Ikon Science, a provider of geoprediction and open subsurface knowledge management software and services, has introduced its state-of-the-art 4D inversion technology tool. This seismic and well data investigation tool is featured in RokDoc 2023.3, Ikon’s geoprediction software platform.
“RokDoc 2023.3 has leveraged user insights to further enhance Ikon’s best-in-class technologies and modernize core work flows, enabling us to lead the geoscience community in 4D reservoir monitoring,” said Alan Mur, product manager for QI Applications at Ikon Science. “Our focus is on helping all of our users find the best answers with the least effort.”
As subsurface exploration becomes increasingly complex, there’s less room for error. For exploration and production activities, quantitative interpretation work flows must produce not only a precise prediction of properties but also a good understanding of uncertainties. Ikon’s Time-Lapse Ji-Fi app offers complete 4D fluid-tracking capabilities for production and injection scenarios and is applicable in most hydrocarbon-production campaigns and carbon capture, use, and storage (CCUS) efforts. The new app provides integration of work flows for a consistent 4D analysis of reservoirs.
As new rock physics models unlock functionality to a larger family of geologic plays and work flows, additional sand, shale, and carbonate rock physics models that capture compaction and clay evolution have been added to the RokDoc and rock physics modeling function (RPML) libraries. Also, a new fractured carbonate rock physics model for feasibility and prediction of fracture density streamlines data sharing and integration with geomechanics work flows.
Useability is enhanced with new cross-plot display options, input filters for easier data management, and a refreshed probability density function management system. Ji-Fi and many other rock physics work flows that rely on facies classification are now easier to use with new, per-working-interval prior proportion inputs for 1D, 2D and Depth Trend Bayesian. These user-centric advances make documentation and summary plots easily repeatable for test comparisons so the models may be rapidly refined and improved.
Deep QI machine learning and rock physics functions automation is expanded with XGBoost, which is now combined with grid search for parameter tuning. This machine learning property prediction and automated RPML algorithm is further augmented to directly calibrate mineral volumes in rock physics models. These enhancements drive work flow efficiencies to deliver immediate value to energy companies.
