Schlumberger Technology Corporation

Purnaprajna Raghavendra Mangsuli

Prashanth Pillai

Aria Abubakar

Atul Laxman Katole

Hiren Maniar

Mandar Shrikant Kulkarni

Aaron Scollard

Preeti Gupta

Ekansh Verma

Omkar Anil Gune

Denzil Francis Crasta

A method can include receiving raster images of logs, where the logs include plots, tracks and headers and where, compared to the plots and the tracks, the headers are underrepresented; processing the raster images using a trained machine learning model to generate segmentation masks for the logs for identification of the plots, the tracks and the headers; and generating digitized versions of the logs using the segmentation masks, where each of the digitized versions of the logs includes a digitized plot with an associated digitized header and an associated digitized track.

Raster image digitization system for field data

A method for digitizing image-based data includes receiving an image file including one or more target objects, generating an intermediate image by removing noise from the image file using a denoising machine learning model, identifying the one or more target objects included in the intermediate image using an object segmentation machine learning model, discretizing the one or more target objects that were identified using the trained object segmentation machine learning model, and storing the one or more target objects that were discretized in a data file, visualizing the one or more target objects, or both.

Raster image digitization using machine learning techniques

A method includes identifying entities in a well record database comprising data representing a plurality of objects and attributes of the objects, determining a data gap for at least one attribute of an object of the objects in the well record database, identifying documents in a document database, wherein identifying the documents include determining that the documents are relevant to the object based at least in part on metadata of the documents, extracting values for the data gap from the documents using a machine learning model, determining a data gap filler by aggregating the extracted values, and inserting the data gap filler into the data gap in the well log database.

Well record quality enhancement and visualization

A method can include receiving well logs, as series data, for wells, where the well logs represent stratigraphic characteristics of a field; for each of the well logs, generating a corresponding vector representation in a dimensional space using a transformer encoder; and determining similarity of the well logs via their corresponding vector representations to characterize well log quality for well log correlation of the field.

Well log correlation system

Systems, computer-readable media, and methods are provided. Relevant documents related to a specific entity are identified based on document metadata. Text and associated spatial coordinates are extracted based on relevant document pages. Significant document entities and associated spatial locations are identified. Page ranking is based on the extracted text and the spatial coordinates, the significant document entities, and image vector representations of the pages. A deep learning language model that utilizes the text and the spatial coordinates, layout information of the document entities, and the image vector representations of the pages is used to extract the user-defined attributes from the relevant document pages. First attribute values associated with the user-defined attributes are aggregated from the pages of one of the relevant documents into a single record. Second attribute values associated with the user-defined attributes are aggregated across the relevant documents. Aggregated records, including a first and second attribute, are written to a database.

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Pune, India

Purnaprajna Raghavendra Mangsuli