Search Results (51)

Intelligent completion technologies, including Inflow Control Valves (ICVs), have become increasingly important for remotely managing zonal production in complex well architectures. However, quantifying flow rates and phase fractions in such systems remains challenging due to space constraints and the harsh downhole environment, which limit the deployment of conventional sensors. Distributed Acoustic Sensing (DAS) provides a promising solution by converting standard fiber-optic cables into dense arrays of acoustic sensors. While DAS has been successfully applied in applications such as integrity monitoring and leak detection, its use for direct two-phase flow characterization within intelligent completions remains largely unexplored. In this study, we present a DAS-based methodology to monitor and analyze oil–water two-phase flow in horizontal experiments that mimic field conditions. Acoustic data collected from DAS are transformed into time–frequency spectrograms using Short-Time Fourier Transform (STFT) to extract dynamic spectral features. These features are then correlated with pressure drop across the ICV and flow rate, revealing distinct frequency band behaviors associated with fluid changes. To quantify flow characteristics, a power-law model is trained using spectral features to predict flow rate and phase fractions. The results demonstrate strong predictive capability for pressure drop and flow rate under controlled laboratory conditions, highlighting the potential of DAS for multiphase flow diagnostics in field applications with intelligent completions, while water cut prediction remains challenging due to the complex and non-unique relationship between flow conditions and DAS response and is left for future work. This research not only provides new insights into the acoustic response of oil–water flows but also introduces a data-driven framework for leveraging DAS in real-time flow monitoring and control within ICV-equipped completions. Full article

Extracting hydrocarbons from complex, ultra-deepwater and high-pressure/high-temperature wells requires precise control of hydrostatic pressure to avoid well control problems. Among these, a gas kick is one of the most serious events, as it can quickly develop into a blowout with severe consequences for both safety and project cost. Traditionally, the industry has depended on reactive surface-based indicators, such as pit volume and delta flow, for early kick detection (EKD). However, these methods are often limited by data transmission delays and frequent false alarms. This review goes beyond a conventional summary by critically examining the key weaknesses of current EKD technologies. In particular, it highlights major challenges in modern sensor systems, including the difficulty of interpreting ultrasonic signals in multiphase flow and the way formation leakage can hide or distort kick indicators. It also provides a detailed and original link between specific Artificial Intelligence (AI) models and the drilling signals they are designed to analyze. Although recent studies have shown progress in downhole sensing and predictive algorithms, a significant gap still exists between theoretical models and the highly dynamic, multiphase conditions found in real wellbores. This makes it necessary to evaluate EKD technologies considering actual field demands rather than idealized assumptions. To address these limitations, this review proposes several practical directions for future work. These include the development of dynamic, multiphase, acoustic computational fluid dynamics (CFD) models to improve ultrasonic signal interpretation, the standardization of unsupervised AI models supported by synthetic data generation, the integration of unified leakage detection frameworks, the mechanical standardization of Managed Pressure Drilling (MPD) systems, and the adoption of rig-based edge computing to enable faster and more reliable real-time decision-making. Full article

Top Submerged Lance (TSL) technology is widely used in non-ferrous smelting, yet in-situ bath dynamics remain challenging to quantify because the process operates in a closed, high-temperature, highly turbulent and optically inaccessible environment. The absence of direct diagnostics limits the ability to relate operating conditions to bubble dynamics, gas penetration and bath agitation and constrains validation of multiphase CFD models under realistic conditions. This study introduces a multimodal sensing framework that combines spectral acoustic analysis with lance-mounted inertial motion sensing to characterize dynamic bath behavior across cold-model, laboratory-scale and pilot-scale systems. Water-glycerin experiments establish repeatable acoustic signatures of individual bubble-collapse events, with dominant emission bands in the 300–900 Hz range and higher-frequency components extending into the kilohertz domain. High-temperature laboratory trials using fayalitic slag reproduce these frequency regions while exhibiting depth-dependent attenuation and clear spectral separation between submerged and non-submerged lance operation. Power Spectral Density (PSD) and cumulative spectral power analyses resolve the influence of gas flow rate and lance submersion depth on acoustic spectral power distribution, while inertial measurements capture corresponding increases in vertical lance acceleration associated with back-pressure fluctuations. Pilot-scale trials at 120 Nm³/h air and 13 L/h diesel confirm that shallow lance submersion substantially increases measured acoustic spectral power below 3 kHz, whereas deeper penetration enhances periodic vertical acceleration response measured by the inertial sensor. The combined acoustic-inertial methodology provides a physically interpretable and cross-scale framework for assessing bubble collapse activity, plume interaction and bath agitation under high-temperature TSL conditions. The approach enables frequency-based diagnostics that can be systematically compared with CFD predictions of plume oscillation and collapse-related dynamics. Once baseline frequency ranges are established for a given slag system, the method can support process monitoring and may provide indirect indicators related to changes in surface agitation or foaming tendency, enabling structured data-driven analysis. The framework thus provides a practical bridge between cold-model experiments, high-temperature measurements, multiphase modeling and industrial TSL operation. Full article

