This study experimentally investigated Free-Fall Gravity Drainage (FFGD) under combination-drive conditions in a two-dimensional Hele-Shaw model representing a water-drive reservoir. An initially high gravity potential from the oil column enabled early oil drainage before aquifer support became dominant. Three water-drive strengths were tested, demonstrating that a stronger aquifer (1.15 psig) accelerated oil recovery to approximately 75% of the original oil in place (OOIP) within 60 minutes, resulting in a final recovery of 79.5%. However, this was accompanied by rapid water breakthrough after 2.5 minutes and high-water cuts exceeding 90%. In contrast, a weaker aquifer (0.725 psig) stabilized the oil–water contact, delaying w

Conducted Althilelat chemical models of crude oil back to the reservoir Fertile from the fields of Baghdad and Kut and models of crude oil back to the reservoir ??????? of Haklbe Tikrit and Baghdad were calculated their properties Alvezaúah Kalkthaqh and weight, quality and degree of August j (API) and know the quality Nfothma that was light or heavy and make the comparison between Alinvtin also conducted chemical analyzes of the two models of Almia associated with each of the oil above Almkmnin and measured Ktvthma and Zojithma and concentrations of some dissolved salts in them and clarify the relationship between the oil reservoir and water associated with oil fields...

RG Majeed, AS Ahmed, Jornal of Al-Muthanna for Agricultural Sciences, 2023

In this research an Artificial Neural Network (ANN) technique was applied for the prediction of Ryznar Index (RI) of the flowing water from WTPs in Al-Karakh side (left side) in Baghdad city for year 2013. Three models (ANN1, ANN2 and ANN3) have been developed and tested using data from Baghdad Mayoralty (Amanat Baghdad) including drinking water quality for the period 2004 to 2013. The results indicate that it is quite possible to use an artificial neural networks in predicting the stability index (RI) with a good degree of accuracy. Where ANN 2 model could be used to predict RI for the effluents from Al-Karakh, Al-Qadisiya and Al-Karama WTPs as the highest correlation coefficient were obtained 92.4, 82.9 and 79.1% respectively. For

This study investigated a novel application of forward osmosis using different types of draw solutions for oilfield produced water treatment from the East Baghdad oilfield affiliated to the Midland Oil Company (Iraq). Magnesium chloride (MgCl2) and magnesium sulfate (MgSO4) were used as draw solutions in forward osmosis process to evaluate their effectiveness. Experiments were conducted in a laboratory scale forward osmosis system with cellulose triacetate hollow fiber membrane. In this work, sodium chloride solution was used as a feed solution with a concentration of 76 g/L (same concentration as the East Baghdad oilfield produced water) and the applied external pressure on the feed solution side was 2 bar. The impact of draw solution (DS)

Total dissolved solids are at the top of the parameters list of water quality that requires investigations for planning and management, especially for irrigation and drinking purposes. If the quality of water is sufficiently predictable, then appropriate management is possible. In the current study, Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models were used as indicators of water quality and for the prediction of Total Dissolved Solids (TDS) along the Tigris River, in Baghdad city. To build these models five water parameters were selected from the intakes of four water treatment plants on the Tigris River, for the period between 2013 and 2017. The selected water parameters were Total Dissolved Solids (TDS

Wearable sensors are a revolutionary tool in agriculture because they collect accurate data on plant environmental conditions that affect plant growth in real-time. Moreover, this technology is crucial in increasing agricultural sustainability and productivity by improving irrigation strategies and water resource management. This review examines the role of wearable sensors in measuring plant water content, leaf and air humidity, stem flow, plant and air temperature, light, and soil moisture sensors. Wearable sensors are designed to monitor various plant physiological parameters in real-time. These data, obtained through wearable sensors, provide information on plant water use and physiology, making our agricultural choices more informed an