Molecular dynamics study of helium bubble pressure in titanium

In this paper, the pressure state of the helium bubble in titanium is simulated by a molecular dynamics （MD） method. First, the possible helium/vacancy ratio is determined according to therelation between the bubble pressure and helium/vacancy ratio; then the dependences of the helium bubble pressure on the bubble radius at different temperatures are studied. It is shown that the product of the bubble pressure and the radius is approximately a constant, a result justifying the pressure-radius relation predicted by thermodynamics-based theory for gas bubble. Furthermore, a state equation of the helium bubble is established based on the MD calculations. Comparison between the results obtained by the state equation and corresponding experimental data shows that the state equation can describe reasonably the state of helium bubble and thus could be used for Monte Carlo simulations of the evolution of helium bubble in metals.

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.

Diffusion-controlled Adsorption Kinetics at Air/Solution Surface Studied by Maximum Bubble Pressure Method

In studying the diffusion-controlled adsorption kinetics of aqueous surfactant solutions at the air/solution surface by means of the maximal bubble pressure method, Fick's diffusion equation for a sphere should be used. In this paper the equation was solved by means of Laplace transformation under different initial and boundary conditions. The dynamic surface adsorption F(t) for a surfactant solution, which was used to describe the diffusion-controlled adsorption kinetics at the solution surface, was derived. Different from the planar surface adsorption, the dynamic surface adsorption F(t) for the short time consists of two terms: one is the same as Ward-Tordai equation and the other reflects the geometric effect caused by the spherical bubble surface. This effect should not be neglected for the very small radius of the capillary. The equilibrium surface tension γeq and the dynamic surface tension γ(t) of aqueous C10E6 [CH3(CH2)9(OCH2CH2)6OH] solution at temperature 25℃ were measuredby means of Wilhelmy plate method and maximal bubble pressure method respectively. As t→ 0, the theoreticalanalysis is in good agreement with experimental results and the dependence of γ(t) on is linear.

Computational study of bubble coalescence/break-up behaviors and bubble size distribution in a 3-D pressurized bubbling gas-solid fluidized bed of Geldart A particles

A computational study was carried out on bubble dynamic behaviors and bubble size distributions in a pressurized lab-scale gas-solid fluidized bed of Geldart A particles.High-resolution 3-D numerical simulations were performed using the two-fluid model based on the kinetic theory of granular flow.A finegrid,which is in the range of 3–4 particle diameters,was utilized in order to capture bubble structures explicitly without breaking down the continuum assumption for the solid phase.A novel bubble tracking scheme was developed in combination with a 3-D detection and tracking algorithm(MS3 DATA)and applied to detect the bubble statistics,such as bubble size,location in each time frame and relative position between two adjacent time frames,from numerical simulations.The spatial coordinates and corresponding void fraction data were sampled at 100 Hz for data analyzing.The bubble coalescence/break-up frequencies and the daughter bubble size distribution were evaluated by using the new bubble tracking algorithm.The results showed that the bubble size distributed non-uniformly over cross-sections in the bed.The equilibrium bubble diameter due to bubble break-up and coalescence dynamics can be obtained,and the bubble rise velocity follows Davidson’s correlation closely.Good agreements were obtained between the computed results and that predicted by using the bubble break-up model proposed in our previous work.The computational bubble tracking method showed the potential of analyzing bubble motions and the coalescence and break-up characteristics based on time series data sets of void fraction maps obtained numerically and experimentally.

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.

Effect of agitation on the characteristics of air dense medium fluidization

In order to study the effect of agitation on the characteristics of air dense medium fluidization, we designed and constructed an agitation device. Analyses were then conducted on the fluidization characteristics curves, the bed density stability and the average bubble rise velocity Uaunder different agitation conditions. The results indicated that a lower bed pressure drop(without considering lower gas velocity in a fixed bed stage) and higher minimum fluidized velocity are achieved with increasing agitation speed.The height d(distance between the lower blades and air distribution plate) at which the agitation paddle was located had a considerable effect on the stability of the bed density at 9.36 cm/s < U < 10.70 cm/s. The higher the value of d, the better the stability, and the standard deviation of the bed density fluctuation r dropped to 0.0364 g/cm^3 at the ideal condition of d = 40 mm. The agitation speed also had a significant influence on the fluidization performance, and r was only 0.0286 g/cm^3 at an agitation speed of N = 75 r/min. The average bubble rise velocity decreased significantly with increasing agitation speed under the operating condition of 1.50 cm/s < U–U_(mf)< 3.50 cm/s. This shows that appropriate agitation contributes to a significant improvement in the fluidization quality in a fluidized bed, and enhances the separation performance of a fluidized bed.

Diffusion-Controlled Adsorption Kinetics of Triton X-100 at Air/Solution Interface

In this paper, the equilibrium surface tension and the dynamic surface tension of aqueous Triton X-100 solution at temperature 25 ℃ were measured by means of Wilhelmy plate method and maximal bubble pressure method respectively. The determined critical micellar concentration（cmc） of Triton X-100 at 25 ℃ is (2.2×10-4) mol/dm3. The adsorption mechanics of Triton X-100 at air/solution was determined. For the submicellar concentrations it is diffusion-controlled. The diffusion coefficient was calculated from the experimental data in the range of short limit. In the range of long time adsorption, the subsurface concentration is fitted from the measured dynamic surface tensions.

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.

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.

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.

Bubble dynamics and its applications

Bubbles have very important applications in many fields such as shipbuilding engineering, ocean engineering, mechanical engineering, environmental engineering, chemical engineering, medical science and so on. In this paper, the research status and the development of the bubble dynamics in terms of theory, numerical simulation and experimental technique are reviewed, which cover the underwater explosion bubble, airgun bubble, spark bubble, laser bubble, rising bubble, propeller cavitation bubble, water entry/exit cavitation bubble and bubble dynamics in other fields. Former researchers have done a lot of researches on bubble dynamics and gained fruitful achievements. However, due to the complexity of the bubble motion, many tough mechanical problems remain to be solved. Based on the research progress of bubble dynamics, this paper gives the future research direction of bubble dynamics, aiming to provide references for researches related to bubble dynamics.

Effects of the plenum chamber volume and distributor geometry on fluidized bed hydrodynamics

Most hydrodynamic fluidized bed models, including CFD codes, neglect any effects of the plenum chamber volume. Experiments were performed in a 0.13 m ID fluidization column to determine plenum chamber volume effects on fluidized bed hydrodynamics for FCC and glass particles. Two low-pressure-drop distributors were used, one with a single orifice, and the other with 33 orifices and the same total open area as the single orifice. The results show two peaks in the frequency spectra for the single-orifice distributor, one representing bubble eruption at the bed surface and the other of higher frequency corresponding to the bubbling frequency at the distributor. The latter decreased slightly with increasing plenum volume and with increasing bed depth. For the multi-orifice distributor, broad frequency spectra from pressure measurements became narrower and moved towards higher frequency with decreasing plenum volume.

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.