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Geoplat AI 25.03 enables the conditioning of original seismic data using machine learning methods. The algorithm, like other our conditioning techniques, was trained on synthetic data. However, a distinctive feature here is the utilization of data with varying frequencies.

This trained neural network is capable of enhancing the resolution of seismic data, enabling a more detailed observation of thin layers. These thin layers become more pronounced through this conditioning, resulting in a more detailed representation of the structural characteristics of the surveyed area.

Российская компания «ГридПоинт Дайнамикс» – одна из ведущих компаний по разработке и технической поддержке программного обеспечения в области поиска, разведки и разработки месторождений нефти и газа. Основным продуктом компании является программная платформа Geoplat Pro, которая направлена на комплексное решение сразу нескольких задач: «сейсмическая интерпретация – геологическое моделирование – гидродинамическое моделирование».

• Structure-Oriented Conditioning: Uses convolutional neural networks trained on hundreds of thousands of synthetic datasets to improve seismic image quality, enhance signal-to-noise ratio, and preserve vertical resolution
• Mean Seismic Conditioning: Suppresses incoherent noise and enhances visibility of fault zones and geological structures
• High-Resolution Conditioning: Enhances seismic data resolution to enable detailed observation of thin stratigraphic layers

• Fault Interpretation: Calculates fault probability distribution, extracts fault surfaces automatically, and handles complex fault types including reverse and listric faults even on poor-quality data
• Horizon Interpretation: Enables automatic tracing of single or multiple horizons while preserving complex fault structures and regional geological features
• Salt Bodies Delineation: Generates salt probability attribute volumes to accurately identify top and base boundaries of salt bodies

• Paleo-Channels Detection: Uses probabilistic modeling to detect channels, sand bodies, fans, and injectites directly from seismic data without requiring additional attributes

• R and D Experience: 8 years of machine learning research
• Academic Partners: 15 leading institutions
• Completed Projects: 80 plus consultancy projects
• Productivity Gain: Up to 70 percent improvement
• Active Users: 500 plus geoscientists
• Training Data: 1 million plus data samples in AI models