Prediction of pressure drop in multiphase horizontal pipe flow

Empirical correlations were tested against reliable two phase pipe flow data for the prediction of pressure drop. Correlations are recommended for the prediction with stratified and annular type flows. When these correlations were adapted to three phase gaswater-oil pipe flow in general they predicted for intermittent slug type flows. Momentum balance models could not be successfully adapted to the prediction of pipe three phase pressure drop

One-dimensional modelling of mixing, dispersion and segregation of multiphase fluids flowing in pipelines

The flow of immiscible liquids in pipelines has been studied in this work in order to formulate a one-dimensional model for the computer analysis of two-phase liquid-liquid flow in horizontal pipes. The model simplifies the number of flow patterns commonly encountered in liquid-liquid flow to stratified flow, fully dispersed flow and partial dispersion with the formation of one or two different emulsions. The model is based on the solution of continuity equations for dispersed and continuous phase; correlations available in the literature are used for the calculation of the maximum and mean dispersed phase drop diameter, the emulsion viscosity, the phase inversion point, the liquid-wall friction factors, liquid-liquid friction factors at interface and the slip velocity between the phases. In absence of validated models for entrainment and deposition in liquid-liquid flow, two entrainment rate correlations and two deposition models originally developed for gas-liquid flow have been adapted to liquid-liquid flow. The model was applied to the flow of oil and water; the predicted flow regimes have been presented as a function of the input water fraction and mixture velocity and compared with experimental results, showing an overall good agreement between calculation and experiments. Calculated values of oil-in-water and water-in-oil dispersed fractions were compared against experimental data for different oil and water superficial velocities, input water fractions and mixture velocities. Pressure losses calculated in the full developed flow region of the pipe, a crucial quantity in industrial applications, are reasonably close to measured values. Discrepancies and possible improvements of the model are also discussed. The model for two-phase flow was extended to three-phase liquid-liquid-gas flow within the framework of the two-fluid model. The two liquid phases were treated as a unique liquid phase with properly averaged properties. The model for three-phase flow thus developed was implemented in an existing research code for the simulation of three-phase slug flow with the formation of emulsions in the liquid phase and phase inversion phenomena. Comparisons with experimental data are presented

Prediction of Pressure Drop in Horizontal and Near-Horizontal Multiphase Flow using Group Method of Data Handling (GMDH) approach with the aim of reducing the curse of dimensionality; A Comparative Study

An accurate prediction on the value of pressure drop during a multiphase flow in pipelines is greatly in need in petroleum industry. Back to 1967, the first empirical correlation was developed to predict the pressure drop in pipelines. Since then, it attracts the interest of many researchers to conduct rigorous studies on this matter. However, the correlations and models that are being used in the petroleum industry nowadays seem to be out dated. At most of the time, it tends to under predict and over predict the pressure as all the correlation have superior relation only with the data used in their experiments. The objective of this study is to construct a model with high accuracy and low complexity, by utilizing Group Method of Data Handling (GMDH) approach. Parameters that govern the pressure drop are studied to understand their significance towards the prediction of pressure drop. Once all the parameters are outlined, a model is developed and is expected to be generalized, where it can be applied in any behavior of multiphase given. GMDH approach is well known for its ability to model the relation between multiple input parameters and an output with the mean of self-organizing. Stopping criterion will be set optimally to ensure that the model will result in accurate prediction. To achieve this, MATLAB Software will be used for coding and simulation and all the results will be further evaluated in Microsoft Excel software. The result possess by GMDH model generated in this study will be compared with Beggs and Brill correlation, Gomez et al. correlation and Xiao et al. mechanistic model as these models are the mostly applied methods to predict pressure drop for horizontal and near-horizontal conditions. From this study, the model generated is very successful in predicting the pressure drop in pipeline where it possess the lowest Average Absolute Percentage Error (AAPE) of 12% compared to other correlation or model. Trend analysis and statistical analysis were conducted to confirm the validity of this model. The author believes that the model generated in this study will be able to predict the pressure drop in much convenient way in petroleum industry

