Through ensemble-based methods, we generate insights from a diverse set of geologically consistent reservoir models. These models are history-matched, and serve as the foundation for production forecasts, helping the user make optimal decisions on reservoir management based on the ratio of knowledge vs uncertainty.
ResX software allows subsurface teams to handle uncertainties in the reservoir modeling and history-matching process. This prevents teams from focusing on a too narrow outcome, and instead use algorithms to find the optimal solution given all data. This increases team efficiency and provide more time to perform a deeper reservoir analysis.
Webinar
In this webinar, we will demonstrate how our innovative approach uses both static and dynamic data concurrently. This allows you to explore and visualize the subsurface across various scales and resolutions while consistently accounting for geological uncertainties at every stage.
Watch nowAutomated and updatable reservoir modeling workflows can reduce turnaround time. Users can use a dedicated simulation manager to submit and monitor simulations and pause and resume simulations easily. The solution also enables the quick creation of new studies from previous iterations.
Through uncertainty-centric workflows that consider both static and dynamic data, we can significantly reduce user overconfidence and enhance trust in the results. Workflow is shared amongst involved disciplines, and promotes a holistic process where scientists can discuss their approach on a common understanding. Users can investigate the effect of structural uncertainty with the ResX software. The software can also condition facies realizations to dynamic data by updating the inputs to the facies modeling algorithm. This can help ensure consistent and geologically reasonable history-matched modes.
Capture, quantify, and retain uncertainties throughout the interpretation and modeling process. The ResX software can help evaluate statistical attributes of model properties and scalars and compare statistics between the initial and conditioned ensembles. Users can also automate data ingestion into an IRMA analytics solution to generate aggregated ensemble statistics and provide specific ensemble-oriented analytics.
