The evolution of the syn rift and transition phases of the central / southern Brazilian and W. African conjugate margins: the implications for source rock distribution in time and space, and their recognition on seismic data.

Gibbs, Peter B.; Brush, Eugene R.; Fiduk, Joseph C.

doi:10.3997/2214-4609-pdb.168.arq_433

Cited by 6 publications

(9 citation statements)

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“…The geodynamic evolution of the Espírito Santo Basin was primarily controlled by four tectonic stages, which were also common along most South Atlantic continental margins: the pre-rift, syn-rift, transitional and drift stages (Ojeda, 1982;Mohriak et al, 1998;Gibbs et at., 2003) (Fig. 2).…”

Section: Geological Settingmentioning

confidence: 99%

“…The seismic dataset used in this study was acquired on the upper (extensional) continental slope of the Espírito Santo Basin, offshore southeast Brazil (Fig 1a). Deformation in this region was greatly influenced by gravitydriven salt tectonics (Demercian et al, 1993;Davison, 1999Davison, , 2007Gibbs et al, 2003;Fiduk et al, 2004;Alves, 2012), and is expressed in the form of complex structures such as salt rollers, vertical salt diapirs, seaward-and landward-dipping listric (roller) faults, normal faults, rollover anticlines and tectonic rafts (Figs. 3 and 4).…”

Section: Geological Settingmentioning

confidence: 99%

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Impact of tectonic rafts’ gravitational instability on fault reactivation and geometry

Maunde

Alves

2020

Journal of Structural Geology

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Section: Geological Settingmentioning

confidence: 99%

Section: Geological Settingmentioning

confidence: 99%

Impact of tectonic rafts’ gravitational instability on fault reactivation and geometry

Maunde

Alves

2020

Journal of Structural Geology

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“…In recent decades, as million tons of high‐quality hydrocarbon reserves have been explored and exploited within the Santos Basin, the geological and other related research of this basin gradually become a hotspot (Alves et al., 2017; Azerêdo et al., 2021; Moreira et al., 2007; Pietzsch et al., 2018; Thompson et al., 2015; Varejão et al., 2021). According to the previous findings, it is discovered that most of the petroleum and natural gas resources are accumulated within the pre‐salt lacustrine reservoir, and hence for the exploration of the Santos Basin, to gain a comprehensive and deepgoing sedimentary understanding of the pre‐salt sections has been routinely regarded as one of the primary and most meaningful tasks for geologists (Alves et al., 2017; Gibbs et al., 2003; Gomes et al., 2020; Moreira et al., 2007; Thompson et al., 2015; Varejão et al., 2021). Then, uncovering the stratigraphic and depositional situations of the target reservoirs is significant and principal, and one fundamental work for this is to get a deep insight of the major lithological characteristics because lithology is an indicator that can favourably manifest geophysical and mineralogical properties of the reservoirs (Azerêdo et al., 2021; Gomes et al., 2020; Moreira et al., 2007; Varejão et al., 2021).…”

Section: Data Source Validation and Discussionmentioning

confidence: 99%

“…Minerals by scanning electron microscopy (QEMSCAN) and X-ray diffraction (XDR) are particularly analysed, which finally reveals that all observed lithologies are principally featured by four elements containing spherulite grain, shrub grain, crystal grain and mud (Figure 3). Spherulite, coquina, grainstone and stromatolite similarly display an higher proportion of shrub grain (averagely 90%, derived from a statistics of 142 thin sections) but are differed from the F I G U R E 3 Classification patterns of lithology and lithofacies, relationship between lithology and lithofacies, and sedimentary model established based on classified lithofacies (modified after Gibbs et al, 2003;Gomes et al, 2020). According to the observations of handled representative thin sections and comprehensive analysis of both quantitative evaluation of minerals by scanning electron microscopy (QEMSCAN) and X-ray diffraction (XDR) data, 12 major lithological components of target lacustrine carbonate formations are discovered, including spherulite, coquina, grainstone, stromatolite, rudstone, packstone, laminate, wackstone, crystalline limestone, silexite, dolomite and mudstone.…”

Section: Geological Settingmentioning

confidence: 99%

Lithofacies prediction driven by logging‐based Bayesian‐optimized ensemble learning: A case study of lacustrine carbonate reservoirs

