Syed Nizamuddin - Academia.edu (original) (raw)
Papers by Syed Nizamuddin
Journal of Petroleum Science and Engineering, 2013
Hydraulic Flow Units or hydraulic units (HUs) concept is becoming popular for grouping reservoir ... more Hydraulic Flow Units or hydraulic units (HUs) concept is becoming popular for grouping reservoir rocks of similar petrophysical properties with the main goal of having a better estimate of permeability. HU approach has an advantage that it addresses the development of permeability in reservoir rocks from fundamentals of geology and physics of flow at pore network scale. The aim of the present study is to predict HUs for the un-cored sections of the wells in a carbonate reservoir using Support Vector Machines (SVMs). HUs for un-cored sections were predicted using wire line logs as input and the associated conventional core values as guides to SVMs. HUs for the core data were identified using three popular correlations such as Kozeny-Carmen (KC) equation, Nooruddin and Hossain equation, and the power law flow unit equation. The experimental results on a Middle East field data show that a better HU prediction accuracy is achieved using Nooruddin and Hossain correlation in comparison with using KC and the power law flow unit correlation. A further analysis to the predicted value reveals that better prediction accuracy is achieved if the granularity of HU class boundary is enlarged to the neighboring classes. Although Nooruddin and Hossain correlation could better relate wire line logs to HUs, however the permeability calculated from the predicted HUs showed less error with the power law flow unit correlation. Considering this, we achieved a maximum of 97% accuracy which encourages the application of SVMs in HU unit prediction using well log data for the un-cored sections of well or for the wells which do not have core data.
GEOPHYSICS, 2021
The mechanical nature of fluid-substitution models has always been recognized as a major cause of... more The mechanical nature of fluid-substitution models has always been recognized as a major cause of their limited predictive power. For instance, saturants are typically treated as simple fluids characterized only by their densities, viscosities, and moduli of elasticity; their chemistry is just ignored, even when that fluid is crude oil. However, crude oil is a complex mixture of several thousand organic compounds characterized by a variety of molecular weights, polarities, and polarizabilities, and the response of its rheological behavior to acoustic wave propagation is difficult to predict, especially when it resides in the pore space of rocks. We have performed ultrasonic-velocity measurements on carbonate core plugs saturated with a brine and with a light crude oil that are mechanically similar (i.e., having comparable densities, viscosities, and moduli of elasticity) and that show a significant and consistent excess of hardening when the saturant is oil. Dispersion and wettabili...
Petroleum Science and Technology, 2014
In the present study, an artificial neural network (ANN) constitutive model was developed to pred... more In the present study, an artificial neural network (ANN) constitutive model was developed to predict bubble point pressure for the case of Canadian data. The accuracy of prediction of bubble point pressure was compared using two sets of inputs to the model. One was based on composition of the oil and the other based on easily available parameters such as solution gas-oil ratio, reservoir temperature, oil gravity, and gas relative density. The performance of bubble point pressure prediction with ANN was compared with that of equation of state (EOS) and other available empirical correlations. It was found that ANN models can produce a more accurate prediction of bubble point pressure than the existing empirical correlations and EOS calculations.
Journal of Petroleum Science and Engineering, 2012
Journal of Petroleum Science and Engineering, 2013
Hydraulic Flow Units or hydraulic units (HUs) concept is becoming popular for grouping reservoir ... more Hydraulic Flow Units or hydraulic units (HUs) concept is becoming popular for grouping reservoir rocks of similar petrophysical properties with the main goal of having a better estimate of permeability. HU approach has an advantage that it addresses the development of permeability in reservoir rocks from fundamentals of geology and physics of flow at pore network scale. The aim of the present study is to predict HUs for the un-cored sections of the wells in a carbonate reservoir using Support Vector Machines (SVMs). HUs for un-cored sections were predicted using wire line logs as input and the associated conventional core values as guides to SVMs. HUs for the core data were identified using three popular correlations such as Kozeny-Carmen (KC) equation, Nooruddin and Hossain equation, and the power law flow unit equation. The experimental results on a Middle East field data show that a better HU prediction accuracy is achieved using Nooruddin and Hossain correlation in comparison with using KC and the power law flow unit correlation. A further analysis to the predicted value reveals that better prediction accuracy is achieved if the granularity of HU class boundary is enlarged to the neighboring classes. Although Nooruddin and Hossain correlation could better relate wire line logs to HUs, however the permeability calculated from the predicted HUs showed less error with the power law flow unit correlation. Considering this, we achieved a maximum of 97% accuracy which encourages the application of SVMs in HU unit prediction using well log data for the un-cored sections of well or for the wells which do not have core data.
GEOPHYSICS, 2021
The mechanical nature of fluid-substitution models has always been recognized as a major cause of... more The mechanical nature of fluid-substitution models has always been recognized as a major cause of their limited predictive power. For instance, saturants are typically treated as simple fluids characterized only by their densities, viscosities, and moduli of elasticity; their chemistry is just ignored, even when that fluid is crude oil. However, crude oil is a complex mixture of several thousand organic compounds characterized by a variety of molecular weights, polarities, and polarizabilities, and the response of its rheological behavior to acoustic wave propagation is difficult to predict, especially when it resides in the pore space of rocks. We have performed ultrasonic-velocity measurements on carbonate core plugs saturated with a brine and with a light crude oil that are mechanically similar (i.e., having comparable densities, viscosities, and moduli of elasticity) and that show a significant and consistent excess of hardening when the saturant is oil. Dispersion and wettabili...
Petroleum Science and Technology, 2014
In the present study, an artificial neural network (ANN) constitutive model was developed to pred... more In the present study, an artificial neural network (ANN) constitutive model was developed to predict bubble point pressure for the case of Canadian data. The accuracy of prediction of bubble point pressure was compared using two sets of inputs to the model. One was based on composition of the oil and the other based on easily available parameters such as solution gas-oil ratio, reservoir temperature, oil gravity, and gas relative density. The performance of bubble point pressure prediction with ANN was compared with that of equation of state (EOS) and other available empirical correlations. It was found that ANN models can produce a more accurate prediction of bubble point pressure than the existing empirical correlations and EOS calculations.
Journal of Petroleum Science and Engineering, 2012