Abstract Leaks and ruptures are the most important possible risks for operational oil and gas pipelines. Due to their hazardous effects on the environment, much research has been conducted to prevent and detect possible ruptures on a pipeline to protect people and the environment by enhancing safe operation. Any improvement in leak detection technologies to increase the accuracy and sensitivity while eliminating false leak and rupture alerts will protect the environment and assure hazard-free operation. Data mining algorithms are widely used in many industries, including the energy industry. They have already been implemented as computational leak detection methodologies. To increase confidence and improve accuracy and sensitivity, different algorithms may be introduced to detect ruptures. In our study, a 36" crude oil pipeline with two pump stations was configured in a pipeline simulator. The pipeline parameters of flow, pressure, and temperature were computed for several leak and rupture cases, and data science algorithms such as Logistic Regression, Neural Network, and Multivariate Adaptive Regression Splines were used as classifiers to detect the leaks and ruptures. Multivariate Adaptive Regression Splines (MARS) is an important statistical learning tool for both classification and regression. MARS is nonparametric, adaptive, and effective in high dimensional problems with a proven record for fitting nonlinear multivariate functions. The contribution from the basis functions together with interaction effects between the predictors are used to determine the response variable: MARS produces a resultant model as an explicit formula. MARS proves itself as a comparative classifier to the already known logistic regression and neural network methods as a new leak and rupture detection computation data science technique for pipeline operators.

Abstract Transportadora Gas del Sur (TGS) gathering system crosses a non-conventional gas field collecting the flow from different producers. The high calorific power characterizes this gas, which condenses within the pipeline. The system has been designed for the full development of the deposit. However, it began operating with flows significantly lower than the design value. This translated into the challenge of guaranteeing the flow assurance at the beginning of the operation, since the low flows generated wide accumulations of liquids inside the pipe, and the consequent risk of big liquid slugs. This paper shows the method that allowed TGS to go through the initial stage, and operate the system minimizing the volume of the slug catcher, avoiding pigging as a measure to control liquids. The results of the simulations made during the design, as well as its later implementation are shown. Introduction and Background TGS built a pipeline of 115 km (71 miles) and 36 inches of diameter that goes through the main non-conventional gas field in Argentina (and third in the world), known as “Vaca Muerta”. The gas pipeline collects rich gas -without previous treatment- from different producers working constantly with two-phase flow. Although the gas flow rate design is 60 MMSm3/day (2100 MMSCFD), it began to work with flows lower than 5 % of the design value. This translates to very low speeds - less than 0.2 m/s (0.7 ft/s), which added to an irregular topography generate large accumulations of condensate along the pipeline - over 20.000 m3 (125800 bbl).

Abstract This paper investigates the technology and business benefits of a web-based model scheduling application, which integrates a transient model to other business applications that provide injection and demand schedules. The application provides a simple method to input complex scenarios, including compressor, regulator, and routing operations. The paper shows how the system allows non-modellers to use a complex transient model, reducing non-optimal operational decisions which could impact the wholesale natural gas price or supply to end user customers such as households or businesses. Other operational metrics are discussed. Introduction and Background Independent System Operators’ (ISOs) and Transmission System Operators’ (TSOs) use of transient pipeline models to manage nominations and allocations is seemingly an easy operational premise. The Australian Energy Market Operator (AEMO) operates the gas Declared Transmission System (DTS) which serves Victoria, a state in Australia. The DTS network consists of approximately 2,200 km of transmission pipelines with various lengths and diameters, and transports gas to a network from up to 10 different suppliers. The high pressure meshed transmission network supplies gas to 148 custody transfer points which spans the geographical extremities of the state. The DTS is becoming increasingly more complex, with changes to flow paths and dynamic demand regimes. AEMO, like other TSOs and ISOs, is utilising transient pipeline models to inform operational strategies. This paper explores the challenges facing ISOs and TSOs in the United Kingdom, The United States, and Victoria Australia. Specific reference is made to AEMO’s gas transmission network. The issues explored cover a range of subjects including financial and macro policy but all lead to the impact on a transmission network. The paper does not attempt to solve each transmission operator’s challenges in detail. However, within the context of AEMO’s operations, it attempts to identify the similarities with other TSOs and highlight how AEMO’s transient hydraulic model helps it deal with its challenges.

