Three-phase Flow Research Articles

Variations in phase conductor size and spacing on power losses on the Nigerian distribution network

Most Nigerian distribution networks are examined on a single-phase basis, which fails to reflect the network's true features. Using three-phase power flow algorithms, this research explores the implications of variations in conductor sizes and spacing on power losses on a Nigerian network. Modified carson's equations were used to model the distribution lines to determine the network's impedance without presuming transposition of the lines. The conductor sizes and spacing were changed to see how they affected network power losses and how they contributed to the distribution network imbalance. The results showed that changing the conductor sizes of certain of the phases increased real power losses by 55.8 and 5.8%, respectively, in phases A and B. Phase C's was reduced by 13.04%. Furthermore, reactive power losses in phases A and B increased by 3.29 and 8.18%, respectively, whereas reactive losses in phase C dropped by 10.32%. Changing the conductor spacing in phases A, B, and C increased real power losses by 825.8, 136.2, and 13.2%, respectively, and reactive power losses by 72.86, 52.30, and 31.89%. Distribution networks should not be evaluated on a single-phase basis since losses differ in each of the three phases. Conductor size and spacing reductions cause huge losses.

High Reporting Rate Smart Metering Data for Enhanced Grid Monitoring and Services for Energy Communities

Capturing the dynamic behavior of the power distribution grids, especially under high penetration of renewables, is of high interest for grid operators. The distribution power grids are not fully observable due to lack of sufficient metering infrastructure, especially downstream of medium-voltage substations. Therefore, fusion of data recorded at significantly different reporting rates is proposed to increase the situational awareness of the system with non-negligible effect on the accuracy of the monitoring tool. Higher reporting rates are possible for next generation smart meters (SM), but they raise higher concerns about data privacy, already an issue for SM rollout. This article proposes a framework for knowledge extraction from high reporting-rate SM data. The process takes place at SM level and with low computation and communication costs and preserving user privacy, with the scope to increase the accuracy of the monitoring tools for distribution power grids. The methodology makes use of statistical metrics able to capture system dynamics relevant for network diagnosis. The proposed approach is validated on a three-phase low-voltage power flow model applied to a realistic testbed microgrid and real field measurements synchronized at 1 s.

A novel type curve for estimating oil recovery factor of gas flooding

A novel type curve is presented for oil recovery factor prediction suitable for gas flooding by innovatively introducing the equivalent water-gas cut to replace the water cut, comprehensively considering the impact of three-phase flow (oil, gas, water), and deriving the theoretical equations of gas flooding type curve based on Tong’s type curve. The equivalent water-gas cut is the ratio of the cumulative underground volume of gas and water production to the total underground volume of produced fluids. Field production data and the numerical simulation results are used to demonstrate the feasibility of the new type curve and verify the accuracy of the prediction results with field cases. The new type curve is suitable for oil recovery factor prediction of both water flooding and gas flooding. When a reservoir has no gas injected or produced, the gas phase can be ignored and only the oil and water phases need to be considered, in this case, this gas flooding type curve returns to the Tong’s type curve, which can evaluate the oil recovery factor of water flooding. For reservoirs with equivalent water-gas cuts of 60%—80%, the regression method of the new type curve works well in predicting the oil recovery factor. For reservoirs with equivalent water-gas cuts higher than 80%, both the regression and assignment methods of the new type curve can accurately predict the oil recovery factor of gas flooding.

Uncertainty propagation and sensitivity analysis of three-phase flow in porous media using polynomial chaos expansion

In this study, polynomial chaos expansion (PCE) will be utilized to investigate propagation and evolution of uncertainty from uncertain model parameters to model outputs. Unknown PCE coefficients are calculated using probabilistic collocation method (PCM), which is a non-intrusive technique. The proposed approach is evaluated on a 2D reservoir under three-phase flow condition. By use of PCEs, dependent model outputs, such as well bottomhole pressure and production rates, are represented as functions of uncertain model parameters, including parameters defining the capillary pressure (Pc) and relative permeability (kr) curves. Accuracy, compatibility and efficiency of the proposed approach are compared with Monte Carlo (MC) method, which is considered as the reference case. Mean values, standard deviations and histograms for well bottomhole pressure and production rates estimated from the PCM-based PCE are closely matched to those obtained from MC method. This discloses the fact that the proposed approach can successfully restore the simulation model to represent propagation of uncertainty from uncertain model parameters toward model outputs and to perform sensitivity analysis (SA). Unlike the MC approach which requires an extremely large number of realizations to obtain accurate results, the PCM-based PCE requires to perform model evaluations only at the selected collocation points, thus the computational cost is significantly reduced. Subsequently, using the proposed PCE as the surrogate model of the reservoir, Sobol's sensitivity indices are calculated analytically. Results of SA reveal that those parameters defining Pc curves have negligible effect on the model outputs compared with those parameters defining kr curves, and thus may be ignored during history matching process. Moreover, it is observed that the effects caused by interactions between uncertain kr and Pc parameters have negligible contribution compared to the individual effect of each parameter.

