Period–Luminosity Relationship forδScuti Stars Revisited

The Gaia DR3 parallax approach was used to estimate the absolute parameters of 2375δScuti stars from the ASAS catalog.The selected stars have a variety of observational characteristics,with a higher than 80%probability of beingδScuti stars.We have displayed all the stars in the Hertzsprung-Russell diagram along with theδScuti instability strip,the Zero Age Main Sequence and the Terminal Age Main Sequence.Then,we determined which fundamental and overtone modes each star belongs to using pulsation constant(Q)calculations.In addition,we evaluated the parameters in the Q calculation equation using three machine learning methods,which showed that surface gravity and temperature have the greatest effect on its calculation.The Period-Luminosity(P-L)relationship of theδScuti stars was also revisited.Eventually,using least squares linear regression,we made four linear fits for fundamental and overtone modes and updated their relationships.

New empirical scaling equations for oil recovery by free fall gravity drainage in naturally fractured reservoirs

Gas-oil gravity drainage is a recognized major contributor to production in fractured reservoirs. While various empirical and analytical methods have been proposed to model this process, many of them contain assumptions that are questionable or require parameters that are not accessible at the field level. The aim of this work is to provide new, easy-to-use scaling equations for estimating the recoverable oil through gravity drainage in naturally fractured reservoirs, considering the effects of resistance capillary pressure. To accomplish this, data from four oilfields undergoing gravity drainage, including rock properties (eight sets), block height (three sets), and fluid properties (four sets), were used to generate a wide range of recovery curves using a single porosity numerical simulation model. Aronofsky's and Lambert's functions were then utilized to match the generated recovery curves. Statistical analysis revealed that the Aronofsky's function is more accurate in replicating the recovery patterns, while the Lambert's function tends to overestimate the early-time oil recovery and underestimate the oil recovery at a later stage in the majority of cases. A sensitivity analysis was subsequently performed, revealing that parameters such as absolute permeability, viscosity of oil, height of block, gas and oil density, characteristics of relative permeability and capillary pressure curves and interfacial tension (IFT) influence the amount of time taken to achieve the final recovery. Of these parameters, absolute permeability has the most significant effect on the amount of time needed to attain the final recovery, while the effect of difference between oil and gas densities is the lowest. Consequently, two different expressions were developed using nonlinear multiple regression analysis of simulated gravity drainage data which can be combined with the Aronofsky model to substitute the rate convergence constant. The new scaling equations include the effects of capillary pressure and other relevant factors in gravity drainage simulations. Both forms show satisfactory accuracy, as evidenced by the statistical parameters obtained (R2 = 0.99 and MSE = 0.0019 for both established correlations). The new correlations were verified using a wide range of oilfield data and are expected to provide a better understanding of the recovery process in naturally fractured reservoirs.

Distinguished discriminatory separation of CO2 from its methane-containing gas mixture via PEBAX mixed matrix membrane

Highly selective separation of CO_2 from its methane-containing binary gas mixture can be achieved by using Poly(ether-block-amide)(PEBAX)mixed matrix membranes(MMMs).According to FESEM and AFM analyses,silica-based nanoparticles were homogenously integrated within the polymer matrix,facilitating penetration of CO_2 through the membrane while acting as barrier for methane gas.The membrane containing 4.6 wt% fumed silica(FS)(PEBAX/4.6 wt%FS)exhibits astonishing selectivity results where binary gas mixture of CO_2/CH_4 was used as feed gas.As detected by gas chromatography,in the permeate side,data showed a significant increase of CO_2 permeance,while CH_4 transport through the mixed matrix membrane was not detectable.Moreover,PEBAX/4.6 wt%FS greatly exceeds the Robeson limit.According to data reported on CO_2/CH_4 gas pair separation in the literature,the results achieved in this work are beyond those data reported in the literature,particularly when PEBAX/4.6 wt%FS membrane was utilized.

Prediction of oil flow rate through an orifice flow meter: Artificial intelligence alternatives compared

Fluid-flow measurements of petroleum can be performed using a variety of equipment such as orifice meters and wellhead chokes.It is useful to understand the relationship between flow rate through orifice meters(Qv)and the five fluid-flow influencing input variables:pressure(P),temperature(T),viscosity(μ),square root of differential pressure(ΔP^0.5),and oil specific gravity(SG).Here we evaluate these relationships using a range of machine-learning algorithms applied to orifice meter data from a pipeline flowing from the Cheshmeh Khosh Iranian oil field.Correlation coefficients indicate that(Qv)has weak to moderate positive correlations with T,P,andμ,a strong positive correlation with theΔP^0.5,and a weak negative correlation with oil specific gravity.In order to predict the flow rate with reliable accuracy,five machine-learning algorithms are applied to a dataset of 1037 data records(830 used for algorithm training;207 used for testing)with the full input variable values for the data set provided.The algorithms evaluated are:Adaptive Neuro Fuzzy Inference System(ANFIS),Least Squares Support Vector Machine(LSSVM),Radial Basis Function(RBF),Multilayer Perceptron(MLP),and Gene expression programming(GEP).The prediction performance analysis reveals that all of the applied methods provide predictions at acceptable levels of accuracy.The MLP algorithm achieves the most accurate predictions of orifice meter flow rates for the dataset studied.GEP and RBF also achieve high levels of accuracy.ANFIS and LSSVM perform less well,particularly in the lower flow rate range(i.e.,<40,000 stb/day).Some machine learning algorithms have the potential to overcome the limitations of idealized streamline analysis applying the Bernoulli equation when predicting flow rate across an orifice meter,particularly at low flow rates and in turbulent flow conditions.Further studies on additional datasets are required to confirm this.