Seismic inversion is the leading method to map and quantify changes in time-lapse (4D) seismic datasets, with applications ranging from monitoring hydrocarbon-producing fields to geological CO2 storage. However, the process of inverting seismic data for reservoir properties is a notoriously ill-posed inverse problem due to the band-limited and noisy nature of seismic data. This comes with additional challenges for 4D applications, given the inaccuracies in the repeatability of time-lapse acquisition surveys. Consequently, adding prior information to the inversion process in the form of properly crafted regularization terms is essential to obtain geologically meaningful subsurface models and 4D effects. In this thesis, I propose a joint inversion-segmentation algorithm for 4D seismic inversion, which integrates total variation and segmentation priors as a way to counteract the missing frequencies and noise present in 4D seismic data. I validate the algorithm with synthetic and field seismic datasets and benchmark it against state-of-the-art 4D inversion techniques. The proposed algorithm shows three main advantages: 1. it produces high-resolution baseline and monitor acoustic impedance models, 2. by leveraging similarities between multiple seismic datasets, the proposed algorithm mitigates the non-repeatable noise and better highlights the real seismic time-lapse changes, and 3. it simultaneously provides a volumetric classification of the acoustic impedance 4D difference model based on user-defined classes, i.e., percentages of seismic time-lapse changes. Such advantages may enable more robust stratigraphic/structural and quantitative 4D seismic interpretation and provide more accurate inputs for dynamic reservoir simulations.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/691526 |
| Date | 04 1900 |
| Creators | Romero, Juan Daniel |
| Contributors | Ravasi, Matteo, Physical Science and Engineering (PSE) Division, Alkhalifah, Tariq Ali, Turkiyyah, George M. |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Relation | https://github.com/DIG-Kaust/4DProximal |
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