Constellation Software has patented a geological exploration method that uses machine learning to predict synthetic curves based on anomaly-free data from boreholes. The method also involves blending the synthetic curve with actual data to generate a more accurate three-dimensional image of underground formations for hydrocarbon extraction planning. GlobalData’s report on Constellation Software 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 Constellation Software, was a key innovation area identified from patents. Constellation Software's grant share as of May 2024 was 39%. Grant share is based on the ratio of number of grants to total number of patents.
Geological exploration method using machine learning for anomaly detection
A recently granted patent (Publication Number: US11965399B2) discloses a novel geological exploration method that involves obtaining measurements and calculating property values along boreholes in a specific area to generate log data with multiple curves. The method includes detecting anomalies along one of the curves, training a machine learning regressor to predict a synthetic curve based on the anomaly-free curve, blending the synthetic curve with the original curve, obtaining seismic data for the area, and using an inversion method to create a three-dimensional image of the underground formation. This three-dimensional image can be utilized to plan hydrocarbon extraction efficiently.
Furthermore, the patent details the process of flagging anomalies detected along the curves and replacing them with values from the synthetic curve during blending. An ensemble of anomaly detection methods such as isolation forest, density-based spatial clustering, and interquartile range techniques are employed to identify anomalies. The method also incorporates a pay zone flag and an anomaly flag to enhance the blending process, with measurements including neutron porosity, density, and hole geometry, and calculated properties encompassing shale volume, water saturation, porosity, permeability, elasticity, and reflectivity coefficient. The patent also introduces an underground exploration apparatus equipped with an interface for data collection and a data processing module for anomaly detection, machine learning regression, blending, and inversion methods to generate a comprehensive plan for hydrocarbon extraction based on the three-dimensional image of the underground formation.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
