Schlumberger Technology Corporation

Fatma Mahfoudh

Yingwei Yu

Cheolkyun Jeong

Velizar Vesselinov

Crispin Chatar

Aymeric Lilian Jan

Sergey Mikhailovich Makarychev-Mikhailov

Gordana Draskovic

Gregory Michael Skoff

Georgia Kouyialis

Yezid Arevalo Romero

Oleh Petryshak

The present disclosure relates to systems, non-transitory computer-readable media, and methods for detecting a washout or other anomaly event in a wellbore. In particular, in one or more embodiments, the disclosed systems receive a plurality of measurements including a measured flow rate into the wellbore, a measured weight on a drill bit in the wellbore, a measured depth of the drill bit in the wellbore, and a measured pressure at a standpipe of the wellbore. In one or more embodiments, the disclosed systems estimate one or more parameters of a physical model for determining a theoretical estimate of the standpipe pressure. In one or more embodiments, the disclosed systems determine a probability that the washout or other anomaly event is occurring in the wellbore based at least partially upon the measurements and the theoretical estimate of the standpipe pressure.

Hybrid neural network for drilling anomaly detection

A method can include receiving input for a drilling operation that utilizes a bottom hole assembly and drilling fluid; generating a set of offset drilling operations using historical feature data, where the historical feature data are processed by computing feature distances; performing an assessment of the offset drilling operations as characterized by at least feature distance-based similarity between the drilling operation and the offset drilling operations; and outputting at least one recommendation for selection of one or more of a component of the bottom hole assembly and the drilling fluid based on the assessment.

Well construction equipment framework

Growing community of inventors

Cambridge, United Kingdom

Fatma Mahfoudh