--> Processing and Interpretation Considerations for Full Waveform Inversion of PSDM Velocity Fields

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Processing and Interpretation Considerations for Full Waveform Inversion of PSDM Velocity Fields

Abstract

Abstract

Full waveform inversion (FWI) technology offers the potential reward of subsurface velocity resolution and PSDM imaging quality never before attainable within the industry. It is performed on prestack data, and prestack processing and interpretive experience with modern datasets are valuable assets in conducting production FWI projects. FWI is a nonlinear inversion and, as such, does not guarantee convergence to a geologically reasonable solution. In practical applications, without continuous monitoring of intermediate data and model updates, the output of the inversion process can diverge and produce non-geological output models and inversion artifacts.

As a new technology, FWI is often presented in the language of mathematicians, which can preclude input by non-FWI experts. This is regrettable because theoretical knowledge is desirable, but often not as important as familiarity with data and expertise of processing and interpretation tools. This paper describes the FWI workflow in terms to which a seismic professional can relate. Also discussed are workflow steps that, at first glance, might appear unfamiliar or disconcerting to FWI novices. These lessons learned are exercised on a synthetic marine case study, which simulates a multitude of challenges encountered during real-life FWI projects. The velocity field has a variety of exploration-relevant velocity features, including shallow channels, low- and high-velocity lenses, layered sequences, and near-vertical velocity discontinuities. In the discussed simulated “blind” test, it was determined that detailed understanding of the input data set, velocity estimation techniques, and imaging artifacts are paramount to a successful inversion project. The FWI case study began by first evaluating three different input velocity models suitable for FWI. Next, prestack data was prepared to generate differential data to be carefully analyzed for optimal processing parameters using a new condensed differential data QC technique. FWI gradients were conditioned based on geologic considerations and an understanding of imaging artifacts. Offset ranges, mutes, and frequency ranges were carefully adjusted based on condensed differential data analysis. After 59 increasingly expensive FWI iterations, a good correlation of velocity to geology and a crisp image with clear fault patterns were obtained.