Novel empirical correlations for estimation of bubble point pressure,saturated viscosity and gas solubility of crude oils

Knowledge of petroleum fluid properties is crucial for the study of reservoirs and their development. Estimation of reserves in an oil reservoir or determination of its performance and economics requires a good knowledge of the fluid physical properties. Bubble point pressure, gas solubility and viscosity of oils are the most important parameters in use for petroleum and chemical engineers. In this study a simple-to-use, straight-forward mathematical model was correlated on a set of 94 crude oil data. Three correlations were achieved based on an exponential regression, which were different from conventional empirical correlations, and were evaluated against 12 laboratory data other than those used for the regression. It is concluded that the new exponential equation is of higher precision and accuracy than the conventional correlations and is a more convenient mathematical formulation.

Clinical Observation on Intractable Insomnia Treated by Point Pressure in 42 Cases

The author have in recent years treated 42 cases of intractable insomnia (with a history of over 2 years) by point pressure, yielding quite satisfactory results when compared with those treated with clonazepam. This is reported as follows.……

Study of asphaltene deposition from Tahe crude oil

Borehole blockage caused by asphaltene deposition is a problem in crude oil production in the Tahe Oilfield, Xinjiang, China. This study has investigated the influences of crude oil compositions, temperature and pressure on asphaltene deposition. The asphaltene deposition trend of crude oil was studied by saturates, aromatics, resins and asphaltenes （SARA） method, and the turbidity method was applied for the first time to determine the onset of asphaltene flocculation. The results showed that the asphaltene deposition trend of crude oil by the turbidity method was in accordance with that by the SARA method. The asphaltene solubility in crude oil decreased with decreasing temperature and the amount of asphaltene deposits of T739 crude oil （from well T739, Tahe Oilfield） had a maximum value at 60℃. From the PVT results, the bubble point pressure of TH 10403CX crude oil （from well TH10403CX, Tahe Oilfield） at different temperatures can be obtained and the depth at which the maximum asphaltene flocculation would occur in boreholes can be calculated. The crude oil PVT results showed that at 50,90 and 130 ℃, the bubble point pressure of TH 10403CX crude oil was 25.2, 26,4 and 27.0 MPa, respectively. The depth of injecting asphaltene deposition inhibitors for TH10403CX was determined to be 2,700 m.

脐带间充质干细胞治疗SUI鼠模及干细胞标记磁共振示踪检测体内的实验效果

Objective To assess the effect of intra-sphincteric injections of human umbilical cord mesenchymal stem cells(HUMSCs)on leak point pressure(LPP)changes in an animal model of stress urinary incontinence(SUI).Meanwhile,to investigate in vivo MRI tracking HUMSCs in SUI rats using a clinically available paramagnetic contrast agent(Gd-DTPA)and commercially available effentence transfection reagents..Materials and Methods HUMSCs were dual labeled with Gd-DTPA and PKH26,the labeling efficiency and longevity of Gd-DTPA maintenance were measured and cell viability and proliferation were assessed.39 female Sprague–Dawley SUI rats.12 normal rats and 12 SUI rats received periurethral injection of PBS and 12 SUI rats were given periurethral injection of dual labeled HUMSCs.3 SUI rat sreceived periurethral injection of u nlabeled HUMSCs.Six weeks after injection,LPP was undertaken in animals.All rats were sacrificed and frozen urethra sections were submitted to pathology and immunohistochemistry assessment.Results The labeling efficiency of Gd-DTPA was up to 80%,the labeling procedure did not influence cell viability and proliferation.The signal intensity on T1-weighted imaging and T1 values of labeled cells were significantly higher than those of unlabeled cells.In vitro,differentiated HUMSCs expressed myosin heavy chain(MHC)and desmin,markers of striated muscles.In vivo,immunohistochemistry of rat urethras revealed dual labeled HUMSCs in situ and at the injection site.LPP was significantly improved in animals injected with HUMSCs.Atrophic urethras with implanted HUMSCs were positively stained for MHC and desmin.The distribution and migration of labeled cells could be tracked by MRI more than 14 days after t ransplantation.Conclusion HUMSCs have the ability to differentiate striated muscles,as demonstrated by MHC and desmin expression.Periurethral injection of HUMSCs in an animal model of SUI restored the damaged external urethral sphincter and significantly improved LPP.MRI can track Gd-DTPA–labled HUMSCs in an animal model of SUI in vivo.

