result, the wavelet estimation converges more quickly than when starting with a zero phase assumption. Minor edits and "stretch and squeeze" may be applied to the well to better align the events. Accurate wavelet estimation requires the accurate tie of the impedance log to the seismic. Errors in well tie can result in phase or frequency artifacts in the wavelet estimation. Once the wavelet is identified, seismic inversion computes a synthetic log for every seismic trace. To ensure quality, the inversion result is convolved with the wavelet to produce synthetic seismic traces which are compared to the original seismic. Components of inversion Inversion includes both seismic field data and well data, where well data serves to add the high frequency below the seismic band and to constrain the inversion. Well logs are first conditioned and edited to ensure there is a suitable relationship between impedance logs and the desired properties. The logs are then converted to time, filtered to approximate the seismic bandwidth and edited for borehole effects, balanced and classified by quality. Seismic data is band-limited, reducing resolution and quality. To extend the frequency band available, low-frequency data is derived from log data, pre-stack depth or time migrated velocities and/or a regional gradient. High frequency can be derived from well control or geostatistical analysis. Initial inversions are often run with relaxed constraints, starting with the seismic and then adding limited-trend data from the wells. This provides a rough overview of the reservoir in an unbiased manner. 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. It is also important to ensure that the wavelet matches the phase and frequency of seismic data. Without a wavelet, the solution is not unique. Deterministic inversions address this problem by constraining the answer in some way, usually to well log data. 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). Post-stack seismic resolution inversion An example of a post-stack seismic resolution inversion technique is the constrained sparse-spike inversion (CSSI). This assumes a limited number of reflection coefficients, with larger amplitude. The inversion results in acoustic impedance (AI), which is the product of rock density and p-wave velocity. Unlike seismic reflection data (which is