Accurate Prediction Of Pressure Drop Research Articles

A novel method for accurate pressure drop prediction in horizontal and near horizontal pipes using adaptive neuro fuzzy inference system based model

Effective flow line and piping network design depends on the accurate prediction of pressure drop in multiphase flow for horizontal and near horizontal pipes. Since early 1950, several empirical correlations and mechanistic models have been developed to predict pressure drop. All correlations used by the industry, in addition to their applicability limitations, fall short of providing the necessary precision of pressure drop predictions. However, compared to empirical correlations, the recently developed mechanistic models improved pressure drop prediction. To design and construct more dependable and economical surface piping networks and wells, it is still necessary to improve prediction accuracy. This study uses the Adaptive Neuro-Fuzzy Inference System (ANFIS) to create a model that predicts pressure drop in horizontal and near-horizontal pipelines with greater accuracy and simplicity. Using the ANFIS method, the fuzzy modelling procedure can gather knowledge about a set of data to determine the membership function parameters that will enable the associated fuzzy inference system to track input/output data most effectively. The model was created and tested using field data encompassing various variables. The model was developed using 450 different data sets that were collected from the Asian continent. 113 data sets were used for testing, and a total of 337 data sets were used for training. Trend analysis was carried out during the model development phase prior to the model’s completion. This is performed to make sure the model is stable and to make sure the created model is physically sound and accurately simulates the real physical process. To determine the percentage of error between the predicted value and the actual measured data, statistical analysis was carried out. To compare the performance of the new ANFIS model to earlier empirical correlations and mechanistic models, graphical and statistical techniques were also used. The new model outperformed known correlations and the most recent mechanistic models by a significant margin in producing incredibly accurate pressure drop predictions. The Dukler et al. empirical correlation, Beggs and Brill empirical correlation, Xiao mechanistic model, and Gomez mechanistic model had values of 25.284, 20.940, 30.122, and 20.817, respectively, while the ANFIS model had a value of 13.256 for the lowest average absolute percentage error. Additionally, the Duckler and Beggs & Brill models came in second and third, with values of 0.908 and 0.906, respectively, and the ANFIS model had the highest coefficient of determination at 0.955.

Pressure drop of cemented high-concentration backfill in pipe flow: Loop test, model comparison and numerical simulation

Cemented high-concentration backfill (CHB) is an indispensable solution for mitigating risks associated with underground mining voids and surface tailings ponds. The accurate prediction of pressure drop of CHB in pipe flow is crucial for the design of backfilling systems. In this study, full factorial loop tests were conducted to obtain observed pressure drop data and rheological parameters of CHB, while considering the variables of binder content, solid fraction, and flow velocity. The rheometer method was also utilized to acquire the rheological parameters of CHB for comparison. Three analytical models and one numerical simulation method, which are considered highly accurate in the literature, were employed to predict the pressure drop of CHB in pipe flow. The findings indicate that the Buckingham model and the Darby–Melson model produce identical results as they are fundamentally equivalent. The Swamee–Aggarwal model and the single-phase flow simulation employ a similar mechanism as the Buckingham model, albeit with minor variations in mathematical treatment. The rheological parameters of CHB obtained through the rheometer method are considerably greater than those acquired by the loop test method, leading to significantly higher predicted pressure drop values from both the three analytical models and single-phase flow simulation when compared to the measured values. Whereas the mean deviation of the three analytical models is within 6.5% when employing rheological parameters of CHB determined by the loop test, with the Swamee–Aggarwal model being the most accurate, the mean error of single-phase flow simulation remains within 10%. It is suggested that the rheological parameters of CHB be determined through small-diameter loop testing. The Buckingham model and single-phase flow simulation are subsequently recommended for predicting pressure drop in industrial straight horizontal pipelines and complex piping systems, respectively. The results of this study facilitate the selection of the simplest method for accurately predicting the pressure drop of CHB in pipe flow.

Simulation of circular pipe flow of thixotropic cemented tailings pastes

Cemented tailings pastes (CTPs) exhibit complex thixotropic behaviors, which considerably affect the accurate prediction of pressure drop in circular pipe transport. In this work, the thixotropy of CTPs is incorporated into the numerical simulation via a constitutive thixotropic model. The analytical solution of the velocity profile across the pipe based on the equilibrium rheological model is presented. Comparisons indicate that the Bingham model cannot accurately characterize the flow behaviors of CTPs. It also overestimates the pressure drop in the circular pipe. Moreover, the pressure drop predicted by the novel equilibrium model is overestimated because of thixotropy. A peak of the structural parameter can be observed near the edge of the plug region, which highly influences shear rate distribution. The effects of solids content and flow rate on pressure drop are analyzed. In summary, thixotropy should be considered in the circular pipe flow simulation of CTPs.