An optimization method of fidelity parameters of formation fluid sampling cylinder while drilling

A design idea of fidelity sampling cylinder while drilling based on surface nitrogen precharging and supplemented by downhole pressurization was proposed, and the working mode and optimization method of sampling parameters were discussed. The nitrogen chamber in the sampling cylinder functions as an energy storage air cushion, which can supplement the pressure loss caused by temperature change in the sampling process to some extent. The downhole pressurization is to press the sample into the sample chamber as soon as possible, and further increase the pressure of sample to make up for the pressure that the nitrogen chamber cannot provide. Through the analysis of working mode of the sampling fidelity cylinder, the non-ideal gas state equation was used to deduce and calculate the optimal values of fidelity parameters such as pre-charged nitrogen pressure, downhole pressurization amount and sampling volume according to whether the bubble point pressure of the sampling fluid was known and on-site emergency sampling situation. Besides, the influences of ground temperature on fidelity parameters were analyzed, and corresponding correction methods were put forward. The research shows that the fidelity sampling cylinder while drilling can effectively improve the fidelity of the sample. When the formation fluid sample reaches the surface, it can basically ensure that the sample does not change in physical phase state and keeps the same chemical components in the underground formation.

Effect of High Pressure on the Melting and Solidifying Behavior of a Railway Frog Steel

Microstructural evolutions of the railway frog steel solidified under different pressure were studied using OM, FEGSEM, and TEM. The influences of pressure on the solidification, grain sizes, and morphology of carbides of the steel were analyzed. It is found that the melting point of the steel increases with the pressure and the solidified microstructure under high pressure does not vary significantly with the melting temperature. The experimental results show that the solidified microstructure consisting of complete equiaxed dendrites is remarkably refined through the increase of pressure, with the mean dendrite arm spacing of about 24, 18, and 8 μm under 3, 6, and 10 GPa, respectively. It is also revealed by TEM observation that the precipitates change from needle-like and rhombic carbide（M3C） forms during normal（atmospheric） pressure solidification into nodulized hexagonal precipitate M7C3 at 3 GPa, and M（23）C6 at 6 GPa and 10 GPa, which is associated with the undercooling and distribution of the trace elements. The diameter of the precipitates is between 80 nm and 200 nm.

Interplay of superconductivity and d-f correlation in CeFeAs_(1-x)P_xO_(1-y)F_y

The recent discovery of high-temperature superconductivity in iron-based pnictides （chalcogenides） not only trig- gers tremendous enthusiasm in searching for new superconducting materials, but also opens a new avenue to the study of the Kondo physics. CeFeAsO is a parent compound of the 1111-type iron-based superconductors. It shows 3d- antiferromagnetic （AFM） ordering below 139 K and 4f-AFM ordering below 4 K. On the other hand, the phosphide CeFePO is a ferromagnetically corelated heavy-fermion （HF） metal with Kondo scale TK 10 K. These properties set up a new platform for research of the interplay among magnetism, Kondo effect, and superconductivity （SC）. In this review, we present the recent progress in the study of chemical pressure effect in CeFeAsOl_yFy （y = 0 and 0.05）. This P/As-doping in CeFeAsO serves as an effective controlling parameter which leads to two magnetic critical points, Xcl -- 0.4 and Xc2 - 0.92, associated with suppression of 3d and 4f magnetism, respectively. We also observe a turning point of AFM-FM ordering of Ce3＋ moment at Xc3 - 0.37. The SC is absent in the phase diagram, which is attributed to the destruction to Cooper pair by Ce-FM fluctuations in the vicinity of Xcl. We continue to investigate CeFeAsl-xPxO0.95Fo.os. With the separation of xcl and xc3, this chemical pressure results in a broad SC region 0〈 x 〈 0.53, while the original HF behavior is driven away by 5% F- doping. Different roles of P and F dopings are addressed, and the interplay between SC and Ce-4f magnetism is also discussed.

Toward connectionist model for predicting bubble point pressure of crude oils: Application of artificial intelligence

Knowledge about reservoir fluid properties such as bubble point pressure(Pb)plays a vital role in improving reliability of oil reservoir simulation.In this work,hybrid of swarm intelligence and artificial neural network(ANN)as a robust and effective method was executed to determine the Pb of crude oil samples.In addition,the exactly precise Pb data samples reported in the literatures were employed to create and validate the PSO-ANN model.To prove and depict the reliability of the smart model developed in this study for estimating Pb of crude oils,the conventional approaches were applied on the same data set.Based on the results generated by PSO-ANN model and other conventional methods and equation of states(EOS),the PSO-ANN model is a reliable and accurate approach for estimating Pb of crude oils.This is certified by high value of correlation coefficient(R2)and insignificant value of average absolute relative deviation(AARD%)which are obtained from PSO-ANN outputs.Outcomes of this study could help reservoir engineers to have better understanding of reservoir fluid behavior in absence of reliable and experimental data samples.

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.

