The Experts below are selected from a list of 81 Experts worldwide ranked by ideXlab platform
H.y. Al-yousef - One of the best experts on this subject based on the ideXlab platform.
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Vertical Multiphase Flow Correlations for High Production Rates and Large Tubulars
SPE Production & Facilities, 1996Co-Authors: M.a. Aggour, H.y. Al-yousefAbstract:Summary Numerous correlations exist for predicting pressure drop in vertical multiphase flow. These correlations, however, were all developed and tested under limited operating conditions that do not match the high production rates and large tubulars normally found in the Middle East Fields. This paper presents a comprehensive evaluation of existing correlations and modifications of some correlations to determine and recommend the best correlation(s) for various field conditions. Over 400 field data sets covering tubing sizes from 2 3/8 in. to 7 in., oil rates up to 23,200 BPD, water cuts up to 95% and GOR up to 927 SCF/STB were used in this study. Considering all data combined, the Beggs and Brill correlation provided the best pressure predictions. However, the Hagedorn and Brown correlation was better for water cuts above 80%, while Hasan and Kabir model was better for total liquid rates above 20,000 BPD. The Aziz et al. correlation was significantly improved when the Orkiszewski flow-pattern transition criteria were used. Introduction A reliable and accurate means for prediction of pressure drop in vertical multiphase flow is essential for proper design of well completions and artificial-lift systems and for optimization and accurate forecast of production performance. Because of the complexity of multiphase flow, mostly empirical or semi-empirical correlations have been developed for prediction of pressure drop. Tens of such correlations have been developed since 1914. However, the correlations which have seen the most application and testing are those of Aziz et al., Hagedorn & Brown, Duns & Ros, Beggs & Brill and Hasan & Kabir. Each of these correlations was developed and/or tested for a specific range of operating conditions. Several studies have been performed to evaluate various multiphase flow correlations. Orkiszewski was the first to perform such a study using 148 well cases. This resulted in the development of his own, better, correlation. A later study by Espanol on 44 wells confirmed the conclusions of Orkiszewski; Camacho used data from 111 high gas-liquid ratio (GLR) wells to test five correlations. He concluded that no method was sufficiently accurate for all ranges of GLR. He reported that the best results were obtained from the Fancher & Brown correlation followed by the correlation of Poettmann and Carpenter.
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Vertical Multiphase Flow Correlations for High Production Rates and Large Tubulars
Spe Production & Facilities, 1996Co-Authors: Aggour, H.y. Al-yousefAbstract:Numerous correlations exist for predicting pressure drop in vertical multiphase flow. These correlations, however, were all developed and tested under limited operating conditions that do not match the high production rates and large tubulars normally found in the Middle East fields. This paper presents a comprehensive evaluation of existing correlations and modifications of some correlations to determine and recommend the best correlation or correlations for various field conditions. More than 400 field data sets covering tubing sizes from 2 3/8 to 7 inches, oil rates up to 23,200 B/D, water cuts up to 95%, and gas/oil ratio (GOR) up to 927 scf/STB were used in this study. Considering all data combined, the Beggs and Brill correlation provided the best pressure predictions. However, the Hagedorn and Brown correlation was better for water cuts above 80%, while the Hasan and Kabir model was better for total liquid rates above 20,000 B/D. The Aziz correlation was significantly improved when the Orkiszewski flow-pattern transition criteria were used.
E Santoyo - One of the best experts on this subject based on the ideXlab platform.
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numerical modeling of flow processes inside geothermal wells an approach for predicting production characteristics with uncertainties
Energy Conversion and Management, 2006Co-Authors: O Garciavalladares, P Sanchezupto, E SantoyoAbstract:Abstract One dimensional steady and transient numerical modeling for describing the heat and fluid dynamic transport inside geothermal wells has been conducted. The mass, momentum and energy governing equations were solved using a segregated numerical scheme. Discretized governing equations for the fluid flow were coupled and solved with a fully implicit step by step method. The mathematical formulation used suitable empirical correlations for estimating the convective heat transfer coefficients as well as the shear stress and the void fraction parameters. Heat conduction across the wellbore materials was solved by an implicit central difference numerical scheme using the tri-diagonal matrix algorithm (TDMA). The flow characteristics of producer geothermal wells (pressure, temperature, enthalpy, heat fluxes, etc.) at each depth node were computed. Analytical data reported in the literature were used to validate the numerical capability of the wellbore simulator developed for this study (GEOWELLS). This simulator, together with another computer code (Orkiszewski), was applied for modeling the heat and fluid flow processes inside some wells drilled in Mexican geothermal fields. The simulated pressure and temperature profiles were statistically compared against stable measured field data (through the computation of the residual sum of squares and Chi-square). A good agreement between the simulated and measured profiles of pressure and temperature was consistently obtained, having the best matching results for the GEOWELLS predictions. An analysis of the sensitivity and uncertainty was finally conducted to estimate the confidence to be accorded the simulation results predicted by GEOWELLS. Matching the sensitivity to variations in some input parameters (e.g., pressure, temperature, enthalpy and void fraction) was examined. The void fraction was identified as one of the most important parameters that affect the GEOWELLS simulations for matching measured field data correctly.
