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Correlation-driven direct sampling method for geostatistical simulation and training image evaluation.

Chen Zuo1, Zhibin Pan1, Zhaoqi Gao1

  • 1School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Physical Review. E
|June 20, 2019
PubMed
Summary
This summary is machine-generated.

Multiple-point geostatistics (MPS) simulations are improved using correlation-driven direct sampling (CDS) and minimum correlation-driven distance (MCD) for training image evaluation. These methods enhance geological realism and computational efficiency.

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Area of Science:

  • Geostatistics
  • Computational Geology
  • Image Analysis

Background:

  • Multiple-point geostatistics (MPS) generates realistic geological models by extracting patterns from training images (TI) and ensuring compatibility with hard data (HD).
  • Existing MPS methods face challenges in simulating complex geological scenarios and evaluating the quality of training images.
  • Addressing these limitations is crucial for improving the accuracy and reliability of geostatistical simulations.

Purpose of the Study:

  • To develop novel methods for high-quality geostatistical simulation and training image evaluation within the MPS framework.
  • To introduce correlation-driven direct sampling (CDS) for enhanced geostatistical simulation.
  • To propose minimum correlation-driven distance (MCD) for improved training image evaluation and address nonstationarity.

Main Methods:

  • Correlation-driven direct sampling (CDS) was developed, using a novel correlation-driven distance metric based on elliptical concepts, correlation coefficients, Gaussian functions, and affine transformations.
  • Minimum correlation-driven distance (MCD) was introduced for training image evaluation, assessing consistency between TI and HD, incorporating a conditioning pattern extraction history strategy for efficiency.
  • Local consistency was implemented to handle nonstationarity by automatically dividing the simulation domain into subareas.

Main Results:

  • CDS demonstrated superior simulation quality compared to previous methods when applied to 2D channelized reservoir and 3D rock images.
  • MCD achieved sensible evaluation accuracy and excellent computational efficiency, effectively handling nonstationary geological features.
  • The proposed methods showed potential for further applications in 2D TI evaluation and 3D simulation domain segmentation.

Conclusions:

  • The developed CDS and MCD methods offer significant improvements in geostatistical simulation realism and training image evaluation accuracy.
  • These techniques provide efficient and robust solutions for complex geological modeling and nonstationary data.
  • The study validates the effectiveness of correlation-driven approaches in advancing multiple-point geostatistics.