DEVELOPMENT AND TESTING OF UNIVERSAL PRESSURE DROP MODELS IN PIPELINES USING ABDUCTIVE AND ARTIFICIAL NEURAL NETWORKS

Determination of pressure drop in pipeline system is difficult. Conventional methods (empirical correlations and mechanistic methods) were not successful in providing accurate estimate. Artificial Neural Networks and polynomial Group Method of Data Handling techniques had received wide recognition in terms of discovering hidden and highly nonlinear relationships between input and output patterns. The potential of both Artificial Neural Networks (ANN) and Abductory Induction Mechanism (AIM) techniques has been revealed in this study by generating generic models for pressure drop estimation in pipeline systems that carry multiphase fluids (oil, gas, and water) and with wide range of angles of inclination. No past study was found that utilizes both techniques in an attempt to solve this problem. A total number of 335 data sets collected from different Middle Eastern fields have been used in developing the models. The data covered a wide range of variables at different values such as oil rate (2200 to 25000 bbl/d), water rate (up to 8424 bbl/d), angles of inclination (-52 to 208 degrees), length of the pipe (500 to 26700 ft) and gas rate (1078 to 19658 MSCFD). For the ANN model, a ratio of 2: 1: 1 between training, validation, and testing sets yielded the best training/testing performance. The ANN model has been developed using resilient back-propagation learning algorithm. The purpose for generating another model using the polynomial Group Method of Data Handling technique was to reduce the problem of dimensionality that affects the accuracy of ANN modeling. It was found that (by the Group Method of Data Handling algorithm), length of the pipe, wellhead pressure, and angle of inclination have a pronounced effect on the pressure drop estimation under these conditions. The best available empirical correlations and mechanistic models adopted by the industry had been tested against the data and the developed models. Graphical and statistical tools had been utilized for comparing the performance of the new models and other empirical correlations and mechanistic models. Thorough verifications have indicated that the developed Artificial Neural Networks model outperforms all tested empirical correlations and mechanistic models as well as the polynomial Group Method of Data Handling model in terms of highest correlation coefficient, lowest average absolute percent error, lowest standard deviation, lowest maximum error, and lowest root mean square error. The study offers reliable and quick means for pressure drop estimation in pipelines carrying multiphase fluids with wide range of angles of inclination using Artificial Neural Networks and Group Method of Data Handling techniques. Graphical User Interface (GUI) has been generated to help apply the ANN model results while an applicable equation can be used for Group Method of Data Handling model. While the conventional methods were not successful in providing accurate estimate of this property, the second approach (Group Method of Data Handling technique) was able to provide a reliable estimate with only three-input parameters involved. The modeling accuracy was not greatly harmed using this technique

DETERMINATION OF PRESSURE DROP IN A TWO-PHASE, LIQUID-LIQUID SYSTEM IN A HORIZONTAL PIPELINE THROUGH MATLAB SIMULATION

The pressure drop of the flow inside the pipeline is an important parameter to be determined before proceeding with the design. This parameter is very important to pipeline size selection and the design of the downstream facilities. Underestimation of pressure drop will give a smaller pipe size than required, thus the transportation capacity will be restricted. In the other hand, overestimation of pressure drop will cause in oversized pipeline, worse sweeping characteristics, and possible solid dropout and corrosion issues. The wrong prediction of pressure drop is likely to occur in a liquid-liquid two phase system which false predictions of interface configurations are made. A tlat intertace is assumed between the phases which actually highly applicable for high-density differential system, such as gas-liquid system under earth condition. However, tor liquid-liquid system with small density differences or in reduced gravity system, the factor of curvature interface must be considered. The interface configuration tor liquid-liquid systems can either be tlat, concave or convex. Hence, to overcome this problem, a model is developed to calculate pressure drop tor liquid-liquid system that will consider the tactor of curvature interface between the phases. In this modelling, two-fluid model is used for prediction of pressure drop and this model is derived to make it applicable tor stratified tlow system only. The model is developed by using MATLAB programming and it is tested with tew sets of input data. The calculated pressure drop from this model is compared with experimental data to check for its reliability. As a conclusion, it is shown that tlat-shape intertace assumption is not the best assumption for this prediction. The percentage difference of prediction is very large when it was compared to experimental data. Curvature intertacial contiguration is assumed to give best prediction, however, in this project, the curvature interfuce assumption not give an expected result. This is due to some ambiguity in cross sectional area and wetted perimeter derivation formula used in this model. Hence, modification in the correlated function has to be developed to prove that calculation using the curved interface will give better assumption of pressure drop

Generation of empirical correlation for predicting drag reduction of oil-water flows with natural polymers

There is an increasing need to accurately predict the behaviour of fluid in the different flow geometry as applicable in the industries. The prediction of drag reduction phenomenon observed during the two-phase oil-water flow with drag reducing polymers in horizontal pipes was investigated. The Power law model was adopted inthis study to empirically correlate the data acquired from our earlier experimental works in a 12-mm ID and 20-mm ID pipes. The model accurately predicts the drag reduction across the horizontal pipes. The agreement between the predicted and experimental drag reductions was better in the 12-mm ID pipe than in the 20-mm ID pipe. More work and data is needed to enhance the predictive accuracy of applicable models.Keywords: Drag reduction; polymers; horizontal pipes, oil-water flow, modellin

