Fold and thrust belts : structural style, evolution and exploration – an introduction

Peer reviewedPublisher PD

Similarities and differences in the dolomitization history of two coeval Middle Triassic carbonate platforms, Balaton Highland, Hungary

Dolomitization of platform carbonates is commonly the result of multiphase processes. Documentation of the complex dolomitization history is difficult if completely dolomitized sections are studied. Two Middle Anisian sections representing two coeval carbonate platforms were investigated and compared in the present study. Both sections are made up of meter-scale peritidal–lagoonal cycles with significant pedogenic overprint. One of the sections contains non-dolomitized, partially dolomitized, and completely dolomitized intervals, whereas the other is completely dolomitized. Based on investigations of the partially dolomitized section, penecontemporaneous dolomite formation and/or very early post-depositional dolomitization were identified in various lithofacies types. In shallow subtidal facies, porphyrotopic dolomite was found preferentially in microbial micritic fabrics. Microbially induced dolomite precipitation and/or progressive replacement of carbonate sediments could be interpreted for stromatolites. Cryptocrystalline to very finely crystalline dolomite, probably of pedogenic origin, was encountered in paleosoil horizons. Fabric-destructive dolomite commonly found below these horizons was likely formed via reflux of evaporated seawater. As a result of the different paleogeographic settings of the two platforms, their shallow-burial conditions were significantly different. One of the studied sections was located at the basinward platform margin where pervasive fabric-retentive dolomitization took place in a shallow-burial setting, probably via thermal convection. In contrast, in the area of the other, smaller platform shallow-water carbonates were covered by basinal deposits, preventing fluid circulation and accordingly pervasive shallow-burial dolomitization. In the intermediate to deep burial zone, recrystallization of partially dolomitized limestone and occlusion of newly opened fractures and pores by coarsely crystalline dolomite took place

2D and 3D GPR imaging and characterization of acarbonate hydrocarbon reservoir analogue

We tested and adapted seismic attributes techniques on a 2-D and 3-D multi frequency GPR dataset to image the network of stratigraphic joints and fractures, the lithological variations and to characterize the rock mass based on the response to the radar wavefield measured in an abandoned limestone quarry. We applied semi-automatic horizon mapping techniques using manually picked seeds (control points) on selected attributes and automatic extrapolation both on inline and crossline, starting from seed positions. The results were integrated and validated with direct outcrop measures and allowed to image an hydrocarbon reservoir analogue in 3-D up to a depth of over 10m below the topographic surface

Integrating clustering and classification techniques: a case study for reservoir facies prediction

The need for integration of different data in the understanding and characterization of reservoirs is continuously growing in petroleum geology. The large amount of data for each well and the presence of different wells to be simultaneously analyzed make this task both complex and time consuming. In this scenario, the development of reliable interpretation methods is of prime importance in order to help the geologist and reduce the subjectivity of data interpretation. In this paper, we propose a novel interpretation method based on the integration of unsupervised and supervised learning techniques. This method uses an unsupervised learning algorithm to objectively and quickly evaluate a large dataset made of subsurface data from different wells in the same field. Then it uses a supervised learning algorithm to predict and propagate the characterization over new wells. To test our approach, we use first hierarchical clustering to then feed several supervised learning algorithms in the classification domain (e.g. decision trees and linear regression)