Initial estimation of hydrate formation temperature of sweet natural gases based on new empirical correlation

Production,processing and transportation of natural gases can be significantly affected by clathrate hydrates.Knowing the gas analysis is crucial to predict the right conditions for hydrate formation.Nevertheless,Katz gas gravity method can be used for initial estimation of hydrate formation temperature （HFT） under the circumstances of indeterminate gas composition.So far several correlations have been proposed for gas gravity method,in which the most accurate and reliable one has belonged to Bahadori and Vuthaluru.The main objective of this study is to present a simple and yet accurate correlation for fast prediction of sweet natural gases HFT based on the fit to Katz gravity chart.By reviewing the error analysis results,one can discover that the new proposed correlation has the best estimation capability among the widely accepted existing correlations within the investigated range.

LARGE DEVIATION FOR THE EMPIRICAL CORRELATION COEFFICIENT OF TWO GAUSSIAN RANDOM VARIABLES

In this article, the author obtains the large deviation principles for the empirical correlation coefficient of two Gaussian random variables X and Y. Especially, when considering two independent Gaussian random variables X, Y with the means EX, EY （both known）, wherein the author gives two kinds of different proofs and gets the same results.

Numerical discussions on complete empirical correlation in Langtry's transition model

Recently, introducing a transition predicting model into the Reynolds averaged Navier-Stokes （RANS） environment has been paid more and more attention. Langtry proposed a correlation-based transition model in 2006, which was built strictly on local variables. However, two core correlations in the model had not been published until 2009. In this paper, after considerable analyses and discussions of the mechanism of this transition model and a series of numerical validations in the skin friction coefficient of flat plate boundary layers, a new correlation based on free-stream turbulence intensity is developed, and the empirical correlation of the transition onset momentum thickness Reynold number aiming at the hypersonic transition is improved. Low-speed/transonic airfoils and a hypersonic double wedge fiat are tested to prove the reliability and practicability of this correlation.

Noise level estimation method with application to EMD-based signal denoising

This paper proposes a new signal noise level estimation approach by local regions. The estimated noise variance is applied as the threshold for an improved empirical mode decomposition（EMD） based signal denoising method. The proposed estimation method can effectively extract the candidate regions for the noise level estimation by measuring the correlation coefficient between noisy signal and a Gaussian filtered signal. For the improved EMD based method, the situation of decomposed intrinsic mode function（IMFs） which contains noise and signal simultaneously are taken into account. Experimental results from two simulated signals and an X-ray pulsar signal demonstrate that the proposed method can achieve better performance than the conventional EMD and wavelet transform（WT） based denoising methods.

External mass transfer from/to a single sphere in a nonlinear uniaxial extensional creeping flow

This work systematically simulates the external mass transfer from/to a spherical drop and solid particle suspended in a nonlinear uniaxial extensional creeping flow.The mass transfer problem is governed by three dimensionless parameters:the viscosity ratio(λ),the Peclet number(Pe),and the nonlinear intensity of the flow(E).The existing mass transfer theory,valid for very large Peclet numbers only,is expanded,by numerical simulations,to include a much larger range of Peclet numbers(1≤Pe≤105).The simulation results show that the dimensionless mass transfer rate,expressed as the Sherwood number(5 h),agrees well with the theoretical results at the convection-dominated regime(Pe>103).Only when E>5/4,the simulated Sh for a solid sphere in the nonlinear uniaxial extensional flow is larger than theoretical results because the theory neglects the effect of the vortex formed outside the particle on the rate of mass transfer.Empirical correlations are proposed to predict the influence of the dimensionless governing parameters(λ,Pe,E)on the Sherwood number(Sh).The maximum deviations of all empirical correlations are less than 15%when compared to the numerical simulated results.

