Enhancing Reservoir Characterization With 3D Geocellular Modelling

| |

3D geocellular modeling
Cellular grids are created so gridlines align to the major structural components of the model such as the faults and horizons.

Greater accuracy in reservoir modeling supports better field development and operational decisions. With the wealth of geologic, stratigraphic, geophysical, and petrophysical data, geoscientists have never been in a better position to build models that characterize reservoirs at the macro and micro level.

At the micro level, 3D geocellular modeling is vital to characterizing reservoir potential and performance, understanding how complex reservoir attributes influence fluid flow. Efficient 3D geocellular modeling uses workflows to bring greater efficiency and consistency to the process, particularly when geoscientists need to vary parameters with the goal of determining best-fit models.

Save Time With 3D Geocellular Modeling

Modern computing hardware environments offer the computational capabilities to handle large datasets with multiple attributes per cell. Powerful gridding algorithms and geostatistics are employed to model density data and produce reliable attribute models, even when sampling is sparse. In many instances, 3D geocellular modeling provides results in order to feed reservoir simulators.
The best modeling methods avoid the limitations of orthogonal grids in favor of polygons with constant density that more accurately match dipped, curved, and faulted horizons. By building models from subsurface data using geologically representative processes like deposition and faulting, geoscientists create more accurate geocellular models.

The Workflow Approach

Given the types and volumes of data, parametric variations used to develop individual models, and iterative aspects of the geocellular modeling, a workflow approach brings efficiency and consistency to the modeling process. A workflow that creates a 3D geocellular model with attributes true to their depositional arrangement might follow these steps:
  1. Input data—interpreted, depth converted seismic and well tops.
  2. Build the earth model based on seismic picks, with fault picks quality controlled and limited by tip-line polygons.
  3. Compute a fault tree that describes the fault framework and fault dependencies.
  4. Grid stratigraphic picks from the seismic interpretation, optionally using well-top picks as constraints on the resulting horizon‘s shape and depth.
  5. Superpose depositional, erosional, or unconformable stratigraphic horizons to form the stratigraphic sequence.
  6. Evaluate input horizon picks in the context of modeled faults and other horizons to produce an earth model geometrically consistent with input data.
  7. Populate model with attribute data using 3D grids within stratigraphic layers and reconstructive techniques to compensate for fault displacements. Grid cell size would be determined by the user.
  8. Conform attribute grids with layer boundaries for better representation of the attribute distribution.
Computational demands of the process depend on cell granularity and the number of attributes associated with a cell, but ultimately, the geocellular model is adequately prepared for reservoir simulation.

3D Geocellular Modeling With EarthVision

To address the need for efficient 3D geocellular modeling, Dynamic Graphics, Inc., a leader in 3D visualization and analytic software offers an optional geocellular module as part of its EarthVision product. Using configurable workflows, the module efficiently converts structure or attribute models into a cellular-grid format for input to reservoir simulators. EarthVision improves the efficiency and accuracy of geocellular modeling by:
  • Using fault-optimized algorithms to calculate cellular reservoir grids, accurately depicting the structure/attribute in the original 3D model.
  • Aligning grid lines with major structural model components (faults and horizons).
  • Calculating attributes within each non-orthogonal 3D cellular grid so that they are ”cell-centered” (averaged over rock volume of a cell).
  • Supporting multiple attributes per cell, with either floating-point or discrete values.
  • Providing upscaling options—arithmetic, geometric, harmonic, and power mean; hybrid schema for heterogeneous attributes like permeability.

The resulting 3D geocellular grid provides a framework for calculating and predicting fluid flows within a reservoir. This 3D geocellular modeling method minimizes the loss of information between the structural modeling and reservoir simulation processes and more accurately reflects the modeled geologic features.

Visualize and Verify Geocellular Models

With EarthVision, the 3D geocellular model can be interactively visualized against a 3D geologic model. Depicting the structural alignment of the grid lines along the faults and horizons facilitates exploration, analysis, and verification by all members of the reservoir management team. Non-orthogonal gridding cells are depicted with smooth intersections along faults with the option of faults employing different methods of conforming to more closely match geologic structures.

A rich 3D environment for visualization and analysis facilitates model building and anomaly calculation where users can collaboratively evaluate the goodness of fit and quickly modify parameters and rerun the workflow if needed. Once the geocellular grid is finalized it can be exported to a wide range of reservoir simulation software products.

With EarthVision, the 3D geocellular model can be interactively visualized against a 3D geologic model.

Computationally Efficient, Visually Detailed 3D Geocellular Models

EarthVision’s 3D geocellular modeling module allows geoscientists to collaboratively develop more accurately characterized subsurface structures and conditions. It is a proven workflow that efficiently manages the process, precise cell modeling of complex fault geometries, rapid calculations that deliver results in minutes, and powerful 3D visualization capabilities give geoscientists the tools they need to develop accurate geocellular models to predict fluid migration.

EarthVision, from Dynamic Graphics, Inc., enables geoscientists to quickly build, visualize, analyze, and update precise 3D models. It enables model examination and interrogation in the context of datasets provided by all members of an asset development team, allowing you to confidently make development and operational decisions that positively impact profit and reduce operational risk. To learn more about EarthVision contact our team.

FURTHER READING

3D Visualization of Hyperspectral Data

Point Loma, California—LiDAR merged with aerial photo. LiDAR data generated for the Scripps Institution of Oceanography by the Center for Space Research, the University of Texas at Austin (CSR), with support provided by the Bureau of Economic Geology, the University...

Reducing Subsurface Uncertainty with Data Integration and Visualization

The above image in CoViz 4D depicts a seismic horizon and velocity model in the time domain (top) and the same horizon, depth converted, along with the depth converted seismic cube, wellbores and horizon picks in the depth domain (bottom). Data used with the...

4D Seismic Monitoring of Reservoir Change Through Visualization

Bringing together reservoir simulation model, production towers and 4D seismic into the same visualization environment along with geological and petrophysical data. Data used with permission of owner.Risks are inevitable with any hydrocarbon asset development, and...

Enhancing Structural Interpretation of Seismic Data With Velocity Modeling

This display from CoViz 4D shows the depth scaled velocity model using well control and the sliced structural depth model in the upper part of the image. The seismic time model along with the average velocity cube used to correct well control is shown below.While...

A Statistical Approach to Depth Uncertainty Analysis for Model Integrity

3D Depth Uncertainty Model: Depth uncertainty imported into well paths and displayed as cones of uncertainty indicating positional uncertainty to 1 Standard Deviation.Dealing with the fundamental uncertainty of subsurface environments and their hydrocarbon resources...

Conducting Oil Well Performance Analysis to Forecast & Optimize Production

Oil well performance analysis can deliver greater value when the calculations, simulations, modeling, and monitoring of production metrics can be evaluated in an environment that facilitates the sharing of all relevant information and collaborative analysis by...

Seismic Reservoir Monitoring Through Visualization

An ideal design, development, and management plan for hydrocarbon asset development comes from having a better understanding of  all known geological and petrophysical aspects of the subsurface. But given the dynamic nature of the reservoir and its attributes, petroleum professionals can encounter some complexities in the process of in-depth analysis.

Share on Social Media