Software - Geoprediction Platform

Rock Property Modeling

Are your data loaded, ready, and verified to continue to the next workflow step? Do they lie on trend? Do they agree with physical models? Use RokDoc to prove you have what you need to proceed.

Seismic Modeling

Gain insight into what drives the seismic responses observed between wells. Use RokDoc’s algorithms to find and validate the best possible seismic wavelet given well and seismic data availability. Investigate the impact of geometry and lateral variation of rock properties on AVO and model seismic amplitudes while considering tuning and mobile fluid contacts. Directly compare actual seismic with 1D to 4D synthetic models.

Pore Pressure and Geomechanical Modeling

RokDoc helps improve safety in drilling operations through integrating well log, laboratory, and seismic data to provide estimates of pore pressure, stress, and borehole stability, to quantify risk. Capture statistical and geological uncertainty as you develop 1D to 4D models for prediction of pore pressure and geomechanical behavior away from well control.

Why Choose RokDoc for Geological Scenario Modeling?

RokDoc helps geoscientists model geological scenarios with greater confidence by combining data-driven analysis, uncertainty testing, and predictive workflows in one integrated platform.

How does RokDoc use machine learning to improve rock physics analysis and reduce interpretation uncertainty?

RokDoc combines advanced rock physics expertise with machine learning to deliver a more scalable and data-driven interpretation workflow. Using captured well knowledge and regional trends, the RPML engine automates model classification and calibration so teams can train and apply models at the wells and in 3D. This gives geoscientists a faster, more consistent, and more reliable way to extend understanding into step-out areas, reduce manual interpretation effort, and generate subsurface models with greater confidence.

How does RokDoc quantify geological uncertainty?

RokDoc quantifies geological uncertainty by automating what-if scenario testing, analyzing spatial variability with variography, and adding statistical noise to datasets to reflect realistic subsurface conditions. This helps geoscientists evaluate probability of success, understand geological trends, and make more reliable interpretation decisions.