The influence of lithology and depth on acoustic velocities in South-east

Tamunobereton-ari, I.

doi:10.5251/ajsir.2010.1.2.279.292

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…The empirical formula method is the most common approach to first estimate the shear wave velocity by constructing a linear relationship between logging parameters and Vs (Castagna et al, 1985;Han et al, 1986;Dvorkin, 2008;Parvizi et al, 2015). However, the empirical relationship is highly lithology-and region-specific (Tamunobereton-Ari et al, 2010). In carbonate formation, which has a complex pore structure and strong heterogeneity, the variation of shear wave velocity in carbonate is the result of an interaction between lithofacies, diagenetic process, and porosity (Rafavich et al, 1984;Anselmetti and Eberli, 1993;Tamunobereton-Ari et al, 2010;Qabany et al, 2011;Wang et al, 2011;Kittridge, 2015;Garia et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…However, the empirical relationship is highly lithology-and region-specific (Tamunobereton-Ari et al, 2010). In carbonate formation, which has a complex pore structure and strong heterogeneity, the variation of shear wave velocity in carbonate is the result of an interaction between lithofacies, diagenetic process, and porosity (Rafavich et al, 1984;Anselmetti and Eberli, 1993;Tamunobereton-Ari et al, 2010;Qabany et al, 2011;Wang et al, 2011;Kittridge, 2015;Garia et al, 2019). Thus, simple linear approximation is insufficient for accurately estimating the Vs The rock physics modeling technique for carbonate has being extensively studied by considering different mineral types, pore structures, and fluid conditions, and the studies have shown promising results (Xu and Payne, 2009;Sun et al, 2012;Zhang et al, 2013;Azadpour et al, 2020;Seifi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Interpretable machine learning model for shear wave estimation in a carbonate reservoir using LightGBM and SHAP: a case study in the Amu Darya right bank

Zhang,

Chai,

Wang

et al. 2023

Front. Earth Sci.

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The shear wave velocity (Vs) is significant for quantitative seismic interpretation. Although numerous studies have proved the effectiveness of the machine learning method in estimating the Vs using well-logging parameters, the real-world application is still hindered because of the black-box nature of machine learning models. With the rapid development of the interpretable machine learning (ML) technique, the drawback of ML can be overcome by various interpretation methods. This study applies the Light Gradient Boosting Machine (LightGBM) to predict the Vs of a carbonate reservoir and uses the Shapley Additive Explanations (SHAP) to interpret the model. The application of ML in Vs estimation normally involves using conventional well-log data that are highly correlated with Vs to train the model. To expand the model’s applicability in wells that lack essential logs, such as the density and neutron logs, we introduce three geologically important features, temperature, pressure, and formation, into the model. The LightGBM model is tuned by the automatic hyperparameter optimization framework; the result is compared with the Xu-Payne rock physics model and four machine learning models tuned with the same process. The results show that the LightGBM model can fit the training data and provide accurate predictions in the test well. The model outperforms the rock physics model and other ML models in both accuracy and training time. The SHAP analysis provides a detailed explanation of the contribution of each input variable to the model and demonstrates the variation of feature contribution in different reservoir conditions. Moreover, the validity of the LightGBM model is further proved by the consistency of the deduced information from feature dependency with the geological understanding of the carbonate formation. The study demonstrates that the newly added features can effectively improve model performance, and the importance of the input feature is not necessarily related to its correlation with Vs

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interpretable machine learning model for shear wave estimation in a carbonate reservoir using LightGBM and SHAP: a case study in the Amu Darya right bank

Zhang,

Chai,

Wang

et al. 2023

Front. Earth Sci.

