Seismic facies analysis based on 3D multiattribute volume classification, La Palma Field, Maracaibo, Venezuela

Linari, Victor; Santiago, Marcelo; Pastore, Carlos; Azbel, Kostia; Poupon, Manuel

doi:10.1190/1.1542752

Cited by 25 publications

(10 citation statements)

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“…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).…”

Section: Three-dimensional Multiattribute Seismic-facies Classificationmentioning

confidence: 99%

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

et al. 2012

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The recognition of paleokarst in subsurface carbonate reservoirs is not straightforward because conventional seismic interpretation alone is generally not sufficient to discriminate karstified areas from their surroundings. In the Loppa High (Norwegian Barents Sea), a protracted episode of subaerial exposure occurring between the late Paleozoic and mid-TriassicLate Permian to Anisian-resulted in a significant overprinting of the previously deposited carbonate units. Here, we map the extension of the karstified areas using an integrated approach consisting of (1) a core study of critical paleokarst intervals, (2) a three-dimensional (3-D) seismic stratigraphic analysis, and (3) a 3-D multiattribute seismic facies (SF) classification. A core retrieved in the flat-topped Loppa High revealed breccia deposits at least 50 m (164 ft) thick, which probably resulted from cave collapses following the burial of the karst terrain. The SF classification was tested on a 3-D cube to (1) discriminate the respective SF related to the breccia deposits compared with other SF and (2) to estimate their spatial extent. Seismic-facies analysis suggests that breccias occupied the topmost area of the structural high, extending up to 12 km (7 mi) in width, 46 km (29 mi) in length, and tens of meters in thickness. The inference of such a large amount of breccia suggests that a significant part of this terrain was derived from

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Section: Three-dimensional Multiattribute Seismic-facies Classificationmentioning

confidence: 99%

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

et al. 2012

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“…This is accomplished by reducing the dimensionality of the data -the number of original quantities -to fewer combinations of those quantities, called principal component variables. PCA examines the cross-plotted patterns and finds the principal directions of variances within the multi-dimensional data (Gurney 1997;Elsayed & Slusarczyk 2001;Coléou et al 2003;Linari et al 2003). PCA compresses the bulk of the variance in data by projecting the data onto a few orthogonal eigenvector components (Kreyszig 1999) by linear transformation of the coordinate system (Kaiser 1958;Davis 1986).…”

Section: Data Reduction and 3d Multi-attribute Volume Classification mentioning

confidence: 99%

“…8). It is, therefore, difficult to relate the results back to the input attributes using PC crossplots (Linari et al 2003), although it is possible to reconstruct the attributes from Fig. 8.…”

Section: Workflowmentioning

confidence: 99%

“…In recent years many aspects of the structural interpretation of seismic data have become automated and more rapid (Brown 1999(Brown , 2005Coléou et al 2003;Linari et al 2003;Marsh et al 2005). The traditional line-by-line interpretation technique has been enhanced/replaced by techniques that operate throughout a seismic volume (e.g.…”

Section: Introductionmentioning

confidence: 97%

“…The second challenge is to classify multi-attribute seismic facies in order to recognize their meaningful 3D patterns and provide a truly 3D interpretation. These challenges are addressed by employing a method for 3D multi-attribute data analysis with a hierarchical classification (Romesburg 1984) of multivariate data in combination with a data reduction technique, namely principal component analysis (PCA) (Gurney 1997;Coléou et al 2003;Linari et al 2003).…”

Section: Introductionmentioning

confidence: 99%

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

Farzadi

Hesthammer

2007

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Depositional facies variability with a secondary overprint of Turonian palaeokarst controls the quality of the Cenomanian Mishrif reservoir, the main oil producer in the southeastern Persian Gulf. Subsequent drowning of the prominent area and successive deepening of the basin during the Coniacian and Santonian enabled the deposition of pelagic marls of the Coniacian Laffan Formation and the development of a carbonate turbidite system within the overlying Santonian Ilam Formation. The Ilam Formation occurs within slope and pelagic carbonates and consists of oil-bearing channel-reworked limestone facies.Although high-quality 3D seismic data exist over the Sirri C/D oil fields studied in this paper, it is a real challenge to map the palaeokarst and turbidite deposits in 3D space using conventional seismic interpretation procedures.This work describes a procedure using Paradigm's Seisfacies software for seismic facies classification and uses this to develop a volume-based interpretation of palaeokarst geobodies and sedimentary patterns of the carbonate turbidite. A hierarchical facies classification technique combined with principal component analysis (PCA) is used to analyse a set of seismic attribute volumes that capture the seismic stratigraphic patterns inherent in the data. PCA as a data reduction algorithm greatly optimized the analysis of the attribute volumes while preserving the essential features of seismic character. A hierarchical facies classifier recognized enough variability within the seismic data to reveal details of the associated geological features. This classification method uses multiple 3D volume attributes as input and generates a single 3D seismic facies volume (a synthesis of different attributes). Using this method, interpretative work can focus directly on geological features in 3D space. This study gives new insights into the internal variability of palaeokarst and carbonate turbidite systems in the Sirri C/D oil fields (SE Persian Gulf) that are essential for the estimation of reservoir volume, connectivity and variability.

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Data mining for large scale 3D seismic data analysis

Deighton

Petrou

2007

Machine Vision and Applications

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Seismic facies analysis based on 3D multiattribute volume classification, La Palma Field, Maracaibo, Venezuela

Cited by 25 publications

References 0 publications

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

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

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

Data mining for large scale 3D seismic data analysis

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