An accurate description of petrophysical properties, such as porosity and permeability, and their spatial variability is essential to understanding the fluid distribution and flow properties of a reservoir. RMS™ offers a full suite of petrophysical modeling tools, from 2D interpolation to 3D stochastic modeling, conditioned to wells, facies and seismic data. Models can be updated globally or locally as new data is acquired, keeping the models accurate and the decisions focused.
The RMS petrophysical modeling solution includes:
- Petrophysical modeling
- Parameter interpolation
- Water saturation modeling
- Sweet spot detection (using machine learning)
A fast and powerful solution
- Fully parallelized to take advantage of the available processing power
- Handles very large numbers of wells quickly and efficiently
- Models can be produced easily by integrating a variety of geological trends, regardless of complexity
- Fully integrated into the RMS Workflow manager, enabling an automated workflow
- The model can be conditioned to a wide range of data, including seismic
- Wide range of data transformations, allowing users to select the best for their data
- Full suite of interpolation, kriging and simulation algorithms
- Choose between full model update or local model update to keep the model continuously correct
Petrophysical modeling should always be based on combining all available data, especially well and seismic, with the geological interpretation of the reservoir. To achieve this, RMS provides fully integrated data analysis tools and a suite of easy-to-use geological trends. These trends are used to incorporate knowledge of the depositional environments and diagenetic processes directly in the petrophysical model.
The petrophysical modeling tools in RMS include standard distance weighting methods, a full suite of kriging methods, and stochastic simulation based on a parallelized Fast Fourier Transform (FFT) algorithm, to ensure accuracy and high-speed performance. The kriging methods include simple, ordinary, universal, Bayesian and co-located co-kriging. The kriging algorithms have been optimized to handle giant fields with thousands of wells.
The petrophysical properties of a reservoir are strongly controlled by depositional facies and a variety of post-depositional diagenetic processes. RMS has an extensive and easy-to-use trend toolbox that can incorporate a wide variety of trends in the petrophysical model, including:
- Large-scale compactional/depositional processes
- Intrabody trends
- Upwards fining or coarsening
- Lateral trends
- 3D trends and Cloud transform
- Skewed distributions including normal score
This module interpolates values seen in the well data into the grid. the user can choose Stratigraphic or Horizontal mode, and whether to condition to a discrete value and/or weight the output with a 3D trend.
The following calculations are available in the integrated Water Saturation Modeling tool:
- Look-up Function
- J-Function (SCAL-based)
- J-function (Simplified)
- User-defined function
Sweet spot detection
Predict potential areas of interest away from wells by using the Sweet Spot detection tool. This is done through a supervised machine learning algorithm called k-nearest neighbors (kNN). Define the values of interest from the well data and train the algorithm to recognize these features in the reservoir to identify potential sweet spots, including their confidence.
Calculate reservoir volumes, taking into account zones, regions and facies if desired. Either define the input per job or use pre-defined region attributes.