CFD Modeling of Pressure Drop through an OCP Server for Data Center Applications

Modeling IT equipment is of critical importance for the simulations of flow and thermal structures in air cooled data centers. Turbulent flow undergoes a significant pressure drop through the server due to the energy losses originating from the internal components. Therefore, there is an urgent need to develop a fast and an accurate method for the calculation of pressure losses inside server components for data center applications. In this study, high resolution numerical simulations were performed on an OCP (Open Compute Project) server under various inlet flow rates for inactive and active conditions. Meanwhile, one key challenge of modeling complete geometry of the server results from using an intense mesh even for a single server. To address this challenge, the server was modeled as a porous zone to mimic inertia and viscous resistance in a realistic way. Comparison of the results of porous and complete models showed that the proposed model could calculate pressure drop accurately even when the number of cells in the server was reduced to 0.3% of the complete model. Porosity coefficients were determined from the numerical simulations conducted in a broad range of air discharge for both active and inactive conditions. Errors in the calculation of pressure drop may result in a significant deviation in the prediction of the temperature rise over the server. Thus, the present model can effectively be used for the fast and accurate prediction of pressure drop inside a server component rather than solving internal flow on an intense mesh, while simulating airflow inside an air-cooled data center, which is crucial for the design safety of data centers. Finally, calculated porosity coefficients can be used for the prediction of the pressure drop in a server, while designing data centers based on numerical simulations.

Effects of density, viscosity and surface tension on flow regimes and pressure drop of two-phase flow in horizontal pipes

Two-phase gas/liquid flow in pipes is a common occurrence in the petroleum, chemical, nuclear and geothermal industry. In the petroleum industry, it is encountered in the production, transportation, and processing of hydrocarbons from the oil and gas fields. In designing these systems, accurate prediction of pressure drop is imperative, which is determined from the flow pattern and flow regime map. Unfortunately, most of the flow regime maps were developed for the air-water system and widely used for the oil/gas system. Despite the practical importance, the general applicability of these maps is not addressed. In order to improve the generality of flow regime maps, it is necessary to evaluate the effect of density, viscosity, and surface tension, which differ by great magnitude on flow regime maps. Thus, this study evaluates the effect of density, viscosity, and surface tension on the flow regime map. To evaluate the effect of fluid properties, experiments were conducted using a horizontal flow loop of 9.15 m (30 ft) pipe length and 0.0254 m (1-inch) pipe diameter with a two-phase air/liquid system. The surface tension was varied using the surfactant solution, viscosity was varied with the aid of glycerin, and density was varied with the aid of calcium bromide. The superficial velocity of the liquid ranges from 0 to 3.048 m/s (0–10 ft/s) and superficial gas velocity ranges from 0 to 18.288 m/s (0–60 ft/s) respectively. The experimental data were used to generate a flow pattern map and to predict the effect of these properties on the variation in the boundaries of different flow patterns and pressure drop. The results show that the flow patterns and transition boundaries were affected by fluid properties. The boundary between the annular and slug-annular flow at high velocity, the slug flow and pseudo-slug flow at medium velocity, and inertial wave and ripple wave flow at low velocities are affected by the variation in surface tension, viscosity, and density. The decrease in surface tension shifted the boundary between annular and slug flow toward the left-bottom corner of the flow regime map, while, similar effects were observed due to an increase in viscosity, which is opposite to the effect of surface tension. The increase in density shifts the boundary toward the top-right corner of the flow regime map. The effect of density on the flow pattern transition is significant compared to the viscosity, while the viscosity effect is higher than the surfactant. The effect of fluid properties on pressure drop is noticeable at high velocities, however, the pressure drop is not evident to reveal the flow pattern and boundary transition quantitatively.

Pressure Drop Determination for Multiphase Flow in a Vertical Well Tubing

Several studies have been carried out, by researchers to predict multiphase flow pressure drop in the oil and gas industry, but yet there seems to be one being generally acceptable for accurate prediction of pressure drop. This is as a result of some constraints in each of these models, which makes the pressure drop predicted by the model far from accurate when compared to measured data from the field. This study is aimed at developing a multiphase fluid flow model in a vertical tubing using the Duns and Ros flow model. Data from six wells were utilized in this study and results obtained from the Modified model compared with that of Duns and Ros model along other models. From the result, it was observed that the newly developed model (Modified Duns and Ros Model) gives more accurate result with a R-squared value of 0.9936 over the other models. The Modified model however, is limited by the choice of correlations used in the computation of fluid properties.

