Using artificial neural networks to estimate the z-factor for natural hydrocarbon gases

Kamyab, Mohammadreza; Sampaio, Jorge H.B.; Qanbari, Farhad; Eustes, A. W.

doi:10.1016/j.petrol.2010.07.006

Cited by 47 publications

(20 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kamyab et al [31] also implemented the ANN modeling to predict compressibility factor for natural gases. The model introduced by them that covers wide ranges of reduced temperature and pressure (1 < T pr < 3.0 and 0 < P pr < 30) exhibits higher accuracy compared to the Normandin et al's model [30].…”

Section: Artificial Intelligent Methodsmentioning

confidence: 99%

Utilization of support vector machine to calculate gas compressibility factor

Chamkalani

Zendehboudi

Chamkalani

et al. 2013

Fluid Phase Equilibria

View full text Add to dashboard Cite

Section: Artificial Intelligent Methodsmentioning

confidence: 99%

Utilization of support vector machine to calculate gas compressibility factor

Chamkalani

Zendehboudi

Chamkalani

et al. 2013

Fluid Phase Equilibria

View full text Add to dashboard Cite

“…It is postulated that a correlation of the equations for analysis of constant-pressure and constant-rate production (Equations 3 and 9, respectively) using the slopes of the auxiliary plots can be applied to the variable pressure/rate cases. In this study, the results of the analysis of numerous synthetic production data (generated by numerical models) revealed that the following correlation can be used effectively for variable pressure/rate cases during transient linear flow period: (10) where p m and q m are, respectively, the slopes of the auxiliary plots of pressure and rate functions. In the constant-pressure cases, p m is equal to zero and Equation 10 reduces to Equation 3.…”

Section: Mathematical Modelmentioning

confidence: 97%

Analysis of Transient Linear Flow in Tight Oil and Gas Reservoirs with Stress-Sensitive Permeability and Multi-Phase Flow

Qanbari

Clarkson

2013

All Days

View full text Add to dashboard Cite

Horizontal wells and hydraulic fracturing are the key technologies that allow commercial production from tight oil and gas reservoirs. However, rigorous analysis of production data from these reservoirs requires incorporation of the impacts of stress-dependent permeability and multi-phase flow. Changes in the stress state of the system during production may reduce the absolute permeability. Furthermore, gas phase formation and flow in presence of supersaturated oil phase affects fluid dynamics in tight oil reservoirs. This study provides a rigorous methodology for incorporation of the effects of non-static permeability and multi-phase flow in rate transient analysis (RTA) of tight oil and gas reservoirs producing at variable rate/flowing pressures during transient linear flow period.Analytical solutions for the approximate linearized form of the flow equation have been widely used as the basis for RTA tools for conventional reservoirs during transient flow period. However, they lead to considerable error when applied to tight oil and gas reservoirs. In particular, during the transient linear flow period, the slope of the square-root-of-time plot obtained from numerical solution differs from the slope calculated by analytical methods. Efforts have been made by some researchers to obtain a correction factor from the numerical solution of the flow equation to correct the slope of the square-root-of-time plot for single phase flow of gas during transient linear flow period. In this study, an iterative method is used for evaluation of the slope correction factor in the presence of multi-phase flow and non-static permeability for constant-pressure production during transient linear flow period. Further, the correction factor is used for analysis of production data from tight oil and gas reservoirs producing at variable rate/flowing pressures.The correction factor is used in the analysis of different sets of synthetic production data for tight oil and gas reservoirs. The results show that the correction factor can reduce/eliminate the considerable errors associated with the conventional analytical methods in initial permeability estimation. For multi-phase flow cases, the producing gas-oil ratio (GOR) is used to estimate the oil saturation-pressure relationship in the reservoir, which is required for calculation pseudo-pressure and the correction factor.The method developed in this study alleviates the need for using numerical simulation models to generate empirical correlations for the correction factor for the square-root-of-time plot. The easy-to-implement iterative procedure of this method only requires the pressure dependencies of the constituent elements of the hydraulic diffusivity. Therefore, this method is applicable for analysis of production profiles for variety of reservoirs with nonlinear flow equations.

show abstract

“…Further, it has been shown that these equations are not suitable for predicting hydrocarbon gas properties (Elsharkawy, 2004). Empirical correlations are another source of determining gas compressibility factor, which is easy and fast to use but is generally associated with erroneous predictions (Kamyab, Sampaio, Qanbari, & Eustes, 2010;Kumar, 2004;Sanjari & Lay, 2012b). A minor estimation error in the compressibility factor of correlations would lead to a false prediction of formation, density and the amount of gas.…”

Section: Introductionmentioning

confidence: 99%

Modeling natural gas compressibility factor using a hybrid group method of data handling

Hemmati-Sarapardeh

Hajirezaie

Soltanian

et al. 2019

Engineering Applications of Computational Fluid Mechanics

View full text Add to dashboard Cite

The natural gas compressibility factor indicates the compression and expansion characteristics of natural gas under different conditions. In this study, a simple second-order polynomial method based on the group method of data handling (GMDH) is presented to determine this critical parameter for different natural gases at different conditions, using corresponding state principles. The accuracy of the proposed method is evaluated through graphical and statistical analyses. The method shows promising results considering the accurate estimation of natural gas compressibility. The evaluation reports 2.88% of average absolute relative error, a regression coefficient of 0.92, and a root means square error of 0.03. Furthermore, the equations of state (EOSs) and correlations are used for comparative analysis of the performance. The precision of the results demonstrates the model's superiority over all other correlations and EOSs. The proposed model can be used in simulators to estimate natural gas compressibility accurately with a simple mathematical equation. This model outperforms all previously published correlations and EOSs in terms of accuracy and simplicity. ARTICLE HISTORY

show abstract

Using artificial neural networks to estimate the z-factor for natural hydrocarbon gases

Cited by 47 publications

References 11 publications

Utilization of support vector machine to calculate gas compressibility factor

Utilization of support vector machine to calculate gas compressibility factor

Analysis of Transient Linear Flow in Tight Oil and Gas Reservoirs with Stress-Sensitive Permeability and Multi-Phase Flow

Modeling natural gas compressibility factor using a hybrid group method of data handling

Contact Info

Product

Resources

About