Abstract
the oil industry is one in constant need of development and technological advancement. Declining oil field productivity as well as volatile oil prices, particularly after the last market crash, will always put pressure on the entire industry to innovate continuously. Although active applications of AI and 4.0 practices are considered relatively behind other industries, there are a few fields in which the oil industry can benefit from AI and the latest in 4.0 advancements. Particularly in the field of oil well surveillance, an innovative approach to diagnosing oil well problems was discovered only two years ago. By treating these wells chemically, millions of dollars in revenue have been gained at a negligible cost, and AI is being used to understand how it all works. This paper will discuss in detail the added value of automation, innovation in well surveillance, and the use of machine learning algorithms to improve our management of oil well emulsions.
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Author Keywords
- oil field,
- emulsion,
- upstream,
- surveillance,
- wells
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IEEE Keywords
- Oils,
- Chemicals,
- Production,
- Petroleum,
- Fluids,
- Pumps,
- Surveillance
Introduction
The Awali field in Bahrain is the oldest oil field in the GCC region. Starting production back in the early 30’s, the field consists of 16 stacked individual layers forming a complex range of reservoirs with different characteristics. Ranging from thick, heavy oil at its shallowest, too gassy reservoirs with light oil at its deepest, the Awali field presents a wide range of ever-changing challenges for the subsurface department.
When a field such as this one is produced for such a long time, it is often referred to as a “brown” field. Brownfields differ from newer ones in that they typically present more complex oil production challenges; declining pressure, increasing undesirable water production, and bacterial corrosion are just a few of the issues that may worsen with time in brownfields.
In December 2009, Tatweer Petroleum was founded as a joint venture between Nogaholding, Occidental Petroleum, and Mubadalah to bring together the latest technologies and increase investment in the field to revitalize production and be better able to face the challenges associated with brownfields. A large emphasis was placed on full-field automation, research and development, and improving Bahraini capabilities. One such example of how these values were combined with sustainable and resilient production solutions is with the treatment of emulsion.
An emulsion is a condition in which oil and water are intermixed at a molecular level to the extent that they do not separate easily. This may either be oil-in-water, water-in-oil, or a combination of both. These mixtures pertain a substantially increased viscosity which makes them far more difficult to flow and produce. As such, it is extremely undesirable to have emulsions in oil wells since they harm productivity. Notably, emulsions contain stabilizing agents that resist the natural separation associated with oil and gas mixtures. Some of these agents include: • Salts • Gas • Sand • Wax • Asphaltenes • Incompatible chemicals • Temperature Emulsion stability is also highly impacted by shearing forces, where flow velocity and even gas may intermix and entrain the fluids into one another. In the case of temperature, higher temperatures weaken emulsions and lower ones strengthen them. The latter is notable due to the fact that it is this behavior that is used to identify the existence of emulsions in oil wells.
Detecting
Emulsion There are over 500 naturally flowing and gas lifted wells at Tatweer Petroleum currently with surface pressure and temperature transmitters used to monitor flow.
As is the case that emulsions worsen with increasing temperature, it has been noted that this phenomenon is presented in the pressure and temperature readings as a fluctuation between day and night. Fluid from wells with emulsion flow with a higher viscosity (lower productivity) at night than they do in the day since the fluid is cooler and thicker at night.
Conclusion
The importance of continuously developing automating systems for well optimization cannot be stated enough. Increasingly aging oil fields provide a stream of problematic wells with dynamic problems. Automating production enhancement systems are the most effective way to face these challenges in order to not only face existing problems but also allow engineers the time to look for future problems and developments. Data science practices are essential for this step as AI models are needed to mimic human intelligence and detect hidden patterns that would otherwise get lost in the sea of data. We believe this to be the future of technological advancements in the oil industry.
Acknowledgment
The authors would like to acknowledge all the invaluable support acquired by the Tatweer Executive Management, Production Engineering, Facilities Engineering, and Information Technology departments in terms of data and support.
About KSRA
The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.
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FULL Paper PDF file:
Applications of AI in Oil Well SurveillanceBibliography
author={},
Year
2020
Title
Applications of AI in Oil Well Surveillance
Publish in
2019 International Conference on Fourth Industrial Revolution (ICFIR), Manama, Bahrain, 2019, pp. 1-5,
Doi
10.1109/ICFIR.2019.8894791.
PDF reference and original file: Click here
Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.
Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.
Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.