Abstract Solids transport models are used to predict the fluid velocity required to transport solid particles in hydraulic and pneumatic systems. It is important that the processes in these applications are designed and operated at a sufficient fluid velocity to avoid solid deposition. Mechanistic models are used to provide a reasonable estimate for the minimum fluid velocity needed to transport the particles. However, those models are commonly applicable in their respective ranges of data fitting; and are limited by the applicability of the empirically based closure relations that are a part of such models. Artificial Intelligence (AI) and Machine learning (ML) offer a wealth of techniques to extract information from data that can be translated into knowledge about the underlying fluid mechanics. The purpose of this work is to investigate the use of several ML models to predict the critical velocities of various single-phase carrier fluids in horizontal and inclined flow conditions. A frame of work is developed to predict critical velocities in pipes via machine learning, using accessible parameters as inputs, namely, liquid and particle properties and inclination angles. The ML algorithms are trained on a large dataset (more than 1400 data points) of critical velocities in single-phase carrier fluid that is collected from open source: articles and dissertations. The trained algorithms are Elastic Net, Support Vector Machine, Random Forest, and Extreme Gradient Boosting Decision Trees. Moreover, the influence of key features in critical velocity prediction was identified by the applied algorithms. Finally, the predictive abilities of the models are cross-compared and were further validated by comparing its performance with well-established mechanistic models based on empirical correlations. The ML approach is observed to have superior performance to other models across a wide range of flow conditions and inclination angles.

Abstract Both in Europe and in North America, the goal to use increasing amounts of renewable energy has created the need for electricity storage. One concept which aims to address this need is to produce hydrogen from surplus electricity, and to use the existing natural gas pipeline system to transport and store said hydrogen. Generally, the hydrogen content in the pipeline flow would be below 20%, thus avoiding the problems of transporting and burning pure hydrogen. The thermodynamic properties of hydrogen-natural gas mixtures, as well as their impact on the combustion process are briefly addressed. To assess the impact of hydrogen addition in various concentrations to a natural gas pipeline, a realistic pipeline system is simulated. The pipeline hydraulic simulation provides the necessary operating conditions for the gas compressors and the gas turbines that drive these compressors. The impact on transportation efficiency in terms of energy consumption as well as the impact on the transport capacity of a pipeline where the equipment was sized originally for natural gas transport, are assessed. Introduction and Background The increased use of renewable energy will require the capability to store energy to balance the large fluctuations in the availability of renewable energy, thus balancing supply and demand. Generating green Hydrogen from surplus electricity using electrolysis and transporting this hydrogen together with natural gas in gas pipelines is a method to provide storage and transportation for hydrogen [1,2,3]. Mixing hydrogen into natural gas pipelines requires a number of considerations regarding the compression of this mixture, the use of the mixture as a fuel for the gas turbines that drive the pipeline compressors, and the impact of pipeline capacity and transport efficiency. There are a number of other concerns, including safety concerns for gas compressor packages, and material issues related to hydrogen embrittlement on rotating parts like impellers, pressure containing vessels (like compressor bodies), and high-pressure pipes. [4].

Abstract The purpose of a pipeline Operator Training System (OTS) is to train operators in an offline environment, while giving the look and feel of real-world operations. The problem with many OTS’s is that, while some aspects of pipeline operation are covered, it can be very challenging to simulate other parts in an effective manner. Real-world pipeline operations encompass many “moving parts” that are often ignored or highly simplified in an OTS, but the inclusion of these parts can vastly improve the training experience. The integration of these many parts poses unique challenges in the design of the pipeline simulation as well as the operation of the simulator. This paper will discuss the challenges and solutions found during the development of an OTS that integrates liquid and gas pipeline operation, pump station power plant startup, PLC control, and power distribution management models. These models were integrated in such a way as to give an operator fully comprehensive training on every aspect of the real-world system included in their operating responsibility. Introduction and Background An Operator Training System (OTS), also called an Operator Training Simulator, is an offline system that dynamically simulates real world processes. The most realistic platform of an OTS is one in which the simulation of real world processes is integrated with a stand-alone copy of the Supervisory Control and Data Acquisition (SCADA) system or Distributed Control System (DCS) that is used in the pipeline control room. This OTS platform allows for training of new operators, retraining existing operators, testing out new operating procedures, validating control logic or PLC logic prior to commissioning, etc. It can also be used for offline engineering analysis and calculations that allow for efficiency improvements or in-depth studies into how equipment modifications may affect the system. OTSs are commonly found in industrial processes such as plant or pipeline operations. Often the scope of an OTS is limited or simplified due to the complexity in modeling real world systems and the extra time and effort required to implement a full-scope simulation outweighs the added benefit derived by those using the system. On the other hand, with advances in computing and OTS conception, very complex systems can be modeled that give the look and feel of a real-world system such that operators training on them may find them difficult to distinguish from the online system, providing a seamless transition from OTS to real world operation. As with all engineering considerations, there are tradeoffs that must be balanced and compared with the end goal of the training system in mind.