Sustainable dewatering of unconventional gas wells using engineered multiphase flow dynamics

This paper aims to investigate and demonstrate, for the first time, the feasibility of using an artificial lift system, referred to as foam assisted gas lift (FAGL) for dewatering unconventional gas wells to depressurise coal and allow gas to be produced. More specifically, this paper investigates the capability of a three-phase foam flow to lift gas, liquid and solid particulates to the surface to optimise gas production by minimising the flowing bottom-hole pressure (FBHP) and minimising downtime, operational costs and production losses caused by downhole pump failures.A laboratory set-up of a 9 m vertical tubing with 80 mm (3.1 in) ID and a 7 m annulus with a casing inner diameter of 170 mm (6.69 in) and tubing outer diameter of 70 mm (2.76 in) are used to investigate the foam flow behaviour in both geometries for a wide range of gas and water flowrates, 40–6500 L/min and 10–80 L/min, respectively. The 80 mm tubing is pressurised up to 6 bar, allowing examining the foam flow characteristics and foam stability at different depths of a gas well, by changing the operating pressure. Foam flow regimes in the tubing and the annulus, which are key in determining the FBHP are characterised by analysing differential pressure signals, collected at 100 Hz frequency. The addition of foamer (sodium dodecylbenzene sulfonate) significantly advanced the transition from churn to the annular flow regime, resulting in a lower required gas flow rate to obtain the least attainable FBHP. A model is developed to evaluate the FBHP of the FAGL, compared against the measured FBHP in 36 gas wells in Australia, equipped with down-hole pumps for dewatering. Our results indicate that FAGL is capable of maintaining a low FBHP for a wide range of gas and water flow rates. The effective particle lifting capacity of foam flow is also demonstrated. Further, the positive impact of water salinity (0.1 M NaCl) in the presence of particles (15–25 g/l) on foam integrity is presented.

Experimental Investigation of Three-Phase Flow Regime Transition in a Large Diameter 90° Bend and the Resultant Pressure Drop Prediction

Experimental Investigation of Three-Phase Flow Regime Transition in a Large Diameter 90° Bend and the Resultant Pressure Drop Prediction

On the Calibration of an Optical, High-Speed, Multiphase Microfluidic Sensor With Droplet Counting Applications in Lab-on-PCB Devices

The integration of microfluidics and droplet-based systems has led to platforms capable of characterizing multiple phases inside microchannels which can be an asset for many industries. Furthermore, recent developments on Lab-on-a-PCB devices focus to meet design specifications such as the ASSURED criteria (Affordable, Sensitive, Specific, User-friendly, Rapid and Robust, Equipment-free, and Deliverable to end-users). However, most of these systems still present external equipment dependencies, complex setup and manufacturing processes, low reproducibility, along reduced information regarding calibration processes for ASSURED-based sensors. In this work, we developed a rapid and fully integrated calibration process for optical droplet-based Lab-on-PCB devices by means of an interfacial distance constant called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> , to obtain reliable and wide spectrum droplet detection and characterization results. To test the proposed calibration process, a low-cost optical droplet sensor was built using commonly available electronics components, consisting only of a fluid channel between a Light-Emitting Diode (LED) and a Light Dependent Resistor (LDR), which voltage variation is measured and processed with an Arduino Uno. After the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> calibration of the platform, we were able to characterize different multiphase flow properties such as velocity, flow rate, droplet lengths, and volume for velocities up to 1000 droplets per second with the Mean Relative Errors (MRE) ranging from 2.4% up to 17%. The lowest MRE value was obtained using a two-phase flow system for flow from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20 \mu \text{L}$ </tex-math></inline-formula> /min up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$425 \mu \text{L}$ </tex-math></inline-formula> /min. In contrast, the highest MRE value we report was found for a three-phase flow system for droplets at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$250 \mu \text{L}$ </tex-math></inline-formula> /min.

