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3D Parametric Inversion for Interpretation of Logging-While-

A work ow is developed for the interpretation of logging-while-drilling density images in high-angle and horizontal wells. The key component of the work ow is 3D parametric inversion using a robust Gauss-Newton optimization engine and a new fast-forward model based on second-order 3D sensitivity functions.
The parametric model used for interpretation includes a 1D multilayer dipping formation, mud properties, 3D borehole geometry, and 3D well trajectory. Lateral (2D) variations in formation and borehole parameters are captured by de ning the models in discrete trajectory segments using an adaptive segmentation based on the local relative dip. Measurement sensitivities are used to design a exible and robust inversion-based work ow for determining optimum parameter values from all the available measurements. A sliding window is used to enforce consistency of models between adjacent segments. The result of the inversion is the accurate layer thicknesses,
shoulder-bed-corrected layer densities, formation dip, and azimuth in each segment. The inversion also produces a borehole-corrected formation density image and a robust caliper that takes into account the layered formation as background.
The work ow is especially tuned for scenarios with wellbore trajectory nearly parallel to layer boundaries, where ambiguity in interpretation is increased because of the dif culty in determining dip, lateral changes in layer properties, and in uence of standoff and nearby noncrossed boundaries.
The work ow is validated on synthetic thin-layer models with variable dip for both high-angle and near parallel scenarios. Several eld datasets are successfully processed using the work ow. Inversion-derived formation- density pro le and borehole caliper are shown to be more accurate than results from conventional processing.
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