Sol–gel-based optical functional sensor coatings were developed for real-time monitoring of multiphase saponification reactions in microreactors. Various pH-sensitive indicator mixtures, including bromocresol green and bromocresol purple (BCG and BCP) and methyl red–methyl orange, were incorporated into sol–gel coatings and evaluated on test plates across pH range of 2–12. Coatings with BCG and BCP 1:3 demonstrated the most pronounced color change at high pH (11–12), with distinct hue (H) transitions providing a reliable measure of local pH. These optimized coatings were integrated into microreactor channels to track the passage of oil and NaOH slugs under varying flow rates. Hue analysis produced reproducible plateaus corresponding to NaOH-rich (H = 50°) and oil-rich (H = 41°) phases, enabling droplet-level resolution of slug flow and detection of flow-regime transitions. The sensor response was fully reversible, highlighting the robustness and reusability of the coatings. Unlike previous high-resolution fluorescence-based systems, this approach relies on simple visible-light imaging and low-cost data extraction, leaving the reaction chemistry unaltered. The results demonstrate that sol–gel coatings coupled with hue-based analysis provide a practical, noninvasive, and real-time monitoring strategy for multiphase reactions in microreactors, with potential for implementation in industrial or IoT-enabled process control systems. Full article

Reliable measurement of multiphase flow is fundamental to production evaluation in complex oil and gas wells. However, conventional sensors often suffer from low integration, limited measurement capability, and potential environmental impact. To address these challenges, a photoelectric composite three-phase flow sensor is developed, integrating multiple electrode rings for water holdup and liquid-phase velocity measurement, with dual optical-fiber probes for gas holdup and gas-phase velocity detection. A slip model is further applied to quantify the dependence of slip velocity on liquid holdup based on measured phase rates. Experimental results demonstrate high sensitivity to bubble-flow structures, accurate extraction of gas holdup and phase velocities, and stable full-range water holdup calibration from 0% to 100% at 5 V and 15 V with effective temperature and salinity compensation. And compared with existing technologies, the sensor designed in this paper has the advantages of high integration, a simple structure, multiple measurement parameters, and higher water-holding capacity resolution in low-saturation areas, providing more advanced technical means for conventional profile three-phase flow logging. Full article

Accurate identification of oil–water two-phase flow patterns is essential for ensuring the safety and operational efficiency of oil and gas extraction systems. While traditional methods using empirical models and sensor technologies have provided basic insights, they often struggle to capture the nonlinear features of complex operational conditions. To address the challenge of data scarcity commonly found in experimental settings, this study employs a data augmentation strategy that combines the Synthetic Minority Over-sampling Technique (SMOTE) with Gaussian noise injection, effectively expanding the feature space from 60 original experimental nodes. Next, a physics-constrained attention mechanism model was developed that incorporates a physical constraint matrix to effectively mask irrelevant feature interactions. Experimental results show that while the standard attention model (83.88%) and the baseline BP neural network (84.25%) have limitations in generalizing to complex regimes, the proposed physics-constrained model achieves a peak test accuracy of 96.62%. Importantly, the model demonstrates exceptional robustness in identifying complex transition regions—specifically Dispersed Oil-in-Water (DO/W) flows—where it improved recall rates by about 24.6% compared to baselines. Additionally, visualization of attention scores confirms that the distribution of attention weights aligns closely with fluid-dynamic mechanisms—favoring inclination for stratified flows and flow rate for turbulence-dominated dispersions—thus validating the model’s interpretability. This research offers a novel, interpretable approach for modeling dynamic feature interactions in multiphase flows and provides valuable insights for intelligent oilfield development. Full article

