Stick/slip motion is an extremely severe torsional oscillation that can cause the drill bit to come to a complete stop and then followed by an extra acceleration of the surface rotation. Due to the lack of downhole sensors for direct speed measurement of logging-while-drilling (LWD) image tools, the depth measured on the surface can appear to be smoother than the downhole sensors’ real movement. Using the smoothed surface depth for time-to-depth gating of the images might lead to the formation features appearing to be compressed or stretched semicyclically in the depth domain when sensor stick/slip occurs.

In this paper, an innovated approach is presented to restore image features altered by the depth desynchronization and stick/slip issue. This method initially identified the drilling status from the interpolated surface depth in a time domain, including sensor movement direction; i.e.,drilling down or reaming down, tool stop, and connection breakdown time. In parallel, the drilling statuses were also identified from a downhole image feature similarity and the artificial intelligence (AI)-aided functions. The surface depth can be synchronized to downhole image measurements in the time domain based on the identified drilling statuses. In the end, the stick/slip time intervals were identified from the synchronized depth when the depth variation exceeded the image's vertical resolution, and the pseudovelocity was filtered and computed in the time domain. The stick/slip image features were restored in the depth domain efficiently after time-to-depth gating using the new processed depth.

This new approach will be applied to an LWD ultrasonic image obtained from a Middle East carbonate reservoir. The drilling statuses were identified confidently in the time domain and no additional stick/slip features were found in the image data in the depth domain. The processed images revealed the reservoir heterogeneity with clear vug features, additional bedding boundary, and confident fracture features.

In a severe stick/slip mode, the LWD resistivity image was processed with downhole speed correction from the multiple sensors’ signal, but the block image features remained on the image in the depth domain. The image features were restored efficiently with an adapted solution from this innovative approach.

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