Estimation of Minimum Miscibility Pressure for Flue Gas Injection Using Soft Experimentations

A new approach is demonstrated in which soft experimentation can be performed for MMP measurements, thus replacing the common practice of slim tube displacement laboratory experiments. Recovery potential from oil reservoirs by miscible flue gas injection was studied by slim tube and field-scale numerical simulation using two flue gases and seven crude oils sampled at different depths in three candidate reservoirs. The soft experimentations were conducted using Eclipse300TM, a three-phase compositional simulator. This study investigates minimum miscibility pressure (MMP), a significant miscible gas injection project screening tool. Successful design of the project is contingent to the accurate determination of the MMP. This study evaluates effects of important factors such as injection pressure, oil component composition, and injection gas composition on the MMP and recovery efficiency for slim tube and field-scale displacements. Two applicable MMP correlations were used for comparison and validation purposes.

Numerical study on the effective thermal conductivity and thermal tortuosity of porous media with different morphologies

Effective thermal conductivity and thermal tortuosity are crucial parameters for evaluating the effectiveness of heat conduction within porous media.The direct pore-scale numerical simulation method is applied to investigate the heat conduction processes inside porous structures with different morphologies.The thermal conduction performances of idealized porous structures are directly compared with real foams across a wide range of porosity.Real foam structures are reconstructed using X-ray computed tomography and image processing techniques,while Kelvin and Weaire-Phelan structures are generated through periodic unit cell reconstruction.The detailed temperature fields inside the porous structures are determined by solving the heat conduction equation at the pore scale.The results present that the equivalent thermal conductivity of Kelvin and Weaire-Phelan structures is similar to and greater than that of the real foam structure with the same strut porosity.The thermal tortuosity of real foam structure is relatively larger and the heat conduction path becomes straighter by adopting the anisotropic design.The thermal tortuosity of the fluid channels for Kelvin,Weaire-Phelan,and real foam structures is close to one.The thermal conductivity of porous structures with heat transfer fluid increases as the thermal conductivity ratio of fluid to solid becomes larger.A small porosity of porous media leads to a larger equivalent thermal conductivity due to the dominant contribution of porous skeleton in the heat conduction process.Correlations derived from parallel and series models,as well as the Maxwell-Eucken models,provide decent predictions of effective thermal conductivity,with an average error of less than 8%in the entire range of thermal conductivity ratio.

Presenting decision tree for best mixing rules and Z-factor correlations and introducing novel correlation for binary mixtures

The significance of gas compressibility factor in petroleum engineering encourages the researchers to employ the most accurate and precise methods for estimation of this factor.Commonly,empirical correlations due to their simplicity have been referred more than other approaches for prediction of Z-factor.There is no clear and reliable report to address an appropriate combination of correlation and mixing rule for each type of gas.In the present study,combination of several empirical correlations and mixing rules is examined and a decision tree is constructed to suggest best combination for each gas system.For this reason,2329 experimental data were used for analysis.According to the results,LelandeMueller mixing rule/Sanjari and Lay correlation is the best combination for sour and natural gas.Also,Van NesseAbbot mixing rule/HalleYarborough correlation,StewarteBurkhardteVoo mixing rule/Heidarian correlation and SattereCampbell mixing rule/Papay correlation are the most appropriate combination for gas condensate,binary and ternary mixtures respectively.For binary mixtures,a robust and novel empirical correlation was developed based on Kay mixing rule to estimate Z-factor.The results employed how good the new correlation is in agreement with the experimental data with significant R-squared 0.9843.

An Empirical Constitutive Correlation for Regular Jugged Discontinuity of Rock Surfaces

This paper presents a physical investigation and mathematical analysis on mechanical behavior of the regular jugged discontinuity.In particular,we focus on the creep property of structural plane with various slope angles under different normal stress through shear creep tests of structural plane under shear stresses.According to the test results,the shear creep property of structural plane was described and the creep velocity and long-term strength of the structural plane during shear creep were also investigated.An empirical formula is finally established to evaluate shear strength of discontinuity and a modified Burger model was proposed to represent the shear deformation property during creep.

