Research Article
Novel Local Demulsifiers for Crude Oil Emulsion Treatment in Oil and Gas Industry
Emuchuo Chukwudi,
Osokogwu Uche*
Issue:
Volume 9, Issue 2, December 2025
Pages:
48-54
Received:
24 June 2025
Accepted:
7 July 2025
Published:
28 July 2025
Abstract: Emulsion is the mixture of two immiscible liquid (water and oil) that found themselves together under agitation and turbulence in the presence of emulsifying agents like resins, fines, paraffins, sand etc. Crude oil emulsion is one of the major challenges in petroleum production and processing operations in the oil and gas industry. Several methods like chemical, thermal, and electrical or combination have been adopted to surmount these production challenges in the industry. In this study, the focus is on the chemical method (demulsifier) hence it is the most widely used method in Nigeria. The aim of this study is to develop a novel local demulsifier to address and avoid the formation of crude oil emulsions in the oil and gas sector while the objectives are to design novel demulsifiers from a local source, treat crude oil emulsion at various bottle test ratios and to determine the percentage of basic sediments and water (BS&W) left in the treated crude emulsion. An emulsion sample of crude oil was obtained and treated with three reagents. The analysis of the three reagents revealed that the treated crude substance formed an emulsion. The LD2 reagent demonstrated the most effective treatment in the confirmatory test, resulting in 86% oil, 12% sludge, and 2% water at a ratio of 0.2. Servo and LD1 both confirmed that the substance is composed of 90% oil and 10% contaminants, with a ratio of 0.6. LD1 outperformed Servo in the following ratios. Locally sourced demulsifiers demonstrate a high capacity to resolve emulsion challenges in the oil and gas sector and can serve as a cost-effective alternative to foreign demulsifiers, given their biodegradable nature.
Abstract: Emulsion is the mixture of two immiscible liquid (water and oil) that found themselves together under agitation and turbulence in the presence of emulsifying agents like resins, fines, paraffins, sand etc. Crude oil emulsion is one of the major challenges in petroleum production and processing operations in the oil and gas industry. Several methods...
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Research Article
Predicting Liquid Loading in Gas Condensate Wells Using Machine Learning to Enhance Production Efficiency
Issue:
Volume 9, Issue 2, December 2025
Pages:
55-66
Received:
21 June 2025
Accepted:
4 July 2025
Published:
30 July 2025
Abstract: Liquid loading in gas condensate wells drastically lowers gas production and increases operating expenses if unmanaged. The traditional empirical model often has difficulty representing the complex behaviours of multiphase flow and typically rely solely on historical data. In contrast, this study introduces a novel machine learning approach using a non-linear regression that integrates both historical and live data to predict liquid loading events in gas condensate wells with greater precision and adaptability. The newly developed machine learning Algorithm exhibited a very significant performance achieving an RMSE of 1.1293Mscf/d, MSE of 1.561 and R2 of 0.9978. The results surpass other machine learning approaches including the hybrid model with an RMSE of 2.8639 and R2 of 0.9978 and the Feed forward neural network, which have the value of R2 of 0.9833 respectively. The model’s streamlined architecture requires moderate data volume and low computational power making it suitable for real time monitoring and seamless integration into digital oil field systems which improves usability. Also, its accuracy relies on high-quality data input, highlighting the importance of a strong sensor network. With lower computing power requirements and the ability to adjust to different field conditions, this makes it a practical, scalable tool and a cost-effective solution that improves decision-making in oil and gas field operations through insight based on data. This dual data driven approach offers a practical advancement over existing models, significantly contributing to the optimization of hydrocarbon recovery.
Abstract: Liquid loading in gas condensate wells drastically lowers gas production and increases operating expenses if unmanaged. The traditional empirical model often has difficulty representing the complex behaviours of multiphase flow and typically rely solely on historical data. In contrast, this study introduces a novel machine learning approach using a...
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