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<p>Proper uniaxial compressive strength (UCS) and other rock mechanical properties are essential parameters for planning and stimulating oil and gas wells, especially for unconventional resources. UCS can be directly measured in the lab using core samples; however, this method is mechanically destructive and provides only discrete point measurements. To avoid such disadvantages, local empirical correlations are utilized to estimate UCS from well logs (sonic, porosity, and density). Unfortunately, Egyptian sedimentary basins lack local correlation tailored to estimate UCS. The present study utilized well-preserved core samples to develop single and multi-parameter correlations using well logs. Core description and measurements, including petrophysical analysis, X-ray diffraction (XRD), and scanning electron microscope (SEM), are accomplished to investigate rock texture and compositional analysis. Then, cores are tested in triaxial cells to get the mechanical properties of rock samples. </p><p>Laboratory-measured uniaxial compressive strength (UCS) values were statistically analyzed in conjunction with well log data, including sonic, neutron porosity, and density logs, to develop new empirical correlations. These correlations enable the accurate and continuous prediction of UCS directly from well logs, thereby significantly reducing the need for costly and time-consuming coring and laboratory testing. Two types of predictive models were established and validated. The first type includes single-parameter correlations, which demonstrated high coefficients of determination (<i>R</i>²), achieving values of 0.96 for the sonic log (Δtₒ), 0.90 for neutron porosity (φₙ), and 0.87 for density (<i>ρ</i>). The second type involves multi-variable correlations that provide a more robust and stable framework for UCS prediction across varying reservoir conditions. </p><p>Model validation was conducted, and the results showed excellent agreement between the predicted and measured UCS, confirming the reliability of the developed correlations.</p>

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New correlations for estimating uniaxial compressive strength of carbonates in the Egyptian Western Desert

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月7日

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  • Mohamed Salah Eid,
  • Abdulaziz M. Abdulaziz,
  • Abdel Sattar Dahab

摘要

Proper uniaxial compressive strength (UCS) and other rock mechanical properties are essential parameters for planning and stimulating oil and gas wells, especially for unconventional resources. UCS can be directly measured in the lab using core samples; however, this method is mechanically destructive and provides only discrete point measurements. To avoid such disadvantages, local empirical correlations are utilized to estimate UCS from well logs (sonic, porosity, and density). Unfortunately, Egyptian sedimentary basins lack local correlation tailored to estimate UCS. The present study utilized well-preserved core samples to develop single and multi-parameter correlations using well logs. Core description and measurements, including petrophysical analysis, X-ray diffraction (XRD), and scanning electron microscope (SEM), are accomplished to investigate rock texture and compositional analysis. Then, cores are tested in triaxial cells to get the mechanical properties of rock samples.

Laboratory-measured uniaxial compressive strength (UCS) values were statistically analyzed in conjunction with well log data, including sonic, neutron porosity, and density logs, to develop new empirical correlations. These correlations enable the accurate and continuous prediction of UCS directly from well logs, thereby significantly reducing the need for costly and time-consuming coring and laboratory testing. Two types of predictive models were established and validated. The first type includes single-parameter correlations, which demonstrated high coefficients of determination (R²), achieving values of 0.96 for the sonic log (Δtₒ), 0.90 for neutron porosity (φₙ), and 0.87 for density (ρ). The second type involves multi-variable correlations that provide a more robust and stable framework for UCS prediction across varying reservoir conditions.

Model validation was conducted, and the results showed excellent agreement between the predicted and measured UCS, confirming the reliability of the developed correlations.