Development of an integrated reservoir-wellbore model to examine the hydrodynamic behaviour of perforated pipes

Perforated pipes are used widely in vertical and horizontal production wellbores. Understanding the fluid flow behaviour through perforated pipes by taking into consideration the wall inflow is crucial for determining the wellbore frictional characteristics. Accurate prediction of pressure drop along the perforated pipes is a key step in completion design of production wellbores. Many empirical and theoretical models have been reported in the literature to predict pressure drop and friction factor along perforated pipes. However, these models show contradictory findings which is resulted from variations in the wall inflow configuration and modelling assumptions. The fluid flow through the surrounding formation and its interactions with wellbore have been simplified in the previous models which limits their range of applicability.In this study, a three-dimensional integrated reservoir-wellbore model of fluid flow through a perforated pipe surrounded by porous media is developed via Computational Fluid Dynamics (CFD) simulation. The model is used to investigate the effect of perforation parameters including perforation density, diameter and phasing angle on the wall friction factor and the pressure drop along the perforated pipe. The simulations are carried out for pipe inlet velocities of 0.5, 2.5, and 5 m/s with inflow to pipe flow rate ratios of 0, 7.5, 15, 30%. The results from this study show that the friction factor varies linearly with the perforation density but does not change remarkably with the perforation diameter or phasing. The observed trends of wall friction factor with perforation parameters are further explained and confirmed by studying the local wall shear stress results. Increasing the number of perforations leads to a higher friction factor as well as a larger pressure drop along the pipe. It is also observed that for perforation phasing angle of 90°, the overall pressure drop has the highest value compared to other phasing angles due to intensified influence of mixing pressure drop. For turbulent flows with high Reynolds number, the accelerational pressure drop is more dominant than the frictional and mixing pressure drop for the same inflow to pipe flow rate ratios. The developed model provides an alternative solution to experimental studies of perforated pipes, while delivering more details on friction factor behaviour and overall pressure drop components.

On developing improved modelling and scale-up procedures for pneumatic conveying of fine powders

Pneumatic transport of fine powders in fluidized dense-phase mode is becoming increasingly popular in various industries, such as power, chemical, cement, refinery, alumina, pharmaceutical, limestone, to list a few, because of the reasons of reduced gas flow rate and power consumption, decreased conveying velocities, improved product quality control, reduced pipeline sizing and wear rate, increased workplace safety etc. For the reliable design of a pneumatic conveying system, it is important to accurately predict the total pipeline pressure drop. However, accurate prediction of pressure drop from an improved understanding of the fundamental transport mechanism of fluidized dense-phase flow condition has only made limited progress till now because of the highly concentrated and turbulent nature of the gas-solids mixture. Power plant fly ash (median particle diameter: 30μm; particle density: 2300kg/m3; loose-poured bulk density: 700kg/m3) was conveyed through different pipelines (69mm I.D.×168m long; 105mm I.D.×168m long; 69mm I.D.×554m long). 8 different fly ash samples were tested in a fluidizing column for their deaeration characteristics and fluidized bulk densities were determined. Governing equations of flow for the dense-phase pneumatic conveying system of fine powders were solved using Runge-Kutta-Fehlberg (RKF45) method for different fluidized bulk densities of fly ash and air flow rates. The results have shown that the particle and actual gas velocities and the ratio of the two velocities increase in the direction of flow, while a reverse trend was apparent for the solids volumetric concentration. The results were compared against the predictions obtained using existing empirical relations for particle velocity. To develop an improved model for solids friction factor, an existing reliable pure dilute-phase model has been modified for dense-phase flow condition by incorporating sub-models for particle to actual gas velocity and impact and solids friction factor. The developed solids friction factor model was validated by using it to predict the total pipeline pressure drops for larger and longer pipelines and by comparing the experimental and predicted pneumatic conveying characteristics. The results have shown improved reliable predictions and that the model is capable of addressing the gradual transition of flow mechanism from dense- to dilute-phase. The accuracy of prediction is similar (in fact better in certain scale-up cases) when compared to a recently developed two-layer based model (developed by some of the authors). The results demonstrate the importance of incorporating particle and actual gas velocity terms in the model of solids friction factor instead of the prevailing techniques that overly depend on using superficial gas velocities.

