In the present study, Al-SiC-ZrO2 nanocomposites were developed and characterized. Towards this direction, the aluminum (Al) matrix was reinforced with nano-sized silicon carbide (SiC) and zirconium dioxide (ZrO2), and the mixture was blended using ball milling technique. The blended powder was compacted and sintered in a microwave sintering furnace at 550 °C with a heating rate of 10 °C/min and a dwell time of 30 min. The amount of SiC reinforcement was fixed to 5 wt.%, while the concentration of ZrO2 was varied from 3 to 9 wt.% to elucidate its effect on the microstructural and mechanical properties of the developed nanocomposites. Microstructural analysis revealed the presence and uniform distribution of reinforcements into the Al matrix without any significant agglomeration. The mechanical properties of Al-SiC-ZrO2 nanocomposites (microhardness and compressive strength) were observed to increase with the increase in the concentration of ZrO2 nanoparticles into the matrix. Al-SiC-ZrO2 nanocomposites containing 9 wt.% of ZrO2 nanoparticles demonstrated superior hardness (67 ± 4 Hv), yield strength (103 ± 5 MPa), and compressive strength (355 ± 5 MPa) when compared to pure Al and other compositions of the synthesized composites. Al-SiC-ZrO2 nanocomposites exhibited the shear mode of fracture under compression loadings, and the degree of deformation was restricted due to the work hardening effect. The appealing properties of Al-SiC-ZrO2 nanocomposites make them attractive for industrial applications.

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This research aims to identify the best machine learning (ML) classification techniques for classifying the flow regimes in vertical gas-liquid two-phase flow. Two-phase flow regime identification is crucial for many operations in the oil and gas industry. Processes such as flow assurance, well control, and production rely heavily on accurate identification of flow regimes for their respective systems' smooth functioning. The primary motivation for the proposed ML classification algorithm selection processes was drilling and well control applications in Deepwater wells. The process started with vertical two-phase flow data collection from literature and two different flow loops. One, a 140 ft. tall vertical flow loop with a centralized inner metal pipe and a larger outer acrylic pipe. Second, an 18-ft long flow loop, also with a centralized, inner metal drill pipe. After extensive experimental and historical data collection, supervised and unsupervised ML classification models such as Multi-class Support vector machine (MCSVM), K-Nearest Neighbor Classifier (KNN), K-means clustering, and hierarchical clustering were fit on the datasets to separate the different flow regions. The next step was fine-tuning the models' parameters and kernels. The last step was to compare the different combinations of models and refining techniques for the best prediction accuracy and the least variance. Among the different models and combinations with refining techniques, the 5- fold cross-validated KNN algorithm, with 37 neighbors, gave the optimal solution with a 98% classification accuracy on the test data. The KNN model distinguished five major, distinct flow regions for the dataset and a few minor regions. These five regions were bubbly flow, slug flow, churn flow, annular flow, and intermittent flow. The KNN-generated flow regime maps matched well with those presented by Hasan and Kabir (2018). The MCSVM model produced visually similar flow maps to KNN but significantly underperformed them in prediction accuracy. The MCSVM training errors ranged between 50% - 60% at normal parameter values and costs but went up to 99% at abnormally high values. However, their prediction accuracy was below 50% even at these highly overfitted conditions. In unsupervised models, both clustering techniques pointed to an optimal cluster number between 10 and 15, consistent with the 14 we have in the dataset. Within the context of gas kicks and well control, a well-trained, reliable two-phase flow region classification algorithm offers many advantages. When trained with well-specific data, it can act as a black box for flow regime identification and subsequent well-control measure decisions for the well. Further advancements with more robust statistical training techniques can render these algorithms as a basis for well-control measures in drilling automation software. On a broader scale, these classification techniques have many applications in flow assurance, production, and any other area with gas-liquid two-phase flow.