Robust correlation to predict dew point pressure of gas condensate reservoirs

When the bottom-hole flowing pressure in a gas condensate reservoir drops below the dew point pressure,liquid starts to build up around the well bore resulting in gas productivity decline.For this reason it is important to be able to accurately either measure or estimate the dew point pressure.The condensate formed in the reservoir will not flow until its saturation reaches the critical saturation and in many cases it might not be entirely recovered.It order to maximize gas production and condensate recovery,the reservoir pressure must be maintained close to the dew point pressure.Several attempts have been made to predict the dew point pressure in case the gas sample becomes unavailable or measured value is unreliable.Unfortunately,most of these attempts have minor success rates and are based on limited data.In this paper we present a robust,cheap,and easy model for predicting the dew point pressure for gas condensate reservoirs.The new model is an intelligent based model called“Gene Expression Programming”that is carried out to generate a precise and accurate correlation to estimate the dew point pressure in condensate gas reservoirs.The new model has been trained and tested using a large data bank collected for the literature.Precision of the suggested correlation has been compared to published correlations.The validity of this model has also been compared to experimental data and other published correlations.

Developing a K-value equation for predict dew point pressure of gas condensate reservoirs at high pressure

This paper proposed a new empirical K-value equation is developed to calculate dew pressure for gas condensate reservoirs.This equation is applicable in the wide ranges of composition,temperature,and pressure by considering the effect of composition via two equations for normal boiling point and critical temperature of the mixture.The range of dew pressure,temperature,heptane plus mole fraction,methane mole fraction,N2 mole fraction,CO2 mole fraction,and H2S mole fraction are fallen into 2666.7e9655 Psia,40e350.87
F,0.0021e0.213,0.3344e0.9668,0e0.4322,0e0.0864,and 0e0.942 respectively.As an important point,the proposed equation has any adjustable parameters,in addition,this equation indicates that in order to predict of dew pressure of gas condensate reservoirs,trial and error was not needed and therefore,computational speed increases beyond the accuracy.Moreover,the accuracy is validated by comparing against the experimental data of 81 gas condensate reservoirs samples from published literature and the results of Wilson,Whitson,and Ghafoori equations.Compared to the experimental data,the absolute average deviations of dew pressure calculations for the proposed equation,Wilson,Whitson,and Ghafoori were 7.6%,97.6%,99.4%,and 94.9%respectively.

Transparent open-box learning network and artificial neural network predictions of bubble-point pressure compared

The transparent open box(TOB)learning network algorithm offers an alternative approach to the lack of transparency provided by most machine-learning algorithms.It provides the exact calculations and relationships among the underlying input variables of the datasets to which it is applied.It also has the capability to achieve credible and auditable levels of prediction accuracy to complex,non-linear datasets,typical of those encountered in the oil and gas sector,highlighting the potential for underfitting and overfitting.The algorithm is applied here to predict bubble-point pressure from a published PVT dataset of 166 data records involving four easy-tomeasure variables(reservoir temperature,gas-oil ratio,oil gravity,gas density relative to air)with uneven,and in parts,sparse data coverage.The TOB network demonstrates high-prediction accuracy for this complex system,although it predictions applied to the full dataset are outperformed by an artificial neural network(ANN).However,the performance of the TOB algorithm reveals the risk of overfitting in the sparse areas of the dataset and achieves a prediction performance that matches the ANN algorithm where the underlying data population is adequate.The high levels of transparency and its inhibitions to overfitting enable the TOB learning network to provide complementary information about the underlying dataset to that provided by traditional machine learning algorithms.This makes them suitable for application in parallel with neural-network algorithms,to overcome their black-box tendencies,and for benchmarking the prediction performance of other machine learning algorithms.

Adsorption,selectivity,and phase behavior in organic nanopores for shale gas and oil development

In a shale gas and oil reservoir,hydrocarbon fluids are stored in organic nanopores with sizes on the order of~1-100 nm.The adsorption,selectivity,and phase behavior of hydrocarbons in the nanopores are crucial for estimating the gas-in-place and predicting the productivity.In this study,to understand the characteristics of the phase behavior of multicomponent hydrocarbon systems in shale reservoirs,the phase behavior of a CH_(4)/n-C_(4)H_(10)binary mixture in graphite nanopores was investigated by Grand Ca-nonical Monte Carlo(GCMC)molecular simulation.The method for determining the dew-point pressure and bubble-point pressure in the nanopores was explored.The condensation phenomenon was observed owing to the difference in the adsorption selectivities of the hydrocarbon molecules on the nanopore surfaces,and hence the dew-point pressure(and bubble-point pressure)of hydrocarbon mixtures in the nanopores significantly shifted.The GCMC simulations reproduced both the higher and lower bubble-point pressures in nanopores in previous studies.This work highlights the crucial role of the selec-tivity in the phase behavior of hydrocarbons in nanopores.