M.a. Aggour - One of the best experts on this subject based on the ideXlab platform.
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An Artificial Neural Network Model for Predicting Bottomhole Flowing Pressure in Vertical Multiphase Flow
All Days, 2005Co-Authors: El-sayed A. Osman, Mohammed Abdalla Ayoub, M.a. AggourAbstract:Abstract Accurate prediction of pressure drop in vertical multiphase flow is needed for effective design of tubing and optimum production strategies.Several correlations and mechanistic models have been developed since 1950.In addition to the limitations on the applicability of all existing correlations, they all fails to provide the desired accuracy of pressure drop predictions.The recently developed mechanistic models provided little improvements in pressure drop prediction over the empirical correlations.However, there is still a need to further improve the accuracy of prediction for a more effective and economical design of wells and better optimization of production operations. This paper presents an Artificial Neural Network (ANN) model for prediction of the bottom-hole flowing pressure and consequently the pressure drop in vertical multiphase flow.The model was developed and tested using field data covering a wide range of variables.A total of 206 field data sets collected from Middle East fields; were used to develop the ANN model. These data sets were divided into training, cross validation and testing sets in the ratio of 3:1:1. The testing subset of data, which were not seen by the ANN model during the training phase, was used to test the prediction accuracy of the model and compare its performance against existing correlations and mechanistic models.The results showed that the present model significantly outperforms all existing methods and provides predictions with higher accuracy.This was verified in terms of highest correlation coefficient, lowest average absolute percent error, lowest standard deviation, lowest maximum error, and lowest root mean square error.A trend analysis was also conducted and showed that the present model provides the expected effects of the various physical parameters on pressure drop. Introduction A reliable and accurate way of predicting pressure drop in vertical multiphase flow is essential for the proper design of well completions and artificial-lift systems and for optimization and accurate forecast of production performance. Because of the complexity of multiphase flow, mostly empirical or semi-empirical correlations have been developed for prediction of pressure drop. Numerous correlations have been developed since the early 1940s. Most of these correlations were developed under laboratory conditions and are, consequently, inaccurate when scaled-up to oil field conditions[1].The most commonly used correlations are those of (Hagedorn and Brown[2]; Duns and Ros[3]; Orkiszewski[4]; Beggs and Brill[5]; Aziz and Govier[6]; Mukherjee and Brill correlation[7]). Numerous studies were done to evaluate and study the applicability of those correlations under different ranges of data[8–15].Most researchers agreed upon the fact that no single correlation was found to be applicable over all ranges of variables with suitable accuracy[1].It was found that correlations are basically statistically derived, global expressions with limited physical considerations, and thus do not render them to a true physical optimization. Mechanistic models are semi-empirical models used to predict multiphase flow characteristics such as liquid hold up, mixture density, and flow patterns. Based on sound theoretical approach, most of these mechanistic models were generated to outperform the existing empirical correlations.The most widely used mechanistic models are those of Hasan and Kabir[16]; Ansari et al.[17].; Chokshi et al.[18]; Gomez et al.[19]. Other studies were conducted to evaluate the validity of such mechanistic models[20–22].Generally, each of these mechanistic models has an outstanding performance in specific flow pattern prediction and that is made the adoption for certain model of specific flow pattern by investigators to compare and yield different, advanced and capable mechanistic models.
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Vertical Multiphase Flow Correlations for High Production Rates and Large Tubulars
SPE Production & Facilities, 1996Co-Authors: M.a. Aggour, H.y. Al-yousefAbstract:Summary Numerous correlations exist for predicting pressure drop in vertical multiphase flow. These correlations, however, were all developed and tested under limited operating conditions that do not match the high production rates and large tubulars normally found in the Middle East Fields. This paper presents a comprehensive evaluation of existing correlations and modifications of some correlations to determine and recommend the best correlation(s) for various field conditions. Over 400 field data sets covering tubing sizes from 2 3/8 in. to 7 in., oil rates up to 23,200 BPD, water cuts up to 95% and GOR up to 927 SCF/STB were used in this study. Considering all data combined, the Beggs and Brill correlation provided the best pressure predictions. However, the Hagedorn and Brown correlation was better for water cuts above 80%, while Hasan and Kabir model was better for total liquid rates above 20,000 BPD. The Aziz et al. correlation was significantly improved when the Orkiszewski flow-pattern transition criteria were used. Introduction A reliable and accurate means for prediction of pressure drop in vertical multiphase flow is essential for proper design of well completions and artificial-lift systems and for optimization and accurate forecast of production performance. Because of the complexity of multiphase flow, mostly empirical or semi-empirical correlations have been developed for prediction of pressure drop. Tens of such correlations have been developed since 1914. However, the correlations which have seen the most application and testing are those of Aziz et al., Hagedorn & Brown, Duns & Ros, Beggs & Brill and Hasan & Kabir. Each of these correlations was developed and/or tested for a specific range of operating conditions. Several studies have been performed to evaluate various multiphase flow correlations. Orkiszewski was the first to perform such a study using 148 well cases. This resulted in the development of his own, better, correlation. A later study by Espanol on 44 wells confirmed the conclusions of Orkiszewski; Camacho used data from 111 high gas-liquid ratio (GLR) wells to test five correlations. He concluded that no method was sufficiently accurate for all ranges of GLR. He reported that the best results were obtained from the Fancher & Brown correlation followed by the correlation of Poettmann and Carpenter.
