Seismic inversion may be pre- or post-stack, deterministic, random or geostatistical; it typically includes other reservoir measurements such as well logs and cores.
Seismic data may be inspected and interpreted on its own without inversion, but this does not provide the most detailed view of the subsurface and can be misleading under certain conditions.
Because of its efficiency and quality, most oil and gas companies now use seismic inversion to increase the resolution and reliability of the data and to improve estimation of rock properties including porosity and net pay.
Although the order presented reflects advances in inversion techniques over the past 20 years, each grouping still has valid uses in particular projects or as part of a larger workflow.
Typically, a reflection coefficient series from a well within the boundaries of the seismic survey is used to estimate the wavelet phase and frequency.
The logs are then converted to time, filtered to approximate the seismic bandwidth and edited for borehole effects, balanced and classified by quality.
It is critical at this point to evaluate the accuracy of the tie between the inversion results and the wells, and between the original seismic data and the derived synthetics.
Stochastic inversions address this problem by generating a range of plausible solutions, which can then be narrowed through testing for best fit against various measurements (including production data).
In many geological environments acoustic impedance has a strong relationship to petrophysical properties such as porosity, lithology, and fluid saturation.
This approximate result is then improved in a final inversion for P-impedance, S-impedance and density, subject to various hard and soft constraints.
Since a different wavelet is computed for each offset volume, compensation is automatically done for offset-dependent bandwidth, scaling and tuning effects.
No prior knowledge of the elastic parameters and density beyond the solution space defined by any hard constraints is provided at the well locations.
This makes comparison of the filtered well logs and the inversion outputs at these locations a natural quality control.
When applied in global mode a spatial control term is added to the objective function and large subsets of traces are inverted simultaneously.
The simultaneous inversion algorithm takes multiple angle-stacked seismic data sets and generates three elastic parameter volumes as output.
This generates reservoir models with geologically-plausible shapes, and provides a clear quantification of uncertainty to assess risk.
Geostatistical inversion integrates data from many sources and creates models that have greater resolution than the original seismic, match known geological patterns, and can be used for risk assessment and reduction.
Seismic, well logs and other input data are each represented as a probability density function (PDF), which provides a geostatistical description based on histograms and variograms.
Individual PDFs are merged using bayesian inference techniques, resulting in a posterior PDF conditioned to the whole data set.
The posterior PDF is then input to a Markov chain Monte Carlo algorithm to generate realistic models of impedance and lithofacies, which are then used to co-simulate rock properties such as porosity.
It is thus possible to exploit "informational synergies" to retrieve details that deterministic inversion techniques blur out or omit.
Well log information is used in the inversion process to derive wavelets, supply the low frequency component not present in the seismic data, and to verify and analyze the final results.
Wavelet analysis is conducted by extracting a filter from each of the seismic volumes using the well elastic (angle or offset) impedance as the desired output.