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“…The importance of using shear velocity is ranging from seismic exploration, calculation of petrophysical properties and evaluation of the well stability. Sometimes not all logs are available, for example the dipole shear sonic imager (DSI) (Rezaee et al 2006, Amunobereton-ari et al 2010). The DSI have both monopole and dipole sonic acquisition abilities for the dependable acoustic measurement of compressional, shear, and Stoneley slowness's.…”

Section: Introductionmentioning

confidence: 99%

New Equations to Determine Shear Velocity from Compressional Velocity in Hf-2 Well, Halfaya Oil Field

Al-Banna

Tareq

Al-Sharaa³

2022

IGJ

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The most important characters of the rock formations are shear and compression velocities, which are used to calculate elastic moduli. The geomechanical properties can be used to evaluate reservoir stability or to calibrate velocity and time/depth conversion, synthetic seismogram and other important applications. The aim of this study is to use the first order least square method to obtain an empirical equation to determine the shear velocity from compressional velocity for Mishrif, Rumaila, Ahmadi, and Mauddud formations in Hf-2 well. The obtained equations depend on sonic log and dipole shear sonic imager data of Hf-2 well and use Techlog software. The results from the obtained equations in comparison with those results of Green Castagna equation and measured data of dipole shear sonic imager of shear velocity for Mishrif, Rumiala and Mauddud formations. It is found from the comparison that the result is consistent each other’s. The consistent reflect the reality of shear waves values obtained in these formations. The comparison in Ahmadi formation also shows a consistent result for the obtained equations but it shows some differences from the dipole shear sonic imager and shear velocity reference values. The authors believed that the difference in some values of the Ahmadi Formation may be due to the high shale volume in comparison to the other formations.

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“…Velocity is one of the most important petrophysical parameters used in oilfield optimization or other geophysical surveys to easily determine and predict horizons, faults, facies, unconformities, stratigraphic boundaries, geologic structures, fluid contents etc. (Tamunobereton-ari et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Discrimination of reservoir fluid contacts using compressional and shear wave velocity

Bello¹,

Onifade²

2016

Glo Jnl Pure Appl Sci

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Improved fluid detection and lithology discrimination using rock properties and attributes cross plots have been attempted using well log data in an Onshore Niger Delta field. Rock properties and attributes were extracted using empirical rock physics models on well logs and used to validate their potentials as pore fluid discriminants. These rock properties and attributes were cross plotted for the primary purpose of investigating their sensitivity to fluid and lithology in cross plot space. V p and V s logs were derived from the inverse of interval transit times of sonic and dipole shear logs respectively. V p /V s ratio, acoustic impedance and porosity were derived from V p , V s and density logs using appropriate relations. The identified depth of reservoir of interest (A2) for Well A and B ranges from 5842 ft to 5964 ft and 5795 ft to 5936 ft respectively. The properties cross plotted comprise V p vsV s, V p /V s vsI p , V p /V s vs Porosity, V p /V s vs Density and V p vs Density. V p vs Density cross plot revealed that the reservoir consists of sand lithology with intercalated shale. V p vsV s shows a linear relationship and does not discriminate fluid in the reservoir. V p /V s ratio vsI p distinguish A2 into hydrocarbon, brine and shale zones. V p /V s ratio vs density and porosity crossplots distinguishes the A2 into gas, oil, brine and shale zones. The analysis validates the fact that V p /V s ratio and their combinations cross plots are more sensitive and robust for fluid discrimination. It also reveals that the ratio of V p /V s is more sensitive to change of fluid type than the use of V p or V s separately.

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The influence of lithology and depth on acoustic velocities in South-east

Cited by 6 publications

References 12 publications

Interpretable machine learning model for shear wave estimation in a carbonate reservoir using LightGBM and SHAP: a case study in the Amu Darya right bank

Interpretable machine learning model for shear wave estimation in a carbonate reservoir using LightGBM and SHAP: a case study in the Amu Darya right bank

New Equations to Determine Shear Velocity from Compressional Velocity in Hf-2 Well, Halfaya Oil Field

Discrimination of reservoir fluid contacts using compressional and shear wave velocity

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