Abstract The paper demonstrates the construction, training, and uncertainty quantification analysis of an artificial neural network model for single phase liquid pipeline leak detection through pressure drop and flow rate monitoring. The demonstrated methods reached acceptable error levels efficiently using theoretical data produced by a theoretical physics model. The results demonstrate that the randomly simulated leakage can be efficiently detected using the trained ANN (Artificial Neural Network) model based on theoretical data derived from physical equations. However, complexity appears when simulated leakage with modeled uncertainty are used in the training of the AI models. The propagation and influence of the uncertainty in the input data on the ANN method are discussed. Randomness following certain probability distributions is introduced into the data to measure the influence on the efficiency and reliability of the training and results of the ANN model. The paper also discusses the influence of the range of input data on the predictability of the ANN model in leak detection. Introduction and Background Data driven Machine Learning technologies such as ANN (Artificial Neural Networks) provide great innovation opportunities towards the design and operation of oil and gas pipeline systems. ANN based models have proved to be efficient in predicting the pressure drop within a pipeline (Brkic, D., and Cojbasic, Z., 2016; Shayya, W.H., and Sablani, S.S, 1998; Salmasi, F., etc. 2012; Fadare, D.A., and Ofidhe, U.I., 2009) as well as solving the inverse problems such as detecting pipeline roughness progression, inner diameter change, and leakage (Cheng, D., Zeosky, D., 2019). The increasing interest towards machine learning models causes the need to highlight the concerns of uncertainty propagation into the engineering applications of the methods. Machine learning models are developed through analysis of data derived either from physics models or measurements.

Abstract Deep subsea gas pipelines are in place or being built in places like the Baltic, the Mediterranean, the Black Sea, and Southeast Asia. At full depth, the height of the water column can produce pressure that exceeds the gas pressure inside the pipeline, particularly toward the delivery end; in those regions, a loss of containment will result in water ingress into the pipeline instead of gas egress. We discuss the detection of water ingress in such pipelines. We present the results of multiphase simulation of ingress at several locations and sizes in two flow regimes. Time scale of the ingress depends strongly on location, which is equivalent to the difference between gas pressure in the pipeline and ambient external pressure from the water column. Larger sizes have greater tendency toward cyclic behavior in the simulator. The expectation that the accumulation of water in the pipeline would displace gas, producing a downstream flow surge, is shown to be naive. Instead, accumulating water decreases the gas flow cross-section, throttling down gas throughput and reducing delivery flow. At equilibrium, water ingress will act as a regulator, causing pressure at the point of ingress to be equal to ambient external pressure. Output of the multiphase simulation is converted to instrument measurements, then simulated in a conventional leak detection RTTM. Ingress is detected as a conventional leak prior to any water reaching the downstream delivery. Location information is partially inverted compared to conventional leaks, but properly corrected can be used to distinguish gas egress from water ingress.

Abstract Software for the simulation of the transient behavior of natural gas pipelines commercially available, however, due to their high computational demands, they do not allow real time simulations. Further, they are difficult to set up if the goal is to match measurement data from SCADA systems or compressor stations for existing pipelines. To overcome this difficulty, a state space approach is used to capture the transient behavior of the pipeline. The general approach takes a published method from the literature that derives transfer functions and state space equations from one-dimensional Navier-Stokes and extends the method to reduce uncertainty in modeling parameters. The present method is validated against results in the literature. Then, a pipeline is modeled with commercially available, well established software, and the transient behavior is simulated and the results are compared with the present method. The main contribution of the paper is the extension of the stat-space formulation to remove uncertainty in pipe-roughness assumptions through systematic estimation of friction factors. Introduction Natural Gas Pipelines are an efficient method to transport energy over large distances. The transportation of natural gas and/or blends of natural gas and hydrogen over large geographic areas requires efficient operation of compressors and other related facilities. The scheduling of flows throughout a piping network is a great challenge to control theory. Many gas pipelines are operated under unsteady operating conditions, which in turn causes significant fluctuations in the operating conditions for the compressor stations in these pipelines. These fluctuations are the result of varying demand, as well as varying gas supply. For a given pipeline segment, the gas supply pressure and flow into the pipe are subject to change as are the pressure and flow demand on the delivery side of the pipe. These conditions, as well as their timing are only predictable to a degree.

