Browsing by Author "Barooah, Abinash"

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    Diagnosis of a Leaky Pipeline Carrying Multiphase Flow under Plug Flow Conditions
    (Avestia Publishing, 2025) Ferroudji, Hicham; Al-Ammari, Wahib A.; Barooah, Abinash; Hassan, Ibrahim; Hassan, Rashid; Hassan, Rashid; Gomari, Sina Rezaei; Rahman, Mohammad Azizur
    Multiphase flows are crucial to the oil and gas industry since most petroleum companies produce and transport both gas and oil simultaneously. Pipeline leaks are frequently caused by corrosion, aging, and metal deterioration. After an incident, the energy sector not only loses money but also raises environmental and safety concerns. Therefore, developing a successful tool for instantaneous leakage identification in pipelines becomes crucial. In the current work, a leaky pipeline carrying multiphase flow is numerically simulated using Ansys-Fluent under plug flow conditions. The obtained numerical results were validated against experimental data collected from an experimental setup. After that, Probability Density Function (PDF), Wavelet Transform (WT), and Empirical Mode Decomposition (EMD) methods were applied to the obtained time series signals. On the other hand, the analysis is complemented by the application of several machine learning models like Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN). For instance, it is observed that the Empirical Mode Decomposition exhibits better performance in leakage identification
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    Dimensionless data-driven model for optimizing hole cleaning efficiency in daily drilling operations
    (2021) Khaled, Mohamed Shafik; Khan, Muhammad Saad; Ferroudji, Hicham; Barooah, Abinash; Rahman, Mohammad Azizur; Hassan, Ibrahim; Hasan, A. Rashid
    Poor cuttings transport in deviated wells limit drill rate, induce excessive torque and drag, or in severe cases result in a stuck pipe. This paper presents a generalized data-driven model that utilizes statistical techniques for optimizing hole cleaning efficiency under different drilling conditions in deviated and extended reach wells. For this purpose, the model is constructed based on three approaches including extensive experiments conducted in our flow loop of 5-m horizontal length (4.5in. × 2in.), a validated Computational Fluid Dynamics (CFD) model was developed, and experimental data were collected from the literature to develop a reliable predictive tool that can estimate cuttings concentration in deviated wells. The developed model utilized a non-linear regression method, and was trained with 75% of the gathered data and validated with the remaining 25% to ensure the capability of the proposed model for accurate estimation of cuttings accumulation under different conditions. Unique dimensionless parameters were developed to shift the model results from lab-scale to field-scale applications. Findings revealed that the developed model provides promising results in estimating cuttings accumulation in deviated wells (20–90° from vertical). Predicted points lay in between 30% error margin in most cases, and the relation between estimated and measured cuttings accumulation has an adjusted R2 = 0.9. The proposed model outperforms the Duan, and Song models and introduces new dimensionless parameters to characterize hole cleaning efficiency during daily operations. The developed model proves to be a robust tool for simulating cuttings transport in real-time, monitoring cuttings accumulation, improving drilling efficiency, and avoiding Non-Productive Time (NPT) related to hole cleaning issues