Seismic waves propagating in sedimentary sequences that include thin beds with large acoustic-impedance variations develop a distinct signature called stratigraphic filtering (SF). The source wavelet transforms into a low-frequency bell-shaped signal at the front end followed by a high-frequency noise coda. These effects produce low-frequency reflections that are delayed and exhibit poor continuity — a consequence of the short-period internal multiples. Applying a high-cut filter to the seismic and multiple synthetics removes noise coda and improves their correlation. Now, the need for multiple suppression of long-period internal multiples is evaluated. Multiple generators are easily identified for the multiple suppression process with synthetics morphing from primary only to primary plus multiples. Likewise, the stretch produced by SF is easily determined by inverting the primary and multiple synthetics for impedance correlation. The reduction in frequency is not desirable in the final product, so the high signal-to-noise low-frequency portion is extended to high frequency in the time-frequency domain. These principles are part of an interpretation workflow applied in Cooper Basin, Australia, which has as many as 50 coal beds of Permian age. By suppressing the SF effects from the Permian coal beds, the quality of fault and stratigraphic interpretation increases for units within the older Warburton Basin below the coal beds.

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With improved seismic data quality, prestack inversion has become a routine process for quantitative seismic interpretation. However, direct products from traditional seismic inversion usually are P-impedance (PI), S-impedance (SI), and, in some cases, density. These elastic properties are only an indirect description of subsurface geology. A bridge must be established from inverted PI, SI, and density to more understandable reservoir properties: lithology, porosity, and water saturation. Reservoir-property inversion is a model-based inversion process to transform PI, SI, and density to lithology, porosity, and water saturation. The inversion is performed in two steps: (1) well-log inversion on log data to estimate optimal elastic properties of rock-grain constituents and (2) reservoir-property inversion to estimate reservoir properties from PI, SI, and density. The underlying rock-physics models are the same for both inversions including mass–balance equation, Gassmann equation, Voigt-Reuss-Hill average, and Krief's relationship, or, optionally, the Xu-White velocity model. The solution of the inversion is considered as optimal in terms of minimum misfit of PI, SI, and density modeled with inverted reservoir properties compared to the input PI, SI, and density. The inversion results honor all the interrelationships between various elastic properties, reservoir properties, and rock-grain properties. A limitation of the proposed inversion includes a requirement for lithology with only two solid constituents, such as sand mixed with shale. It also requires a density volume as one of the primary input data for the inversion. Due to the inversion's sensitivity to fluid contents, estimated water saturation in many cases may not be reliable. This paper presents the inversion methodology and the inversion results from a set of modeled data as well as a real case study to demonstrate the inversion's capability.

Seismic exploration in the PNG Highland's has experienced a 'mini renaissance' over the last few years where the permit Operators have engaged a group of independent contractors to acquire several hundred kilometres of 2D seismic. The cost of acquisition is upwards of US$ 50,000 per kilometre which generally rules out in-field experimentation and places a greater emphasis on processing and pre-survey modelling. Many of the seismic programs have traversed thick, (massive, from 500m->1000m) karst Darai Limestone at surface. The karst limestone, combined with the structural complexity of the thrust belt produces seismic images which range from clear to undecipherable. Full waveform elastic modelling based on wells at Gobe, Gobe-1X and Hides provides insights into seismic propagation through the limestone and lead to better acquisition design. In turn this may improve data quality to the point where pre-stack migration can be applied successfully.

Through a combination of innovative survey design, new technology and the introduction of novel operational techniques, the trace density of a 3D seismic survey in the Cooper Basin was increased from a baseline of 140 000 to 1 600 000 traces km–2, the bandwidth of the data was extended from four to six octaves, and the dataset was acquired in substantially the same time-frame and for the same cost as the baseline survey.