Use genetic models to constrain facies models and ensure accuracy and realism

Posted on by Geovariances
Tagged Oil & Gas, , , , , , , , ,

Geological modeling in Isatis can be achieved using MPS methodology or Flumy, our applications for meandering system modeling. Do you know that you can combine both of them to ensure model accuracy and realism? See the tests done for a NOC.


Geovariances has recently conducted some tests for a South-American Oil Company showing the advantage of combining process-based modeling techniques (Flumy) with MPS geostatistical simulation method for building accurate meandering geological models accounting for well data, deposition rules and complex geometric relationships between facies.

The idea has been to build first a training image with Flumy to be used as input of MPS. An erodability map has been defined to constrain the channel migration during the life of the turbiditic depositional system. Sand (Point Bars) is deposited preferentially in high erodibility areas.

 

Combining Flumy and MPS in Isatis
2D erodability map

 

Combining Flumy and MPS in Isatis
Genetic model built with Flumy (stacked channels – horizontal section)

 

Combining Flumy and MPS in Isatis
Genetic model (Flumy) taking into account the erodability map (stacked channels – horizontal section): we can see that less sand (yellow) is deposited near the grid borders

 

Combining Flumy and MPS in Isatis
Single channel from the flumy model. Horizontal section of the Flumy model showing the main channel. It will be used as input training image for MPS simulation.

 

Then we have run MPS taking into account several auxiliary variables including local azimuths, local affinity and distance to channel. At simulation step,  these variables allow to bring non-stationarity to a training image (note that two auxiliary variables can now be used simultaneously in Isatis).

Combining Flumy and MPS in Isatis
The distance to channel derived from the previous training image is used as an auxiliary variable in MPS simulation. It allows taking into account the non-stationarity in facies distribution.

 

Combining Flumy and MPS in Isatis
We have used the a priori knowledge we have from the global geology to build a distance to channel variable on the MPS simulation grid. This variable is also used as an auxilliary variable to  take into account the non-stationarity in facies distribution.

 

Combining Flumy and MPS in Isatis
Result: a MPS simulation using a Flumy simulation as training image

This case study allowed demonstrating that combining Flumy with MPS has several advantages:

  • Conditioning to data is easy
  • Many controls are available and are easy to manage
  • Flumy is an efficient way of getting 3D training images
  • It takes into account training image variability
  • Final model is realistic.

 

> LEARN MORE ABOUT FLUMY

 

INDUSTRIES:
Mining (13)
Nuclear Decommissioning (7)
Oil & Gas (6)
Hydrogeology (5)
Contaminated sites (5)

TAGS:
Background images (1)
Big data (1)
Conditional simulations (6)
Contaminated sites (2)
Contaminated sites management (2)
Drill hole spacing analysis (2)
Facies modeling (2)
Flow modeling (1)
Geological modeling (3)
H2020 INSIDER (1)
Horizon mapping (1)
Hydrogeological modeling (1)
Ice content evaluation (1)
Isatis (11)
Isatis.neo (15)
Kartotrak (6)
Machine Learning (1)
Mapping (3)
MIK (2)
Mineral resource estimation (6)
Monitoring network optimization (1)
Ore Control (1)
Plurigaussian simulations (1)
Post-accidental situation (2)
Python (1)
Recoverable resource estimation (3)
Resource classification (2)
Resource reporting (1)
Resources workflow (3)
Risk analysis (3)
Sampling clustering (1)
Sampling optimization (3)
Sampling strategy (1)
Scripting procedures (3)
Simulation post-processing (1)
Simulation Reduction (1)
Site characterization (2)
Soil contamination mapping (4)
Spatial Sampling Density Variance (1)
Time-to-Depth conversion (1)
Uncertainty analysis (2)
Uniform Conditioning (5)
Variography (2)
Water quality modeling (1)
Workflow automatization (1)

AUTHORS:

DATES:
2022 (2)
2021 (2)
2020 (2)
2019 (8)
2018 (4)
2017 (3)
2016 (3)