Multi-point Statistics (MPS)

Rapidly generate facies models

Apart from standard algorithms, JewelSuite™ Subsurface Modeling supports next generation facies modeling using MPS (Multi-point Statistics). You can model complex and heterogeneous geological environments through the use of comprehensive library of training images which describe the geometrical characteristics of the deposition environment to model.

MPS simulations can be effectively conditioned to external data in the form of wells, 3D facies proportion, and depositional azimuth data. The simulations also produce complex and laterally persistent patterns observed in outcrops and present-day analogs.

MPS facies modeling involves two steps. First, a 2D or 3D training image is analyzed for occurring facies patterns. Second, the registered patterns are used to create a simulation model, honoring external data. Models can be generated without detailed knowledge of the underlying math and statistical assumptions, concentrating more on geological concepts.

JewelSuite Subsurface Modeling uses IMPALA™, a fully parallelized algorithm for MPS simulation. JewelSuite Subsurface Modeling is superior to other MPS implementations in two critical areas:

  • supports the use of trend or auxiliary properties, which means that nonstationary training images can be used to generate simulations containing complex trends. This approach is far more intuitive and easier to control than facies proportions. This is a vital enabler for nested simulations, such as first modeling large-scale patterns and filling in details.
  • makes efficient use of multiple processors and clusters. The Impala plug-in is optimized by using a list approach rather than the common search tree approach and full parallelization. Both improvements facilitate simulation of big models with many facies. Simulations, which are computation and memory intensive, can be updated for user-defined regions, such as around a new well.

Training images are separate 3D grids that are detached from the actual simulation model. This setup facilitates easy exchange of training images between projects, such as importing from a library of training images for various depositional geometries.

Other significant benefits include:

  • workflow guidance: detailed access for the expert and tested defaults for newer users
  • efficiency: common controls, such as depositional azimuth, are wrapped into intuitive GUIs and powerful automated methods
  • ease of use: extensive Graphical Editor options enable easy and even fun-to-build 2D and 3D training models.

Impala is a trademark of Ephesia Consult SA.

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