Modelling of multiphase flow through a subsea recirculation line equipped with a choke

A peculiar problem encountered in engineering practices for multiphase flows is the pressure loss in piping systems. Because of the variations in viscosities, densities, and velocities of the fluid phases, multiphase systems design requirements are different from those of singlephase flows. Irrespective of the number of phases involved, pressure loss occurs at different points of the pipe. The severity is however more in multiphase flows due to the variations in the fluid compositions across the length of the pipe. Works of literature on the pressure drop across chokes or valves for multiphase fluids are very limited due to the complexities and flow regimes bothered around the valve system. Moreso, most researchers only bother with the frictional losses along the pipeline as these are considered to constitute most of the losses. Current practices are only just interested in designing and sizing valves based solely on the pressure drop across a valve for single-phase flows. In this work, Daniel Bernoulli’s model or equation was evaluated against empirical data from OneSubsea company in a bid to predict the pressure drop across a choke in a subsea recirculation line for multiphase flow. The equation was used to quantify and evaluate the performance characteristics of a valve handling multiphase gas-water-oil flow, as this kind of flow is commonly seen in processing industries. The received measured data include a 6-in-diameter pipe, with 60 meters equivalent length, and twenty-six bends. Stem travel from 7.1% to 70.5% for a recirculation pipe was evaluated against 118 data values of Gas Volume Factor GVF and Water Liquid Ratio WLR to obtain the control valve coefficients at different flow rates under varying temperatures and pressure. The results showed a good correlation in line with the principle of energy conservation or continuity equation when the flow rate ‘Q’ was measured against the pressure drop across the valve. Other quantitative relationships evaluating the effects of GVF, Bulk density of the fluids mixture against the pressure drop across the valve were also determined. The detailed evaluation carried out allows for local flow characteristics of pressure drop, flow rates, and GVF determination within the valve. The parameters can be incorporated in the sizing methodology of control valve systems for multiphase oil-water-gas flow.Master's Thesis in Process TechnologyPRO399MAMN-PR

Pressure Drop in Vertical Multiphase Flow using Neuro Fuzzy Technique; A Comparative Approach

The sole objective of this study is to develop a model for estimating the pressure drop in vertical multiphase flow using one of the artificial intelligence techniques which is Neuro Fuzzy Systems with a good and acceptable accuracy that can work for a wide range of well flowing conditions that can replace the rigorous empirical and mechanistic correlations. In this study a number of 206 data sets collected from some fields in the Middle East were used to develop the Neuro Fuzzy Model. Many attempts have been done to estimate the pressure drop in vertical multiphase flow starting from the homogeneous models, the empirical models and the mechanistic models. But yet, none of the traditional correlations works well for the variety of well conditions that are found in the oil industry. Thus, the accuracy of the old pressure drop correlations cannot be raised to a generally accepted level. For this purpose, one of the artificial intelligence techniques (Neuro Fuzzy System) is used to have a significant reduction in the error involved with estimating the pressure drop. The Neuro Fuzzy Model was developed through 3 stages; Training, Validation, Testing. The developed Neuro Fuzzy Model has successfully achieved the lowest Average Absolute Percentage Error (AAPE%) of 2.92% that could overcome all the empirical and mechanistic correlations when tested against the same set of data. It can be concluded that Neuro Fuzzy system has overcame the performance of the models currently used in the industry

Predicton of interface level height of stratified liquid-liquid flow using artficial neural network

In this study, artificial neural network (ANN) was used to predict the interface level height (ILH) of two immiscible liquids flowing in a horizontal pipe. A three-layer feed-forward back-propagation (FFBP) neural network was constructed and trained with experimental data of two different liquid-liquid flow systems reported in the literature. The all studied flow patterns were stratified flow (stratified smooth and stratified wavy with or without droplets at interface ). The input parameters of the ANN model were superficial velocity of phases, pipe diameter, the ratio of the lighter phase density to the heavier phase density (ρlp/ρhp) and the ratio of the lighter phase viscosity to the heavier phase viscosity (μlp/μhp), while the interface level height (ILH) of phases was its output. The Levenberg–Marquardt (LM) algorithm, the hyperbolic tangent sigmoid and the linear activation functions were used for training and developing the ANN. Optimal configuration of the ANN model was determined using minimizing the mean absolute percent error (MAPE) and mean square errors (MSE) between experimental and predicted ILH data by the ANN model. The results showed that the optimal configuration was a network with five neurons in hidden layer that was highly accurate in predicting the interface level. MAPE and correlation coefficient (R) between the experimental and predicted values were determined as 1.8% and 0.9962 for training, and 1.52% and 0.9996 for testing date sets, respectively