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公开(公告)号：US20250139751A1
公开(公告)日：2025-05-01
申请号：US18722166
申请日：2023-05-24
Applicant: Schlumberger Technology Corporation
Inventor： Sophie Androvandi , Karim Bondabou , Francois Wantz , Matthias Francois , MaryEllen Loan , Tetsushi Yamada , Simone Di Santo
Abstract: A method for generating a depth log of cuttings obtained during a subterranean drilling operation includes acquiring images of the cuttings and labeling the images with a lagged depth at a rig site; generating a clustering of lithology types in the acquired images at a rig the site; transferring the images and the clustering of lithology types from the rig site to an offsite location; evaluating the images and the clustering of lithology types to label each of the lithology types at the offsite location; and generating a description and/or depth log of the labeled lithology types at the offsite location.
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Patent Agency Ranking
公开(公告)号：US11443149B2
公开(公告)日：2022-09-13
申请号：US17067806
申请日：2020-10-12
Applicant: Schlumberger Technology Corporation
Inventor： Matthias Francois , Youssef Tamaazousti , Josselin Kherroubi
IPC: G06K9/62 , G01N15/08 , G01N21/25 , G01N33/24 , G06V10/147
Abstract: Apparatus and methods for ascribing one of multiple predetermined sub-classes to multiple pixels of an image of an unknown rock sample retrieved from a geological formation. The ascription utilizes a deep learning model trained with an annotated training dataset. The annotated training dataset includes multi-pixel images of known rock samples and, for each known rock sample image, which sub-class corresponds to at least a subset of pixels of that image. For each pixel of the unknown rock sample image having an ascribed sub-class, which one of predetermined meta-classes is associated with that pixel is derived based on the sub-class ascribed to that pixel. The meta-classes represent different predetermined rock types. At least one property of the formation is predicted utilizing the ascription-derived meta-classes, including which rock type(s) are present in the formation.
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公开(公告)号：US20240420299A1
公开(公告)日：2024-12-19
申请号：US18719961
申请日：2023-05-25
Applicant: Schlumberger Technology Corporation
Inventor： Richa Sharma , Karim Bondabou , Matthias Francois
Abstract: Systems and methods are provided for imaging drill cuttings, which employ a UV source including a UV LED, which is configured to illuminate a sample volume with UV radiation that interacts with oil-bearing cuttings to cause fluorescence emission. A camera system is configured to capture at least one image of the cuttings based on fluorescence emission. In another aspect, methods are provided for characterizing oil content in drill cuttings that involve capturing at least one WE image of the cuttings illuminated by white light, capturing at least one UV image of the cuttings based on fluorescence emission from UV radiation, processing the at least one WE image to determine a first pixel count for all cuttings, processing the at least one UV image to determine a second pixel count for oil-bearing cuttings, and determining a parameter representing oil content of the cuttings based on the first and second pixel counts.
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公开(公告)号：US20250027409A1
公开(公告)日：2025-01-23
申请号：US18779680
申请日：2024-07-22
Applicant: Schlumberger Technology Corporation
Inventor： Josselin Kherroubi , Matthias Francois , Tetsushi Yamada
Abstract: Systems and methods are provided for analyzing sample images, such as for rock particles obtained during drilling of a geologic formation. The system and techniques utilize a Large Foundation Model (LFM) in the segmentation of rock particles. The LFM can receive an image (or image data) of rock particles as an input and generates segmentation of the image at a pixel level (i.e., each pixel of the image is classified) as a segmented image. Additionally, active annotation can be provided in conjunction with a graphics user interface (GUI) to allows for user interaction with images as well as selective segmentation of the images.
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公开(公告)号：US12154324B1
公开(公告)日：2024-11-26
申请号：US18720042
申请日：2023-05-31
Applicant: Schlumberger Technology Corporation
Inventor： Maxime Marlot , Matthias Francois
IPC: G06V10/98 , G06V10/26 , G06V10/771 , G06V10/82 , G06V20/10
Abstract: A method for evaluating drill cuttings includes acquiring a first digital image and processing the first digital image with a trained neural network (NN) to generate a first segmented image including a plurality of labeled segments in which at least one label includes a lithology type. The segmented image and the acquired first digital image are processed to retrain the NN. A second digital image is then be processed with the retrained NN to generate a second segmented image including a plurality of labeled segments in which at least one label includes a lithology type.
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公开(公告)号：US20210319257A1
公开(公告)日：2021-10-14
申请号：US17067806
申请日：2020-10-12
Applicant: Schlumberger Technology Corporation
Inventor： Matthias Francois , Youssef Tamaazousti , Josselin Kherroubi
Abstract: Apparatus and methods for ascribing one of multiple predetermined sub-classes to multiple pixels of an image of an unknown rock sample retrieved from a geological formation. The ascription utilizes a deep learning model trained with an annotated training dataset. The annotated training dataset includes multi-pixel images of known rock samples and, for each known rock sample image, which sub-class corresponds to at least a subset of pixels of that image. For each pixel of the unknown rock sample image having an ascribed sub-class, which one of predetermined meta-classes is associated with that pixel is derived based on the sub-class ascribed to that pixel. The meta-classes represent different predetermined rock types. At least one property of the formation is predicted utilizing the ascription-derived meta-classes, including which rock type(s) are present in the formation.
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