GPU-based SPH-DEM Method to Examine the Three-Phase Hydrodynamic Interactions between Multiphase Flow and Solid Particles

Particulate flows in a gas-liquid multiphase mixture (i.e., three-phase flows) are frequently encountered in various fields as well as the chemical engineering, resource engineering, ocean engineering, and nuclear engineering domains. Thus, the numerical modeling for such three-phase flows is essential to evaluate and enhance the fluidic design of the existing industrial devices or increase the engineering safety/efficiency. In terms of the solid particle phase, the discrete element method (DEM) has demonstrated outstanding capability in numerically modeling solid–solid interactions. For multiphase CFD model, several numerical techniques, ranging from the Eulerian–Eulerian two-fluid model to a fully resolved DNS solver, have been coupled with the DEM to model the three-phase flow. Most of the existing research on three-phase particulate flows was based on coupled grid-based CFD and DEM techniques. Considering that many three-phase particulate flows in industries are driven by a vapor/gas phase that forms a sharp interface with the liquid, coupling DEM with particle-based Lagrangian CFD methods, which can handle multi-phase flows without any interface capturing or associated correlations, can also be a good option or alternative for modeling such three-phase flows. In this respect, a three-phase flow solver was established in this study by coupling the DEM with a Lagrangian smoothed particle hydrodynamics (SPH) model while adopting the unresolved phase coupling between the two techniques. In order to overcome the low computational efficiency of these two numerical methods, GPU-based acceleration is adopted for speedup of SPH-DEM coupled solver due to its high compatibility for Lagrangian methods offered by thousands of GPU processors. The results of a scalability analysis indicated that the accelerated version exhibited an enhanced performance especially when many Lagrangian nodes are used in simulation. Several benchmark simulations were conducted to demonstrate the capability and applicability of the SPH-DEM solver in various multi-phase applications associated with particulate flows. The results of numerical simulations validated the feasibility of the momentum exchange model, and the potential and applicability of the SPH-DEM coupled solver for three-phase flow was demonstrated through a three-phase SPH-DEM simulation on the gas-driven leveling behavior of a particulate bed.

Industrial process monitoring based on optimal active relative entropy components

The multivariate statistic method has been widely applied, but no clear mapping relationship exists between the latent variables and the fault information, which leads to various information in different latent variables. The traditional methods cause the loss of useful fault information and the decline of monitoring performance. In this paper, a process monitoring method based on the optimal active relative entropy component is proposed. Based on global and local preserving projection, relative entropy is selected as the monitoring statistic. The influence of latent variable selection on relative entropy calculation is analyzed, and a relative entropy activity index is proposed to select the relative entropy component beneficial to process monitoring. The parameters are optimized to generate the optimal active relative entropy component for process monitoring using the mayfly algorithm. Finally, a numerical case, Tennessee Eastman (TE) process, and three-phase flow facility are used to verify the superiority of the monitoring algorithm.

Intelligent fault diagnosis of machine under noisy environment using ensemble orthogonal contractive auto-encoder

Despite significant advances in auto-encoder (AE) based intelligent fault diagnosis recently, their assumption of high-quality sensor data is unrealistic due to noisy environment. Aiming at such problem, this work proposes a novel ensemble orthogonal contractive auto-encoder (EOCA) method to improve diagnosis accuracy. By designing a new cost function including contractive term and orthogonal constrain, OCA can learn robust and non-redundant features from noisy input. To make fault patterns separable, fisher discriminant analysis is cascaded with OCA for mapping these features into a new coordinate space with maximum class separability. Considering the limitation of a single model, sixteen different OCAs with complementary activation functions as well as a new ensemble strategy based on expert model are developed to produce a stable diagnosis result. Additionally, to prove the effectiveness, error bound of EOCA is analyzed and theoretical basis is found for supporting OCA design. EOCA is verified on three cases including three-phase flow facility, gear box and self-priming centrifugal pump. Experiments are conducted by introducing various strength of white Gaussian noise. Results show that EOCA is better than single OCA, canonical AE, sparse AE, denoising AE and several advanced ensemble methods under strong noise.

