Schlumberger Technology Corporation

Services Petroliers Schlumberger

Geoquest Systems B.v.

Wei Chen

Yuelin Shen

Yingwei Yu

Velizar Vesselinov

Richard John Meehan

Minh Trang Chau

Qiuhua Liu

Zhengxin Zhang

Sylvain Chambon

Cheolkyun Jeong

Huseyin Murat Panayirci

Rongbing Chen

Maria Lucia Cazares Gomez

Gregory Michael Skoff

A system and method that include receiving sensor data during drilling of a portion of a borehole in a geologic environment. The system and method also include selecting a drilling mode from a plurality of drilling modes based at least on a portion of the sensor data. The system and method additionally include simulating drilling of the borehole using the selected drilling mode and generating a state of the borehole in the geologic environment based on the simulated drilling of the borehole. The system and method further include generating a reward using the state of the borehole and a planned borehole trajectory and using the reward through deep reinforcement learning to maximize future rewards for drilling actions.

Drilling control

A method of detecting wear of a downhole tool implemented in a subject wellbore includes receiving offset wellbore data for one or more offset wellbores and, based on the offset wellbore data, determining an expected downhole tool index for the downhole tool at one or more measurement depths including an active measurement depth of the subject wellbore. The method further includes receiving subject wellbore data and, based on the subject wellbore data, determining a subject downhole tool index in real time for the downhole tool at the active measurement depth. The method further includes determining the wear of the downhole tool based on comparing the subject downhole tool index to the expected downhole tool index at the active measurement depth in real time.

Systems and methods for determining wear of downhole tools

A method for drilling a well includes generating a plurality of proposed drilling actions using a plurality of working agents based on a working environment, simulating drilling responses to the proposed drilling actions using a plurality of validation agents in a validation environment that initially represents the working environment, determining rewards for the proposed drilling actions based on the simulating, using the validation agents, selecting one of the proposed drilling actions, and causing a drilling rig to execute the selected one of the proposed actions.

Multi-agent drilling decision system and method

A method can include receiving sensor data during drilling of a portion of a borehole in a geologic environment; determining a drilling mode from a plurality of drilling modes using a trained neural network and at least a portion of the sensor data; and issuing a control instruction for drilling an additional portion of the borehole using the determined drilling mode.

Methods, computing systems, and computer-readable media for interpreting drilling dynamics data are described herein. The method can include receiving drilling dynamics data simulated by a processor or collected by a sensor positioned in a drilling tool. The method can further include extracting a feature map from the drilling dynamics data, and determining that a feature zone from the feature map corresponds to a predetermined dynamic state. The feature zone can be determined using a neural network trained to associate feature zones with dynamic states. Additionally, the method can include selecting a drilling parameter for a drill string based on the feature zone.

Automatic interpretation of drilling dynamics data

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Katy, TX, United States of America

Wei Chen