In oil and gas well cementing, a strong cement sheath is wanted to insure long-term safety of the wells. Successful completion of cementing job has become more complex, as drilling is being done in highly deviated and high pressure-high temperature wells. Use of nano materials in enhanced oil recovery, drilling fluid, oil well cementing and other applications is being investigated. This study is an attempt to investigate the effect of nano materials on oil well cement properties. Two types of nano materials were investigated, which are Nano silica (>40 nm) and Nano Alumina (80 nm) and high sulfate-resistant glass G cement is used. The investigated properties of oil well cement included compressive strength, thickening

Abstract:          

                Interest in the topic of prediction has increased in recent years and appeared modern methods such as Artificial Neural Networks models, if these methods are able to learn and adapt self with any model, and does not require assumptions on the nature of the time series. On the other hand, the methods currently used to predict the classic method such as Box-Jenkins may be difficult to diagnose chain and modeling because they assume strict conditions.

In this paper the use of a circular array antenna with adaptive system in conjunction with modified Linearly Constrained Minimum Variance Beam forming (LCMVB) algorithm is proposed to meet the requirement of Angle of Arrival (AOA) estimation in 2-D as well as the Signal to Noise Ratio (SNR) of estimated sources (Three Dimensional 3-D estimation), rather than interference cancelation as it is used for. The proposed system was simulated, tested and compared with the modified Multiple Signal Classification (MUSIC) technique for 2-D estimation. The results show the system has exhibited astonishing results for simultaneously estimating 3-D parameters with accuracy approximately equivalent to the MUSIC technique (for estimating elevation and a

In high-dimensional semiparametric regression, balancing accuracy and interpretability often requires combining dimension reduction with variable selection. This study intro- duces two novel methods for dimension reduction in additive partial linear models: (i) minimum average variance estimation (MAVE) combined with the adaptive least abso- lute shrinkage and selection operator (MAVE-ALASSO) and (ii) MAVE with smoothly clipped absolute deviation (MAVE-SCAD). These methods leverage the flexibility of MAVE for sufficient dimension reduction while incorporating adaptive penalties to en- sure sparse and interpretable models. The performance of both methods is evaluated through simulations using the mean squared error and variable selection cri

The growing demand for energy, coupled with the continued dominance of fossil fuels as the primary energy source, necessitates eco-friendly technologies that simultaneously enhance oil recovery (EOR) and reduce the impact of their emissions. Only one task, which is the CO2-EOR project, can combine these two sustainable development goals. Further, employing green nanotechnology, including nanoparticles and nanofluids, ensures a sustainable approach to controlling and enhancing rock wettability, thereby enhancing hydrocarbon production and carbon storage. However, the performance of nanofluids in subsurface formations is limited by the stability of these nano-dispersions at the harsh conditions of reservoirs. This work thus synthesize

We observed strong nonlinear absorption in the CdS nanoparticles of dimension in the range 50-100 nm when irradiant with femtosecond pulsed laser at 800 nm and 120 GW/cm 2 irradiance intensity. The repetition rate and average power were 250 kHz and