A computational fluid dynamics investigation of a novel flooded-bed dust scrubber with vibrating mesh

This work proposes a vibrating mesh screen as an alternative to the static mesh screen currently used in conventional flooded-bed dust scrubbers for removing airborne coal mine dust in the continuous mining environment. Fundamental assessments suggest that a vibrating screen may improve the dust collection efficiency of scrubber systems and mitigate the clogging issues associated with the conventional design. To evaluate this hypothesis, computational fluid dynamics (CFD) simulations were carried out to assess the effects of vibration conditions (i.e., frequency and amplitude) on the dust particle-mesh interaction and mesh wetting conditions, which are the two decisive factors in determining the dust collection efficiency. The results suggest that the vibrating mesh screen can enhance dust particle collision opportunities on the mesh and increase mesh wetted area as compared to the static mesh screen. The effects of mesh screen aperture, coal dust concentration, and spray nozzle flow rate on the performance of the vibrating mesh are also evaluated. Finally, a simplified three-phase flow simulation including airflow, dust particles, and water droplet spray is performed, and the results reflect a significant improvement of dust collection efficiency in the liquid-coated vibrating mesh screen.

A latent feature oriented dictionary learning method for closed-loop process monitoring

Industrial cyber–physical system (ICPS), by its powerful computing, communication, precise control and remote operation functions, has become the mainstream of modern industrial process. The observed variables of the closed-loop process in ICPS are subject to the degradation of equipment and other factors, resulting in exhibiting a stationary/nonstationary mixture feature and dynamic feature. Moreover, due to the frequent change of working conditions in the closed-loop process, the traditional open-loop process monitoring method always triggers false alarms, which will impose a negative impact on the safety and trustworthiness of ICPS. Therefore, for the closed-loop process in ICPS, a latent feature oriented dictionary learning (LFDL) method is proposed, which realizes the precise separation of latent features of raw data through three stages. First, closed-loop process variables are separated into stationary and nonstationary variables to mine the local information spatially. Then, from the temporal viewpoint, the static and dynamic features were extracted for stationary and nonstationary variables on the basis of the slow feature analysis method and cointegration analysis for local monitoring. Finally, the global monitoring results are obtained by utilizing the dictionary learning method to fuse respectively the local monitoring results of the static and dynamic features. Since the proposed method has taken the feature of the close-loop process from temporal and spatial viewpoints simultaneously, it can distinguish the normal change of operating conditions and actual faults accurately. Extensive experiments including the three-phase flow, the Tennessee Eastman process and an industrial roasting process are conducted to demonstrate the feasibility and effectiveness of the proposed method.

Investigating the migration of immiscible contaminant fluid flow in homogeneous and heterogeneous aquifers with high-precision numerical simulations.

Numerical modeling of the migration of three-phase immiscible fluid flow in variably saturated zones is challenging due to the different behavior of the system between unsaturated and saturated zones. This behavior results in the use of different numerical methods for the numerical simulation of the fluid flow depending on whether it is in the unsaturated or saturated zones. This paper shows that using a high-resolution shock-capturing conservative method to resolve the nonlinear governing coupled partial differential equations of a three-phase immiscible fluid flow allows the numerical simulation of the system through both zones providing a unitary vision (and resolution) of the migration of an immiscible contaminant problem within a porous medium. In particular, using different initial scenarios (including impermeable “lenses” in heterogeneous aquifers), three-dimensional numerical simulation results are presented on the temporal evolution of the contaminant migration following the saturation profiles of the three-phases fluids flow in variably saturated zones. It is considered either light nonaqueous phase liquid with a density less than the water, or dense nonaqueous phase liquid, which has densities greater than the water initially released in unsaturated dry soil. Our study shows that the fate of the migration of immiscible contaminants in variably saturated zones can be accurately described, using a unique mathematical conservative model, with different evolution depending on the value of the system’s physical parameters, including the contaminant density, and accurately tracking the evolution of the sharp (shock) contaminant front.

