Asphaltene Deposition Measurement and Modeling for Flow Assurance of Subsea Tubings and Pipelines

Author(s):  
Kamran Akbarzadeh ◽  
Dmitry Eskin ◽  
John Ratulowski ◽  
Shawn David Taylor
2020 ◽  
Author(s):  
Naima Bestaoui-Spurr ◽  
Frances DeBenedictis ◽  
Marty Usie ◽  
Sumit Bhaduri

2019 ◽  
Vol 25 (8) ◽  
pp. 113-128
Author(s):  
Ali Anwar Ali ◽  
Mohammed S. Al-Jawad ◽  
Abdullah A. Ali

Asphaltene is a component class that may precipitate from petroleum as a highly viscous and sticky material that is likely to cause deposition problems in a reservoir, in production well, transportation, and in process plants. It is more important to locate the asphaltene precipitation conditions (precipitation pressure and temperature) before the occurring problem of asphaltene deposition to prevent it and eliminate the burden of high treatment costs of this problem if it happens. There are different models which are used in this flow assurance problem (asphaltene precipitation and deposition problem) and these models depend on experimental testing of asphaltene properties. In this study, the used model was equation of state (EOS) model and this model depends on PVT data and experimental data of asphaltene properties (AOP measurement) and its content (asphaltene weight percent). The report of PVT and flow assurance of the live oil from the well (HFx1) of the zone of case study (Sadi formation in Halfaya oil field) showed that there is a problem of asphaltene precipitation depending on asphaltene onset pressure (AOP) test from this report which showed high AOP greater than local reservoir pressure. Therefore this problem must be studied and the conditions of forming it determined. In the present work, the asphaltene precipitation of Halfaya oil field was modeled based on the equation of state (EOS) by using Soave-Redlich-Kwong (SRK) equation which gave the best matching with the experimental data. The main result of this study was that the reservoir conditions (pressure and temperature) were located in the asphaltene precipitation region which means that the asphaltene was precipitated from the oil and when the pressure of the reservoir decreases more with oil production or with time it will cause asphaltene deposition in the reservoir by plugging the pores and reducing the permeability of the formation.  


2021 ◽  
Author(s):  
John Lovell ◽  
Dalia Salim Abdallah ◽  
Rahul Mark Fonseca ◽  
Mark Grutters ◽  
Sameer Punnapala ◽  
...  

Abstract Asphaltene deposition presents a significant flow assurance to oil production in many parts of the Middle East and beyond. Until recently, there had been no intervention-free approach to monitor deposition in the asphaltene affected wells. This prompted ADNOC to sponsor MicroSilicon to develop of an intervention less real-time sensor device to monitor asphaltene deposition. This new state-of-the-art device is currently installed and automatically collecting data at the wellhead and nearby facilities of an ADNOC operated field. Historic ways of measuring asphaltene in oil relied upon laboratory processes that extracted the asphaltene using a combination of solvents and gravimetric techniques. Paramagnetic techniques offer a potentially simpler alternative because it is known that the spins per gram of an oil is a constant property of that oil, at least when the oil is at constant temperature and pressure. Taking the device to the field means that any interpretation needs to be made independent of these properties. Additionally, the fluid entering the sensor is multiphase and subject to varying temperature and pressure which raises challenges for the conversion of raw spectroscopic data into asphaltene quantity and particle size. These challenges were addressed with a combination of hardware, software and cloud-based machine learning technologies. Oil from over two dozen wells has been sampled in real-time and confirmed that the asphaltene percentage does not just vary from well to well but is also a dynamic aspect of production, with some wells having relatively constant levels and others showing consistent variation. One other well was placed on continuous observation and showed a decrease in asphaltene level following a choke change at the surface. Diagnostic data enhanced by machine learning complements the asphaltene measurement and provides a much more complete picture of the flow assurance challenge than had been previously been available.


SPE Journal ◽  
2021 ◽  
pp. 1-16
Author(s):  
Minhui Qi ◽  
Rouzbeh Ghanbarnezhad Moghanloo ◽  
Xin Su ◽  
Mingzhong Li

Summary Asphaltene deposition triggers serious flow assurance issues and can significantly restrict the production capacity. Because of the complexity associated with asphaltene deposition that includes several mechanisms acting simultaneously, an accurate prediction of asphaltene blockage along the wellbore requires integration of asphaltene precipitation, aggregation, and deposition. In this work, an integrated simulation approach is proposed to predict the asphaltene deposition profile along the wellbore. The proposed approach is novel because it integrates various deposition patterns of particulate flow (which depends on hydrodynamics) with aggregation processes to investigate how the distribution of asphaltene particle size varies (governed by molecular dynamics) after being precipitated out of the oil phase (controlled by thermodynamics). To improve the predictability capability of simulations, a direct input from the wellbore flow simulator is used to update the velocity profile after the wellbore radius changes beyond a certain predefined threshold. The fraction of asphaltene precipitation is determined using the asphaltene solubility model and combined with aggregation models to feed into deposition calculations. Wellbore blockage was examined for two cases with and without the aggregation mechanism included. A sensitivity analysis was carried out to study parameters that affect the severity of blockage, such as range of pressure-temperature along the wellbore, flow velocity, and radial distribution of asphaltene particles. The simulation approach proposed in this paper provides an in-depth understanding of the wellbore flow assurance issues caused by asphaltene deposition and thus provides useful insights for improving the predictions of production performance.


2011 ◽  
Vol 26 (1) ◽  
pp. 495-510 ◽  
Author(s):  
Kamran Akbarzadeh ◽  
Dmitry Eskin ◽  
John Ratulowski ◽  
Shawn Taylor

2016 ◽  
Author(s):  
Hassan Karimi ◽  
Erni Dharma Putra ◽  
Kapil Kumar Thakur ◽  
Rahel Yusuf ◽  
Azwan Shaharun ◽  
...  

Author(s):  
Bohui Shi ◽  
Shangfei Song ◽  
Yuchuan Chen ◽  
Xu Duan ◽  
Qingyun Liao ◽  
...  

2010 ◽  
Author(s):  
Ali Rezaian ◽  
Amin Kordestany ◽  
Mohammad Jamialahmadi ◽  
Jamshid Moghadasi ◽  
Mohammad Yousefi Khoshdaregi ◽  
...  

2005 ◽  
Author(s):  
Paul H. Javora ◽  
Xiaolan Wang ◽  
Jack Burman ◽  
Kevin Dale Renfro ◽  
Michael Andrew Weaver ◽  
...  

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