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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Research/Technical Note
Leveraging Data-driven Approach for Sand Production Prediction and Management in Oil and Gas Wells
Issue:
Volume 9, Issue 2, December 2025
Pages:
67-83
Received:
4 July 2025
Accepted:
21 July 2025
Published:
5 August 2025
Abstract: Sand production poses a major challenge in wells completed within sandstone reservoirs, despite their inherent benefits. It could lead to erosion of surface and subsurface equipment, well plugging from sand grain deposition and accumulation, and potential collapse of sections in horizontal wells, resulting in non-productive time, costly cleanup, and remedial or work-over operations. Various researchers have proposed strategies and models to tackle sand production and its associated problems. While these models have successfully predicted the onset of sand production, cavity stability, and rock collapse, they often struggle to accurately forecast the sand production rate due to the complexity of influencing parameters and the difficulty in fully capturing all reservoir and wellbore processes. Early prediction of sand production before drilling and field development is critical for selecting appropriate equipment and mitigation strategies. A thorough evaluation of sanding potential based on key reservoir and wellbore properties is essential to inform field development plans. In this study, a ν-support vector classification (ν-SVC) model was used to classify sanding potential, achieving 100% accuracy on the training set and 87% on the test set. For wells identified as having sanding potential, the sand occurrence was predicted using critical drawdown pressure estimated by a Random Forest model, also with 97.65% training and 88.89% test accuracy. The expected volume of sand produced over time was then predicted using an AdaBoost regression model with 99.51% training and 79.07% test accuracy. Together, these models form a predictive pipeline for proactive sand control and management. Their effectiveness was validated using publicly available field data, demonstrating strong predictive capability for real-world applications. The results indicate that these machine learning models can support completion engineers in designing timely and effective sand management strategies, minimizing production losses and operational risks.
Abstract: Sand production poses a major challenge in wells completed within sandstone reservoirs, despite their inherent benefits. It could lead to erosion of surface and subsurface equipment, well plugging from sand grain deposition and accumulation, and potential collapse of sections in horizontal wells, resulting in non-productive time, costly cleanup, an...
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