Michael W. Lieberman - One of the best experts on this subject based on the ideXlab platform.
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γ-Glutamyl Leukotrienase, a γ-Glutamyl Transpeptidase Gene Family Member, Is Expressed Primarily in Spleen
The Journal of biological chemistry, 1998Co-Authors: Bing Z. Carter, Zheng Zheng Shi, Roberto Barrios, Michael W. LiebermanAbstract:Abstract We have recently identified a mouse enzyme termed γ-glutamyl leukotrienase (GGL) that converts leukotriene C4 (LTC4) to leukotriene D4(LTD4). It also cleaves some other glutathione (GSH) conjugates, but not GSH itself (Carter, B. Z., Wiseman, A. L., Orkiszewski, R., Ballard, K. D., Ou, C.-N., and Lieberman, M. W. (1997) J. Biol. Chem. 272, 12305–12310). We have now cloned a full-length mouse cDNA coding for GGL activity and the corresponding gene. GGL and γ-glutamyl transpeptidase constitute a small gene family. The two cDNAs share a 57% nucleotide identity and 41% predicted amino acid sequence identity. Their corresponding genes have a similar intron-exon organization and are located 3 kilobases apart. A search of Genbank and reverse transcription-polymerase chain reaction analysis failed to identify additional family members. Mapping of the GGL transcription start site revealed that the GGL promoter is TATA-less but contains an initiator, a control element for transcription initiation. Northern blots for GGL expression were negative. As judged by ribonuclease protection,in situ hybridization, and measurement of enzyme activity, spleen had the highest level of GGL expression. GGL is also expressed in thymic lymphocytes, bronchiolar epithelial cells, pulmonary interstitial cells, renal proximal convoluted tubular cells, and crypt cells of the small intestine as well as in cerebral, cerebellar, and brain stem neurons but not in glial cells. GGL is widely distributed in mice, suggesting an important role for this enzyme.
O Garciavalladares - One of the best experts on this subject based on the ideXlab platform.
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numerical modeling of flow processes inside geothermal wells an approach for predicting production characteristics with uncertainties
Energy Conversion and Management, 2006Co-Authors: O Garciavalladares, P Sanchezupto, E SantoyoAbstract:Abstract One dimensional steady and transient numerical modeling for describing the heat and fluid dynamic transport inside geothermal wells has been conducted. The mass, momentum and energy governing equations were solved using a segregated numerical scheme. Discretized governing equations for the fluid flow were coupled and solved with a fully implicit step by step method. The mathematical formulation used suitable empirical correlations for estimating the convective heat transfer coefficients as well as the shear stress and the void fraction parameters. Heat conduction across the wellbore materials was solved by an implicit central difference numerical scheme using the tri-diagonal matrix algorithm (TDMA). The flow characteristics of producer geothermal wells (pressure, temperature, enthalpy, heat fluxes, etc.) at each depth node were computed. Analytical data reported in the literature were used to validate the numerical capability of the wellbore simulator developed for this study (GEOWELLS). This simulator, together with another computer code (Orkiszewski), was applied for modeling the heat and fluid flow processes inside some wells drilled in Mexican geothermal fields. The simulated pressure and temperature profiles were statistically compared against stable measured field data (through the computation of the residual sum of squares and Chi-square). A good agreement between the simulated and measured profiles of pressure and temperature was consistently obtained, having the best matching results for the GEOWELLS predictions. An analysis of the sensitivity and uncertainty was finally conducted to estimate the confidence to be accorded the simulation results predicted by GEOWELLS. Matching the sensitivity to variations in some input parameters (e.g., pressure, temperature, enthalpy and void fraction) was examined. The void fraction was identified as one of the most important parameters that affect the GEOWELLS simulations for matching measured field data correctly.