Zhang

Wang³

et al. 2022

Geophysical Prospecting

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Although lithofacies routinely is featured by distinct logging responses from each other, many types of lithofacies in practical cases show similar measuring characteristics on logs, and then to achieve a desirable solution from logging‐based lithofacies prediction actually is challengeable. Since the mathematical essence of lithofacies prediction can be explained as an issue of pattern recognition, a light gradient boosting machine, a state‐of‐the‐art ensemble learning, specifically developed to address supervised classification, could be a potential solver. Nonetheless, due to an incompatibility of inherent exclusive feature bundling algorithm for logs and usage of a great deal of hyper‐parameters, a raw light gradient boosting machine might not be suitable or functional to predict lithofacies. Thus, continuous restricted Boltzmann machine and Bayesian optimization, respectively, employed to process original logging data and optimize hyper‐parameters, are adopted as technical assistants for the light gradient boosting machine, and accordingly a new ensemble learning‐based predictor called continuous restricted Boltzmann machine – Bayesian optimization – the light gradient boosting machine, is proposed for lithofacies. To validate classifying capability and robust nature of new predictor, three experiments are designed purposefully based on the application of a dataset collected from the wells located within pre‐salt lacustrine carbonate reservoirs of the Santos Basin. Simultaneously, to highlight validating effect, other three sophisticated classifiers are introduced as competitors in all experiments, including supper vector machine, random forest and extreme gradient boosting. According to the analysis and comparison of all experimental results, overall four points are verified: (1) the integration of continuous restricted Boltzmann machine and Bayesian optimization indeed is beneficial for the light gradient boosting machine in data preprocess and modelling stage; (2) the light gradient boosting machine cored predictor shows more capability of producing reliable predicted lithofacies information compared to other three competitors; (3) training more learning samples is a simple and effective approach to enhance computing capability of any validated predictor, and under this circumstance the light gradient boosting machine cored predictor still performs relatively better; (4) the light gradient boosting machine cored predictor is characterized with a better robustness as its predicting outcomes, even derived from a sparse‐condition dataset, are also proved more qualified. Consequently, the new proposed predictor is demonstrated as a high‐efficient and robust solver for the prediction of lithofacies and deserves a widespread application in the geological, geophysical and petrophysical research.

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“…Continental rifting started in the Early Cretaceous (Valanginian) in association with the magmatism in the Serra Geral (Gibbs et al, 2003;Cainelli et al, 1999). The Syn-Rift stage extended from the Berriasian to the Early Aptian, producing narrow, lacustrine basins with flanking fluvio-deltaic sediments (Ojeda, 1982;Chang et al, 1992;Gibbs et al, 2003). The end of the Syn-Rift stage is marked by a Transitional stage of evaporitic deposition that took place from Early to Late Aptian/Albian (Chang et al, 1992).…”

Section: Geological Settingmentioning

confidence: 99%

Unpredictable geometry and depositional stacking patterns of mass-transport complexes in salt minibasins

Biancardi

Alves

Martins-Ferreira

2020

Marine and Petroleum Geology

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Mass-transport complexes in a salt minibasin of the Espírito Santo Basin (SE Brazil) are investigated using a high-quality 3D seismic volume and borehole data. A series of six (6), stacked MTCs were identified from the sea floor down to an approximate depth of 1.5 kilometres. These MTCs exhibit a high variability in size and internal structures. Three of the MTCs contain single, discrete landslide deposits while the other three MTCs contain multiple, contemporaneous landslides that merge to span the entire salt minibasin. The data in this work show that Area/Length relationships and the number of contemporaneous landslide deposits within an MTC are random, revealing no obvious relationship with relative location or depth. As such, there are no clear stacking patterns for the MTCs in this minibasin. This implies that landslide deposits can be encountered anywhere within a salt minibasin and, furthermore, the extent of the slope failure and its internal structure are unpredictable. This work concludes that slope instability can be the dominant process of sediment filling Miocene salt minibasins on the continental slope of Espírito Santo. Moreover, there is a strong link between halokinesis and the triggering of landslides in the salt minibasins, but the identification of MTCs 2 becomes challenging with increasing depth and there is the potential for them to be misrepresented.The identification of basal ramps therefore becomes critical in any analysis; except for the youngest MTC 1, all other complexes show clear basal ramps, and for one of the MTCs the basal ramp is its sole identifying character.

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The evolution of the syn rift and transition phases of the central / southern Brazilian and W. African conjugate margins: the implications for source rock distribution in time and space, and their recognition on seismic data.

Cited by 6 publications

References 0 publications

Impact of tectonic rafts’ gravitational instability on fault reactivation and geometry

Impact of tectonic rafts’ gravitational instability on fault reactivation and geometry

Lithofacies prediction driven by logging‐based Bayesian‐optimized ensemble learning: A case study of lacustrine carbonate reservoirs

Unpredictable geometry and depositional stacking patterns of mass-transport complexes in salt minibasins

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