The aim of this research is to study the influence of additives on the properties of soap greases, such as lithium, calcium, sodium, lithium-calcium grease, by adding varies additives, such as graphite, molybdenum disulfide, carbon black, corrosion inhibitor, and extreme pressure.
These additives have been added to grease to obtain the best percentages that improve the properties of grease such as load carrying, wear resistance, corrosion resistance, drop point, and penetration.
The results showed the best weight percentages to all types of grease which give good properties are 1.5% extreme pressure additive, 3% graphite, 1% molybdenum disulfide, 2.5% carbon black.
The other hand, the best weight percentage for corrosion inhibitor is 1% to lithium-calcium grease, 2% to lithium grease, and 3% to sodium grease. It was concluded that there is no need to add corrosion inhibitor to calcium grease
Effect of Additives on the Properties of Different Types of Greases
Greases
Lubrication
Lubricating oil
Publication Date
Sun Jun 23 2019
Journal Name
American Rock Mechanics Association
Using an Analytical Model to Predict Collapse Volume During Drilling: A Case Study from Southern Iraq
reservoir geomechanics
strength
Rock mechanics
borehole collapse
Reservoir Characterization
Upstream Oil & Gas
pore pressure
Wellbore Design
horizontal stress
Drilling
Zubair Formation is one of the richest petroleum systems in Southern Iraq. This formation is composed mainly of sandstones interbedded with shale sequences, with minor streaks of limestone and siltstone. Borehole collapse is one of the most critical challenges that continuously appear in drilling and production operations. Problems associated with borehole collapse, such as tight hole while tripping, stuck pipe and logging tools, hole enlargement, poor log quality, and poor primary cement jobs, are the cause of the majority of the nonproductive time (NPT) in the Zubair reservoir developments. Several studies released models predicting the onset of borehole collapse and the amount of enlargement of the wellbore cross-section. However, assump
... Show MorePublication Date
Mon May 04 2020
Journal Name
Offshore Technology Conference
Hydrate Equilibrium Model for Gas Mixtures Containing Methane, Nitrogen and Carbon Dioxide
Abstract<p>Gas hydrate formation is considered one of the major problems facing the oil and gas industry as it poses a significant threat to the production, transportation and processing of natural gas. These solid structures can nucleate and agglomerate gradually so that a large cluster of hydrate is formed, which can clog flow lines, chokes, valves, and other production facilities. Thus, an accurate predictive model is necessary for designing natural gas production systems at safe operating conditions and mitigating the issues induced by the formation of hydrates. In this context, a thermodynamic model for gas hydrate equilibrium conditions and cage occupancies of N2 + CH4 and N2 + CO4 gas mix</p>
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gas hydrate
cavity
equilibrium condition
flow assurance
modeling & simulation
hydrate equilibrium condition
upstream oil & gas
cage occupancy
hydrate
gas mixture
(6)
Publication Date
Thu Mar 18 2010
Journal Name
Spe Projects, Facilities & Construction
Correlating Optimum Stage Pressure for Sequential Separator Systems
Summary<p>A study to find the optimum separators pressures of separation stations has been performed. Stage separation of oil and gas is accomplished with a series of separators operating at sequentially reduced pressures. Liquid is discharged from a higher-pressure separator into the lower-pressure separator. The set of working separator pressures that yields maximum recovery of liquid hydrocarbon from the well fluid is the optimum set of pressures, which is the target of this work.</p><p>A computer model is used to find the optimum separator pressures. The model employs the Peng-Robinson equation of state (Peng and Robinson 1976) for volatile oil. The application of t</p>
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separation and treating
PVT measurement
optimization problem
Upstream Oil & Gas
Phase Behavior
objective function
optimum stage pressure
GOR
Artificial Intelligence
(15)
(13)
