Abstract: A large quantity of world-s oil reserves exists in
carbonate reservoirs. Carbonate reservoirs are very sensitive to
chemical enhanced oil recovery process because of containing large
amount of calcite, dolomite and calcium sulfate minerals. These
minerals cause major obstacles during alkali-surfactant-polymer
(ASP) flooding. Alkali reacts with these minerals and form undesired
precipitations which plug effective porous openings, reduce
permeability and cause scale occurrence at the wellbore. In this
paper, a new chemical combination consists of acrylic acid and alkali
was used to minimize precipitation problem during ASP flooding. A
series of fluid-fluid compatibility tests were performed using acrylic
acid and different concentrations of alkaline. Two types of alkalis
namely; sodium carbonate and sodium metaborate were screened. As
a result, the combination of acrylic acid and sodium carbonate was
not effective in preventing calcium and magnesium precipitations.
However, acrylic acid and sodium metaborate showed promising
results for keeping all solutions without any precipitations. The ratio
of acrylic acid to sodium metaborate of 0.7:1.0 was found to be
optimum for achieving a compatible solution for 30 days at 80oC.
Abstract: Performance of a cobalt doped sol-gel derived silica (Co/SiO2) catalyst for Fischer–Tropsch synthesis (FTS) in slurryphase reactor was studied using paraffin wax as initial liquid media. The reactive mixed gas, hydrogen (H2) and carbon monoxide (CO) in a molar ratio of 2:1, was flowed at 50 ml/min. Braunauer-Emmett- Teller (BET) surface area and X-ray diffraction (XRD) techniques were employed to characterize both the specific surface area and crystallinity of the catalyst, respectively. The reduction behavior of Co/SiO2 catalyst was investigated using the Temperature Programmmed Reduction (TPR) method. Operating temperatures were varied from 493 to 533K to find the optimum conditions to maximize liquid fuels production, gasoline and diesel.
Abstract: The aim of this research is to use artificial neural networks computing technology for estimating the net heating value (NHV) of crude oil by its Properties. The approach is based on training the neural network simulator uses back-propagation as the learning algorithm for a predefined range of analytically generated well test response. The network with 8 neurons in one hidden layer was selected and prediction of this network has been good agreement with experimental data.