Physical properties of Cretaceous to Eocene platform-to-basin carbonates from Albania

Jaballah, Jalel; Reijmer, John J.G.; El-Husseiny, Ammar; Goff, Johan Le; Hairabian, Alex; Slootman, Arnoud

doi:10.1016/j.marpetgeo.2021.105022

Cited by 14 publications

(4 citation statements)

References 87 publications

(165 reference statements)

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“…Then, the dry rock was constructed by four elastic boundary theory, with Voight for upper limit (black line), Reuss for lower limit (red line), Hashin-Shtrikman for upper and lower limit (blue and pink line), and V-R-H for average of equivalent elastic modulus of the medium. According to some studies, porosity has important influence on the wave velocity of rocks [15][16][17][18]. The data depicted in Figure 3 aligns with the notion that the increase of porosity can significantly reduce P-and S-wave velocity, indicating that porosity directly determines wave velocity.…”

Section: Experimental Conditions and Basic Petrophysical Characteristicssupporting

confidence: 76%

“…Figure 2 shows the AutoLab system and scheme of preparation for wave velocity measurements in samples. P-wave and S-wave velocity according to the direction of vibration and propagation were evaluated, namely According to some studies, porosity has important influence on the wave velocity of rocks [15][16][17][18]. The data depicted in Figure 3 aligns with the notion that the increase of porosity can significantly reduce P-and S-wave velocity, indicating that porosity directly determines wave velocity.…”

Section: Experimental Conditions and Basic Petrophysical Characteristicsmentioning

confidence: 69%

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Wave Velocity in Sandstone and Mudstone under High Temperature and Overpressure in Yinggehai Basin

Liu¹,

Du²,

Zhu³

et al. 2022

Energies

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Wave velocity under different pressure and temperature (PT) conditions plays an important role in the exploration of oil and gas reservoirs. We obtained the mineral composition and porosity of 20 underground sandstone and mudstone samples in Yinggehai Basin via X-ray diffraction and porosity measurements. Using high-frequency ultrasound, the P- and S-wave velocities of four samples under high temperature and overpressure conditions were found to vary significantly, owing to the material composition and porosity. According to the comparison between the experimental conclusion and the well-logging data, the genesis of false bright spot and dark spot gas reservoirs in the study area was analyzed. The variation in P-wave velocity under different temperature and pressure conditions was explained with the PT coefficient. The traditional pressure–velocity and temperature–velocity prediction methods were improved and applied to well-logging data. Herein, the velocity of P- or S-waves of sand and mudstone under high temperature and overpressure via rock physics experiments and the genesis of false bright spot and dark spot gas reservoirs in the Yinggehai Basin was observed. Overall, the results serve as a theoretical basis for seismic exploration in the study area.

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Section: Experimental Conditions and Basic Petrophysical Characteristicssupporting

confidence: 76%

Section: Experimental Conditions and Basic Petrophysical Characteristicsmentioning

confidence: 69%

Wave Velocity in Sandstone and Mudstone under High Temperature and Overpressure in Yinggehai Basin

Liu¹,

Du²,

Zhu³

et al. 2022

Energies

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“…Such EPAR values suggest that these samples are dominated by microporosity and interparticle porosity which is consistent with observations from thin sections as mentioned earlier (Figures 6 and 7). The occasional increase of EPAR seen in a few samples could be attributed to the presence of some mouldic and intraparticle porosity (Figure 6D) (Fournier et al, 2014;Hairabian et al, 2014;Jaballah et al, 2021), but the impact of such pore types remains limited since they were not well-developed in most of the studied samples. These observations and the reported EPAR values are consistent with results obtained by several studies for microporosity and interparticle porosity dominated carbonates (Baechle et al, 2008;Fournier et al, 2011Fournier et al, , 2014.…”

Section: Petrophysical Propertiesmentioning

confidence: 99%

Petrophysics and sediment variability in a mixed alluvial to lacustrine carbonate system (Miocene, Madrid Basin, Central Spain)

Reijmer

Blok

El-Husseiny

et al. 2021

The Depositional Record

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“…If the model is relevant to well data, it can significantly contribute to the prediction of the rock texture. In inclusion models, the pore space is represented as inclusions, typically observed in carbonate rocks (Fournier et al, 2018;Jaballah et al, 2021;Teillet et al, 2021). However, in grainbased models, the rock is represented as a combination of ideally rounded and identically shaped grain packs.…”

Section: Rock Physics Models (Transforms)mentioning

confidence: 99%

Rock physics and machine learning comparison: elastic properties prediction and scale dependency

Suleymanov,

El-Husseiny,

Glatz

et al. 2023

Front. Earth Sci.

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Rock physics diagnostics (RPD) established based upon the well data are used to deterministically predict elastic properties of rocks from measured petrophysical rock parameters. However, with the recent advances in statistical methods, machine learning (ML) can help to build a shortcut between raw well data and rock properties of interest. Several studies have reported the comparison of rock physics and machine learning methods for the prediction of rock properties, but the scale dependence of the ML models was never investigated. This study aims at comparing the results from rock physics and machine learning models for predicting elastic properties such as bulk density (ρb), P-wave velocity (Vp), S-wave velocity (Vs), as well as Poisson’s ratio (v) and acoustic impedance (Ip) in a well from the Gulf of Mexico (GOM) in two different scale scenarios: the well log and seismic scales. The well data under examination was split into training and testing subsets to optimize and test the developed ML models. The RPD approach was also used to validate and compare the accuracy of predicted elastic properties. Backus averaging was later applied to upscale the well data to the seismic scale to examine the scale dependence and prediction accuracy of aforementioned physics-driven and data-driven approaches. Results show that RPD and ML methods provided consistent results at both well log and seismic scales, suggesting the scale independence of both approaches. Moreover, ML models showed better estimation of rock properties due to their “apparent” match with measured data at both scales compared to the RPD approach where a significant mismatch between measured and predicted rock properties was found in the reservoir section of the well. However, by conducting further quality control of the sonic data, it was found that the measured Poisson’s ratio was extremely high in the gas-saturated interval. Hence, the prediction from ML models in this particular case cannot be trusted as ML models were trained based on poor-quality well data with non-realistic Vs and v values. Such an issue, however, could be identified and corrected using RPD as presented in this study. We demonstrate the importance of incorporating domain knowledge, i.e., rock physics, to check data quality and validate results from data-driven models.

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Physical properties of Cretaceous to Eocene platform-to-basin carbonates from Albania

Cited by 14 publications

References 87 publications

Wave Velocity in Sandstone and Mudstone under High Temperature and Overpressure in Yinggehai Basin

Wave Velocity in Sandstone and Mudstone under High Temperature and Overpressure in Yinggehai Basin

Petrophysics and sediment variability in a mixed alluvial to lacustrine carbonate system (Miocene, Madrid Basin, Central Spain)

Rock physics and machine learning comparison: elastic properties prediction and scale dependency

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