Seismic methods for fluid discrimination in areas with complex geologic history — A case example from the Barents Sea

Autor: Nazmul Haque Mondol, Per Avseth, Ivan Lehocki
Rok vydání: 2020
Předmět:
Zdroj: Interpretation. 8:SA35-SA47
ISSN: 2324-8866
2324-8858
DOI: 10.1190/int-2019-0057.1
Popis: We have developed a new scheme for calculation of density ratio, an attribute that can be directly linked to hydrocarbon saturation, and applied it to seismic amplitude variation with offset (AVO) data from the Hoop area in the Barents Sea. The approach is based on the inversion of Zoeppritz’s equation for PP-wave. Furthermore, by using interval velocities, we quantified uplift magnitude for a given interval beneath Base Cretaceous unconformity (BCU) horizon in the Hoop area. Depending on the temperature gradient, the maximum burial depth can be estimated, a crucial factor affecting the elastic properties of the rocks. Coupling uplift map with temperature history for key stratigraphic units from basin modeling enabled us to extend the training data away from well control. By doing so, we created nonstationary AVO probability density functions (PDFs) for calibration and classification of seismic attributes in the test area. This decreases the likelihood of misclassification of pore fluid type as opposed to the case where the training data are created based only on sparse well-log data. We tested and compared the methods on the Barents Sea seismic data set, and the results were validated at four well locations. Finally, maps of fluid distribution obtained from stochastic rock-physics modeling honoring burial history were compared against the density ratio map. Four maps revealed the same anomalous zones, the major difference being the detection of the down-flank presence of oil associated with some of the predicted gas anomalies in the prospect area, in the case of density ratio map. Possible gas caps were detected/predicted only for certain temperature constraints during the AVO classifications and were most obvious in the density ratio map.
Databáze: OpenAIRE