Modelling the direct virus exposure risk associated with respiratory events

The outbreak of the COVID-19 pandemic highlighted the importance of accurately modelling the pathogen transmission via droplets and aerosols emitted while speaking, coughing and sneezing. In this work, we present an effective model for assessing the direct contagion risk associated with these pathogen-laden droplets. In particular, using the most recent studies on multi-phase flow physics, we develop an effective yet simple framework capable of predicting the infection risk associated with different respiratory activities in different ambient conditions. We start by describing the mathematical framework and benchmarking the model predictions against well-assessed literature results. Then, we provide a systematic assessment of the effects of physical distancing and face coverings on the direct infection risk. The present results indicate that the risk of infection is vastly impacted by the ambient conditions and the type of respiratory activity, suggesting the non-existence of a universal safe distance. Meanwhile, wearing face masks provides excellent protection, effectively limiting the transmission of pathogens even at short physical distances, i.e. 1 m.

Multi-phase Rate Transient Behaviors of the Multi-fractured Horizontal Well with Complex Fracture Networks

Complex fracture networks (CFN) provide flow channels and significantly affect well performance in unconventional reservoirs. However, traditional rate transient analysis (RTA) models barely consider the effect of CFN on production performance. The impact of multi-phase flow on rate transient behaviors is still unclear especially under CFN. Neglecting these effects could cause incorrect rate transient response and erroneous estimation of well and fracture parameters. This paper investigates multi-phase rate transient behaviors considering CFN, and tries to investigate in what situations the multi-phase models should be used to obtain more accurate results. Firstly, an embedded discrete fracture model (EDFM) is generated instead of LGR method to overcome time-intensive computation. The model is coupled with reservoir models using non-neighboring connections (NNCs). Secondly, eight cases are designed using the EDFM technology to analyze effect of natural fractures, formation permeability, and relative permeability on rate transient behaviors. Thirdly, Blasingame plot, log-log plot, and linear flow plot are used to analyze the differences of rate transient response between single-phase and multi-phase flow in reservoirs with CFN. For multi-phase flow, severe deviations can be observed on RTA plots compared with single-phase model. Combination of three RTA type curves can characterize the differences from early to late flow regimes and improve the interpretation accuracy as well as reduce the non-unicity. Finally, field data analysis in Permian Basin demonstrates that multi-phase RTA analysis are required for analyzing production and pressure data since single-phase RTA analysis will lead to big errors especially under high water cut during fracturing fluid flowback period, early production of unconventional gas wells or after waterflooding or water huff-n-puff.

Novel In-Line Inspection Tool for Deposit Characterization in Pipelines

Deposition formation inside pipelines is a major and growing problem in the oil and gas industry. The optimal use of prevention and remediation tools such as chemical inhibitors and cleaning processes could lead to major savings due to minimized production problems and optimized pipe cleaning costs. This requires characterization and quantification of the actual deposits inside pipelines and downholes. Recently, a novel deposition inline inspection sensor moving inside the pipeline has been proposed based on "inside-out" electrical tomography. In this sensor, the distribution of electrical properties between the sensor and the pipe wall are estimated based on measurements carried out using electrodes around the sensor. In this study, the next generation sensor moving inside the pipeline is described and a deep neural network based approach to deposit estimation is introduced. Test results from a 70 m long semi-industrial scale flow loop containing paraffin wax and calcium carbonate deposits of different thicknesses are shown. Challenges include the changing position and orientation of the sensor during the low. The results show that the sensor is able to measure both deposit thickness and type with good accuracy which indicates that the sensor is suitable for industrial use. Accurate knowledge about deposits allows future blockage prevention, detecting build-up locations in the early phase, increasing accuracy of multi-phase flow and deposition models, optimization of chemical use and validation of deposit cleaning tools before integrity campaigns leading to overall reduced pipeline operation costs.

An Innovative IR 4.0 Solution Against Leaks Utilizing a Fully Integrated Pipelines Management System

This paper demonstrates the Saudi Aramco Khurais Facility (KhPD) successful commissioning of the A Fully Integrated Pipelines Management System, in an effort to enhance its environmental emission performance. The project team conducted an assessment conceptually right from the beginning, to ensure that the value creations from this initiative can be realized, and the project remain cost effective and safely executed while meeting environmental objectives. Following successful deployment, the Khurais team carried out post installation performance assessment to ensure the outcomes and objectives from this project has been impacted positively. This paper covers the fully implemented solution to manage pipelines assets and enchantments followed by Saudi Aramco Khurais producing facility (KhCPF) Objectives: Share how a corrosion challenge of multi-phase flow within pipelines led to installation of a comprehensive solution to Pipeline Management Systems (common header connects all compressors) and how it was resolved through integration between two different systems. In addition, highlight how this approach enhanced the pipelines reliability, safety and most important the big environmental impact that helped Saudi Aramco to reduce its carbon footprint.

Best Practice Methodology to Enhance Modeling Inflow Control Technologies in Dynamic Reservoir Simulators

The methodology presented here will expand on current modeling of Autonomous Inflow Control Devices (AICD) to generalize for a wider range of fluid flow rates and phases. It will also address the challenges of modeling multiphase behavior of the reservoir fluid flow. This paper presents proposed methods for selected devices, and device models supported by simulations. The proposed methods show the potential for qualified benchmarking of Inflow Control Technology (ICT) completed wells in dynamic reservoir simulations compared to the generic models currently in use. New single-phase models for segregated and sequential flow are presented, and these have a potential for greatly simplifying mass flowrate predictions for multi-phase flow leading to more accurate analysis within dynamic reservoir simulators.

Propulsion simulation on fully appended ship model

This study presents the numerical investigation for the flow around the propeller of the ONR Tumblehome combatant in open water and for the flow around the same ship in the case of self-propulsion with actuator disk method. Computational Fluid Dynamics based on RANS-VOF solver have been used in order to analyse the flow. The free surface treatment is multi-phase flow approach, incompressible and nonmiscible flow phases are modelled through the use of conservation equations for each volume fraction of phase. Accuracy involves close attention to the physical modelling, particularly the effects of turbulence, as well as the numerical discretization.

Thermodynamics-Informed Neural Network (TINN) for Phase Equilibrium Calculations Considering Capillary Pressure

The thermodynamic properties of fluid mixtures play a crucial role in designing physically meaningful models and robust algorithms for simulating multi-component multi-phase flow in subsurface, which is needed for many subsurface applications. In this context, the equation-of-state-based flash calculation used to predict the equilibrium properties of each phase for a given fluid mixture going through phase splitting is a crucial component, and often a bottleneck, of multi-phase flow simulations. In this paper, a capillarity-wise Thermodynamics-Informed Neural Network is developed for the first time to propose a fast, accurate and robust approach calculating phase equilibrium properties for unconventional reservoirs. The trained model performs well in both phase stability tests and phase splitting calculations in a large range of reservoir conditions, which enables further multi-component multi-phase flow simulations with a strong thermodynamic basis.