Schlumberger Technology Corporation

Ettore Mirto

Helen Xiaoyan Zhong

Ping Zhang

Mark T Frey

Jean P Seydoux

Aria Abubakar

Denis Heliot

Weixin Dong

Steve F Crary

Christophe Dupuis

QingFeng Zhu

Hui Xie

Yao Feng

Qingfeng Zhu

Yan Kai Xu

A method for drilling includes obtaining formation-measurement pairings, training a machine-learning model using the formation-measurement pairings, receiving measurements obtained by a tool positioned in a well formed in a formation, and generating a formation model of at least a portion of the formation using the machine-learning model and the measurements. The formation model represents one or more physical parameters of the formation, one or more structural parameters of the formation, or both.

Geological interpretation with artificial intelligence

Methods capable of determining a depth of investigation of a logging tool can include generating an error distribution model for a logging tool. The methods can also include defining a detection threshold above which a measured signal from a measurement channel of the logging tool can be considered reliable based on output from the error distribution model, and generating a simulated formation model to determine the depth of investigation. The depth of investigation can be biased by the detection threshold.

Methods and systems to analyze bed boundary detection

A method for integrating graphical logging content of a subterranean wellbore includes processing a plurality of digitized display images and relevant context information to generate a single synchronized display image including representations of each of the plurality of display image. The plurality of digitized display images and relevant context information are received at a central processor from a corresponding plurality of source tools or source processors. The display images are representative of subterranean wellbore conditions.

Visualization integration of graphical logging content

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Sugar Land, TX, United States of America

Ettore Mirto