[1] A 3D seismic volume from the Nankai Trough accretionary wedge (SE Japan) is used to evaluate the subsurface distribution of gas hydrates as a function of structural and stratigraphic complexity, variable heat flow patterns and the presence of subsurface fluid conduits. Eleven equations were modified for depth, pressure, and temperature, modeled in 3D, and compared with the distribution of BottomSimulating Reflections (BSRs) offshore Nankai. The results show that the equations produce overlapping-and thus potentially consistent-predictions for the distribution of BSRs, leading us to propose the concept of a ''BSR Stability Envelope'' as a method to quantify the subsurface distribution of gas hydrates on continental margins. In addition, we show that the ratio (R) between shallow and deep BSRs of seven subenvelopes, which are defined by BSR stability equations, indicates local gas hydrate equilibrium conditions. Values of R < 1 relate to cooler regions, whereas when R > 1 the majority of BSRs are located in warmer structural traps. The method in this paper can be used to recognize any divergence between observed and theoretical depths of occurrence of BSRs on 3D or 4D (time lapse) seismic volumes. In the Nankai Trough, our results point out for equilibrium conditions in BSRs located away from the Megasplay Fault Zone and major thrust faults. This latter observation demonstrates the applicability of the method to: (a) the recognition of subsurface fluid conduits and (b) the prediction of maximum and minimum depths of occurrence of gas hydrates on continental margins, under distinct thermal and hydrologic conditions.
Today, the major challenge in reservoir management is improving reservoir characterization to better understand variations in rock properties and distribution of fluids away from the wells. A further challenge is the proper characterziation of thin multi-layered heterogeneous reservoirs. In this paper we present a new workflow to delineate reservoirs at and below seismic resolution. A reservoir characterization study is performed for the Reservoir Z of the Giant field. The reservoir seqeuce consists of multiple reservoir units (1, 2, and 3) with variable average porosities between 11 and 28%. The study attempts to delineate separate units through an innovative Bayesian inversion technique that jointly solves for impedances and facies (Kemper and Gunning, 2014). Four different seismic facies were determined through an inversion feasibility study from variations in mineralogy and porosity. Elastic property trends as a function of time were built from the petrophysics of two wells. The low frequency component was driven by these trends and prior facies distributions were specified to constrain the results to be geologically reasonable. Three angle stacks were simultaneously inverted using extracted wavelets, each with specified noise estimates. An inversion was run using this technique on post stack data, as well as a coloured inversion for comparison. Despite varaible data quality, the pre-stack facies based Bayesian inversion was able to invert for three separate layers of high porosity in the reservoir sequence in a number of blind wells. This was a noteable improvement on both the coloured inversion and post-stack inversion. It was concluded that the higher frequenices in the near stack data combined with increased rock physics contraints of the inversion resulted in better thin layer detection.
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