Seismic Facies Analysis Based on 3d Multi‐attribute Volume Classification, Dariyan Formation, Se Persian Gulf

Abstract

Interpretation of recently acquired 3D seismic data from the adjacent Sirri C and D oilfields in the SE Persian Gulf indicates that a 3D interpretation of seismic facies is crucial to resolve the internal stratal geometries of the Aptian Dariyan Formation. This carbonate formation passes southward into the Shu'aiba Formation, a prolific reservoir rock of similar facies in the UAE. Lack of exposures and limited cored intervals have forced reliance on the seismic data for evidence of the depositional environment and the internal architecture of potential reservoir rocks. The progradational nature of the Dariyan Formation and the occurrence of carbonate build‐ups within it make this stratal geometry complex. The complex internal heterogeneity of the build‐ups and presence of seismic noise make mapping of the build‐ups in 3D space using conventional seismic interpretation tools difficult, despite the availability of high‐quality 3D seismic data covering the area. The high quality seismic and limited well data from this field is one of the few datasets of this kind presented in the literature. A procedure for the hierarchical multi‐attribute analysis of seismic facies using Paradigm's Seis Facies software is used in this study to provide a 3D interpretation of the stratal patterns. Principal component analysis reduces the noise and redundant data by representing the main data variances as a few vector components in a transformed coordinate system. Cluster analysis is performed using those components which have the greatest contribution to the maximum spread of the data variability. Six seismic attribute volumes are used in this study and the result is a single 3D classified volume. Important new information obtained from within the Dariyan Formation gives new insights into its stratigraphic distribution and internal variability. This method of processing seismic data is a step towards exploring for subtle stratigraphic traps in the study area, and may help to identify exploration targets.

Geological reservoir characterization of the Lower Cretaceous Dariyan Formation (Shu'aiba equivalent) in the Persian Gulf, southern Iran

“…1), the stratigraphic counterpart of the Shu'aiba Formation is the Dariyan Formation, as a unit of Khami Group (Fig. 2) In Iran, both in the Zagros area and in the Persian Gulf, few studies have been conducted on the Lower Cretaceous Dariyan Formation that have dealt with its paleontology, stratigraphic architecture, diagenesis and facies characteristics (e.g., Farzadi, 2006;Sharp et al, 2010;Schroeder et al, 2010;van Buchem et al, 2010;Moosavizadeh et al, 2013). This study is the first report of sedimentological and reservoir geological aspects of this formation in subsurface sections of the Persian Gulf.…”

Diagnosis of the Upper Cretaceous palaeokarst and turbidite systems from the Iranian Persian Gulf using volume-based multiple seismic attribute analysis and pattern recognition

“…The generated classification volume is shown here to be more advantageous if either 'proportional slicing' (Posamentier et al 1996) or 'stratal slicing' (Zeng et al 1998a, b;Zeng & Hentz 2004, Farzadi 2005, 2006b, which follow depositional surfaces more accurately, are applied for sequential seismic facies displays.…”

“…The interpreter of multi-attribute volumes faces a two-fold challenge. The first is to find the true dimensionality of a huge number of data samples (Aminzadeh 1987;Mari et al 1999;Elsayed & Slusarczyk 2001) and their 3D organization in order to minimize noise and redundant data (Farzadi 2005(Farzadi , 2006b. The second challenge is to classify multi-attribute seismic facies in order to recognize their meaningful 3D patterns and provide a truly 3D interpretation.…”

“…Although the use of automated horizon interpretation while moving through a seismic volume is valuable, it can provide insights into the variability only at the top and base of intervals and not within them. A horizon-based interpretation approach cannot provide the interpreter with a truly 3D interpretation of complex geological features (Farzadi 2006b). In addition, caution should be used if attempting to use a single seismic attribute (Brown 1996) for the interpretation.…”

Characterization of a deeply buried paleokarst terrain in the Loppa High using core data and multiattribute seismic facies classification

“…A 3-D automated SF classification was subsequently applied, to separate the response of different SF in the 3-D seismic cube, by combining different seismic attributes (i.e., mathematical transforms of the seismic data) in an artificial neural network (Coleou et al, 2003;Linari et al, 2003;Carrillat et al, 2005;Tebo and Hart, 2005;Farzadi, 2006;Herrera et al, 2006;Baaske et al, 2007;Farzadi and Hesthammer, 2007). The classification method used here has been successfully applied for reservoir characterization (e.g., Coleou et al, 2003;Linari et al, 2003;Carrillat et al, 2005;Farzadi, 2006;Herrera et al, 2006;Farzadi and Hesthammer, 2007).…”