Schlumberger Technology Corporation

Francisco Jose Gomez

Maurice Ringer

Paul Bolchover

Cheolkyun Jeong

Paul Muller

Ram Sunder Kalyanraman

Adrian Rodriguez Herrera

Saad Kisra

James Minton

Athithan Dharmaratnam

Assef Mohamad Hussein

Karsten Fischer

Ivan Diaz Granados Pertuz

Mohamed Saad Kisra

A system and method that includes querying a database to obtain offset well data collected while drilling previously drilled wells. The system and method also include determining if at least one risk is identified with respect to a planned well based on the offset well data. The system and method additionally include generating a machine learning model based on the at least one risk that is identified based on the offset well data. The system and method further include predicting at least one drilling risk based on the machine learning model, wherein a drill plan that includes drilling parameters is adjusted based on the at least one predicted drilling risk.

Automated offset well analysis

A system and method are provided for extracting information regarding a drill site including forming one or more documents having one or more raw comments regarding a well site. Raw data may be extracted from the one or more documents to produce extracted raw data. The extracted raw date may be pre-processed by removing ambiguity, artifacts, and/or formatting errors from the one or more raw comments to produce pre-processed data. Topics data may be extracted from the pre-processed data using a natural language processing (NLP) algorithm to produce extracted topics data. Measurement data may also be extracted from the pre-processed data using the NLP algorithm to produce extracted measurement data. The extracted topics data and the extracted measurement data may be aggregated to form a set of discrete data points, such as calibration points, per comment to produce aggregated data and one more calibration points may be identified from the aggregated data. The results of the one or more calibration points may then be presented.

Information extraction from daily drilling reports using machine learning

A method, computing system, and non-transitory computer-readable medium, of which the method includes receiving offset well data collected while drilling one or more offset wells, generating a machine learning model configured to predict drilling risks from drilling measurements or inferences, based on the offset well data, receiving drilling parameters for a new well, determining that the drilling parameters are within an engineering design window, generating a drilling risk profile for the new well using the machine learning model, and adjusting one or more of the drilling parameters for the new well, after determining the drilling parameters are within the engineering design window, and after determining the drilling risk profile, based on the drilling risk profile.

Methods and systems for determining a property of a tubular are described. Measurement data of cross-sectional shapes of the tubular at a plurality of depth positions is provided. A three-dimensional mesh representing the tubular based on the cross-sectional shapes is generated. A stress simulation using the three-dimensional mesh to provide an integrity assessment of the tubular is performed.

Methods and systems for determining integrity and operational boundaries of subterranean wells

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Oxford, United Kingdom

Francisco Jose Gomez