Estimation of PVT Properties Using Artificial Neural Networks and Comparison of Results with Experimental Data

The importance of pressure, volume and temperature (PVT) properties, bubble pressure, gas-to-oil solubility ratio and oil volume factor have made it necessary to precisely determine these properties for the calculation of reservoir performance. In the absence of laboratory measurements to determine the PVT properties of crude oil, two methods, used commonly, include the equations of state and the experimental relations of PVT. The equation of state is based on the information concerned with the fluid composition details of the reservoir, which is very expensive and time-consuming to determine. Whereas, PVT relationships are based on data obtained from easily measured ground layers. These data include reservoir pressure, reservoir temperature, and the specific gravity of oil and gas. Recent Studies show that artificial neural networks have a great ability to predict the PVT properties. Due to the training capability in neural networks, these networks were rapidly applied in engineering and were widely used in petroleum engineering. Estimation of porosity and permeability of reservoirs, prediction of outflow generated by oil wells, estimation of oil recovery, prediction of asphaltene deposition and estimation of PVT properties are the most important applications of artificial neural networks in petroleum engineering. By preparing and collecting more than 1000 PVT data related to southern Iran reservoirs, 577 data were selected to be used in the project and were randomly divided into two parts, 486 data for network training and 91 data for testing. The three-layer structure (one hidden layer with 6 neurons) was selected as the best structure and the batch backpropagation training method as the best learning algorithm. The results of the network were in a good agreement with experimental data, which the average relative error using training set in estimation of the volume factor and densities of oil were 0.557 and 0.509% respectively and using test data were 1.032 and 1.104% respectively

Produced Water Treatment by Using Nanofibrous Polyvinylidene Fluoride Membrane in Air Gap Membrane Distillation

The removal of oil from produced water is one of the main environmental topics in oil and gas reservoir exploitations and industrial operations. In this study, a new method was introduced for efficient and cost effective treatment of produced water by using nanofibrous polyvinylidene fluoride (PVDF) in air gap membrane distillation (AGMD) process. The PVDF membrane was fabricated through electrospinning technique and characterized by means of various methods such as scanning electron microscopy (SEM), liquid entry pressure (LEP) test, gas permeation and contact angle goniometry. The prepared membranes were tested for permeation flux and oil separation efficiency in AGMD process. The results indicate that the optimally modified membrane yields high permeate flux of around 17.5 kg/m2h and total oil removal efficiency of 99.9 % compared to commercial membranes

A Comparison of the Catalytic Activity of Cu-X2 (X=Mn, Co) Nano Mixed Oxides toward Phenol Remediation from Wastewater by Catalytic Wet Peroxide Oxidation

The catalytic wet peroxide oxidation of phenol from aqueous wastewater and COD reduction over Cu-Mn2 and Cu-Co2 nano mixed oxides are reported. The effects of process variables of pH, reaction time and hydrogen peroxide dosage were investigated in the process over both catalysts. The catalysts were characterized by X-ray diffraction (XRD) and it was concluded that the mixed oxides are in the form of the spinel structure. However, a little bit CuO was found in the mixed oxides. The morphology and particles size of the catalysts were investigated by scanning electron microscope (SEM). The morphologies and particle size of the catalyst were approximately the same with an average range of 40-60 nm. The catalytic results indicated the higher activity of CuCo2O4 spinel. The phenol oxidation on Cu-Co2 oxide was 82% after 40 min, whereas on the Cu-Mn2 oxide was 78% even after 50 min. The COD reduction The higher activity and reusability of the Cu-Co2 catalyst is attributed to the high synergistic effect between CuO particles and Cu–Co2 spinel, promoting phenol degradation

Synthesis, characterization and in vitro evaluation of magnetic nanoparticles modified with PCL-PEG-PCL for controlled delivery of 5FU.

Magnetic nanoparticles have properties that cause to apply them in cancer therapy and vehicles for the delivery of drugs such as 5FU, especially when they are modified with biocompatible copolymers. The aim of this study is to modify superparamagnetic iron oxide nanoparticles (SPIONPs) with PCL-PEG-PCL copolymers and then utilization of these nanoparticles for encapsulation of anticancer drug 5FU. The ring-opening polymerization (ROP) was used for the synthesis of PCL-PEG-PCL copolymer by ε-caprolactone (PCL) and polyethylene glycol (PEG2000). We used the double emulsion method (water/oil/water) to prepare 5FU-encapsulated Fe3O4 magnetic nanoparticles modified with PCL-PEG-PCL copolymer. Chemical structure and magnetic properties of 5FU-loaded magnetic-polymer nanoparticles were investigated systematically by employing FT-IR, XRD, VSM and SEM techniques. In vitro release profile of 5FU-loaded NPs was also determined. The results showed that the encapsulation efficiency value for nanoparticles were 90%. Moreover, the release of 5FU is significantly higher at pH 5.8 compared to pH 7.4. Therefore, these nanoparticles have sustained release and can apply for cancer therapy