Deepwater oil–gas–water three-phase flow is widely regarded as a multiphase system. Intense interfacial interactions cause significant nonuniform fluid distributions in the wellbore, giving rise to complex nonlinear dynamics. In this study, a distributed conductance sensor (DCS) was developed to capture local flow information from a horizontal oil–gas–water simulation well. To quantify the complexity of nonlinear time series, phase permutation entropy (PPE) was first validated using artificial data, including the Tent map, Hénon map, and Lorenz system. PPE demonstrates superior capability in detecting abnormal dynamical changes compared with permutation entropy (PE). Subsequently, PPE is combined with the multiscale approach, i.e., multiscale phase permutation entropy (MPPE), to analyze the DCS signals and uncover the complexity of horizontal oil–gas–water flows. The results show that the MPPE analysis can reveal the spatial distribution characteristics of elongated gas bubbles, gas paths, dispersed bubbles and oil droplets. Full article

It is demonstrated that the general representation of a dynamic multiphase wellbore flow model may be identified from the available physical measurements. The proposed approach is based on the techniques of numerical optimization and also requires the availability of solvers for the general type of partial differential equations describing two-phase gas–oil flow. A solution is obtained both for the case of the homogeneous no-slip model and the drift-flux model with velocity slip. The feasibility of the proposed approach for system identification and parameter estimation has been demonstrated using simulated flow data. Two distinct scenarios have been considered: firstly, when the well is fully instrumented with multiple pressure sensors and a multiphase flow meter, and secondly, when only a single downhole pressure gauge is available. Full article

This paper considers the problem of estimating the quantitative parameters of a two-phase fluid flow in a well based on the dynamic physical flow model. This is a challenging problem in the oil and gas industry, where the knowledge of multiphase production rates plays an important role during reservoir characterization, production optimization and reservoir management. As the direct measurement of these rates is not easily available, they can be inferred from conventional sensors (e.g., pressure gauges) in combination with a dynamic multiphase flow model. The methodology proposed in this work uses inverse modeling concepts to estimate flow rates that are not measured directly. The mismatch between the available data and model prediction is numerically minimized, leading to the optimal set of dynamic flow variables characterizing the flow. Two different scenarios are considered: firstly, when the well has only a flow meter located at the wellhead (minimum amount of available information), and when the well has distributed pressure sensors in addition to the topside flow meter (maximum amount of information). The feasibility of the proposed concept is assessed via several simulation-based case studies. Full article

This work presents a comprehensive review of the evolution in modeling reactive extrusion (REx), tracing developments from early analytical models to advanced computational fluid dynamics (CFD) simulations. Additionally, it highlights the key challenges and future directions in this field. Analytical models to describe the velocity profiles were proposed in the 1950s, involving certain geometrical simplifications. However, numerical models of melt polymeric flow in extruders have proven to be crucial for optimizing screw design and predicting process characteristics. The state-of-the-art CFD models for single and twin-screw extruders design address the impact of geometry (type of mixing elements and geometrical simplifications of CFD geometries), pressure and temperature gradients, and quantification of mixing. Despite the extensive work conducted, modeling reactive extrusion using CFD remains challenging due to the intricate interplay of mixing, heat transfer, chemical reactions, and non-Newtonian fluid behavior under high shear and temperature gradients. These challenges are further intensified by the presence of multiphase flows and the complexity of extruder geometries. Future advancements should enhance simulation accuracy, incorporate multiphase flow models, and utilize real-time sensor data for adaptive modeling approaches. Full article

Bubble multiphase systems are crucial in industries such as biotechnology, medicine, oil and gas, and water treatment. Optical data analysis provides critical insights into bubble characteristics, such as the shape and size, complementing physical sensor data. Existing detection techniques rely on classical computer vision algorithms and neural network models. While neural networks achieve a higher accuracy, they require extensive annotated datasets, and classical methods often struggle with complex systems due to their lower accuracy. This study proposes a novel framework to address these limitations. Using Superformula parameter regression, we introduce an advanced border detection method for accurately identifying gas inclusions and complex-shaped objects in multiphase environments. The framework also includes a new approach for generating realistic artificial bubble images based on physical flow conditions, leveraging the Superformula to create extensive, labeled datasets without manual annotation. Tested on real bubble flows in mass transfer equipment, the algorithms enable bubble classification by shape and size, enhance detection accuracy, and reduce development time for neural network solutions. This work provides a robust method for object detection and dataset generation in multiphase systems, paving the way for more precise modeling and analysis. Full article

