurements can help optimize production strategy and identify areas where hydrocarbons have been bypassed. However, the seismic signal associated with such changes can be negligible, especially in heterogeneous carbonate reservoirs. To measure this 4D signal, the seismic acquisition must be repeated. Data vintages should be processed together to minimize differences unrelated to production. Repeatability of data acquisition is sometimes impossible to achieve in the Middle East due to environmental changes (e.g., dunes, currents, field facilities). Due to high cost and inadequate sampling, attempts to permanently bury seismic sources and receivers have failed. In this study, least-square pre-stack depth migration (LSM) co-processing was applied to remove the influence of survey design on the final image and pinching mark compared to conventional Kirchhoff pre-stack depth migration (KPSDM). The 4D physical, geometric, and seismic attributes are analyzed in the field as key diagnostic tools to evaluate the probability of a sighted 4D difference independent of two different acquisition geometries. The 4D analysis was performed on two case studies offshore Abu Dhabi, to determine which workflow and algorithm are most likely to ensure that the complete and optimal 4D processing sequence relaxes the need for seismic acquisition repeatability.
Original language | English |
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Pages (from-to) | 135-149 |
Number of pages | 15 |
Journal | Journal of Petroleum Exploration and Production Technology |
Volume | 13 |
Issue number | 1 |
DOIs | |
Publication status | Published - Jan 2023 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2022, The Author(s).
Keywords
- 4D
- Carbonate reservoir
- Co-processing
- Heterogeneous
- Imaging