Nanofluids (i.e. nanoparticles dispersed in a fluid) have tremendous potential in a broad range of applications, including pharmacy, medicine, water treatment, soil decontamination, or oil recovery and CO2 geo-sequestration. In these applications nanofluid stability plays a key role, and typically robust stability is required. However, the fluids in these applications are saline, and no stability data is available for such salt-containing fluids. We thus measured and quantified nanofluid stability for a wide range of nanofluid formulations, as a function of salinity, nanoparticle content and various additives, and we investigated how this stability can be improved. Zeta sizer and dynamic light scattering (DLS) principles were used to investigate zeta potential and particle size distribution of nanoparticle-surfactant formulations. Also scanning electron microscopy was used to examine the physicochemical aspects of the suspension. We found that the salt drastically reduced nanofluid stability (because of the screening effect on the repulsive forces between the nanoparticles), while addition of anionic surfactant improved stability. Cationic surfactants again deteriorated stability. Mechanisms for the different behaviour of the different formulations were identified and are discussed here. We thus conclude that for achieving maximum nanofluid stability, anionic surfactant should be added.
Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.
In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation
... Show MoreDrilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.
In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation
... Show MoreThe aim of this paper is introducing the concept of (ɱ,ɳ) strong full stability B-Algebra-module related to an ideal. Some properties of (ɱ,ɳ)- strong full stability B-Algebra-module related to an ideal have been studied and another characterizations have been given. The relationship of (ɱ,ɳ) strong full stability B-Algebra-module related to an ideal that states, a B- -module Ӽ is (ɱ,ɳ)- strong full stability B-Algebra-module related to an ideal , if and only if for any two ɱ-element sub-sets and of Ӽɳ, if , for each j = 1, …, ɱ, i = 1,…, ɳ and implies Ạɳ( ) Ạɳ( have been proved..