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排序方式： 共有178条查询结果，搜索用时 15 毫秒
91.
Mengxiang Chen Jafar Sasanipour Sayyed Ali Kiaian Mousavy Ebrahim Khajeh Majid Kamyab 《Petroleum Science and Technology》2013,31(19):1582-1588
AbstractThis study implements an adaptive neuro-fuzzy inference system (ANFIS) approach to predict the precipitation amount of the asphaltene using temperature (T), dilution ratio (Rv), and molecular weight of different n-alkanes. Results are then evaluated using graphical and statistical error analysis methods, confirming the model’s great ability for appropriate prediction of the precipitation amount. Mean squared error and determination coefficient (R2) values of 0.036 and 0.995, respectively are obtained for the proposed ANFIS model. Results are then compared to those from previously reported correlations revealing the better performance of the proposed model. 相似文献
92.
《能源学会志》2014,87(3):208-214
Minimum miscible pressure (MMP) is an important indicator to evaluate the miscibility of CO2 with oil, and it is of paramount importance to the implementation of CO2 flooding. In this study, the sensitivities of MMP to its influencing factors were analyzed quantitatively. And the MMP correlations applying for pure and impure CO2–oil in low permeability reservoir were presented. These correlations are conducive to predicting MMP quickly and precisely when limited experimental data are available. In low permeability reservoirs, the main sensitive factors of MMP are reservoir temperature, oil components (C5+ molecular weight, volatiles and intermediates) and the components of injected gas (characterized with pseudo-critical temperature). MMP increases with the volatile/intermediate ratio, especially in the neighborhood of unity and decreases with the pseudo-critical temperature of impure CO2. MMP shows strong sensibility to the pseudo-critical temperature of impure CO2 when the critical temperature is less than that of pure CO2. 相似文献
93.
Alireza Baghban Peyman Abbasi Peyman Rostami Meysam Bahadori Zainal Ahmad Tomoaki Kashiwao 《Petroleum Science and Technology》2016,34(13):1129-1136
Wellhead chokes are widely used in the petroleum industry. Owning to the high sensitivity of oil and gas production to choke size, an accurate correlation to specify choke performance is vitally important. The aim of this contribution was to develop effective relationships among the liquid flow rate, gas liquid ratio, flowing wellhead pressure, and surface wellhead choke size using the support vector machines (SVMs). The accurate data set was gathered from the 15 different fields containing 100 production samples from the vertical wells at wide ranges of wellhead choke sizes. This computational model was compared with the previous developed correlations in order to investigate its applicability for subcritical two phase flow regimes through wellhead chokes. Results confirmed amazing capability of the SVM to predict liquid flow rates. The value of R2 obtained was 0.9998 for the SVM model. This developed predictive tool can be of massive value for petroleum engineer to have accurate estimations of liquid flow rates through wellhead chocks. 相似文献
94.
Gas hydrates may form in the petroleum and gas industry and can lead to significant problems such as plugging the pipelines and increasing velocity movement of the hydrate plugs in the pipelines. In this contribution, a simple strategy based on principal component analysis and partial least square methods has been utilized in order to estimate hydrate formation condition of carbon dioxide and tetra butyl ammonium chloride. In this regard, the developed tool has been evaluated by some reported data points in order to obtain its accuracy. This tool was simple to apply and can be of great help for gas transmission engineers to have an accurate estimation of hydrate conditions. 相似文献
95.
Alireza Baghban Peyman Abbasi Peyman Rostami 《Petroleum Science and Technology》2016,34(20):1698-1704
The resources of heavy oil and bitumen are more than those of conventional light crude oil in the world. Diluting the bitumen with liquid solvent can decrease viscosity and increase the empty space between molecules. Tetradecane is a candidate as liquid solvent to dilute the bitumen. Owning to the sensitivity of enhanced oil recovery process, the accurate approximation for the viscosity of aforementioned mixture is important to decrease uncertainty. The aim of this study was to develop an effective relation between the viscosity of Athabasca bitumen and heavy n-alkane mixtures based on temperature, pressure, and weight percentage of n-tetradecane using the least square support vector machine. This computational model was compared with the previous developed correlation and its accuracy was confirmed. The value of R2 and MSE obtained 1.00 and 1.02 for this model, respectively. This developed predictive tool can be applied as an accurate estimation for any mixture of heavy oil with liquid solvent. 相似文献
96.
《International Journal of Hydrogen Energy》2019,44(11):5324-5333
Artificial neural network has generally been used for a quantity of tasks such as classification, prediction, clustering and association analysis in different application fields. To the best of our knowledge, there are few researches on breakthrough curve used artificial neural network. In this paper, an artificial neural network model is established for breakthrough curves prediction in relation to a ternary components gas with a two-layered adsorbent bed piled up with activated carbon (AC) and zeolite, and an optimization is concluded by the artificial neural network. The performance data which acquired by Aspen model has been utilized for training artificial neural network (ANN) model. The ANN model trained has great competence for making prediction of hydrogen purification performance of PSA cycle with impressive speed and rational accuracy. On the strength of the ANN model, we implemented an optimization for seeking first-rank PSA cycle parameters. The optimization is concentrated on the effect of inlet flow rate, pressure and layer ratio of activated carbon height to zeolite height. Furthermore, this paper shows that the PSA cycle's optimal operation parameters can be obtained by use of ANN model and optimization algorithm, the ANN model has been trained according to the data generated by Aspen adsorption model. 相似文献
97.