This paper presents a follow-up study to Manikonda et al. (2021), which identified the best machine learning (ML) models for classifying the flow regimes in vertical gas-liquid two-phase flow. This paper replicates their study but with horizontal, gas-liquid two-phase flow data. Many workflows in the energy industry like horizontal drilling and pipeline fluid transport involve horizontal two-phase flows. This work and Manikonda et al. (2021) focus on two-phase flow applications during well control and extended reach drilling. The study started with a comprehensive literature survey and legacy data collection, followed by additional data collection from original experiments. The experimental data originates from a 20-ft long inclinable flow loop, with an acrylic outer tube and a PVC inner tube that mimics a horizontal drilling scenario. Following these data collection and processing exercises, we fit multiple supervised and unsupervised machine learning (ML) classification models on the cleaned data. The models this study investigated include K-nearest-neighbors (KNN) and Multi-class support vector machine (MCSVM) in supervised learning, along with K-means and Hierarchical clustering in unsupervised learning. The study followed this step with model optimization, such as picking the optimal K for KNN, parameter tuning for MCSVM, deciding the number of clusters for K-means, and determining the dendrogram cutting height for Hierarchical clustering. These investigations found that a 5-fold cross-validated KNN model with K = 50 gave an optimal result with a 97.4% prediction accuracy. The flow maps produced by KNN showed six major and four minor flow regimes. The six significant regimes are Annular, Stratified Wavy, Stratified Smooth at lower liquid superficial velocities, followed by Plug, Slug, and Intermittent at higher liquid superficial velocities. The four minor flow regions are Dispersed Bubbly, Bubbly, Churn, and Wavy Annular flows. A comparison of these KNN flow maps with those proposed by Mandhane, Gregory, and Aziz (1974) showed reasonable agreement. The flow regime maps from MCSVM were visually similar to those from KNN but severely underperformed in terms of prediction accuracy. MCSVM showed a 99% training accuracy at very high parameter values, but it dropped to 50% - 60% at typical parameter values. Even at very high parameter values, the test prediction accuracy was only at 50%. Coming to unsupervised learning, the two clustering techniques pointed to an optimal cluster number between 13-16. A robust horizontal two-phase flow classification algorithm has many applications during extended reach drilling. For instance, drillers can use such an algorithm as a black box for horizontal two-phase flow regime identification. Additionally, these algorithms can also form the backbone for well control modules in drilling automation software. Finally, on a more general level, these models could have applications in production, flow assurance, and other processes where two-phase flow plays an important role.

Gas kick is a well control problem and is defined as the sudden influx of formation gas into the wellbore. This sudden influx, if not controlled, may lead to a blowout problem. An accidental spark during a blowout can lead to a catastrophic oil or gas fire. This makes early gas kick detection crucial to minimize the possibility of a blowout. The conventional kick detection methods such as the pit gain and flow rate method have very low sensitivity and are time-consuming. Therefore, it is required to identify an alternative kick detection method that could provide real-time readings with higher sensitivity. In this study, Electrical Resistance Tomography (ERT) and dynamic pressure techniques have been used to investigate the impact of various operating parameters on gas volume fraction and pressure fluctuation for early kick detection. The experiments were conducted on a horizontal flow loop of 6.16 m with an annular diameter ratio of 1.8 for Newtonian fluid (Water) with varying pipe inclination angle (0–10°) and annulus eccentricity (0–30%), liquid flow rate (165–265 kg/min), and air input pressure (1–2 bar). The results showed that ERT is a promising tool for the measurement of in-situ gas volume fraction. It was observed that the liquid flow rate, air input pressure and inclination has a much bigger impact on gas volume fraction whereas eccentricity does not have a significant influence. An increase in the liquid flow rate and eccentricity by 60% and 30% decreased the gas volume fraction by an average of 32.8% and 5.9% respectively, whereas an increase in the inclination by 8° increased the gas volume fraction by an average 42%. Moreover, it was observed that the wavelet analysis of the pressure fluctuations has good efficacy for real-time kick detection. Therefore, this study will help provide a better understanding of the gas-liquid flow and potentially provide an alternative method for early kick detection.