Modeling solids friction factor for fluidized dense-phase pneumatic transport of powders using two layer flow theory

Fluidized dense-phase pneumatic conveying of powders is becoming increasingly popular in various industries, such as power, chemical, cement, refinery, alumina, pharmaceutical, limestone, to list a few, due to the reasons of reduced gas flows and power consumption, improved product quality control, reduced pipeline sizing and wear rate, increased workplace safety etc. An accurate estimation of total pipeline pressure drop is of paramount importance for the reliable design of a pneumatic conveying system. However, because of the highly concentrated and turbulent nature of the gas–solids mixture, fundamentally understanding the flow mechanism and accurately predicting the pressure drop as an important design parameter has only made limited progress so far. This paper results from an ongoing investigation into developing a validated modeling procedure for solids friction factor for the accurate prediction of pressure drop and optimal operating conditions for fluidized dense-phase pneumatic conveying systems. Under the present study, a two-layer based model has been developed by separately considering the solids friction contributions of the non-suspension (dense) bed of powders flowing along the bottom of pipe and the suspension (dilute-phase flow) of particles occurring on top of the non-suspension layer. Volumetric loading ratio and dimensionless velocity have been used to model the non-suspension dune flow layer. A solids impact and friction term and dimensionless velocity have been employed to model the dilute-phase flow due their established reliability. Models have been developed using the straight-pipe conveying data of two types of fly ash, cement and ESP dust (median particle diameter: 7 to 30μm; particle density: 2300 to 3637kg/m3; loose-poured bulk density: 610–1080kg/m3). The developed models for solids friction were validated for their scale-up accuracy by using them to predict the pressure drops in five larger and longer pipelines (69mm I.D.×168m long; 105mm I.D.×168m long; 69mm I.D.×554m long, 65mm I.D.×254m long and 80/100mm I.D.×407m long pipes) and by comparing the experimental versus predicted pneumatic conveying characteristics. The two-layer model provided improved accuracy compared to existing models indicating that the model is able to adequately address the dense- to dilute-phase transition criteria.

Improved Model for Predicting Annulus Pressure Drop during Underbalanced Drilling

Most time during underbalanced drilling or cleaning of well, formation fluid (oil and water) influx is usually experienced as soon as productive zone is penetrated because formation pore pressure gradient are higher than hydrostatic pressure gradient. Successful flow of fine drilling cuttings and formation fluid (gas, oil and water) to the surface flow facilities therefore demands for accurate prediction of pressure drop along the well bore during underbalanced drilling. Previous studies on pressure profile prediction in the wellbore during underbalanced drilling have not taken into account the effect of cuttings volume. The purpose of this study is therefore to develop an improved model that predicts the effect of cuttings volume on annulus pressure drop in the wellbore during underbalanced drilling. This is an extension of Guo and Fadairo model for investigating the effect of cutting volume pressure drop during underbalanced drilling. The result of the study shows that the cutting flow rate increases with small pressure differential while it decreases with large pressure differential. This depicts that cutting retards fluid flow and increases bottom-hole pressure and fluid density, resulting in low fluid velocity and cutting lifting capacity of the drilling fluid. This may result in cutting accumulation, ultimately reducing the ability of the drilling fluid to effectively lift drill cuttings from bottom-hole to the surface and eventually lead into stuck drilling string.

A New Model for Accurate Prediction of Pressure Drop in Gas Well

The analytical model for predicting the pressure at any point in a flow string from the bottom hole to the well head is essential in determining optimum production string dimension and in the design of gas-lift installations.A variety of models for predicting pressure transience in flowing wells have been reported in the literatures. Most of the early models were based on steady state fluid flow equation that did not consider time factor which results in inaccurate at early production time. Hence, there is the need for developing a model capable of estimating pressure transverse accurately at all times in flowing well bore.This study presents equation for pressure drop in flowing vertical well without neglecting any term in the momentum equation by the inclusion of accumulation and kinetic term. The analytical solution of the resulting differential equation gives functional relationship between flow rates and pressure at any point in flowing well at any given time. The results show improvement over previous studies as the assumptions previously neglected were all considered.

