Spectra derived from fast Fourier transform (FFT) analysis of time-domain data intrinsically contain statistical fluctuations whose distribution depends on the number of accumulated spectra contributing to a measurement. The tail of this distribution, which is essential for separation of the true signal from the statistical fluctuations, deviates noticeably from the normal distribution for a finite number of the accumulations. In this paper we develop a theory to properly account for the statistical fluctuations when fitting a model to a given accumulated spectrum. The method is implemented in software for the purpose of automatically fitting a large body of such FFT-derived spectra. We apply this tool to analyze a portion of a dense cluster of spikes recorded by our FST instrument ) during a record-breaking event (Cerruti et al. 2006) that occurred on 06 Dec 2006. The outcome of this analysis is briefly discussed. Subject headings:

A complete well-seismic integration project was successfully performed in a gas field located in the Lower Magdalena Basin, Colombia. The reservoir level corresponds to a laterally limited, mixed clastic turbidte system (laminated, shaly sands) which makes prospect identification and delineation difficult. The study can be divided in three phases: well log analysis, seismic analysis and interpretation/integration. Well log analysis included petrophysical volumetric estimation and rock physics modeling. Seismic analyses included gather conditioning and pre-stack simultaneous inversion. The interpretation/integration phase pulled together the results from the phases described above. Based on the rock physics modelling and the rock physics template, there is a clear separation between gas shaly sands and other facies (wet shaly sands and shales) in the Poisson's ratio vs. P-impedance domain. During the interpretation phase, seismic inversion attributes guided by the rock physics information made it possible to separate the shaly gas sands in order to identify new prospective areas in the field. New successful wells were drilled, validating the workflow.

The Plato Depression in the Lower Magdalena Basin is a Miocene depocenter where a thick, shale-prone marine sequence known as the Porquero Formation was laid down in basin-floor conditions. Seismic inversion carried out on new and existing 2D seismic data helped to focus early exploration on a shallow stratigraphic gas-sand play associated with what seemed to be isolated shale diapirs with shallow roots. A subsequent land 3D survey helped to locate the first exploratory well, which resulted in the discovery of the Guama gas-condensate field. The main reservoir consists of laminar, low-permeability sands in a relatively thick shale-prone sequence of Early and Middle Miocene age. Sequential application of acoustic and elastic inversion and AVO analysis was used to build an evolving 3D predictive model of gas sands, extracted from an otherwise featureless seismic cube. Workflows were based on careful rock-physics analysis, simultaneous seismic inversion, and AVA analysis supported by custom well-log and seismic-gather conditioning. Work routines carried out in parallel became essential to applying quality control and fine-tuning the model, which supported three additional successful wells, early reservoir planning, and key volumetrics.

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We have evaluated a case study, in which a class-1 amplitude variation with offset (AVO) turbiditic system located offshore Cote d’Ivoire, West Africa, is characterized in terms of rock properties (lithology, porosity, and fluid content) and stratigraphic elements using well-log and prestack seismic data. The methodology applied involves (1) the conditioning and modeling of well-log data to several plausible geologic scenarios at the prospect location, (2) the conditioning and inversion of prestack seismic data for P- and S-wave impedance estimation, and (3) the quantitative estimation of rock property volumes and their geologic interpretation. The approaches used for the quantitative interpretation of these rock properties were the multiattribute rotation scheme for lithology and porosity characterization and a Bayesian lithofluid facies classification (statistical rock physics) for a probabilistic evaluation of fluid content. The result indicates how the application and integration of these different AVO- and rock-physics-based reservoir characterization workflows help us to understand key geologic stratigraphic elements of the architecture of the turbidite system and its static petrophysical characteristics (e.g., lithology, porosity, and net sand thickness). Furthermore, we found out how to quantify and interpret the risk related to the probability of finding hydrocarbon in a class-1 AVO setting using seismically derived elastic attributes, which are characterized by having a small level of sensitivity to changes in fluid saturation.