Schlumberger Technology Corporation

Sergey Mikhailovich Makarychev-Mikhailov

Yingwei Yu

Velizar Vesselinov

Crispin Chatar

Cheolkyun Jeong

Débora Campos De Faria

Fatma Mahfoudh

Gregory Michael Skoff

Georgia Kouyialis

Yezid Arevalo Romero

Oleh Petryshak

Isabelle Atheaux

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

Multivariate machine learning (ML) techniques can be applied to an XRF spectra and mitigate matrix effects and enable simultaneous quantification of composition, even when markers elements or ions of interest are imperceptible in the XRF spectra. Physical (e.g., density) and chemical (e.g., total dissolved solids and hardness) properties of the material can be also quantified using ML techniques.

X-ray fluorescence spectroscopy analysis

Growing community of inventors

Cambridge, United Kingdom

Sergey Mikhailovich Makarychev-Mikhailov