A comprehensive comparison of the performance of several popular models to predict pressure drop in stratified gas–liquid flow with low liquid loading

The accurate prediction of pressure drop in gas–liquid flow through a pipeline under low liquid loading condition is the most prominent factor in designing gas-condensate transmission pipelines. The primary focus of this paper is on comparing the capabilities of several important mechanistic models introduced in the literature to predict the pressure gradient of gas–liquid flow through a pipeline in low liquid loading condition. An experiment was conducted to find the pressure gradients of air–water flow through a pipe with diameter of 25 mm at pressure of 1–3 bar for liquid holdups ranging from 0.01 to 0.08. The results of the models introduced by Taitel and Dukler (1976), Hart el al. (1989), Grolman-Fortuin (1997), Hamersma-Hart (1987), Chen et al. (1997), Baker et al. (1988), Zhang et al. (2003) and Fan (2005) were compared with the obtained experimental data for different range of liquid holdups. As a result, it was concluded that Taitel–Dukler and Zhang et al. models provided more reliable predictions than the other models did for a wider range of liquid holdups. The results also demonstrated that Zhang et al., Fan, Grolman-Fortuin, Hart et al. and Hamersma–Hart models are more successful in predicting the experimental data than Taitel–Dukler is where the liquid holdup is less than 0.02. However, there is no significant correlation between the accuracy of Taitel–Dukler model predictions and the amount of liquid holdup.

Compressible and low Mach number LES of a swirl experimental burner

Large-Eddy Simulations (LES) of a swirl experimental burner are performed using a compressible and a low Mach number solver. The investigations are focused on the modeling strategies in LES aimed at validating the flow predictions and principally the associated pressure losses. Accurate prediction of pressure drop through complex geometries, such as those typically encountered in industrial swirlers, is indeed of paramount importance to design and optimize the engine efficiency. LES is here probed and tested to identify the model parameters affecting pressure losses: grid resolution, wall treatment or solver accuracy, with the aim of highlighting the requirements for accurate pressure drop predictions. Results show that for the high Reynolds number flow considered, the wall law model provides the best predictions and minimizes the error compared to experimental findings with a reasonable overall CPU cost.

Analysis of Production Logging Data to Develop a Model to Predict Pressure Drop in Perforated Gas Condensate Wells

Accurate prediction of pressure drop in perforated wells with a high fluid flow rate is a challenge in production engineering calculations. In this study, production logging data obtained from an Iranian gas condensate field were analyzed to develop a model to predict the pressure drop in perforated wells. To calculate the pressure drop due to the friction in the perforated section of the wall, a relation between perforation friction factor, pipe and wall Reynolds number, and Moody friction factor was developed by means of nonlinear regression. Perforation effects were also considered in total pressure drop, which occur due to the mixing of axial and radial momentum fluxes in the perforated section. The results predicted by our developed model showed good agreement with the measured production logging data from the studied field within 14% error. This model can be used to accurately estimate the well performance curve.

Predicting bottomhole pressure in vertical multiphase flowing wells using artificial neural networks

Over the years, accurate prediction of pressure drop has been of vital importance in vertical multiphase flowing oil wells in order to design an effective production string and optimum production strategy selection. Various scientists and researchers have proposed correlations and mechanistic models for this purpose since 1950, most of which are widely used in the industry. But in spite of recent improvements in pressure prediction techniques, most of these models fail to provide the desired accuracy of pressure drop, and further improvement is still needed. This study presents an artificial neural network (ANN) model for prediction of the bottomhole flowing pressure and consequently the pressure drop in vertical multiphase flowing wells. The model was developed and tested using field data covering a wide range of variables. A total of 413 field data sets collected from Iran fields were used to develop the ANN model. These data sets were divided into training, validation and testing sets in the ratio of 4:1:1. The results showed that the research model significantly outperforms all existing methods and provides predictions with higher accuracy, approximately 3.5% absolute average percent error and 0.9222 correlation coefficient.

Modeling Solids Friction for Dense-Phase Pneumatic Conveying of Powders

This article results from an ongoing investigation aimed at developing a new validated test-design procedure for the accurate prediction of pressure drop for dense-phase pneumatic conveying of powders. Models for combined pressure drop coefficient (“K”) for solids-gas mixture were derived using the concept of “suspension density” by using the steady-state “straight pipe” pressure drop data between two different tapping locations of the same pipe and also for two different diameter pipes. It was observed that the derived models were different depending on the location of tapping points (for the same pipe) and selected pipe diameters. The derived models were then evaluated by predicting the pressure drop for pipelines with various diameters or lengths (69 mm I.D. × 168 m, 105 mm I.D. × 168 m, 69 mm I.D. × 554 m) for the conveying of power station fly ash. A comparison between the predicted pneumatic conveying characteristics (PCC) and the experimental plots showed that the models resulted in significant over-predictions. In the second part of the article, the “system” approach of scaleup was evaluated. “Total” pipeline pressure drop characteristics for test-rig pipelines were scaled up to predict the PCC for larger/longer pipes. It was found that the “system” approach generally resulted in grossly inaccurate predictions. It was concluded that further studies are needed for a better understanding of the solids-gas flow mechanism under dense-phase conditions.

