Schlumberger Technology Corporation

Tetsushi Yamada

Simone Di Santo

Karim Bondabou

Steven Philip Young

Reda Karoum

Ilaria De Santo

Maneesh Pisharat

Wael Abdallah

A method for evaluating solid drilling fluid additives such as lost cuttings materials (LCM) includes acquiring a calibrated digital image of solid particles separated from drilling fluid circulating in a wellbore. The calibrated digital image is processed to identify individual ones of the solid particles depicted in the image. Color features and/or texture features are extracted from the identified solid particles depicted in the image. The extracted color and/or texture features are processed to identify LCM particles among the identified solid particles and to classify each of the identified LCM particles into one of a plurality of LCM classes and thereby obtain an LCM particle classification.

Automated identification and quantification of solid drilling fluid additives

A method for estimating a porosity of a rock sample includes acquiring a calibrated digital image of the rock sample, extracting color and texture features from the digital image of the rock sample, and estimating the porosity of the rock sample using a model to evaluate the extracted color features and texture features. The model is configured to correlate digital image color and texture features with porosity.

Formation porosity estimation from digital images

Systems and methods are configured to receive one or more photographs depicting a plurality of cuttings, to identify one or more individual cuttings of the plurality of cuttings depicted in the one or more photographs, to extract morphological, color, texture, grain size, and grain distribution data from each individual cutting of the one or more individual cuttings, to perform lithological classification of the one or more individual cuttings at a plurality of hierarchical levels based at least in part on the extracted morphological, color, texture, grain size, and grain distribution data or based at least in part on features directly extracted from the one or more individual cuttings that represent the morphological, color, texture, grain size, and grain distribution data, and to present a consolidated results summary of the lithological classification of the one or more individual cuttings at the plurality of hierarchical levels via the analysis and control system.

Systems and methods for performing lithological classification of cuttings based on calibrated photographs of rock particles

Systems and methods presented herein are configured to train a neural network model using a first set of photographs, wherein each photograph of the first set of photographs depicts a first set of objects and include one or more annotations relating to each object of the first set of objects; to automatically create mask images corresponding to a second set of objects depicted by a second set of photographs; to enable manual fine tuning of the mask images; to re-train the neural network model using the second set of photographs, wherein the re-training is based at least in part on the manual fine tuning of the mask images; and to identify one or more individual objects in a third set of photographs using the re-trained neural network model.

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Cambridge, MA, United States of America

Tetsushi Yamada