The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti

The subject of social capital is of great importance, as it provides an analytical framework for researchers interested in understanding the impact of the Internet on many dimensions of human and social life. And because social networking sites have infiltrated societies and produced new variables at the level of human and social communication, this study came to examine the relationship between the use of the social networking site Facebook and the accumulation of social capital among a sample of University of Algeria 3 students, by researching the variables of intensity of use and patterns of use as independent variables and their relationship to social capital. The results of the field study revealed a strong relationship between the

In the present work the performance of semifluidized bed adsorber was evaluated for removal of phenolic compound from wastewater using commercial activated carbon as adsorbent. P-chlorophenol (4-Chlorophenol) and o-cresol (2-methylphenol) was selected as a phenolic compound for that purpose. The phenols percent removal, in term of breakthrough curves were studied as affected by hydrodynamics limitations which include minimum and maximum semifluidization velocities and packed bed formation in the column by varying various parameters such as inlet liquid superficial velocity (from U_minsf to 8U_minsf m/s), and retaining grid (sometimes referred to as adsorbent loading) to initial static bed height ratio (from 3-4.5). In

In this study, from a total of 856 mastitis cases in lactating ewes, only 34 Streptococcus agalactiae isolates showed various types of resistance to three types of antibiotics (Penicillin, Erythromycin and Tetracycline). St. agalactiae isolates were identified according to the standard methods, including a new suggested technique called specific Chromogenic agar. It was found that antibiotic bacterial resistance was clearly identified by using MIC-microplate assay (dilution method). Also, by real-time PCR technique, it was determined that there were three antibiotics genes resistance ( pbp2b, tetO and mefA ). The high percentage of isolate carried of a single gene which was the Tetracycline (20.59%) followed by percentage Penicillin was

This study investigates the potential of biogas recovery from used engine oil (UEO) by co-digestion with animals’ manure, including cow dung (CD), poultry manure (PM), and cattle manure (CM). The experimental work was carried out in anaerobic biodigesters at mesophilic conditions (37°C). Two groups of biodigesters were prepared. Each group consisted of 4 digesters. UEO was the main component in the first group of biodigesters with and without inoculum, whereby a mix of UEO and petroleum refinery oily sludge (ROS) was the component in the second group of biodigesters. The results revealed that for UEO-based biodigesters, maximum biogas production was 0.98, 1.23, 1.93, and 0 ml/g VS from UEO±CD, UEO±CM, UEO±PM, and U

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

New nanotechnology-based approaches are increasingly being investigated for enhanced oil recovery (EOR), with a particular focus on heavy oil reservoirs. Typically, the addition of a polymer to an injection fluid advances the sweep efficiency and mobility ratio of the fluid and leads to a higher crude oil recovery rate. However, harsh reservoir conditions, including high formation salinity and temperature, can limit the performance of such polymer fluids. Recently, nanofluids, that is, dispersions of nanoparticles (NPs) in a base fluid, have been recommended as EOR fluids; however, such nanofluids are unstable, even under ambient conditions. In this work, a combination of ZrO2 NPs and the polyacrylamide (PAM) polymer (ZrO2 NPs–PAM) was us

Particulate matter (PM) emitted from diesel engine exhaust have been measured in terms of mass, using
99.98 % pure ethanol blended directly, without additives, with conventional diesel fuel (gas – oil),to
get 10 % , 15 %, 20 % ethanol emulsions . The resulting PM collected has been compared with those
from straight diesel. The engine used is a stationary single cylinder, variable compression ratio Ricardo
E6/US. This engine is fully instrumented and could run as a compression or spark ignition.
Observations showed that particulate matter (PM) emissions decrease with increasing oxygenate
content in the fuel, with some increase of fuel consumption, which is due to the lower heating value of
ethanol. The reduction in