No abstract

No abstract

No abstract

Recently, some works showed that magnetic fields may reduce the paraffin crystallization and the viscosity of some types of oil. This Article shows the main results obtained in an attempt to determine some factors responsible for the oil interaction with magnetic fields, which caused the rheological properties change in crude oil samples. Under the influence of a magnetic field (1.3 T in 1 min exposure), one of the six brazilian crude oil samples studied (sample 1) showed 39% reduction on its viscosity and a reduction on the viscoelastic properties (loss modulus and storage modulus). However, the other five samples did not show any considerable modification of their rheological properties. We analyzed all six samples using spectroscopy to detect what kind of component was present in sample 1 that could interact with the magnetic field and cause the aforementioned rheological properties change and that was not present in the other samples. The major differences observed in sample 1 were the presence of the Mn 2+ paramagnetic ion (EPR spectroscopy); Sr and Br (XRF spectroscopy); highest aromatic/aliphatic molecules ratio (NMR spectroscopy); and the highest water content (10% v/v, NMR spectroscopy). Thus, the results show that the paraffin could not be the unique factor responsible for the change on the rheological properties of the crude oil samples caused by magnetic fields, as some authors suggested previously.

This work is concerned with the development of an artificial neural network (ANN), capable of classifying two-phase flow patterns, such as discrete bubbles, stratified, slug-flow, intermittent and annular regimes. Experimental operating data from the literature and the physical properties of the fluids were used to define and calculate dimensionless numbers. These numbers constitute the inputs of the neural model. They successfully describe the flow because they account for the competing forces occurring within the multiphase fluid. The training procedure was performed using a Levenberg-Marquardt algorithm. The methodology used to find the best network architecture is described in detail. All the flow regimes were accurately classified presenting only a small deviation. The final goal is to develop an automatic classification tool for multiphase flow patterns aimed at laboratories and field applications.

No abstract

In many industrial applications, especially in the oil industry, the requirements of multiphase flow measurement pose numerous technological challenges as it oftentimes involve harsh media, strict safety regulations, access difficulties, long distances, and aggressive surroundings. The fluids produced from oil wells most often emerge as a multiphase mixture of oil, natural gas, water, and a variety of solids (sand, hydrates, and asphaltenes). It is generally recognized that multiphase flow metering (MFM), i.e. measuring the flow rates of the individual phases in a multiphase flow, could have many advantages in terms of costs, layout of production facilities, well testing, reservoir management, subsea and downhole metering. In this regard, the ultrasonic technique has been receiving increasing attention in the past years because it is noninvasive, fast responding, and suitable for operation in harsh environments. In the present paper, initially a review is made of selected commercially available separation-type and in-line MFM systems; the multiphase flow parameters measured in each system and the techniques used as well as general comments regarding the system performance are provided. Next, ultrasonic attenuation and transit time data are presented for oil-continuous oil-air-sand and oil-air-water mixtures in steel pipes. A discussion is also presented on attempts to sort out the air and sand concentrations in water-air-sand mixtures from ultrasonic signals. It is hoped that the data presented will help establish the potential of the ultrasonic technique for use in MFM systems in combination with or replacing specific instruments already in use.

No abstract