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Updated: Jun 12, 2026

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography
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Automatic optimization method of horizontal well formation model based on natural gamma while drilling.

Fujun Long1, Heng Tian2, Haoyu Zhang1

  • 1School of Nuclear Science and Technology, Lanzhou University, Lanzhou, Gansu, 730000, China; Frontiers Science Center for Rare Isotopes, Lanzhou University, Lanzhou, Gansu, 730000, China.

Applied Radiation and Isotopes : Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine
|June 10, 2026
PubMed
Summary

This study introduces a fast method for simulating natural gamma-ray logging in horizontal wells. The new approach improves formation model accuracy and aids in developing complex oil and gas reservoirs.

Keywords:
Horizontal wellInversionNatural gamma ray loggingOptimizationStratigraphic model

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Area of Science:

  • Petroleum Engineering
  • Geophysics
  • Computational Science

Background:

  • Declining conventional oil and gas resources necessitate unconventional reservoir exploration.
  • Horizontal well technology is advancing rapidly for resource extraction.
  • Accurate horizontal stratigraphic models are crucial for well log interpretation but manual methods are complex and time-consuming.

Purpose of the Study:

  • To develop a fast forward simulation method for natural gamma-ray logging using localized model construction.
  • To create an automatic generation framework for synthetic gamma-ray logging data.
  • To introduce an automated while-drilling formation model optimization method based on gamma-ray logging.

Main Methods:

  • Localized model construction for fast forward simulation of natural gamma-ray logging.
  • Utilizing Monte Carlo method or azimuthal sensitivity matrices for enhanced computational efficiency.
  • Developing an automatic generation framework for synthetic gamma-ray logging data and an automated optimization method.

Main Results:

  • Significantly enhanced computational efficiency compared to full-scale simulations.
  • Successful application to real horizontal well data, demonstrating efficient and accurate formation profile reconstruction.
  • Improved formation identification accuracy and expanded application potential for geosteering.

Conclusions:

  • The proposed method offers efficient and accurate reconstruction of formation profiles in horizontal wells.
  • The approach provides strong technical support for horizontal well development in complex geological environments.
  • Automated data generation and optimization enhance data-driven interpretation and geosteering applications.