The Experts below are selected from a list of 18 Experts worldwide ranked by ideXlab platform

Chengli Dong - One of the best experts on this subject based on the ideXlab platform.

  • downhole fluid analysis and asphaltene science for Petroleum Reservoir Evaluation
    Annual Review of Chemical and Biomolecular Engineering, 2014
    Co-Authors: Oliver C Mullins, Andrew E Pomerantz, Chengli Dong
    Abstract:

    Petroleum Reservoirs are enshrouded in mysteries associated with all manner of geologic and fluid complexities that Mother Nature can inspire. Efficient exploitation of Petroleum Reservoirs mandates elucidation of these complexities; downhole fluid analysis (DFA) has proven to be indispensable for understanding both fluids and Reservoir architecture. Crude oil consists of dissolved gases, liquids, and dissolved solids, known as the asphaltenes. These different fluid components exhibit fluid gradients vertically and laterally, which are best revealed by DFA, with its excellent precision and accuracy. Compositional gradient analysis falls within the purview of thermodynamics. Gas-liquid equilibria can be treated with a cubic equation of state (EoS), such as the Peng-Robinson EoS, a modified van der Waals EoS. In contrast, the first EoS for asphaltene gradients, the Flory-Huggins-Zuo (FHZ) EoS, was developed only recently. The resolution of the asphaltene molecular and nanocolloidal species in crude oil, which is codified in the Yen-Mullins model of asphaltenes, enabled the development of this EoS. The combination of DFA characterization of gradients of Reservoir crude oil with the cubic EoS and FHZ EoS analyses brings into view wide-ranging Reservoir concerns, such as Reservoir connectivity, fault-block migration, heavy oil gradients, tar mat formation, huge disequilibrium fluid gradients, and even stochastic variations of Reservoir fluids. New Petroleum science and DFA technology are helping to offset the increasing costs and technical difficulties of exploiting ever-more-remote Petroleum Reservoirs.

Oliver C Mullins - One of the best experts on this subject based on the ideXlab platform.

  • downhole fluid analysis and asphaltene science for Petroleum Reservoir Evaluation
    Annual Review of Chemical and Biomolecular Engineering, 2014
    Co-Authors: Oliver C Mullins, Andrew E Pomerantz, Chengli Dong
    Abstract:

    Petroleum Reservoirs are enshrouded in mysteries associated with all manner of geologic and fluid complexities that Mother Nature can inspire. Efficient exploitation of Petroleum Reservoirs mandates elucidation of these complexities; downhole fluid analysis (DFA) has proven to be indispensable for understanding both fluids and Reservoir architecture. Crude oil consists of dissolved gases, liquids, and dissolved solids, known as the asphaltenes. These different fluid components exhibit fluid gradients vertically and laterally, which are best revealed by DFA, with its excellent precision and accuracy. Compositional gradient analysis falls within the purview of thermodynamics. Gas-liquid equilibria can be treated with a cubic equation of state (EoS), such as the Peng-Robinson EoS, a modified van der Waals EoS. In contrast, the first EoS for asphaltene gradients, the Flory-Huggins-Zuo (FHZ) EoS, was developed only recently. The resolution of the asphaltene molecular and nanocolloidal species in crude oil, which is codified in the Yen-Mullins model of asphaltenes, enabled the development of this EoS. The combination of DFA characterization of gradients of Reservoir crude oil with the cubic EoS and FHZ EoS analyses brings into view wide-ranging Reservoir concerns, such as Reservoir connectivity, fault-block migration, heavy oil gradients, tar mat formation, huge disequilibrium fluid gradients, and even stochastic variations of Reservoir fluids. New Petroleum science and DFA technology are helping to offset the increasing costs and technical difficulties of exploiting ever-more-remote Petroleum Reservoirs.

Debarun Bhattacharjya - One of the best experts on this subject based on the ideXlab platform.

  • Simulation–Regression Approximations for Value of Information Analysis of Geophysical Data
    Mathematical Geosciences, 2017
    Co-Authors: Jo Eidsvik, Geetartha Dutta, Tapan Mukerji, Debarun Bhattacharjya
    Abstract:

    Value of information analysis is useful for helping a decision maker evaluate the benefits of acquiring or processing additional data. Such analysis is particularly beneficial in the Petroleum industry, where information gathering is costly and time-consuming. Furthermore, there are often abundant opportunities for discovering creative information gathering schemes, involving the type and location of geophysical measurements. A consistent Evaluation of such data requires spatial modeling that realistically captures the various aspects of the decision situation: the uncertain Reservoir variables, the alternatives and the geophysical data under consideration. The computational tasks of value of information analysis can be daunting in such spatial decision situations; in this paper, a regression-based approximation approach is presented. The approach involves Monte Carlo simulation of data followed by linear regression to fit the conditional expectation expression that is needed for value of information analysis. Efficient approximations allow practical value of information analysis for the spatial decision situations that are typically encountered in Petroleum Reservoir Evaluation. Applications are presented for seismic amplitude data and electromagnetic resistivity data, where one example includes multi-phase fluid flow simulations.

