Pydiraju Yalamanchi - Academia.edu (original) (raw)

Papers by Pydiraju Yalamanchi

Research paper thumbnail of Using rock physics analysis driven feature engineering in ML-based shear slowness prediction using logs of wells from different geological setup

Acta Geophysica, Jan 4, 2024

Research paper thumbnail of Estimation of pore structure and permeability in tight carbonate reservoir based on machine learning (ML) algorithm using SEM images of Jaisalmer sub-basin, India

Scientific Reports, Jan 8, 2024

Analyzing the pore structure in carbonate reservoirs plays a crucial role in predicting fluid flo... more Analyzing the pore structure in carbonate reservoirs plays a crucial role in predicting fluid flow characteristics within these formations. The goal of the study was to use machine learning techniques for pore structure analysis and estimation of permeability in carbonate reservoirs. We implemented these algorithms by examining 2D scanning electron microscope (SEM) images of carbonate samples from the Jaisalmer sub-basin captured at various magnifications. In the initial stage of the analysis, various binarization algorithms were applied to determine carbonate sample porosity. Among these algorithms, the MaxEntropy algorithm gave a porosity value closely aligned with those obtained through petrography analysis. We employed the watershed algorithm to find the pore network parameters of carbonate samples at various magnifications. We observed that changes in magnification affected pore network parameters, resulting in a reduction in pore size distribution, throat radius, and grain size. Subsequently, we employed the numerical lattice Boltzmann method (LBM) to estimate the permeability of carbonate samples and compared to values derived from well logs. We employed machine learning (ML) algorithms, specifically Artificial Neural Network (ANN) and Support Vector Machine (SVM), to predict the permeability of carbonate samples. The input features for these models were the pore network parameters, while the LBM permeability values served as the output. We examine the prediction performance of these methods against the measured LBM permeability by conducting the error analysis and the coefficient of determination (R 2) calculation. Our findings revealed that the ANN models outperformed the SVM models. Specifically, the ANN model displayed an impressive R 2 value of 0.892, along with root mean square error (RMSE), mean squared error (MSE) and, mean absolute error (MAE) values of 1.927, 3.716 and 1.580, respectively. In contrast, the SVM model yielded an R 2 value of 0.849, with RMSE, MSE and, MAE values of 2.324, 5.401 and, 2.166 respectively, when assessed on testing data of measured permeability. This study found that ANN is more dependable, robust, and precise than SVM in forecasting carbonate sample permeability. Pore structure and permeability are crucial in the study of geoscience and petroleum engineering for oil & gas exploration. Pore structure and permeability play a crucial role in simulating fluid flow within the heterogeneous geometry of carbonate porous materials 1-5. To investigate the single and multiphase fluid flow, pore network modeling and its characterization are crucial 6. Permeability, which describes the flow of fluids through porous media, is one of the most important properties. Pore structure parameters, including porosity, tortuosity, connectivity, pore size, as well as pore shape and aspect ratio 7,8 significantly influenced the permeability of porous media 7,8. Several direct experimental approaches have developed to analyse the pore structure character and permeability of the porous medium. These approaches include mercury injection porosimetry (MIP), nuclear magnetic resonance (NMR), core analysis method developed by Gas Research Institute (GRI), and pulse

Research paper thumbnail of Selection of a Suitable Rock Mixing Method for Computing Gardner’s Constant Through a Machine Learning (ML) Approach to Estimate the Compressional Velocity: A study from the Jaisalmer sub-basin, India

Pure and Applied Geophysics, 2021

The frequent variability of petrophysical properties makes hydrocarbon exploration challenging in... more The frequent variability of petrophysical properties makes hydrocarbon exploration challenging in carbonate reservoirs. Nowadays, quantitative interpretation (QI) is an essential part of hydrocarbon exploration in a complex reservoir, which needs adequate rock physics data at the well level. However, sometimes the relevant data are not available in earlier discovered oil and gas fields. We observed that the old oil and gas fields in the onshore parts of India have a scarcity of density and compressional velocity (Vp) data at the well level. Gardner's empirical expression provides the scope to estimate Vp from acquired density data and vice versa. However, there are two constants in this relationship, and these are different for different saturation cases of the reservoir due to different mineralogical content in the reservoir rock. The current study aims to identify suitable rock mineral mixing methods and their related uncertainty for estimating Gardner's constants. This un...

