scholarly journals An Improved CO2-Crude Oil Minimum Miscibility Pressure Correlation

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Hao Zhang ◽  
Dali Hou ◽  
Kai Li
Keyword(s):  
Experimental Data ◽  
Crude Oil ◽  
Relative Error ◽  
Oil And Gas ◽  
Empirical Correlation ◽  
Volatile Components ◽  
Absolute Relative Error ◽  
Minimum Miscibility Pressure ◽  
New Correlation ◽  
Average Absolute Relative Error

Minimum miscibility pressure (MMP), which plays an important role in miscible flooding, is a key parameter in determining whether crude oil and gas are completely miscible. On the basis of 210 groups of CO2-crude oil system minimum miscibility pressure data, an improved CO2-crude oil system minimum miscibility pressure correlation was built by modified conjugate gradient method and global optimizing method. The new correlation is a uniform empirical correlation to calculate the MMP for both thin oil and heavy oil and is expressed as a function of reservoir temperature, C7+molecular weight of crude oil, and mole fractions of volatile components (CH4and N2) and intermediate components (CO2, H2S, and C2~C6) of crude oil. Compared to the eleven most popular and relatively high-accuracy CO2-oil system MMP correlations in the previous literature by other nine groups of CO2-oil MMP experimental data, which have not been used to develop the new correlation, it is found that the new empirical correlation provides the best reproduction of the nine groups of CO2-oil MMP experimental data with a percentage average absolute relative error (%AARE) of 8% and a percentage maximum absolute relative error (%MARE) of 21%, respectively.

scholarly journals A new empirical correlation of minimum miscibility pressure for produced gas reinjection

2020 ◽  
Vol 38 (4) ◽  
pp. 867-883
Author(s):  
Congge He ◽  
Zifei Fan ◽  
Chenshuo Zhang ◽  
Anzhu Xu ◽  
Lun Zhao ◽  
...  
Keyword(s):  
Crude Oil ◽  
Relative Error ◽  
Empirical Correlation ◽  
Critical Parameters ◽  
Average Relative Error ◽  
Cell Method ◽  
Minimum Miscibility Pressure ◽  
Miscible Gas Injection ◽  
Acidic Components ◽  
Key Parameter

Minimum miscible pressure is a key parameter to screen and design miscible gas injection projects. The aim of this paper is to establish a correlation with only a few input parameters to easily and accurately predict minimum miscible pressure for the reinjection of produced gas with high acidic components. First, the critical parameters of equation of state for each component of the crude oil were obtained through fitting pressure-volume-temperature (PVT) experimental results. Based on the analytically calculated minimum miscible pressures from mixing-cell method, an empirical correlation for predicting minimum miscible pressure in the displacement of crude oil by produced gas was regressed. Finally, the correlation’s accuracy was tested by comparing the minimum miscible pressures predicted from the new proposed correlation to other previous correlations and 20 experimental slim-tube minimum miscible pressures of 12 oil samples. The results indicate that the analytically calculated minimum miscible pressures from the mixing-cell method have a relative error of 0.5% compared to the slim-tube experiment results, which supports its reliability. Furthermore, the new proposed correlation is observed to be superior in terms of the average relative error being only 6.4% for all the 75 analytically calculated minimum miscible pressures and 20 experimental slim-tube minimum miscible pressures, which is lower than the average relative error obtained from other previous correlations.

scholarly journals Constitutive Equations for Describing the Warm and Hot Deformation Behavior of 20Cr2Ni4A Alloy Steel

Metals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1169
Author(s):  
Haoran Wang ◽  
Wei Wang ◽  
Ruixue Zhai ◽  
Rui Ma ◽  
Jun Zhao ◽  
...  
Keyword(s):  
Flow Stress ◽  
Alloy Steel ◽  
Relative Error ◽  
Constitutive Equations ◽  
Type Equation ◽  
Coefficient Of Determination ◽  
Original Strain ◽  
Compression Tests ◽  
Absolute Relative Error ◽  
Average Absolute Relative Error

