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

Abstract

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

Three-Dimensional Geophysical Characterization of Deeply Buried Paleokarst System in the Tahe Oilfield, Tarim Basin, China

“…Using a 3D seismic amplitude dataset, Zeng et al [27,28] and Basso et al [29] characterized the structure of paleocave reservoirs and pointed out the associated relationship between faults and paleocaves. Sayago et al, on the other hand, used the multiple seismic attribute classification method to characterize a paleokarst system of the Loppa highlands [30]. Tian et al used an acoustic impedance inversion dataset to identify the heterogeneity of a paleokarst system in Tarim Basin [10,31].…”

Reconstructing large‐scale karst paleogeomorphology at the top of the Ordovician in the Ordos Basin, China: Control on natural gas accumulation and paleogeographic implications

“…Karstification of carbonate strata refers to dissolution of the contained carbonate and associated rocks in the same strata . The development of karstification has been demonstrated to significantly influence hydrocarbon accumulation . The various processes involving in karstification are complex and include dissolution, collapse, and filling, resulting in marked reservoir heterogeneity and presenting challenges to the accurate prediction of hydrocarbon occurrence .…”

“…However, characterizing karst paleogeomorphology is subject to several constraints, such as poor seismic data quality, erroneous seismic interpretation, and insufficient well data for control. Consequently, the description of karst paleogeomorphology remains a challenging aspect of karst geology and also of petroleum geological assessments of carbonate rocks …”

Classificação de sismofáceis carbonáticas a partir da técnica Self-Organizing Maps (SOM).

“…(BROWN, 1996). A utilização de técnicas de aprendizado de máquina ou "machine learning", redes neurais e mineração de dados possibilitam a integração de diversos atributos que proporcionam informações importantes para a análise das sismofácies (COLÉOU, POUPON;AZBEL, 2003;GUO;LIU, 2009;KURODA, 2016;SAYAGO et al, 2012;SKIRIUS et al, 1999). Dessa maneira, ao se explorar a utilização dos atributos sísmicos com estas técnicas seria possível obter classificações de sismofácies com maior correspondência às fácies litológicas e, consequente redução do risco exploratório.…”

“…As redes neurais artificiais (RNA's) são ferramentas capazes de capturar estas relações entre os atributos inclusive relações não lineares(HAGAN et al, 2002). As comparações entre a aplicação das RNA's supervisionadas e não supervisionadas nas geociências mostram que a escolha é dependente da análise em cada caso, não existe uma regra a ser aplicada a todos os casos (BHATTACHARYA; CARR;PAL, 2016;SAYAGO et al, 2012). No caso específico deste trabalho, optamos por utilizar a técnica SOM.…”