Numerical and experimental study of the impact of temperature on relative permeability in an oil and water system.

Relative permeability is affected by several flow parameters, predominantly operating temperature and fluid viscosity. Fluid viscosity changes with temperature, which correspondingly affects the relative permeability. Temperature is believed to have a considerable effect on oil–water relative permeability and thus is a vital input parameter in petroleum reservoir development modelling. The actual effect of temperature on oil–water relative permeability curves has been a subject of debate within the scientific community. This is based on contradictory experimental and numerical results concerning the effect of temperature on oil–water relative permeability in literature. This study investigates the effect of temperature on multiphase flow physics in a porous media under varying temperature conditions. A computational fluid dynamics approach was adopted for a pore-scale study of the temperature effect on oil recovery factor under a water- and oil-wet condition. For the oil–water relative permeability investigation, a series of coreflooding experiments were conducted with well-sorted unconsolidated silica sandpacks, adopting the unsteady-state relative permeability method. The series of experiments were performed at different temperatures (range between 40 to 80 °C). Three levels of injection flowrates (0.5, 0.75 and 1.0 mL/min) and two oil viscosities (43 cP motor and 21 cP mineral oil – at 60 oC) were used in the study. A history matching approach using the commercial software Sendra was used to determine the oil-water relative permeability for each respective temperature, flowrate and oil viscosity. A support vector regression algorithm was later implemented for the machine learning modelling aspect of this work, which can predict reliable temperature dependent oil–water relative permeability. The pore-scale results showed that the displacement behaviour of water and oil-wet system is strongly affected by the contact angle with a profound effect on the oil recovery factor. The water-wet system resulted in about 35 – 45 % more oil recovery than the oil-wet system, with the unrecovered oil mainly adhering to the wall region of the pore bodies of the oil–water system. The results from all the experimental cases showed that the oil–water relative permeability is a function of temperature, water injection flowrate and oil viscosity. In addition, the experimental findings show a decreasing residual oil saturation of the more viscous fluid with increasing injection flowrate. The end-point water relative permeability varies slightly for the set of experiments, with the values higher for the less viscous oil under the same flowrate condition. Generally, the profile of oil and water relative permeability curve changes with varying oil viscosity and water injection flowrate at the same operating condition. This behaviour shows that the viscosity of oil is an important factor to be considered when selecting displacement flowrate to guarantee high oil production. Furthermore, an increment in temperature results in a corresponding rise in the relative permeability of both oil and water. Comparison of the experimental and machine learning results showed a good match, with consistency across all datasets. In addition to the machine learning model, this study proposes a modified empirical model using nonlinear least square regression for application in unconsolidated porous media. The output from this model can be applied for relative permeability prediction, preliminary evaluation in experimental design and as a valuable benchmarking tool for future laboratory experiments under varying temperature conditions

Gas-liquid multiphase flow measurement in venturi tube through data-driven modelling

Precise quantification of multiphase flow rates holds paramount significance in the context of process surveillance and enhancement in the energy sector. Conventional methodologies depend on the physical partitioning of phases prior to the use of single-phase meters, presenting a labor-intensive and economically demanding procedure. Recent developments in the field of machine learning present innovative data-driven methodologies for approximating multiphase flow rates by leveraging sensor-derived measurements. This research explores the examination of neural network architectures, specifically exploring deep neural networks (DNN) and convolutional neural networks (CNN), with the aim of predicting multiphase flow rates in Venturi tubes. Temporal data series and mean values pertaining to variables such as differential pressure, temperature, as well as throat and recovery differential pressure serve as inputs for the model. The primary objective of these data-centric methodologies is to ascertain gas and liquid flow rates directly, eliminating the need for the identification of flow patterns. Both instantaneous and time-averaged predictions are studied. Academic parlance entails subjecting models to training and testing processes using empirical datasets across diverse multiphase flow scenarios. The findings unequivocally establish the viability and efficacy of the suggested DNN and CNN architectures for addressing the complexities inherent in this demanding application. Accuracy is gauged using MSE, RMSE, MAE, and R-squared to assess the disparities between predictions and reference measurements. The enhancement of sensor inputs, customization of network architectures, and the implementation of field testing are integral aspects within the purview of Outlook. These measures are undertaken to bolster resilience across various facilities and operating conditions, thereby contributing to an augmented level of robustness.</p