  • Simulation-Regression Approximations for Value of Information Analysis of Geophysical Data
    Mathematical Geosciences, 2017
    Co-Authors: Jo Eidsvik, Geetartha Dutta, Tapan Mukerji, Debarun Bhattacharjya
    Abstract:

    Value of information analysis is useful for helping a decision maker evaluate the benefits of acquiring or processing additional data. Such analysis is particularly beneficial in the Petroleum industry, where information gathering is costly and time-consuming. Furthermore, there are often abundant opportunities for discovering creative information gathering schemes, involving the type and location of geophysical measurements. A consistent Evaluation of such data requires spatial modeling that realistically captures the various aspects of the decision situation: the uncertain Reservoir variables, the alternatives and the geophysical data under consideration. The computational tasks of value of information analysis can be daunting in such spatial decision situations; in this paper, a regression-based approximation approach is presented. The approach involves Monte Carlo simulation of data followed by linear regression to fit the conditional expectation expression that is needed for value of information analysis. Efficient approximations allow practical value of information analysis for the spatial decision situations that are typically encountered in Petroleum Reservoir Evaluation. Applications are presented for seismic amplitude data and electromagnetic resistivity data, where one example includes multi-phase fluid flow simulations.

Andrew E Pomerantz - One of the best experts on this subject based on the ideXlab platform.

  • downhole fluid analysis and asphaltene science for Petroleum Reservoir Evaluation
    Annual Review of Chemical and Biomolecular Engineering, 2014
    Co-Authors: Oliver C Mullins, Andrew E Pomerantz, Chengli Dong
    Abstract:

    Petroleum Reservoirs are enshrouded in mysteries associated with all manner of geologic and fluid complexities that Mother Nature can inspire. Efficient exploitation of Petroleum Reservoirs mandates elucidation of these complexities; downhole fluid analysis (DFA) has proven to be indispensable for understanding both fluids and Reservoir architecture. Crude oil consists of dissolved gases, liquids, and dissolved solids, known as the asphaltenes. These different fluid components exhibit fluid gradients vertically and laterally, which are best revealed by DFA, with its excellent precision and accuracy. Compositional gradient analysis falls within the purview of thermodynamics. Gas-liquid equilibria can be treated with a cubic equation of state (EoS), such as the Peng-Robinson EoS, a modified van der Waals EoS. In contrast, the first EoS for asphaltene gradients, the Flory-Huggins-Zuo (FHZ) EoS, was developed only recently. The resolution of the asphaltene molecular and nanocolloidal species in crude oil, which is codified in the Yen-Mullins model of asphaltenes, enabled the development of this EoS. The combination of DFA characterization of gradients of Reservoir crude oil with the cubic EoS and FHZ EoS analyses brings into view wide-ranging Reservoir concerns, such as Reservoir connectivity, fault-block migration, heavy oil gradients, tar mat formation, huge disequilibrium fluid gradients, and even stochastic variations of Reservoir fluids. New Petroleum science and DFA technology are helping to offset the increasing costs and technical difficulties of exploiting ever-more-remote Petroleum Reservoirs.

Jo Eidsvik - One of the best experts on this subject based on the ideXlab platform.

  • Simulation–Regression Approximations for Value of Information Analysis of Geophysical Data
    Mathematical Geosciences, 2017
    Co-Authors: Jo Eidsvik, Geetartha Dutta, Tapan Mukerji, Debarun Bhattacharjya
    Abstract:

    Value of information analysis is useful for helping a decision maker evaluate the benefits of acquiring or processing additional data. Such analysis is particularly beneficial in the Petroleum industry, where information gathering is costly and time-consuming. Furthermore, there are often abundant opportunities for discovering creative information gathering schemes, involving the type and location of geophysical measurements. A consistent Evaluation of such data requires spatial modeling that realistically captures the various aspects of the decision situation: the uncertain Reservoir variables, the alternatives and the geophysical data under consideration. The computational tasks of value of information analysis can be daunting in such spatial decision situations; in this paper, a regression-based approximation approach is presented. The approach involves Monte Carlo simulation of data followed by linear regression to fit the conditional expectation expression that is needed for value of information analysis. Efficient approximations allow practical value of information analysis for the spatial decision situations that are typically encountered in Petroleum Reservoir Evaluation. Applications are presented for seismic amplitude data and electromagnetic resistivity data, where one example includes multi-phase fluid flow simulations.

  • Simulation-Regression Approximations for Value of Information Analysis of Geophysical Data
    Mathematical Geosciences, 2017
    Co-Authors: Jo Eidsvik, Geetartha Dutta, Tapan Mukerji, Debarun Bhattacharjya
    Abstract:

    Value of information analysis is useful for helping a decision maker evaluate the benefits of acquiring or processing additional data. Such analysis is particularly beneficial in the Petroleum industry, where information gathering is costly and time-consuming. Furthermore, there are often abundant opportunities for discovering creative information gathering schemes, involving the type and location of geophysical measurements. A consistent Evaluation of such data requires spatial modeling that realistically captures the various aspects of the decision situation: the uncertain Reservoir variables, the alternatives and the geophysical data under consideration. The computational tasks of value of information analysis can be daunting in such spatial decision situations; in this paper, a regression-based approximation approach is presented. The approach involves Monte Carlo simulation of data followed by linear regression to fit the conditional expectation expression that is needed for value of information analysis. Efficient approximations allow practical value of information analysis for the spatial decision situations that are typically encountered in Petroleum Reservoir Evaluation. Applications are presented for seismic amplitude data and electromagnetic resistivity data, where one example includes multi-phase fluid flow simulations.