An adaptive neuro-fuzzy inference system white-box model for real-time multiphase flowing bottom-hole pressure prediction in wellbores

The majority of published empirical correlations and mechanistic models are unable to provide accurate flowing bottom-hole pressure(FBHP)predictions when real-time field well data are used.This is because the empirical correlations and the empirical closure correlations for the mechanistic models were developed with experimental datasets.In addition,most machine learning(ML)FBHP prediction models were constructed with real-time well data points and published without any visible mathematical equation.This makes it difficult for other readers to use these ML models since the datasets used in their development are not open-source.This study presents a white-box adaptive neuro-fuzzy inference system(ANFIS)model for real-time prediction of multiphase FBHP in wellbores.1001 real well data points and 1001 normalized well data points were used in constructing twenty-eight different Takagi eSugeno fuzzy inference systems(FIS)structures.The dataset was divided into two sets;80%for training and 20%for testing.Statistical performance analysis showed that a FIS with a 0.3 range of influence and trained with a normalized dataset achieved the best FBHP prediction performance.The optimal ANFIS black-box model was then translated into the ANFIS white-box model with the Gaussian input and the linear output membership functions and the extracted tuned premise and consequence parameter sets.Trend analysis revealed that the novel ANFIS model correctly simulates the anticipated effect of input parameters on FBHP.In addition,graphical and statistical error analyses revealed that the novel ANFIS model performed better than published mechanistic models,empirical correlations,and machine learning models.New training datasets covering wider input parameter ranges should be added to the original training dataset to improve the model's range of applicability and accuracy.

An artificial neural network visible mathematical model for real-time prediction of multiphase flowing bottom-hole pressure in wellbores

Accurate prediction of multiphase flowing bottom-hole pressure(FBHP)in wellbores is an important factor required for optimal tubing design and production optimization.Existing empirical correlations and mechanistic models provide inaccurate FBHP predictions when applied to real-time field datasets because they were developed with laboratory-dependent parameters.Most machine learning(ML)models for FBHP prediction are developed with real-time field data but presented as black-box models.In addition,these ML models cannot be reproduced by other users because the dataset used for training the machine learning algorithm is not open source.These make using the ML models on new datasets difficult.This study presents an artificial neural network(ANN)visible mathematical model for real-time multiphase FBHP prediction in wellbores.A total of 1001 normalized real-time field data points were first used in developing an ANN black-box model.The data points were randomly divided into three different sets;70%for training,15%for validation,and the remaining 15%for testing.Statistical analysis showed that using the Levenberg-Marquardt training optimization algorithm(trainlm),hyperbolic tangent activation function(tansig),and three hidden layers with 20,15 and 15 neurons in the first,second and third hidden layers respectively achieved the best performance.The trained ANN model was then translated into an ANN visible mathematical model by extracting the tuned weights and biases.Trend analysis shows that the new model produced the expected effects of physical attributes on FBHP.Furthermore,statistical and graphical error analysis results show that the new model outperformed existing empirical correlations,mechanistic models,and an ANN white-box model.Training of the ANN on a larger dataset containing new data points covering a wider range of each input parameter can broaden the applicability domain of the proposed ANN visible mathematical model.

Implementation of multilayer perceptron(MLP)and radial basis function(RBF)neural networks to predict solution gas-oil ratio of crude oil systems

Exact determination of pressure-volume-temperature(PVT)properties of the reservoir oils is necessary for reservoir calculations,reservoir performance prediction,and the design of optimal production conditions.The objective of this study is to develop intelligent and reliable models based on multilayer perceptron(MLP)and radial basis function(RBF)neural networks for estimating the solution gas–oil ratio as a function of bubble point pressure,reservoir temperature,oil gravity(API),and gas specific gravity.These models were developed and tested using a total of 710 experimental data sets representing the samples of crude oil from various geographical locations around the world.Performance of the developed MLP and RBF models were evaluated and investigated against a number of well-known empirical correlations using statistical and graphical error analyses.The results indicated that the proposed models outperform the considered empirical correlations,providing a strong agreement between predicted and experimental values,However,the developed RBF exhibited higher accuracy and efficiency compared to the proposed MLP model.

