Schlumberger Technology Corporation

Saudi Arabian Oil Company

Simone Di Santo

Tetsushi Yamada

Shouxiang Mark Ma

Wael Abdallah

Karim Bondabou

Ilaria De Santo

Ali Jasim A Al Solial

Ngoc Hoang Lan Huynh

A method can include using a digital camera of a digital microscopy system, acquiring a digital image of an engineered three-dimensional object positioned on a base and illuminated by a light source; using the digital camera, acquiring a digital image of a color checker card positioned on the base and illuminated by the light source; determining a light source criterion by assessing position of the light source based at least in part on a shadow in the digital image of the engineered three-dimensional object as cast by the engineered three-dimensional object and based at least in part on saturation of color in the digital image of the color checker card; and calibrating the digital microscopy system using the light source criterion to generate a calibrated digital microscopy system.

Digital microscopy system for 3D objects

Methods and systems are provided for predicting thermal properties of a subsurface rock formation. A training dataset is derived from petrophysical properties of a plurality of formation rock samples and thermal properties of the plurality of formation rock samples. The training dataset is used to train a machine learning model that predicts label data representing the predefined set of thermal properties given input data representing the predefined set of petrophysical properties of an arbitrary formation rock sample. The machine learning model can be validated and deployed for use in predicting thermal properties of subsurface rock formations.

Methods and systems for predicting formation thermal properties

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

Digital image processing a digital image of sample drill cuttings retrieved from a geological formation and related methods include identifying individual zones in the image that depict at least a predetermined minimum heterogeneity of a first physical property, extracting particles in each identified zone that depict a second physical property within a predetermined quantitative range, and measuring a third physical property of each extracted particle. The first physical property may be texture, size, color, or spectral response within the zone. The second physical property may be brightness, color, contrast, hue, saturation, or wavelet energy. The third physical property may be size or color.

Cuttings imaging for determining geological properties

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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Dhahran, Saudi Arabia

Simone Di Santo