Multiphase flow measurement and data analytic based on multi-modal sensors

Accurate multiphase flow measurement is crucial in the energy industry. Over the past decades, separation of the multiphase flow into single-phase flows has been a standard method for measuring multiphase flowrate. However, in-situ, non-invasive, and real-time imaging and measuring the key parameters of multiphase flows remain a long-standing challenge. To tackle the challenge, this thesis first explores the feasibility of performing time-difference and frequency-difference imaging of multiphase flows with complex-valued electrical capacitance tomography (CVECT). The multiple measurement vector (MMV) model-based CVECT imaging algorithm is proposed to reconstruct conductivity and permittivity distribution simultaneously, and the alternating direction method of multipliers (ADMM) is applied to solve the multi-frequency image reconstruction problem. The proposed multiphase flow imaging approach is verified and benchmarked with widely adopted tomographic image reconstruction algorithms. Another focus of this thesis is multiphase flowrate estimation based on low-cost, multi-modal sensors. Machine learning (ML) has recently emerged as a powerful tool to deal with time series sensing data from multi-modal sensors. This thesis investigates three prevailing machine learning methods, i.e., deep neural network (DNN), support vector machine (SVM), and convolutional neural network (CNN), to estimate the flowrate of oil/gas/water three-phase flows based on the Venturi tube. The improvement of CNN with the combination of long-short term memory machine (LSTM) is made and a temporal convolution network (TCN) model is introduced to analyse the collected time series sensing data from the Venturi tube installed in a pilot-scale multiphase flow facility. Furthermore, a multi-modal approach for multiphase flowrate measurement is developed by combining the Venturi tube and a dual-plane ECT sensor. An improved TCN model is built to predict the multiphase flowrate with various data pre-processing methods. The results provide guidance on data pre-processing methods for multiphase flowrate measurement and suggest that the proposed combination of low-cost flow sensing techniques and machine learning can effectively translate the time series sensing data to achieve satisfactory flowrate measurement under various flow conditions

Prediction of Liquid Accumulation in Gas Wells to Forecast the Critical Flowrate and the Loading Status of Individual Wells

Liquid accumulation is a major problem in gas wells. The inability of gas to lift coproduced liquids to the surface imposes back pressure on the reservoir, limits the ultimate recovery and ultimately kills the well if improperly managed. Therefore, accurate prediction of its occurrence and reliable monitoring strategy is key to effectively handling liquid accumulation in gas wells. In this study, machine learning algorithms were used to develop regression and classification models to accurately predict the critical flowrate and the loading status of individual wells. The regression models used are the feed-forward neural network and a least squares support vector machine models while the decision trees model was used as the classification model to characterize the loading status of the wells investigated. These models were validated using actual published data and it was observed that the feed-forward neural network performed better in predicting the critical rate compared to the least squares support vector machine model with an R2 value of 0.9833, and thus was adopted. The feed-forward neural network model was further compared with other critical rate models; and a consistent result with least percent error of 5.571% was also observed.&nbsp; Form this study, it is obvious that the neural network model provide benefits and good prospects in investigating liquid loading phenomena in gas wells compared to empirical models that apply so many simplifying assumptions

Application of Soft Computing Techniques to Multiphase Flow Measurement: A Review

After extensive research and development over the past three decades, a range of techniques have been proposed and developed for online continuous measurement of multiphase flow. In recent years, with the rapid development of computer hardware and machine learning, soft computing techniques have been applied in many engineering disciplines, including indirect measurement of multiphase flow. This paper presents a comprehensive review of the soft computing techniques for multiphase flow metering with a particular focus on the measurement of individual phase flowrates and phase fractions. The paper describes the sensors used and the working principle, modelling and example applications of various soft computing techniques in addition to their merits and limitations. Trends and future developments of soft computing techniques in the field of multiphase flow measurement are also discussed