The Deep QI add-on has best-in-class machine learning (ML) algorithms to predict missing data not only using training datasets but also guided with geological knowledge from petrophysics and rock physics. This toolkit accelerates and improves analysis of regional information due to its flexibility in handling data sets of varied size and volume. The functions span the RokDoc platform by leveraging well data, rock physics models, and pressure trends. Streamline rock property trending for Ji-Fi and use trained ML models for petrophysical property prediction.
Specific to the Reservoir Characterization module of RokDoc, Ji-Fi (Joint Impedance and Facies Inversion) is a unique Ikon Science technology for estimating facies and impedances directly from seismic data. The method has multiple advantages over both conventional and other facies-based seismic inversion techniques. It is facies-aware and rock physics consistent, and therefore frames the inverse problem correctly. It is a true 3D inversion and allows geological and data uncertainties to be captured and investigated.
Quickly bridge gaps between Petrel (SLB) and RokDoc with a live project and data connection. Dynamically update existing seismic interpretations by leveraging RokDoc rock physics and seismic modeling functions.
Robust rock physics tools to predict and model scenarios for diverse play types
Leading seismic modeling, conditioning, inversion, and reservoir characterization tools
Knowledge, machine learning, hardware and, data connectivity across applications