Isothermal hot compression tests of 20Cr2Ni4A alloy steel were performed under temperatures of 973–1273 K and strain rates of 0.001–1 s−1. The behavior of the flow stress of 20Cr2Ni4A alloy steel at warm and hot temperatures is complicated because of the influence of the work hardening, the dynamic recovery, and the dynamic recrystallization. Four constitutive equations were used to predict the flow stress of 20Cr2Ni4A alloy steel, including the original strain-compensated Arrhenius-type (osA-type) equation, the new modified strain-compensated Arrhenius-type (msA-type) equation, the original Hensel–Spittel (oHS) equation and the modified Hensel–Spittel (mHS) equation. The msA-type and mHS are developed by revising the deformation temperatures, which can improve prediction accuracy. In addition, we propose a new method of solving the parameters by combining a linear search with multiple linear regression. The new solving method is used to establish the two modified constitutive equations instead of the traditional regression analysis. A comparison of the predicted values based on the four constitutive equations was performed via relative error, average absolute relative error (AARE) and the coefficient of determination (R2). These results show the msA-type and mHS equations are more accurate and efficient in terms of predicting the flow stress of the 20Cr2Ni4A steel at elevated temperature.

scholarly journals Determination and evaluation of minimum miscibility pressure using various methods: experimental, visual observation, and simulation

2019 ◽  
Vol 74 ◽  
pp. 55 ◽  
Author(s):  
Muslim Abdurrahman ◽  
Wisup Bae ◽  
Asep Kurnia Permadi
Keyword(s):  
Interfacial Tension ◽  
Crude Oil ◽  
Oil And Gas ◽  
Visual Observation ◽  
Tension Test ◽  
Tube Test ◽  
Minimum Miscibility Pressure ◽  
Strong Point ◽  
Two Samples ◽  
Valuable Method

This research proposes a simultaneous technique using various methods to yield the most reliable Minimum Miscibility Pressure (MMP) value. Several methods have been utilized in this study including slim tube test, swelling test, vanishing interfacial tension test, visual observation during swelling test and vanishing interfacial tension test, and simulation. The proposed method may reduce the uncertainty and avoid doubtful MMP. The method can also demonstrate discrepancies among the results. There were two samples used in this study namely Crude Oil AB-5 and Crude Oil AB-4. It showed that for Crude Oil AB-5 the discrepancies among the results from that of the slim tube test were between 3.9% and 10.4% and 0% and 5.9% for the temperature of 60 °C and 66 °C, respectively. The highest discrepancy was shown by the results from the visual observation during vanishing interfacial tension test and the lowest discrepancy was shown by the results from the swelling test. The vanishing interfacial tension test was found to be the fastest method for predicting the MMP. The method also consumed a smaller amount of oil and gas samples for the experiment. The simultaneous method proposed in this study is considered as more proper and exhibits a valuable method for predicting the MMP. This technique has never been found to be performed by previous researchers and accordingly it becomes the strong point of this study to contribute to the global research in the area of MMP determination.

scholarly journals An Investigation of the Robustness of Physical and Numerical Vanishing Interfacial Tension Experimentation in Determining CO2 + Crude Oil Minimum Miscibility Pressure

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Dayanand Saini
Keyword(s):  
Interfacial Tension ◽  
Crude Oil ◽  
Oil And Gas ◽  
Capillary Rise ◽  
Equilibrium Phase ◽  
Petroleum Industry ◽  
Optical Cell ◽  
Gas Phases ◽  
Minimum Miscibility Pressure ◽  
Measured Input

Different experimental and theoretical methods are used for predicting the minimum miscibility pressure (MMP) of complex CO2 + reservoir crude oil systems that are of particular interest to petroleum industry. In this paper, published physical and numerical vanishing interfacial tension (VIT) experimentations are critically examined for identifying best practices to reliably predict the CO2 + crude oil MMP. Some of the reported physical VIT experimentation studies appear to follow a portion of full scale VIT experimentation (i.e., a combination of the pendent drop method and the capillary rise technique). The physical VIT experimentation method in which the IFT measurements are made at varying pressures but with the same initial load of live oil and gas phases in the optical cell seems to be the most robust mechanistic procedure for experimentally studying the pressure dependence of IFT behaviors of complex CO2 + crude oil systems and thus determining the MMP using the VIT technique. The results presented here suggest that a basic parachor expression based on numerical VIT experimentation can reasonably follow the physical VIT experimentation in low IFT region, provided measured input data such as equilibrium phase densities and compositions are used in calculations.

