Seismic Characterization of Carbonate Stringers using Machine Learning techniques: an example from the Western Flank of South Oman Salt Basin

Carbonate stringers are defined as a slab of carbonate bodies encased inside salt. In Oman, the intra-salt carbonate stringers are a very common target, especially in South Oman Salt Basin (SOSB). These stringers contain a large amount of hydrocarbon resources. In this study, the behavior of carbonate stringer bodies hosted in Ara salt structures from the SOSB is investigated. Both synthetic and real seismic data are use. A geological model is created from the structural interpretation of a newly acquired and processed seismic data from the area. The synthetic model is used to generate different seismic and elastic quantities such as P-wave, S-wave, density, synthetic gathers, and stack seismic data. Different attributes are computed from the synthetic gathers before inverting them to acoustic impedance, shear impedance, and density. In order to help us detect carbonate stringers and predict their distribution, Machine Learning, namely, Artificial Neural Network (ANN), is deployed to combine all the computed attributes into one probability cube. The cube highlights only stringers detected by the trained ANN. The ANN demonstrated a promising capability of detection of the targeted stringers. The above experiment is repeated on real prestack data after converting them to impedances using prestack seismic inversion. Likewise, the ANN was able to delineate the stringers and predict the distribution in the 3-D data. After several tests, a final 3-D distribution model of the stringers was obtained. The cube has allowed us to find new potential zones that might be good prospects in any future drilling in the area.