Changing oil-wet surfaces toward higher water wettability is of key importance in subsurface engineering applications. This includes petroleum recovery from fractured limestone reservoirs, which are typically mixed or oil-wet, resulting in poor productivity as conventional waterflooding techniques are inefficient. A wettability change toward more water-wet would significantly improve oil displacement efficiency, and thus productivity. Another area where such a wettability shift would be highly beneficial is carbon geo-sequestration, where compressed CO2 is pumped underground for storage. It has recently been identified that more water-wet formations can store more CO2. We thus examined how silica based nanofluids can induce such a wettability shift on oil-wet and mixed-wet calcite substrates. We found that silica nanoparticles have an ability to alter the wettability of such calcite surfaces. Nanoparticle concentration and brine salinity had a significant effect on the wettability alteration efficiency, and an optimum salinity was identified, analogous to that one found for surfactant formulations. Mechanistically, most nanoparticles irreversibly adhered to the oil-wet calcite surface (as substantiated by SEM–EDS and AFM measurements). We conclude that such nanofluid formulations can be very effective as enhanced hydrocarbon recovery agents and can potentially be used for improving the efficiency of CO2 geo-storage.
In this study, gamma-ray spectrometry with an HPGe detector was used to measure the specific activity concentrations of 226Ra, 232Th, and 40K in soil samples collected from IT1 oil reservoirs in Kirkuk city, northeast Iraq. The “spectral line Gp” gamma analysis software package was used to analyze the spectral data. 226Ra specific activity varies from 9 0.34 Bq.kg-1 to 17 0.47 Bq.kg-1. 232Th specific activity varies from 6.2 0.08 Bq.kg-1 to 18 0.2 Bq.kg-1. 40K specific activity varies from 25 0.19 Bq.kg-1 to 118 0.41 Bq.kg-1. The radiological hazard due to the radiation emitted from natural r
... Show MoreThis study is concerned with making comparison in using different geostatistical methods for porosity distribution of upper shale member - Zubair formation in Luhais oil field which was chosen to study.
Kriging, Gaussian random function simulation and sequential Gaussian simulation geostatistical methods were adopted in this study. After preparing all needed data which are contour map, well heads of 12 wells, well tops and porosity from CPI log. Petrel software 2009 was used for porosity distribution of mentioned formation in methods that are showed above. Comparisons were made among these three methods in order to choose the best one, the comparing cri
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
... Show MoreGas-lift technique plays an important role in sustaining oil production, especially from a mature field when the reservoirs’ natural energy becomes insufficient. However, optimally allocation of the gas injection rate in a large field through its gas-lift network system towards maximization of oil production rate is a challenging task. The conventional gas-lift optimization problems may become inefficient and incapable of modelling the gas-lift optimization in a large network system with problems associated with multi-objective, multi-constrained, and limited gas injection rate. The key objective of this study is to assess the feasibility of utilizing the Genetic Algorithm (GA) technique to optimize t
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
... Show MoreOil pollution of the soil due to a leakage in oil tubes, transportation of products, or during oil excavations can change the soil physical and mechanical, chemical, and biological properties. Consequently, the soil may or may not be eligible for engineering construction projects and it may need a significant treatment. Therefore, it is required to have a better understanding of the general behavior and the corresponding geotechnical properties upon pollution particularly for those areas associated with oil explorations and industry like Thi-Qar Governorate. Fine and coarse soils from two sites at the University of Thi-Qar are artificially contaminated with oil products ranging from 0% to 10% of their dry weight. Testing programs have been
... Show MoreThe Jeribe reservoir in the Jambour Oil Field is a complex and heterogeneous carbonate reservoir characterized by a wide range of permeability variations. Due to limited availability of core plugs in most wells, it becomes crucial to establish correlations between cored wells and apply them to uncored wells for predicting permeability. In recent years, the Flow Zone Indicator (FZI) approach has gained significant applicability for predicting hydraulic flow units (HFUs) and identifying rock types within the reservoir units. This paper aims to develop a permeability model based on the principles of the Flow Zone Indicator. Analysis of core permeability versus core porosity plot and Reservoir Quality Index (RQI) - Normalized poros
... Show MoreThis effort is related to describe and assess the performance of the Iraqi cement sample planned for oil well-cementing jobs in Iraq. In this paper, major cementing properties which are thickening time, compressive strength, and free water in addition to the rheological properties and filtration of cement slurry underneath definite circumstances are experimentally tested. The consequences point to that the Iraqi cement after special additives encounter the requests of the API standards and can consequently is used in cementing jobs for oil wells. At this research, there is a comparative investigation established on experimental work on the effectiveness of some additives that considered as waste materials which are silica fume, baux
... Show MoreThis effort is related to describe and assess the performance of the Iraqi cement sample planned for oil well-cementing jobs in Iraq. In this paper, major cementing properties which are thickening time, compressive strength, and free water in addition to the rheological properties and filtration of cement slurry underneath definite circumstances are experimentally tested. The consequences point to that the Iraqi cement after special additives encounter the requests of the API standards and can consequently is used in cementing jobs for oil wells. At this research, there is a comparative investigation established on experimental work on the effectiveness of some additives that considered as waste materials which are silica fume, bauxite,
... Show MoreThis review examines how artificial intelligence (AI) including machine learning (ML), deep learning (DL), and the Internet of Things (IoT) is transforming operations across exploration, production, and refining in the Middle Eastern oil and gas sector. Using a systematic literature review approach, the study analyzes AI adoption in upstream, midstream, and downstream activities, with a focus on predictive maintenance, emission monitoring, and digital transformation. It identifies both opportunities and challenges in applying AI to achieve environmental and economic goals. Although adoption levels vary across the region, countries such as Saudi Arabia, the UAE, and Qatar are leading initiatives that align with global sustainability targets.
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