Computational fluid dynamic simulation of multi-phase flow in fractured porous media during water-alternating-gas injection process

Naturally fractured reservoirs (NFRs) are among major oil and gas producing reservoirs that are commonly targeted for various enhanced oil recovery (EOR) techniques, particularly water-alternating-gas (WAG) injection. Production from NFRs is complicated due to the flow communication between the matrix and fractures in such porous media. The implementation of WAG injection in NFRs features inherent complexities not only related to the three-phase flow, saturation history, and cycle-dependent hysteresis of the individual phases, but also the fracture-matrix communication, fingering, and early breakthrough of the injected phases particularly during gas injection processes. This paper provides the key results on the computational fluid dynamics (CFD) simulation of WAG injection in a fractured system. We evaluate the impacts of hysteresis, fracture characteristics (aperture, orientation, and fracture density in the network), and the three-phase relative permeability of available phases during the WAG injection. The CFD model simulates an immiscible WAG injection, and the CFD results are compared to the experimental data in a strongly water-wet sand-pack. Similar to the experiments, we simulate Maroon crude as the oil phase, and synthetic brine, and pure CO2 at 100 °C and atmospheric outlet pressure. The CFD results are in excellent agreement with the experimental data. The absolute relative error is less than 12% for predicting the ultimate oil recovery factors (RFs) in water flooding (WF) and gas injection (GI) cycles. Including the three-phase hysteresis significantly increases the accuracy of the WAG process simulation, while excluding hysteresis underestimates the instantaneous RFs at each cycle (especially GI cycles) and the ultimate RF by 4%. We also analyze the fracture pattern and configuration; adding fractures to the system increases the system effective permeability, leading to more contact between the injecting fluid and trapped oil, and consequently higher oil recovery. Connecting a vertical fracture to the horizontal fracture enhances the recovery through strong connections between the vertical and horizontal blocks. An increase in the fracture aperture from 0.5 to 3 mm increases the RF from 50% to 59%. The fracture inclination angle from 30° to 90° increases the ultimate RF by only 2%. Including gravity forces in vertical model orientation results in overall improvement in RF through engaging both matrix and fracture media in all cycles. As the permeability contrast between the matrix and fracture media decreases, the flow communication between the two regions increases, which improves the recovery performance of the WAG process. Our simulation results can help to further understand the effect of various parameters and operational conditions on WAG injection performance in fractured reservoirs.

Critical Concurrent Feature Selection and Enhanced Heterogeneous Ensemble Learning Approach for Fault Detection in Industrial Processes

Machine learning approaches have been successfully applied to fault detection in complex industrial processes, while it is still challenging to design an optimal feature space and a stable model for various faults. To solve this problem, a hybrid approach with critical concurrent feature selection and enhanced heterogeneous ensemble learning is proposed. First, raw process variables from field instruments and slow features obtained by simple linear transformation are ingeniously combined to construct the concurrent feature space so as to contain static and dynamic information. Then, the critical concurrent feature space of each individual learner is automatically obtained by the optimal selection process, and the feature diversity of ensemble is realized simultaneously. Finally, an enhanced heterogeneous ensemble model is constructed by different optimized individual learners, which effectively improves the classification accuracy and stability in fault detection. The performance of the proposed approach is evaluated in a simulated Tennessee Eastman benchmark and a real-word three-phase flow process. The experimental results illustrate that the performance of each optimized individual learner outperforms classic random forest algorithm. The generalization and stability of the ensemble model are further improved. Compared with traditional classification algorithms, the proposed approach achieves superior performance with accuracy that exceeds 89% for the Tennessee Eastman process and exceeds 88% for the three-phase flow process respectively. Additionally, the selected critical concurrent features indicate that both static and dynamic information play important roles in fault detection of industrial processes.

Phase fraction measurement of oil–gas–water three-phase flow with stratified gas by ultrasound technique

Phase fraction is one of important indexes to characterize multiphase flow. In order to measure each phase fraction of oil–gas–water three-phase flow, liquid level height is detected by time-of-flight—TOF of reflected ultrasound at gas–liquid interface, while oil phase fraction in reflection path is calculated according to the ultrasound attenuation. By studying interactions between multiphase flow and the ultrasound propagation in certain flow patterns, a prediction model for phase fraction measurement of three-phase flow is proposed based on ultrasound transmission attenuation and reflection TOF in the process of horizontal flow with actual phase distributions. Simulation and experimental results under conditions of oil–water two-phase structure with stratified gas in a horizontal pipe show that the proposed method and the established model can accurately detect gas–liquid interface, so that measure oil, gas, water phase fraction. The mechanism prediction model and the measurement device effectively solve the nonlinear response of the ultrasonic measurement parameter, so that can estimate phase fractions of liquids and gas in two-phase as well as three-phase flows simultaneously, which extends the measurement range and the applicable scope of ultrasonic technique to multiphase flow.