Experimental Study and Modeling of Slug Dissipation in a Horizontal Enlarged Impacting Tee-Junction

SPE Journal ◽  
2020 ◽  
Vol 25 (05) ◽  
pp. 2508-2520
Author(s):  
Mobina Mohammadikharkeshi ◽  
Ramin Dabirian ◽  
Ram S. Mohan ◽  
Ovadia Shoham
Keyword(s):  
Experimental Data ◽  
Liquid Velocity ◽  
Gas Velocity ◽  
Absolute Relative Error ◽  
Average Absolute Relative Error ◽  
Oil Flow ◽  
Flow Mechanisms ◽  
Dissipation Model ◽  
Tee Junction ◽  
Superficial Gas Velocity

Summary A novel experimental and theoretical study on slug dissipation in a horizontal enlarged impacting tee-junction (EIT) is carried out. Both flowing-slug injection and stationary-slug injection into the EIT are studied, and the effects of inlet slug length and liquid-phase fluid properties on the slug dissipation in the EIT are investigated. A total of 161 experimental data are acquired for air-water and air-oil flow. The flowing-slug data (with a horizontal inlet) show that the slug dissipation length increases with increasing mixture velocity, demonstrating a nonlinear trend with a steeper slope at lower mixture velocities. The effect of superficial gas velocity on the slug dissipation length is more pronounced compared with the effect of superficial liquid velocity. For stationary-slug injection into the EIT (with a 5° upward inclined inlet), the injected slug lengths vary between 40d to 100d (d is the inlet diameter). The data reveal that, when increasing the superficial gas velocity or the inlet slug size, the dissipation length in the EIT branches increases. For this case, the ratio of the slug dissipation length to the inlet slug length is higher for air-water compared with air-oil. A slug dissipation model is developed using the slug-tracking approach, which is based on the flow mechanisms of liquid shedding at the back of the slug and liquid drainage and penetration of bubble turning at the front of the slug. These phenomena result in different translational velocities at the back and the front of the slug, which result in the dissipation of the slug body. Evaluation of model predictions against the acquired experimental data shows an average absolute relative error of less than 11%.

scholarly journals Hybrid Machine Learning Optimization Approach to Predict Hot Deformation Behavior of Medium Carbon Steel Material

Metals ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 1315 ◽  
Author(s):  
Mohanraj Murugesan ◽  
Muhammad Sajjad ◽  
Dong Won Jung
Keyword(s):  
Experimental Data ◽  
Flow Stress ◽  
Carbon Steel ◽  
Medium Carbon Steel ◽  
Medium Carbon ◽  
Absolute Relative Error ◽  
Average Absolute Relative Error ◽  
Steel Material ◽  
Stress Prediction ◽  
Bp Model

The isothermal tensile test of medium carbon steel material was conducted at deformation temperatures varying from 650 to 950 ∘ C with an interval of 100 ∘ C and strain rates ranging from 0.05 to 1.0 s − 1 . In addition, the scanning electron microscopy (SEM) procedures were exploited to study about the surface morphology of medium carbon steel material. Using the experimental data, the artificial neural network (ANN) model with a back-propagation (BP) algorithm was proposed to predict the hot deformation behavior of medium carbon steel material. For model training and testing purpose, the variables such as deformation temperature, strain rate, and strain data were considered as inputs and the flow stress data were used as targets. Before running the neural network, the test data were normalized to effectively run the problem and after solving the problem, the obtained results were again converted in order to achieve the actual data. According to the predicted results, the coefficient of determination ( R 2 ) and the average absolute relative error between the predicted flow stress and the experimental data were determined as 0.999 and 1.335%, respectively. For improving the model predictability, the constrained nonlinear function based optimization procedures was adopted to obtain the best candidate selections of weights and biases. By evaluating each test conditions, it was found that the average absolute relative error based on the optimized ANN-BP model varied from 0.728% to 1.775%. Overall, the trained ANN-BP models proved to be much more efficient and accurate by means of flow stress prediction against the experimental data for all the tested conditions. These optimized results displayed that an ANN-BP model is more accurate for flow stress prediction than that of the conventional flow stress models.

