Abstract
Generating 4D attributes in time-lapse monitoring is typically performed in the post-stack domain, with full and sub-angle stacks commonly used for such conventional 4D analysis. For instance, the post-stack 4D time-shift attribute is usually computed on a partial stack of baseline and monitor, limited to a few wide angles to avoid the high noise content on pre-stack gathers. However, examining pre-stack time-lapse data shows that the recorded time-shifts are offset-dependent and estimating the 4D signature based on the post-stack domain either for the full angle range stack or partial sub-angle stacks could be smeared because the 4D response offset dependency has not been taken into consideration to some degree. Because of the sensitivity of the 4D amplitude differences to the error in the time-shift estimation, pre-stack 4D offset-dependent analysis may be required.
In this paper, We propose a pre-stack approach for 4D analysis, by correcting the 4D time-shifts in the pre-stack domain and stacking the resultant pre-stack 4D attributes for a representative and cleaner post-stack 4D signature. We implement this approach in the North Sea data example with promising results.
In this paper, We propose a pre-stack approach for 4D analysis, by correcting the 4D time-shifts in the pre-stack domain and stacking the resultant pre-stack 4D attributes for a representative and cleaner post-stack 4D signature. We implement this approach in the North Sea data example with promising results.
Original language | English |
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Pages | 1-5 |
Number of pages | 5 |
DOIs | |
Publication status | Published - 5 Jun 2023 |
Event | 84th EAGE Annual Conference & Exhibition 2023 - Vienna, Austria Duration: 5 Jun 2023 → 8 Jun 2023 |
Conference
Conference | 84th EAGE Annual Conference & Exhibition 2023 |
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Country/Territory | Austria |
City | Vienna |
Period | 5/06/23 → 8/06/23 |