Multiphase fluids are common in many industries, such as oil and petrochemical, and volume fraction measurement of their phases is a vital subject. Hence, there are lots of scientists and researchers who have introduced many methods and equipment in this regard, for example, photon attenuation sensors, capacitance-based sensors, and so on. These approaches are non-invasive and for this reason, are very popular and widely used. In addition, nowadays, artificial neural networks (ANN) are very attractive in a lot of fields and this is because of their accuracy. Therefore, in this paper, to estimate volume proportion of a three-phase homogeneous fluid, a new system is proposed that contains an MLP ANN, standing for multilayer perceptron artificial neural network, a capacitance-based sensor, and a photon attenuation sensor. Through computational methods, capacities and mass attenuation coefficients are obtained, which act as inputs for the proposed network. All of these inputs were divided randomly in two main groups to train and test the presented model. To opt for a suitable network with the lowest rate of mean absolute error (MAE), a number of architectures with different factors were tested in MATLAB software R2023b. After receiving MAEs equal to 0.29, 1.60, and 1.67 for the water, gas, and oil phases, respectively, the network was chosen to be presented in the paper. Hence, based on outcomes, the proposed approach’s novelty is being able to predict all phases of a homogeneous flow with very low error. Full article

This article discusses the use of distributed acoustic sensing (DAS) for monitoring gas–liquid two-phase slug flow in horizontal pipes, using standard telecommunication fiber optics connected to a DAS integrator for data acquisition. The experiments were performed in a 14 m long, 5 cm diameter transparent PVC pipe with a fiber cable helically wrapped around the pipe. Using mineral oil and compressed air, the system captured various flow rates and gas–oil ratios. New algorithms were developed to characterize slug flow using DAS data, including slug frequency, translational velocity, and the lengths of slug body, slug unit, and the liquid film region that had never been discussed previously. This study employed a high-speed camera next to the fiber cable sensing section for validation purposes and achieved a good correlation among the measurements under all conditions tested. Compared to traditional multiphase flow sensors, this technology is non-intrusive and offers continuous, real-time measurement across long distances and in harsh environments, such as subsurface or downhole conditions. It is cost-effective, particularly where multiple measurement points are required. Characterizing slug flow in real time is crucial to many industries that suffer slug-flow-related issues. This research demonstrated the DAS’s potential to characterize slug flow quantitively. It will offer the industry a more optimal solution for facility design and operation and ensure safer operational practices. Full article

Multiphase flowmeters (MPFMs) measure the flow rates of oil, gas, and brine in a pipeline. MPFMs provide remote access to real-time well production data that are essential for efficient oil field operations. Most MPFMs are complex systems requiring frequent maintenance. An MPFM that is operationally simple and accurate is highly sought after in the energy industry. This paper describes an MPFM that uses only pressure sensors to measure gas and liquid flow rates. The design is an integration of a previously developed densitometer with an innovative Venturi-type flowmeter. New computing models with strong analytical foundations were developed, aided by empirical correlations and machine-learning-based flow-regime identification. A prototype was experimentally validated in a multiphase flow loop over a wide range of field-like conditions. The accuracy of the MPFM was compared to that of other multiphase metering techniques from similar studies. The results point to a robust, practical MPFM. Full article

A new three-phase downhole flow measurement methodology is developed based on measurements of speed of sound at different locations along the well, where the pressure is greater than the bubble-point pressure at the first location and smaller at the second location. A bulk velocity measurement is also required at the second location. The fluid at the first location is a mixture of two phases, but becomes a mixture of three phases at the second location due to the liberation of gas from the oil phase. The flow equations are first solved for two-phase flow at the first location to obtain the first phase fraction, water-in-liquid ratio, and then this information is fed into the flow equations after adjustment to the local pressure and temperature conditions to solve for three-phase flow at the second location to obtain the second phase fraction, namely the liquid volume fraction. These two phase fractions along with the bulk velocity at the second location are sufficient to calculate the three-phase flow rates. The methodology is fully explained and the analytical solutions for three-phase flow measurement is explicitly provided in a step-by-step process. A Lego-like approach may be used with various sensor technologies to obtain the required measurements, although distributed acoustic sensing systems and optical flowmeters are ideal to easily and efficiently adopt the current methodology. This game-changing new methodology for measuring downhole three-phase flow can be implemented in existing wells with an optical infrastructure by adding a topside optoelectronics system. Full article