ResX, a smarter and faster way for subsurface teams to work

ResX delivers ensemble-based modeling and data conditioning in one single step, for increased reservoir understanding and unparalleled quantification of uncertainty. Unlike other traditional, ResX simultaneously conditions the ensemble to static and dynamic data, making it truly different.

Combining efficient algorithms with your subsurface know-how ResX delivers an ensemble of models that respect the most up-to-date static and dynamic measurements. This, without compromising on the reservoir physics or on the number of uncertain parameters considered.

ResX – Overiew

ResX allows subsurface teams to embrace uncertainties in the reservoir modeling and history matching process. ResX is based on ensemble-based methods, where the full range of geologically consistent reservoir models are history matched and used as a basis for production forecasts. By continuous data conditioning of static and dynamic data, ResX dramatically increases your team’s efficiency. Freeing up time to explore better options for reservoir management or to perform more analysis to learn about your reservoir.

Create initial ensemble

Key to successful application of ResX is to have an initial ensemble that captures key model uncertainties and that are plausible given both measured static and dynamic data.

Robust ensemble

With ensemble-based modeling it is wise to spend time creating a robust initial ensemble. The Create Initial Ensemble process in ResX, together with automated workflows, greatly speeds up the process of generating a prior collection of models to be history matched in an ensemble study.

Adaptive pluri-Gaussian (APG) facies modelling

The APG facies method within the Create Initial Ensemble process offers an elegant way to consistently integrate both static and dynamic data. This, while ensuring geological consistency. When running an ensemble study, each facies realization is conditioned to dynamic data by updating the latent Gaussian variables and the facies probabilities.

Ensemble based study

Choose which initial ensemble to use, set up the objective function, specify model uncertainties and localization setup and run your history matching study and production forecast. ResX works equally well on small fields with limited number of data measurements, as for truly giant fields with decades of production data. ResX scales by taking a geostatistical approach to modelling, combining ensemble Kalman principles with an iterative smoother.

Objective function

The objective function in ResX supports well production data, RFT & PLT logs, DST data as well as 4D seismic data.


An important step in your ensemble study is to define your localization setup. It tells ResX which areas it can update when conditioning to dynamic data. Specify a radius, a custom localization property or leave it to ResX to do auto localization.

Monitoring & run management

ResX comes bundled with a dedicated simulation manager which submits and monitors your simulations. With ResX you can resume studies that have stopped or create a new study from any previous iteration.

Ensemble analysis

The main goal of an ensemble-based study is to gain new insights to the reservoir. Hence, one should always evaluate the statistical attributes of properties and scalars of all the models making up the ensemble, and compare these to the statistics of the initial ensemble.  ResX offers a set of tools to let you analyze and understand the initial ensemble and the resulting conditioned ensemble after an ensemble study has been run. Furthermore, IRMA automatically ingests and manages your ensemble data, giving you the full benefits of the aggregated statistics of the ensemble and provides you with specific ensemble-oriented analytics.

Initial ensemble analysis

The Aggregated grid properties process in ResX let you evaluate the statistical properties of the initial ensemble for all model properties and scalars. This, to ensure that the values of the prior distribution are according to model assumptions. Use the Ensemble analysis process to QC your objective function and check that you span and capture key trends for all wells.

Insights from a conditioned ensemble

Extracting insights from an ensemble is all about using all the models in the ensemble to test your hypotheses. Where should we drill? How many wells? How do we optimally operate the field? A conditioned ensemble gives you more information spatially about the resevoir. ResX and IRMA deliver an uncertainty-centric approach based on an end-to-end integrated and automated workflow that quickly closes the loop between input data and output ensemble, providing insights and decision support.

ResX – Conditioning to 4D seismic data

The ResX 4D module enables a 4D seismic data history matching workflow. ResX 4D allows for maximum flexibility in terms of what response and in what domain the comparison between simulation results and 4D signal is done. The module increases utilization of 4D data for reservoir characterization. Get a better understanding of reservoir connectivity, flow barriers and bypassed hydrocarbons by using ResX with your 4D data.

Qualitative use of 4D seismic data in ResX

A vital part of ResX 4D module are the qualitative ensemble analysis visualization and QC tools that are available. Perform statistical analysis of the objective function and visually analyze 2D surfaces and 3D properties to quickly identify areas of interest. Use interactive filters to pinpoint areas where the prior model assumptions should be revisited.

Quantitative use of 4D seismic data in ResX

In the ResX objective function 4D seismic data is represented as a 2D surface. Multiple surfaces can be included, for example one per extraction window and per time-lapse interval. When used in an ensemble based study, the output of the 4D workflow is an ensemble of reservoir models that are consistent with static, production and 4D seismic data, while capturing uncertainty. This leads to better decision-making for assets employing 4D seismic data. Putting you in a better position to increase higher ultimate recovery.

Seamless integration with IRMA – rapid and continuous learning

ResX can automatically push ensemble data to IRMA.

ResX and IRMA deliver speed, control and power to look at all available data as a whole. Immediately assessing how modeling choices and data interpretation affect the resulting ensembles of models and enabling teams to quickly iterate and continuously learn and improve.

ResX and IRMA deliver an uncertainty-centric approach based on an end-to-end integrated and automated workflow that quickly closes the loop between input data and output ensemble, providing analytics for insights and decision support.



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