Advancements in horizontal drilling technologies are utilized to develop unconventional resources, where reservoir temperatures and pressures are very high. However, the flocculation of bentonite in traditional fluids at high temperature and high pressure (HTHP) environments can lower cuttings transportation efficiency and even result in problems such as stuck pipe, decreased rate of penetration (ROP), accelerated bit wear, high torque, and drag on the drill string, and formation damage. The major purpose of the present research is to investigate the performance of low bentonite content water-based fluids for the hole cleaning operation in horizontal drilling processes. Low bentonite content water-based drilling fluids were formulated by replacing a specified quantity of bentonite with a small fraction of cellulose nanoparticles (CNPs), including cellulose nanocrystals (CNCs) and cellulose nanofibers (CNFs). The concentration of CNPs was changed from 0.15 wt% up to 0.60 wt% and the bentonite content was reduced from 6 to 0 wt%, which leads to a reduction of solid contents from 13.34 to 6.71 wt%. The flow-loop experiments were accomplished on a sophisticated purpose-built flow rig by circulating the tested fluid samples into the test section in a horizontal position, considering the influence of drill pipe rotation, flow rates, cuttings sizes, and drill pipe eccentricity. The results show that the low solid fluids displayed a considerable enhancement in cuttings removal efficiency, especially with 0.15 wt% of the concentration CNPs and 4.5 wt% of the bentonite contents. The morphology of CNPs played a vital role in improving the rheological properties of the water-based drilling fluids.
Solar distillers are a sustainable and simple solution for addressing water scarcity, but their limited productivity restricts their effectiveness. This work aimed to assess the thermal performance of a novel tracked, tilted, hexagonal tubular solar still (HTSS) of four-sectioned U-channel receiver. Two identical HTSSs were side-to-side tested in Baghdad-Iraq (33.3°N, 43.3°E) from June to September 2024. The thermal evaluation of single-axis tracking solar still, tilted at (5° to 15°) with the horizontal axis and charged with and without hydrogel beads for water depth of 60 mm. The still's thermal performance is assessed by analyzing heat transfer coefficients, energy and exergy efficiencies, as well as conducting cost and environment
... Show MoreOil well drilling fluid rheology, lubricity, swelling, and fluid loss control are all critical factors to take into account before beginning the hole's construction. Drilling fluids can be made smoother, more cost-effective, and more efficient by investigating and evaluating the effects of various nanoparticles including aluminum oxide (Al2O3) and iron oxide (Fe2O3) on their performance. A drilling fluid's performance can be assessed by comparing its baseline characteristics to those of nanoparticle (NPs) enhanced fluids. It was found that the drilling mud contained NPs in concentrations of 0,0.25, 0. 5, 0.75 and 1 g. According to the results, when drilling fluid was used without NPs, the coeff
... Show MoreIn this study, the results of x-ray diffraction methods were used to determine the Crystallite size and Lattice strain of Cu2O nanoparticles then to compare the results obtained by using variance analysis method, Scherrer method and Williamson-Hall method. The results of these methods of the same powder which is cuprous oxide, using equations during the determination the crystallite size and lattice strain, It was found that the results obtained the values of the crystallite size (28.302nm) and the lattice strain (0.03541) of the variance analysis method respectively and for the Williamson-Hall method were the results of the crystallite size (21.678nm) and lattice strain (0.00317) respectively, and Scherrer method which gives the value of c
... Show MoreThis research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.
