Halliburton Energy Services, Inc.

Exxonmobil Technology and Engineering Company

Yunfei Yang

Kristoffer Thomas Walker

Jan Schmedes

Ratnanabha Sain

Amit Kushwaha

A method and system for modeling a subsurface region include applying a trained machine learning network to an initial petrophysical parameter estimate to predict a geologic prior model; and performing a petrophysical inversion with the geologic prior model, geophysical data, and geophysical parameters to generate a rock type probability model and an updated petrophysical parameter estimate. Embodiments include managing hydrocarbons with the rock type probability model. Embodiments include checking for convergence of the updated petrophysical parameter estimate; and iteratively: applying the trained machine learning network to the updated petrophysical parameter estimate of a preceding iteration to predict an updated rock type probability model and another geologic prior model; performing a petrophysical inversion with the updated geologic prior model, geophysical seismic data, and geophysical elastic parameters to generate another rock type probability model and another updated petrophysical parameter estimate; and checking for convergence of the updated petrophysical parameter estimate.

Petrophysical inversion with machine learning-based geologic priors

A method is disclosed for radiaiiy profiling shear velocities of flexural wave modes in a formation. The method includes establishing sensitivity kernels with two non-dimensionalized parameters and using said sensitivity kernels to perform an inversion for radial shear wave velocity profiles. This method may be used for LWD, MWD, or wireline logging operations.

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Palo Alto, CA, United States of America

Yunfei Yang