Research paper thumbnail of Using rock physics analysis driven feature engineering in ML-based shear slowness prediction using logs of wells from different geological setup

Acta Geophysica, Jan 4, 2024

Research paper thumbnail of Estimation of pore structure and permeability in tight carbonate reservoir based on machine learning (ML) algorithm using SEM images of Jaisalmer sub-basin, India

Scientific Reports, Jan 8, 2024

Analyzing the pore structure in carbonate reservoirs plays a crucial role in predicting fluid flo... more Analyzing the pore structure in carbonate reservoirs plays a crucial role in predicting fluid flow characteristics within these formations. The goal of the study was to use machine learning techniques for pore structure analysis and estimation of permeability in carbonate reservoirs. We implemented these algorithms by examining 2D scanning electron microscope (SEM) images of carbonate samples from the Jaisalmer sub-basin captured at various magnifications. In the initial stage of the analysis, various binarization algorithms were applied to determine carbonate sample porosity. Among these algorithms, the MaxEntropy algorithm gave a porosity value closely aligned with those obtained through petrography analysis. We employed the watershed algorithm to find the pore network parameters of carbonate samples at various magnifications. We observed that changes in magnification affected pore network parameters, resulting in a reduction in pore size distribution, throat radius, and grain size. Subsequently, we employed the numerical lattice Boltzmann method (LBM) to estimate the permeability of carbonate samples and compared to values derived from well logs. We employed machine learning (ML) algorithms, specifically Artificial Neural Network (ANN) and Support Vector Machine (SVM), to predict the permeability of carbonate samples. The input features for these models were the pore network parameters, while the LBM permeability values served as the output. We examine the prediction performance of these methods against the measured LBM permeability by conducting the error analysis and the coefficient of determination (R 2) calculation. Our findings revealed that the ANN models outperformed the SVM models. Specifically, the ANN model displayed an impressive R 2 value of 0.892, along with root mean square error (RMSE), mean squared error (MSE) and, mean absolute error (MAE) values of 1.927, 3.716 and 1.580, respectively. In contrast, the SVM model yielded an R 2 value of 0.849, with RMSE, MSE and, MAE values of 2.324, 5.401 and, 2.166 respectively, when assessed on testing data of measured permeability. This study found that ANN is more dependable, robust, and precise than SVM in forecasting carbonate sample permeability. Pore structure and permeability are crucial in the study of geoscience and petroleum engineering for oil & gas exploration. Pore structure and permeability play a crucial role in simulating fluid flow within the heterogeneous geometry of carbonate porous materials 1-5. To investigate the single and multiphase fluid flow, pore network modeling and its characterization are crucial 6. Permeability, which describes the flow of fluids through porous media, is one of the most important properties. Pore structure parameters, including porosity, tortuosity, connectivity, pore size, as well as pore shape and aspect ratio 7,8 significantly influenced the permeability of porous media 7,8. Several direct experimental approaches have developed to analyse the pore structure character and permeability of the porous medium. These approaches include mercury injection porosimetry (MIP), nuclear magnetic resonance (NMR), core analysis method developed by Gas Research Institute (GRI), and pulse

Research paper thumbnail of Selection of a Suitable Rock Mixing Method for Computing Gardner’s Constant Through a Machine Learning (ML) Approach to Estimate the Compressional Velocity: A study from the Jaisalmer sub-basin, India

Pure and Applied Geophysics, 2021

The frequent variability of petrophysical properties makes hydrocarbon exploration challenging in... more The frequent variability of petrophysical properties makes hydrocarbon exploration challenging in carbonate reservoirs. Nowadays, quantitative interpretation (QI) is an essential part of hydrocarbon exploration in a complex reservoir, which needs adequate rock physics data at the well level. However, sometimes the relevant data are not available in earlier discovered oil and gas fields. We observed that the old oil and gas fields in the onshore parts of India have a scarcity of density and compressional velocity (Vp) data at the well level. Gardner's empirical expression provides the scope to estimate Vp from acquired density data and vice versa. However, there are two constants in this relationship, and these are different for different saturation cases of the reservoir due to different mineralogical content in the reservoir rock. The current study aims to identify suitable rock mineral mixing methods and their related uncertainty for estimating Gardner's constants. This un...