Petroliam Nasional has filed a patent for a device utilizing sweet spot-based machine learning (SSML) and completion-based machine learning (COMML). The device processes production data normalized with geology and geophysics data to predict sweet spot locations for structure placement using 3D graphics. GlobalData’s report on Petroliam Nasional gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Petroliam Nasional, Bioethanol production GMOs was a key innovation area identified from patents. Petroliam Nasional's grant share as of January 2024 was 58%. Grant share is based on the ratio of number of grants to total number of patents.
Machine learning device for identifying sweet spot locations
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The patent application (Publication Number: US20240013092A1) describes a device and method for sweet spot-based machine learning (SSML) in the context of geology and geophysics (G&G) data normalization. The device includes an input portion to receive output signals from connected apparatus, processed through machine learning to generate visually perceivable 3D productivity volumes for identifying sweet spot locations related to structural placements. The method involves processing completion-based machine learning (COMML) by normalizing production data with completion data to create prediction signals for deriving predictive machine learning models.
Furthermore, the processing method includes pre-processing completion data, normalization of production data, and generating prediction signals for model validation, evaluation, and optimization purposes. The device and method aim to enhance decision-making in identifying optimal locations for structures like completed oil wells. By utilizing machine learning techniques on production and completion data, the system provides a visual representation of productivity volumes, aiding in the identification of sweet spot locations for efficient structural placement. The patent application emphasizes the integration of geology and geophysics data normalization in machine learning processes to optimize model validation and evaluation for improved decision-making in completion-based operations.
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