The accurate estimations of processes in gas engineering need a high degree of accuracy in calculations of gas properties. One of these properties is gas density which is straightly affected by pressure and temperature. In the present work, the Adaptive neuro fuzzy inference system (ANFIS) algorithm joined with Particle Swarm Optimization (PSO) to estimate gas density in terms of pressure, temperature, molecular weight, critical pressure and critical temperature of gas. In order to training and testing of ANFIS-PSO algorithm a total number of 1240 experimental data were extracted from the literature. The statistical parameters, Root mean square error (RMSE), coefficient of determination (R2) and average absolute relative deviation (AARD) were determined for overall process as 0.14, 1 and 0.039 respectively. The determined statistical parameters and graphical comparisons expressed that predicting mode is a robust and accurate model for prediction of gas density. Also the predicting model was compared with three correlations and obtained results showed the better performance of the proposed model respect to the others. 相似文献
98.
Nanofluids and low-salinity water(LSW)flooding are two novel techniques for enhanced oil recovery.Despite some efforts on investigating benefits of each method,the pros and cons of their combined application need to be evaluated.This work sheds light on performance of LSW augmented with nanoparticles through examining wettability alteration and the amount of incremental oil recovery during the displacement process.To this end,nanofluids were prepared by dispersing silica nanoparticles(0.1 wt%,0.25 wt%,0.5 wt% and 0.75 wt%)in 2,10,20 and 100 times diluted samples of Persian Gulf seawater.Contact angle measurements revealed a crucial role of temperature,where no wettability alteration occurred up to 80 ℃.Also,an optimum wettability state(with contact angle 22°)was detected with a 20 times diluted sample of seawater augmented with 0.25 wt% silica nanoparticles.Also,extreme dilution(herein 100 times)will be of no significance.Throughout micromodel flooding,it was found that in an oil-wet condition,a combination of silica nanoparticles dispersed in 20 times diluted brine had the highest displacement efficiency compared to silica nanofluids prepared with deionized water.Finally,by comparing oil recoveries in both water-and oil-wet micromodels,it was concluded that nanoparticles could enhance applicability of LSW via strengthening wettability alteration toward a favorable state and improving the sweep efficiency. 相似文献
99.
《International Journal of Hydrogen Energy》2020,45(58):33274-33287
Accurate determination of sulfur solubility in pure hydrogen sulfide (H2S) and sour gas mixtures has a leading role and a fundamental importance in handling and addressing sulfur deposition issues. In this study, rigorous paradigms based on two artificial neural network (ANN) types, namely multilayer perceptron (MLP) and cascaded forward neural network (CFNN), optimized by Levenberg–Marquardt (LM) algorithm were proposed as machine learning (ML) modeling tools to predict the solubility of sulfur in sour gas mixtures and pure H2S. Besides, explicit and simple-to-use correlations were established using gene expression programming (GEP). The paradigms derived from the methods aforementioned were developed using widespread experimental database. The obtained results indicated that the outcomes gained from the proposed MLP, CFNN and GEP-based correlations are in a high coherence and agreement with the experimental data. In addition, it was found that among the all suggested schemes, CFNN models are the most accurate paradigms for estimating the solubility of sulfur in sour gas mixtures and pure H2S with root mean square error (RMSE) of 0.0232 and 3.8101, respectively. Furthermore, a comparison between the performance of CFNN and the prior alternatives demonstrated that the CFNN models predict the solubility of sulfur in sour gas mixtures and pure H2S more accurately. Moreover, based on the trend analysis, it was concluded that the predictions of CFNN follow the real tendency of sulfur solubility in pure H2S and sour gas mixtures with respect to the input parameters. Besides, the sensitivity analysis dictated that pressure and temperature have the most significant impact on sulfur solubility calculation in pure H2S and sour gas mixtures. The results reported in this investigation revealed that implication of the considered soft computing approaches in the estimation of sulfur solubility in sour gas mixtures and pure H2S can lead to the generation of more reliable predictive paradigms which can be integrated in other related applications. Lastly, the findings of this study can help for effective prediction of the solubility of sulfur in sour gas mixtures and pure H2S while simulating different natural gas processes. 相似文献
100.
Today, due to extensive applications of supercritical fluids technology in various chemical engineering process and industrial fields, predicting thermal conductivity of supercritical carbon dioxide is vital. In this research, two simple-to-apply models have been developed to estimate thermal conductivity of supercritical CO2 as a function of temperature, pressure and density over broad ranges. This research presents a predictive tool based on LSSVM to predict thermal conductivity of supercritical CO2. Genetic algorithm is employed to determine hyper-variables which are included in the LSSVM approach. In this regard, a set of accessible data containing 745 data points has been gathered from the previous published papers. Estimations are found to be in excellent agreement with reported data. Moreover, statistical analyses have been applied to evaluate the performance of two models. The obtained values of Mean Squared Error and R-Square were 7.415866, 0.9935 and 0.046527, 1.00 for the correlation and LSSVM model, respectively. The developed tools can be of immense practical value for chemical engineers to have a quick check of thermal conductivity of supercritical CO2 at an extensive range of conditions. 相似文献