Physical and quantitative interpretation of seismic attributes for rocks and fluids identification.

摘要

This dissertation focuses on the link between seismic attributes and reservoir properties like lithology, porosity, and pore-fluid saturation. The key contribution of this dissertation is a novel inversion technique, which combines rock physics and multiple-point geostatistics. The inversion of seismic data is only one particular application of the technique.; In general, seismic attributes are all the information that can be obtained from seismic data. Using statistical rock-physics, the type of seismic attributes that are direct functions (analytically defined) of the elastic properties, can be probabilistically transformed sample-by-sample, independently one of each other, into reservoir properties. For these wavelet-independent seismic attributes, the wavelet or scale effects are removed during calculation; hence, they can be interpreted as the response from a well-localized reservoir zone.; In contrast, wavelet-dependent seismic attributes directly describe some characteristic of the seismic trace (e.g. amplitude, shape); thus, the wave-propagation effects must be included in any quantitative interpretation attempt. Elastic properties and their spatial arrangement (geometric distribution) must be considered. Fundamentally, the interpretation of wavelet-dependent attributes is an inverse problem with non-unique solution.; This dissertation presents contributions to the understating and interpretation of both types of seismic attributes. Converted P-to-S elastic impedance (PSEI) as a wavelet-independent attribute is introduced. The benefits of using PSEI are discussed, particularly in situations that the key elastic properties, needed for discriminating lithology and/or pore-fluids, are not captured with enough accuracy by attributes derived from P-to-P seismic data.; A novel inversion technique for wave let-dependent attributes, which combines rock physics and multiple-point geostatistics, is presented. The rock-physics component makes it possible to predict situations not sampled by log data. The multiple-point geostatistics component uses geological knowledge to guide the search for solutions. The method can be extended to satisfy multiple physical constraints simultaneously. Therefore, the solutions can be conditioned with different types of geophysical data. This inversion technique, which is the primary contribution of this dissertation, lays the foundation for innovative, multi-physics, multipoint inversions of geophysical data.