Modeling of steam distillation mechanism during steam injection process using artificial intelligence

Steam distillation as one of the important mechanisms has a great role in oil recovery in thermal methods and so it is important to simulate this process experimentally and theoretically. In this work, the simulation of steam distillation is performed on sixteen sets of crude oil data found in the literature. Artificial intelligence (AI) tools such as artificial neural network (ANN) and also adaptive neurofuzzy interference system (ANFIS) are used in this study as effective methods to simulate the distillate recoveries of these sets of data. Thirteen sets of data were used to train the models and three sets were used to test the models. The developed models are highly compatible with respect to input oil properties and can predict the distillate yield with minimum entry. For showing the performance of the proposed models, simulation of steam distillation is also done using modified Peng-Robinson equation of state. Comparison between the calculated distillates by ANFIS and neural network models and also equation of state-based method indicates that the errors of the ANFIS model for training data and test data sets are lower than those of other methods

Modeling of Steam Distillation Mechanism during Steam Injection Process Using Artificial Intelligence

Steam distillation as one of the important mechanisms has a great role in oil recovery in thermal methods and so it is important to simulate this process experimentally and theoretically. In this work, the simulation of steam distillation is performed on sixteen sets of crude oil data found in the literature. Artificial intelligence (AI) tools such as artificial neural network (ANN) and also adaptive neurofuzzy interference system (ANFIS) are used in this study as effective methods to simulate the distillate recoveries of these sets of data. Thirteen sets of data were used to train the models and three sets were used to test the models. The developed models are highly compatible with respect to input oil properties and can predict the distillate yield with minimum entry. For showing the performance of the proposed models, simulation of steam distillation is also done using modified Peng-Robinson equation of state. Comparison between the calculated distillates by ANFIS and neural network models and also equation of state-based method indicates that the errors of the ANFIS model for training data and test data sets are lower than those of other methods

Promotion of GM-PHD Filtering Approach for Single-Target Tracking in Raw Data of Synthetic Aperture Radar in Spotlight Imaging Mode

So far multi-antenna techniques have been used in Synthetic Aperture Radar (SAR) to track moving targets. These techniques carry out the tracking of moving targets in an imaging area, using a combination of the data received by two or several antennas. The aim of this paper is single-target tracking in SAR Spotlight imaging mode based on the promoted PHD filter. In most applications, target tracking in densely cluttered environment using radar system demands robust filtering so as to increase the tracking efficiency. Therefore, tracking of moving targets in the presence of high density clutters in environment, as the particular capability of the PHD filter, has turned it into a robust approach in SAR to track moving targets. Also as the simulation results show, using Range Cell Migration Compensation (RCMC) on SAR raw data before tracking, makes it possible to track a moving target with high quality

Modeling asphaltene precipitation in live crude oil

The presented data are the data used for modeling asphaltene precipitation in live crude oil by performing CPA-EOS. These data are related to a manuscript entitled "Modeling Asphaltene Precipitation in Live Crude Oil Using Cubic Plus Association (CPA) Equation of State". This data bank is gathered from Burke et al., 1990 (J Pet Tech. 1990;42:1440-1446)

Asphaltene-augmented gel polymer system

The presented data are the experimental results of the static test used for finding the optimum conditions of the proposed asphaltene-augmented gel polymer system

Optimum conditions of the presented gel polymer system

The presented data are the experimental results of the static test used for finding the optimum conditions of the proposed asphaltene-augmented gel polymer system

Modeling asphaltene precipitation in live crude oil

The presented data are the data used for modeling asphaltene precipitation in live crude oil by performing CPA-EOS. These data are related to a manuscript entitled "Modeling Asphaltene Precipitation in Live Crude Oil Using Cubic Plus Association (CPA) Equation of State". This data bank is gathered from Burke et al., 1990 (J Pet Tech. 1990;42:1440-1446)

A STUDY OF CHANNEL ESTIMATION TECHNIQUES WITH CARRIER-FREQUENCY OFFSET ESTIMATION IN SISO- OFDM SYSTEMS

The channel estimation techniques for OFDM systems based on pilot arrangement are investigated. That on this basis pilots were inserted among subcarriers in transmitter with distances emerged of sampling theory. The objective of this study is improving channel estimation accuracy in OFDM systems because channel state information is required for signal detection at receiver and its accuracy affects the overall performance of system and it is essential to improve the channel estimation for more reliable communications. For improving the quality of channel estimation, different iterative algorithms are available, such as EM, RLS and LMS, among them LMS is chosen due to its less complexity comparing with other methods and its acceptable performance. The low complexity proposed receiver including LMS algorithm, has a higher efficiency than conventional methods and it can work in lower amount of SNRs. According to this, using maximum likelihood estimation algorithm (MLE), the carrier frequency offset (CFO) can be estimated. Then we estimate channel’s coefficients for these systems. To accurately estimate the channel’s coefficients, carrier frequency offset mitigation is necessary. The efficiency of these algorithms can be investigated with simulation and the results of estimation will come to a comparison

Evaluation of different thermodynamic models in predicting asphaltene precipitation: A comparative study

One of the major issues in the oil industry is asphaltene precipitation. Modeling asphaltene precipitation is still considered as a complex problem due to various characteristics of different heavy components existing in the crude oil. Thermodynamic models have been found as accurate models for studying asphaltene precipitation in the past few years and a great deal of effort has been devoted to model this process by using different empirical models and equations of state. In this study, the obtained results of asphaltene precipitation from different models based on perturbed-chain statistical associating fluid theory (PC-SAFT), cubic-plus-association (CPA), solid model, Flory-Huggins (FH), and the modified Flory-Huggins (MFH) are compared and their accuracy and reliability are analyzed in detail. For this purpose, twelve crude oil types with different characteristics and asphaltene precipitation behavior are used. Additionally, the performance of the introduced models in predicting asphaltene precipitation during gas injection into the studied oil is investigated. Results demonstrated that PC-SAFT and CPA models have the highest accuracy for both precipitation estimation and behavior trend prediction. Afterward, sensitivity analysis is performed by using Monte-Carlo algorithm for better understanding of the effect of different adjusting parameters, which were used during the tuning process, on each model outputs. Results indicated that cross-association energy between asphaltene and heavy component (HC), self-association energy of asphaltene, and binary interaction coefficient between asphaltene and CO2 are the most sensitive tuning variables for PC-SAFT, CPA, and solid models, respectively. Finally, the CPU times of various models for simulating this process were compared. This comparison showed that the PC-SAFT model has more computational time due to the involved iterative processes for phase equilibrium calculations