Assessment of rock geomechanical properties and estimation of wave velocities

Guo, Shaoqiang; Zhang, Yao; Iraji, Amin; Gharavi, Hoorman; Deifalla, Ahmed Farouk

doi:10.1007/s11600-022-00891-8

Cited by 6 publications

(3 citation statements)

References 79 publications

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“…The high prediction precision of both MLR and GEP techniques confirms the results of previous investigations reporting the robustness of these algorithms in V s estimation [46][47][48][49][50][51]. To improve the predictability of these techniques, two innovative works can be carried out: the ensemble of the regression model [45,66] and the coupling of deep learning with machine learning models [75].…”

Section: Discussionsupporting

confidence: 74%

“…The MLR and GEP techniques were also applied to predict the V s in rocks [45][46][47][48][49][50][51]. Upom et al used the MLR and an ensemble (EN-PSO) model to predict the V s values in soils [45].…”

Section: Introductionmentioning

confidence: 99%

“…Behnia et al used the GEP and ANFIS techniques to extract mathematical relations for V s prediction in limestone rocks in Iran [49]. In their study, the input parameters were density, porosity, and compressional wave velocity (V p ).…”

Section: Introductionmentioning

confidence: 99%

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Comparing Artificial Intelligence Algorithms with Empirical Correlations in Shear Wave Velocity Prediction

Khalilidermani,

Knez

2023

Applied Sciences

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Accurate estimation of shear wave velocity (Vs) is crucial for modeling hydrocarbon reservoirs. The Vs values can be directly measured using the Dipole Shear Sonic Imager data; however, it is very expensive and requires specific technical considerations. To address this issue, researchers have developed different methods for Vs prediction in underground rocks and soils. In this study, the well logging data of a wellbore in the Iranian Aboozar limestone oilfield were used for Vs estimation. The Vs values were estimated using five available empirical correlations, linear regression technique, and two machine learning algorithms including multivariate linear regression and gene expression programming. Those values were compared with the real Vs data. Furthermore, three statistical indices including correlation coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the effectiveness of the applied techniques. The mathematical correlation obtained by the GEP algorithm delivered the most accurate Vs values with R2 = 0.972, RMSE = 0.000290, and MAE = 0.000208. Compared to the available empirical correlations, the obtained correlation from the GEP approach uses multiple parameters to estimate the Vs, thereby leading to more precise predictions. The new correlation can be used to estimate the Vs values in the Aboozar oilfield and other geologically similar reservoirs.

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Section: Discussionsupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

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Comparing Artificial Intelligence Algorithms with Empirical Correlations in Shear Wave Velocity Prediction

Khalilidermani,

Knez

2023

Applied Sciences

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Prediction of the static elastic modulus of limestone using downhole seismic test in Asmari formation

et al. 2023

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Estimation of static Young’s modulus of sandstone types: effective machine learning and statistical models

Liu,

Sun,

Wang

et al. 2024

Earth Sci Inform

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The elastic modulus is one of the important parameters for analyzing the stability of engineering projects, especially dam sites. In the current study, the effect of physical properties, quartz, fragment, and feldspar percentages, and dynamic Young’s modulus (DYM) on the static Young’s modulus (SYM) of the various types of sandstones was assessed. These investigations were conducted through simple and multivariate regression, support vector regression, adaptive neuro-fuzzy inference system, and backpropagation multilayer perceptron. The XRD and thin section results showed that the studied samples were classified as arenite, litharenite, and feldspathic litharenite. The low resistance of the arenite type is mainly due to the presence of sulfate cement, clay minerals, high porosity, and carbonate fragments in this type. Examining the fracture patterns of these sandstones in different resistance ranges showed that at low values of resistance, the fracture pattern is mainly of simple shear type, which changes to multiple extension types with increasing compressive strength. Among the influencing factors, the percentage of quartz has the greatest effect on SYM. A comparison of the methods' performance based on CPM and error values in estimating SYM revealed that SVR (R2 = 0.98, RMSE = 0.11GPa, CPM = + 1.84) outperformed other methods in terms of accuracy. The average difference between predicted SYM using intelligent methods and measured SYM value was less than 0.05% which indicates the efficiency of the used methods in estimating SYM.

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Assessment of rock geomechanical properties and estimation of wave velocities

Cited by 6 publications

References 79 publications

Comparing Artificial Intelligence Algorithms with Empirical Correlations in Shear Wave Velocity Prediction

Comparing Artificial Intelligence Algorithms with Empirical Correlations in Shear Wave Velocity Prediction

Prediction of the static elastic modulus of limestone using downhole seismic test in Asmari formation

Estimation of static Young’s modulus of sandstone types: effective machine learning and statistical models

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