Integration of data from fully cored stratigraphic holes with an extensive grid of seismic reflection lines in McMurdo Sound, Antarctica, has allowed the formulation of a new model for the evolution of the Cenozoic Victoria Land Basin of the West Antarctic Rift. The Early Rift phase (Eocene to Early Oligocene) is recorded by wedges of strata confined by early extensional faults, and which conta...

Attributes remain to be of the utmost importance for the characterization of seismic data. Especially in the scale-range where tuning effects play, the understanding of what is happening, and the subsequent technology to extract the proper attributes from the huge seismic data-volumes remains current. This study starts with the analysis of a set of typical petrophysical log shapes. These log sh...

To first order, seismic data provides the geologist and the geophysicist with information on the geometrical relationships between subsurface lithologic boundaries. Generally, this spatial information is used to construct conceptual geologic and process models with which to explain the origins of stratigraphic features. From such models we infer lithologic content. Various methods (e.g. AVO, Co...

Introduction And Geologic Overview The Upper Jurassic (Oxfordian) Smackover Formation is a stratigraphically complex carbonate formation, and a major producer of hydrocarbons in the northeastern Gulf of Mexico (Baria et al., 1982; Salvador, 1991). The main reservoir facies in the Smackover Formation are the microbial reefs, which were formed in a gently sloping to distally steepened carbonate r...

The seismic response of geological reservoirs is a function the elastic properties porous rocks, which depends on rock types, petrophysical features, and environments. Such characteristics are generally classified into facies. We propose to use convolutional neural networks in Bayesian framework predict facies based data quantify uncertainty classification. A variational approach adopted approx...

Stationarity of the random function is the key property of stochastic models to the extent that wrong hypotheses could lead to very unrealistic reservoir modeling for flow simulations. When non stationarity is suspected at the scale of the domain to be modeled, geologists usually propose either geological drift or seismic attributes to constrain stochastic models. However, in both cases, confid...

Deep neural networks (DNNs) can learn accurately from large quantities of labeled input data but often fail to do so when are scarce. DNNs sometimes generalize on test sampled different distributions. Unsupervised deep domain adaptation (DDA) techniques have been proven useful no labels available and distribution shifts observed in the target (TD). In this study, experiments performed seismic i...

With the increased size and complexity of seismic surveys, manual labeling facies has become a significant challenge. Application automatic methods for interpretation could significantly reduce labor subjectivity particular interpreter present in conventional methods. A recently emerged group techniques is based on deep neural networks. These approaches are data-driven require large labeled dat...

F our-component ocean-bottom-cable (4-COBC) seismic data acquired in deep water across the Gulf of Mexico were used to study near-sea-floor geologic characteristics of fluid-gas expulsion systems. Although these 4-C OBC data were acquired to evaluate oil and gas prospects far below the sea floor, the data have great value for studying near-sea-floor geology. The research results summarized here...

A detailed, high-resolution stratigraphic analysis of the Mediterranean Outflow contourite system at the continental slope of the Gulf of Cadiz has been carried out through the correlation between a dense network of seismic reflection profiles (sparker, airgun, 3.75 kHz and parametric echosounder — TOPAS), Calypso giant piston and standard gravity cores. From such correlation we determine a sta...