A Flownet-Based Method for History Matching and Production Prediction of Shale or Tight Reservoirs with Fracturing Treatment

Summary Based on a newly developed physics-based data-driven model FlowNet, this paper presents an effective method for history matching and production prediction of fractured shale or tight reservoirs without any prior information about fracture geometry. In this method, four types of well nodes including fracturing cluster nodes, fracture nodes, stimulated reservoir volume (SRV) nodes, and matrix nodes are allocated in the reservoir. Then, the reservoir model is simplified as a flow network composed of some 1D connection elements between these nodes. Some grids are divided on each connection element, and the grids on the same connection element are of equal width and permeability. Subsequently, a fully implicit nonlinear solver is used to solve flow equations in this FlowNet grid system to obtain pressure, phase saturation, and production rates, etc. Efficient history-matching procedure based on the FlowNet model of the fractured reservoir is used to determine the parameters of connection elements, and then fast production prediction can be conducted. Five numerical examples including single-well depletion, waterflooding development with natural fractures, multiple-well interference, three-phase flow, and an actual waterflooding field case validate that this presented FlowNet-based method can achieve good history matching and production prediction for various flow problems in shale or tight reservoirs with fracturing treatment, and the history-matched transmissibility and volume of connection elements can reflect the existence of high-conductivity fractures.

Subsurface multiphase reactive flow in geologic CO2 storage: Key impact factors and characterization approaches

Subsurface multiphase reactive flow in geologic CO2 storage: Key impact factors and characterization approaches

Dual-energy CT scan protocol optimization to monitor transient fluid saturation distributions during three-phase flow in sand columns

Surfactant foam injection is a promising in situ remediation technique for aquifers impacted by Non-Aqueous Phase Liquids (NAPL). Though the viscous properties of foam can promote immiscible displacement, pore plugging by foam is a concern in an environmental remediation (ER) context, as maintaining flow conditions may require the application of pressures that exceed soil matrix failure thresholds. Optimization of the technology for ER may be furthered by gaining a better understanding of the relationship between foam morphology and NAPL desaturation. This relationship is poorly understood, partly because the interactions between organic phases and foam occurring at specific positions within the flow field at any given time are difficult to assess quantitatively. A Dual-energy X-ray Computed Tomography (CT) technique was developed to enable the measurement of sand column characteristics, as well as transient fluid saturation profiles within the porous medium during foam injection. The available settings of a medical CT scanner were systematically explored in order to identify the acquisition and reconstruction parameters best suited to image the fluids of interest: 0.4 mm slice thickness, a medium kernel (U40u), peak tube voltages of 140 kVp and 80 kVp, and the addition of a doping agent to the NAPL at a concentration of 20 wt%. Using datasets produced by imaging sand columns at various stages of foam injection experiments, an algorithm was developed in order to extract saturation profiles from CT images. The algorithm included multiple scan averaging to reduce image noise, image registration, and specific matrix operations required for calculating fluid saturation values in two- and three-phase systems. The accuracies of the CT-extracted porosity and fluid saturation data were compared to, and found to be in good agreement with, experimentally determined average porosity and saturation values. The scan parameter optimization method developed here can be applied to future studies involving any combination of site-specific natural materials.

Modeling and simulation of non-isothermal three-phase flow for accurate prediction in underbalanced drilling

The present study aims at investigating the effect of temperature variation due to heat transfer between the formation and drilling fluids considering influx from the reservoir in the underbalanced drilling condition. Gas-liquid-solid three-phase flow model considering transient thermal interaction with the formation was applied to simulate wellbore fluid to calculate the wellbore temperature and pressure and analyze the influence of different parameters on fluid pressure and temperature distribution in annulus. The results show that the non-isothermal three-phase flow model with thermal consideration gives more accurate prediction of bottom-hole pressure (BHP) compared to other models considering geothermal temperature. Viscous dissipation, the heat produced by friction between the rotating drilling-string and well wall and drill bit drilling, and influx of oil and gas from reservoir have significant impact on the distribution of fluid temperature in the wellbore, which in turn affects the BHP. Bottom-hole fluid temperature decreases with increasing liquid flow rate, circulation time, and specific heat of liquid and gas but it increases with increasing in gas flow rate. It was found that BHP is strongly depended on the gas and liquid flow rates but it has weak dependence on the circulation time and specific heat of liquid and gas. BHP increase with increasing liquid flow rate and decreases with increasing gas flow rate.