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| 1 | Big Data analytics in oil and gas industry: An emerging trend显示文摘This paper reviews the utilization of Big Data analytics,as an emerging trend,in the upstream and downstream oil and gas industry.Big Data or Big Data analytics refers to a new technology which can be employed to handle large datasets which include six main characteristics of volume,variety,velocity,veracity,value,and complexity.With the recent advent of data recording sensors in exploration,drilling,and production operations,oil and gas industry has become a massive data intensive industry.Analyzing seismic and micro-seismic data,improving reservoir characterization and simulation,reducing drilling time and increasing drilling safety,optimization of the performance of production pumps,improved petrochemical asset management,improved shipping and transportation,and improved occupational safety are among some of the applications of Big Data in oil and gas industry.Although the oil and gas industry has become more interested in utilizing Big Data analytics recently,but,there are still challenges mainly due to lack of business support and awareness about the Big Data within the industry.Furthermore,quality of the data and understanding the complexity of the problem are also among the challenging parameters facing the application of Big Data. | Mehdi Mohammadpoor Farshid Torabi | 2020 | Petroleum2020，6，4： | 4 |
| 2 | Extensive experimental investigation of the effect of drainage height and solvent type on the stabilized drainage rate in vapour extraction (VAPEX) process显示文摘The low cost of the injected solvent,which can be also recovered and recycled,and the applicability of VAPEX technique in thin reservoirs are among the main advantages of VAPEX process compared to thermal heavy oil recovery techniques.In this research,an extensive experimental investigation is carried out to first evaluate the technical feasibility of utilization of various solvents for VAPEX process.Then the effect of drainage height on the stabilized drainage rate in VAPEX process was studied by conducting series of experiments in two large-scale 2D VAPEX models of 24.5 cm and 47.5 cm heights.Both models were packed with low permeability Ottawa sand(#530)and saturated with a heavy oil sample from Saskatchewan heavy oil reservoirs with viscosity of 5650 mPa s.Propane,butane,methane,carbon dioxide,propane/carbon dioxide(70%/30%)and propane/methane(70%/30%)were considered as respective solvents for the experiments,and a total of twelve VAPEX tests were carried out.Moreover,separate experiments were carried out at the end of each VAPEX experiment to measure the asphaltene precipitation at various locations of the VAPEX models.It was found that injecting propane would result in the highest drainage rate and oil recovery factor.Further analysis of results showed stabilized drainage rate significantly increased in the larger physical model. | Mehdi Mohammadpoor Farshid Torabi | 2015 | Petroleum2015，1，3： | 2 |
| 3 | Application of Radial Basis Function Learning Algorithm in Petroleum Engineering: Bottom-Hole Pressure Prediction显示文摘 | Mehdi Mohammadpoor Farshid Torabi | 2011 | 材料科学与工程(中英文A版)2011，1，4X： | 1 |
| 4 | Utilization of CO2 Injection to Improve Oil Recovery of the Handsworth Bakken Formation显示文摘 | Farshid Torabi Jamie Lee French Tyler Scharnatta Mehdi Mohammadpoor Ryan Richard Wilton | 2012 | Journal of Earth Science and Engineering2012，2，6： | 0 |
| 5 | Predicting the performance of steam assisted gravity drainage (SAGD) method utilizing artificial neural network (ANN)显示文摘As the price of oil decreases,it is becoming increasingly important for oil companies to operate in the most costeffective manner.This problem is especially apparent in Western Canada,where most oil production is dependent on costly enhanced oil recovery(EOR)techniques such as steam-assisted gravity drainage(SAGD).Therefore,the goal of this study is to create an artificial neural network(ANN)that is capable of accurately predicting the ultimate recovery factor of oil reservoirs by steam-assisted gravity drainage(SAGD).The developed ANN model featured over 250 unique entries for oil viscosity,steam injection rate,horizontal permeability,permeability ratio,porosity,reservoir thickness,and steam injection pressure collected from literature.The collected data set was entered through a feed-forward back-propagation neural network to train,validate,and test the model to predict the recovery factor of SAGD method as accurate as possible.Results from this study revealed that the neural network was able to accurately predict recovery factors of selected projects with less than 10%error.When the neural network was exposed to a new simulation data set of 64 points,the predictions were found to have an accuracy of 82%as measured by linear regression.Finally,the feasibility of ANN to predict the recovery performance of one of the most complicated enhanced heavy oil recovery techniques with reasonable accuracy was confirmed. | Areeba Ansari Marco Heras Julianne Nones Mehdi Mohammadpoor Farshid Torabi | 2020 | Petroleum2020，6，4： | 0 |
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