Gas/Condensate Wellbore Modeling Using a Fundamental Approach

This article, written by Technology Editor Dennis Denney, contains highlights of paper SPE 102479, "Gas/Condensate Wellbore Modeling Using a Fundamental Approach," by A.A. Sadegh, SPE, J.S. Zaghloul, and M.A. Adewumi, SPE, Pennsylvania State U., prepared for the 2006 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 24–27 September. Multiphase flow in the wellbore poses a challenge for production engineers because characterizing the prevailing flow regime determines which pressure-drop calculation method is appropriate. The problem is complicated further when the flowing fluid under-goes phase changes. A model was developed that comprises a flow-pattern-detection routine, a hydrodynamic model, and an equation-of-state-based phase-behavior package. Introduction As the demand for energy rises, production companies are exploring and exploiting more-complex reservoir systems. It is necessary to optimize production and conserve the natural drive mechanisms within those reservoirs. The production system comprises the reservoir, near-wellbore region, wellbore, and the surface production facility. Optimization of this system requires accurate prediction of pressure drop in the wellbore. However, single-phase flow seldom occurs in the wellbore. More commonly, multiphase flow prevails in the wellbore as well as in gas- and oil-transmission and -distribution pipelines. The engineering calculations required to design and operate these systems are complicated by several physical phenomena. These phenomena include flow-regime transitions, turbulence, and thermodynamic phase changes with pressure and temperature. Hence, the use of empirical correlations that are based on limited laboratory or field data often leads to erroneous predictions. Many tools used by production engineers are based on empirical correlations. The approach taken in developing these models was to generate an extensive database by varying parameters thought to affect pressure drop. These parameters include gas-flow rate, liquid-flow rate, pipe diameter, fluid viscosities, and liquid surface tension. The pressure-drop data then are correlated to dimensionless groups according to relevant variables. However, this approach cannot be extrapolated safely to predict situations for which the actual well parameters are outside the range of data initially used to develop the correlation. Additionally, it was observed that different pressure gradients arose for a varied distribution of the flowing phases in the pipe. The regime transitions also were a function of the physical parameters of the flow system under consideration. Therefore, it is necessary to predict the prevailing flow regime and then use a pressure-drop prediction method suitable for that particular flow regime. Flow of gas/condensate, in addition to the complexities inherent in all two-phase-flow modeling, is further complicated with retrograde condensation. The in-situ liquid fraction may change with a change in pressure, temperature, or fluid composition. This problem requires incorporating compositional modeling in predicting fluid splits and gas and liquid properties. It is necessary to integrate hydrodynamic modeling with hydrocarbon-phase-behavior modeling and flow-regime-transition determination to obtain reasonable predictions. The intent of this research was to address the challenges of accurately predicting pressure drop, liquid holdup, and fluid compositions for gas/condensate wells.

ARTIFICIAL NEURAL NETWORK MODEL FOR ACCURATE PREDICTION OF PRESSURE DROP IN HORIZONTAL AND NEAR-HORIZONTAL-MULTIPHASE FLOW

ABSTRACT Accurate prediction of pressure drop for multiphase flow in horizontal and near horizontal pipes is needed for effective design of flow lines and piping networks. The increased application of horizontal wells further signified the need for accurate prediction of pressure drop. Several correlations and mechanistic models have been developed since 1950. In addition to the limitations on the applicability of all existing correlations, they all fails to provide the desired accuracy of pressure drop predictions. The recently developed mechanistic models provided some improvements in pressure drop prediction over the empirical correlations. However, there is still a need to further improve the accuracy of prediction for a more effective and economical design of wells and surface piping networks. This paper presents an Artificial Neural Network (ANN) model for prediction of pressure drop in horizontal and near-horizontal multiphase flow. The model was developed and tested using field data covering a wide range of variables. A total of 225 field data sets were used for training- and 113 sets data for cross-validation of the model. Another 112 sets of data were used to test the prediction accuracy of the model and compare its performance against existing correlations and mechanistic models. The results showed that the present model significantly outperforms all other methods and provides predictions with accuracy that has never been possible. A trend analysis was also conducted and showed that the present model provides the expected effects of the various physical parameters on pressure drop.