Development of an artificial neural network model for prediction of bubble point pressure of crude oils

Bubble point pressure is one of the most important pressureevolumeetemperature properties of crude oil,and it plays an important role in reservoir and production engineering calculations.It can be precisely determined experimentally.Although,experimental methods present valid and reliable results,they are expensive,time-consuming,and require much care when taking test samples.Some equations of state and empirical correlations can be used as alternative methods to estimate reservoir fluid properties(e.g.,bubble point pressure);however,these methods have a number of limitations.In the present study,a novel numerical model based on artificial neural network(ANN)is proposed for the prediction of bubble point pressure as a function of solution gaseoil ratio,reservoir temperature,oil gravity(API),and gas specific gravity in petroleum systems.The model was developed and evaluated using 760 experimental data sets gathered from oil fields around the world.An optimization process was performed on networks with different structures.Based on the obtained results,a network with one hidden layer and six neurons was observed to be associated with the highest efficiency for predicting bubble point pressure.The obtained ANN model was found to be reliable for the prediction of bubble point pressure of crude oils with solution gaseoil ratios in the range of 8.61e3298.66 SCF/STB,temperatures between 74 and 341.6
F,oil gravity values of 6e56.8 API and gas gravity values between 0.521 and 3.444.The performance of the developed model was compared against those of several well-known predictive empirical correlations using statistical and graphical error analyses.The results showed that the proposed ANN model outperforms all of the studied empirical correlations significantly and provides predictions in acceptable agreement with experimental data.

Frictional resistance of supercritical pressure RP-3 flowing in a vertically downward tube at constant heat fluxes

Based on the demands of compact heat exchangers and micro cooling channels applied for aviation thermal protection, the flow resistance characteristics of aviation kerosene RP-3 were experimentally studied in a vertically downward circular miniature tube with an inner diameter of 1.86 mm at supercritical pressures and constant heat fluxes. A long and short tube method was used to accurately calculate the frictional pressure drop, and experimental conditions are supercritical pressures of 4 MPa, mass flow rates of 2–4 g/s(i.e., mass fluxes of 736–1472 kg/(m^(2)·s)), heat fluxes of 100–500 kW/m^(2), and inlet temperatures of 373–673 K. Results show that the sharp variations of thermophysical properties, especially density, have significant influences on frictional resistances.Generally, the frictional pressure drop and the friction factor increase with increasing inlet temperatures, and this trend speeds up in the relatively high-temperature region. However, the friction factor has a sudden decline when the fuel outlet temperature exceeds the pseudo-critical temperature.The frictional pressure drop and the friction factor basically remain unchanged with increasing heat flux when the inlet temperature is relatively low, but increase quickly when the inlet temperature is relatively high. Besides, a larger mass flux yields a higher pressure drop but does not necessarily yield a higher friction factor. Finally, an empirical friction factor correlation is proposed and shows better predictive performance than those of previous models.

Effect of turbulent fluctuation on the ignition of millimeter particle:Experimental studies and numerical modelling with a new correlation of nusselt number Author links open overlay panel

Understanding the influencing mechanism of turbulent fluctuation on the ignition characteristics of millimeter coal particles is essential.In this work,to study the effect of turbulent fluctuation on ignition time,millimeter coal particles are subjected to a specific flow field,generated in a furnace with symmetric fans.A one-dimensional model with the new proposed correlation and the Ranz-Marshall(R-M)correlation for Nu(Nusselt number)is established to simulate the coal ignition process.In addition,the effects of fan speed,temperature,particle diameter,particle distance and coal type on the ignition time are investigated.It is found that an increase in fan speed from 0 to 3000 rpm leads to a particle Reynolds number Re_(p)increase from 0 to 22.5,and a turbulent particle Reynolds number Re_(t)*increase from 0 to 71.5.With a consideration of the fluctuation effect,the new correlation of Nu gives a better prediction of ignition time compared to the R-M correlation.Moreover,the ignition time is revealed to decrease with an increasing fan speed and an elevating temperature.While the ignition time shows merely an initial boost with enlarging particle distance,it exhibits a linearity with the term of particle diameter dp1.3-1.7 and Reynolds numbers(Nu*/Nu)-0.6(Nu*is turbulent Nusselt number).Based on this relationship,the difference of predicted ignition time is calculated at different Re_(p)and Re_(t)*.It is shown that at low Re_(p)or high Re_(t)*values,the new correlation should substitute for the R-M correlation.