Experimental Study on Wax-Deposition Characteristics of a Waxy Crude Oil Under Single-Phase Turbulent-Flow Conditions
Crude oil, having a paraffin nature, has been studied extensively in the small-scale flow loop at Tulsa University Paraffin Deposition Projects (TUPDP). The effects of turbulence/shear and thermal driving force on wax-deposition characteristics were experimentally studied using a waxy crude oil from the Gulf of Mexico. The test matrix consisted of a total of 15 experiments, which included 12 short-term tests and three long-term tests. The tests were conducted under different operating conditions with a wide range of Reynolds numbers from 3,700 to 20,500. The shear stress ranged from 5.4 to 53.9 Pa.
It was observed that paraffin deposition is highly dependent on the thermal effective driving force, which is the temperature difference between oil bulk and initial inner pipe wall, and also on turbulence effects. The deposit thickness obtained using both the pressure-drop method and a direct measurement was found to decrease with increasing shear stress and decreasing thermal driving force. The wax content showed a gradual increase with an increase in flow rate. For the short-term tests, the deposit mass with no entrained oil seemed to increase and then decrease with an increase in initial shear stress and decrease in effective thermal driving force, whereas the total deposit mass was found to decrease with an increase in initial shear stress or decrease in effective thermal driving force.
Addressing the Gaps in Subsea Produced Water Treatment
Operators are looking for ways to better handle water coming from subsea wells, which is typically treated at topside facilities. Subsea separation systems are not equipped to discharge water back into the reservoir, so how do companies close the gaps?
Neural Networks Plus CFD Speed Up Simulation of Fluid Flow
High-fidelity 3D engineering simulations are valuable in making decisions, but they can be cost-prohibitive and require significant amounts of time to execute. The integration of deep-learning neural networks with computational fluid dynamics may help accelerate the simulation process.
Greedy Pursuit: Algorithms Show Promise in Measuring Multiphase Flow
“Greedy pursuit” in the realm of algorithms is a good thing. Saudi Aramco studied such algorithms to produce images simulating the flow inside a pipe’s cross section, possibly reducing the need for separator-based multiphase flowmeters.
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04 June 2019
03 June 2019