Schlumberger Technology Corporation

Prashanth Pillai

Purnaprajna Raghavendra Mangsuli

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

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

Prashanth Pillai