Munich Re has patented a method involving machine learning models to predict future activity-related attributes for user activities. The method includes refining model parameters based on training data and bias criteria until a termination criterion is met. The model aims to improve performance metrics in well drilling operations. GlobalData’s report on Munchener Ruckversicherungs-Gesellschaft Aktiengesellschaft (Munich Re) 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 Munchener Ruckversicherungs-Gesellschaft Aktiengesellschaft (Munich Re), was a key innovation area identified from patents. Munchener Ruckversicherungs-Gesellschaft Aktiengesellschaft (Munich Re)'s grant share as of February 2024 was 40%. Grant share is based on the ratio of number of grants to total number of patents.
Machine learning model for well drilling performance prediction
A recently granted patent (Publication Number: US11914680B2) outlines a method for improving well drilling performance through machine learning models. The method involves receiving a request for a well drilling performance model, refining model parameters iteratively, and selecting machine learning models based on the request. The models are trained using drilling data records to predict non-productive time (NPT) performance metrics and identify outlier events. The system also includes features for predicting downhole equipment failures, setting drilling goals, and classifying outlier data elements. The method utilizes various independent variables such as planned runs, distance drilled, and vibration severity to optimize drilling performance and reduce non-productive time.
Furthermore, the system described in the patent incorporates outlier analytics and extreme well drilling condition models to enhance drilling performance predictions. The system is capable of generating data selection vectors, filtering training data sets, and training outlier classifier models to classify outlier data elements accurately. By utilizing convolutional neural networks and bias criteria, the system aims to provide a production-ready well drilling performance machine learning model that can be used in real-time drilling environments. The method and system outlined in the patent offer a comprehensive approach to optimizing well drilling operations, predicting equipment failures, and improving overall drilling efficiency through advanced machine learning techniques.
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