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Quantitative classification evaluation model for tight sandstone reservoirs based on machine learning.

Xinglei Song1,2, Congjun Feng3,4, Teng Li5,6,7

  • 1State Key Laboratory of Continental Dynamics, Northwest University, Xi'an, 710069, China.

Scientific Reports
|September 5, 2024
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Summary
This summary is machine-generated.

This study developed a new dual-coupled model to classify tight sandstone reservoirs based on pore structure and shape parameters. The model accurately predicts reservoir quality, guiding exploration in the Ordos Basin.

Keywords:
High-pressure mercury injectionImage recognitionMachine learningPore structureQuantitative evaluationTight sandstone

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Area of Science:

  • Petroleum Geology
  • Sedimentology
  • Reservoir Engineering

Background:

  • Tight sandstone reservoirs are crucial for petroleum exploration but challenging to classify due to heterogeneity.
  • Existing classification methods have limited applicability for complex micropore structures.

Purpose of the Study:

  • To develop a quantitative classification and evaluation system for tight sandstone reservoirs.
  • To establish a dual-coupled model integrating pore structure and shape parameters for improved prediction.
  • To identify favorable exploration areas within the Chang 8 tight reservoir.

Main Methods:

  • High-pressure mercury intrusion, casting thin sections, and scanning electron microscopy were employed.
  • Image recognition technology extracted pore shape parameters.
  • Grey relational analysis, analytic hierarchy process, and entropy weight method were used for quantitative analysis.

Main Results:

  • A dual-coupled model was developed, achieving 93.3% accuracy in classifying tight sandstone reservoirs.
  • Initial productivity positively correlates with porosity, permeability, and pore shape parameters (perimeter, circularity, major axis length).
  • Type I reservoirs, indicating high potential, were identified in the northwest of the study area, recommending exploration prioritization.

Conclusions:

  • The developed model provides a novel and effective method for classifying and evaluating tight sandstone reservoirs.
  • The study successfully identified favorable exploration zones based on quantitative analysis of pore characteristics.
  • This research offers valuable guidance for future exploration and development of tight sandstone reservoirs globally.