scholarly journals Comparative Study of Accurate Descriptions of Hot Flow Behaviors of BT22 Alloy by Intelligence Algorithm and Physical Modeling

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Shaoling Ding ◽  
Chao Fang ◽  
Shulin Zhang
Keyword(s):  
Physical Model ◽  
Relative Error ◽  
Nonlinear Flow ◽  
Support Vector ◽  
Experimental Stress ◽  
Stress Strain ◽  
Absolute Relative Error ◽  
Model Average ◽  
Average Absolute Relative Error ◽  
Strain Data

The nonlinear flow behaviors of BT22 alloy were investigated by thermal simulation experiments at different temperature and strain rates. Taking the experimental stress-strain data as samples, the support vector regression (SVR) model and back propagation artificial neural network (BPANN) model were established by cross-validation (CV) method to describe the nonlinear flow behaviors of BT22 alloy. Genetic algorithm (GA) was used to optimize the parameters of the SVR model and establish the GA-SVR model. At the same time, the physical model optimized by GA algorithm is compared with the machine learning model. Average absolute relative error (AARE), absolute relative error (ARE), and correlation coefficient (R) were used to evaluate the predictive ability of the four models. The results show that the order of model accuracy and generalization ability is GA-SVR > BPANN > SVR > physical model. The AARE value of the GA-SVR model is 1.5752%, and the R value is as high as 0.9984, which can accurately predict the flow behaviors of BT22 alloy. According to the GA-SVR model, the flow behaviors under other conditions could be predicted to expand the experimental stress-strain data and avoid a large number of artificial tests.

Application of Optimized Least Square Support Vector Machine and Genetic Programming for Accurate Estimation of Drilling Rate of Penetration

2018 ◽  
Vol 7 (4) ◽  
pp. 92-108
Author(s):  
Meysam Naderi ◽  
Ehsan Khamehchi
Keyword(s):  
Relative Error ◽  
Flow Rate ◽  
Rotation Speed ◽  
Gel Strength ◽  
Least Square ◽  
Accurate Estimation ◽  
Support Vector ◽  
Absolute Relative Error ◽  
Average Absolute Relative Error ◽  
Weight On Bit

This article describes how the accurate estimation of the rate of penetration (ROP) is essential to minimize drilling costs. There are various factors influencing ROP such as formation rock, drilling fluid properties, wellbore geometry, type of bit, hydraulics, weight on bit, flow rate and bit rotation speed. This paper presents two novel methods based on least square support vector machine (LSSVM) and genetic programming (GP). Models are a function of depth, weight on bit, rotation speed, stand pipe pressure, flow rate, mud weight, bit rotational hours, plastic viscosity, yield point, 10 second gel strength, 10 minute gel strength, and fluid loss. Results show that LSSVM estimates 92% of field data with average absolute relative error of less than 6%. In addition, sensitivity analysis showed that factors of depth, weight on bit, stand pipe pressure, flow rate and bit rotation speed account for 93% of total variation of ROP. Finally, results indicate that LSSVM is superior over GP in terms of average relative error, average absolute relative error, root mean square error, and the coefficient of determination.

Fast Prediction Method of Radon Concentration in Environment Air

2014 ◽  
Vol 539 ◽  
pp. 819-822
Author(s):  
He Xi Wu ◽  
Qiang Lin Wei ◽  
Bo Yang ◽  
Qing Cheng Liu
Keyword(s):  
Relative Error ◽  
Radon Concentration ◽  
Gamma Ray ◽  
Transport Model ◽  
Prediction Method ◽  
Field Tests ◽  
Mean Value ◽  
Absolute Relative Error ◽  
Average Absolute Relative Error ◽  
Fast Prediction

Base on the theory that 222Rn can transport in any medium, fast prediction model of radon concentration in environment air can be acquired. And it has been proved accurate by an experiment in laboratory. Many field tests also showed that the average absolute relative error is 8.78% between mean value of measurement and that of fast prediction. It can be predict fleetly the radon concentration by 226Ra which is acquired from the airborne gamma-ray spectra. The relative error between measurement and model is-11.7%. Therefore, the transport model can be effectively applied to predict radon concentration in environment air.

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