Abstract There are occasions when one might want to use results from one simulator as input to another simulator, for example, when preparing data for a leak sensitivity study where data from an offline simulator is used to synthesize measurement data for a real time model. Alternatively, one might want to compare results between different simulators, as with the PSIG test cases. Clearly, ensuring the pipeline parameters and fluid parameters are the same in both simulators is an essential step in this process. However, nuances and differences in modelling philosophies can lead to differences in the results that can be difficult to explain. In this paper we present an incremental approach to ensuring that results between simulators match to within desired tolerances and, where they do not match, provide insight to where the source of the differences lie. The key parameters used to measure the agreement, or otherwise, between the simulators are explained and the effect of the different modeling parameters investigated. To demonstrate, the approach is applied to an offline single-phase simulator, an offline multiphase simulator, a spreadsheet model and a Real Time Transient Model. In the example presented the various steps in the process are described and differences in modelling approaches are highlighted. Finally, we demonstrate that, for the example used, excellent agreement between the different simulators can be achieved. Motivation and Overview The reasons for wanting to ensure that different simulators agree are many and varied. For example, we might want to generate data using one simulator for consumption by another simulator, such as when undertaking a leak sensitivity study. Another example would be independent verification of a flow assurance study.

Abstract Liquid Pipeline scheduling varies from being simple to very complex as the size of the network grows and a point will be reached where an initially deployed legacy or home-made solution may no longer support the current requirements. This problem is further exacerbated by an ageing work force and lack of knowledge transfer from one generation of schedulers to the next. It is often the case that when pipelines were constructed initially, the process and systems were lacking in complexity and functionality leaving most matters being dealt with by using standard rule of thumb or set practices. But with increasingly complex network growth, numbers of products being transported, stringent interface management and increased focus on optimization, the legacy systems and spreadsheets may no longer support the increasing demands. Migrating from a legacy application to a standard industry bench-marked solution helps the pipeline company to reduce dependency on individuals and makes scheduling a more process driven approach, enabling greater control over the scheduling process. A modern product-based solution also helps in identifying beneficial practices that were previously unapproachable such as optimization of the costs incurred while running combinations of pumps, or optimized usage of drag reduction agents, or a combination of both. Besides offering greater visibility of the schedule for a pre-defined period across various hierarchical levels, a modern scheduling tool will also have interfaces to enterprise applications such as SAP and SCADA thereby facilitating the improvement in the fidelity of the scheduling by utilizing the most recent data for e.g., tank levels or line fill data. This paper pulls together experiences of implementations of scheduling solutions and their varying level of user acceptance. The paper highlights common reasons that hinder a scheduler from fully utilizing an available system. The paper identifies the most common amongst these (like non-availability of external interfaces that leads to a lot of data entry) which when provided may enable a higher user level acceptance of an implementation. The paper also tries to define the optimal time when a pipeline company with a medium to complex pipeline network should consider migrating from a legacy application to a standard, or a tailor made, solution by considering the level of maturity of systems and the processes that exist at various levels within the organization. Recognizing that the pipeline system will continue to evolve indicates that the tolls supporting the pipeline will also continue to evolve and therefore the paper considers what steps should be in place for scaling up the solution from time to time. Finally, the paper discusses what is required to achieve the all-important user buy-in and acceptance of the system.

ABSTRACT Due to the constant need of improving flow, pressure and pipeline throughput for single phase flow in fluid pipelines and due to extensive injection of DRA into pipeline networks, whether to supply an increased demand of customers or uprate an overall throughput of a pipeline system, a need to find ways to optimize the effect of DRA injection on the flow rate and pressure has become essential in the pipeline industry. This study looked into the effect of drag reducing agents on the flow rate and pressure of single phase flow in fluid pipelines. Reviewing the literature helped a lot in planning a methodology that explains how the investigation is carried. The methodology started with selecting a pipeline network to be case studied and then utilizing an advance tool that can perform hydraulic pipeline calculations to simulate various cases. After that, results are evaluated, analyzed, and discussed to make sure that they match with the obtained current pipeline data. To optimize, a hydraulic simulation tool called SPS is used to simulate cases where effect of DRA on the flow rate and pressure is improved. Having gone through several investigations on how to optimize the effect of DRA on the flow rate and pressure of single phase fluid pipelines, a number of recommendations has been made to modify several factors that eventually lead to optimization of the effect of DRA. Initially, the definition and characteristics of DRAs has been made clear and understood to be able to know more about what is being researched and investigated. Additionally, the mechanics behind drag reduction have been understood and the effect of DRAs on the flow rate and pressure of pipelines once injected has been understood. Factors such as location of DRA injection, DRA type vs. %Dr, and DRA concentration have been proven to be effective in optimizing the effect of DRA based on the results of the simulated cases. Savings reaching up to $4 Million on spending for one type of polymeric DRA injection were the results of only one factor contributing to optimization which was the location of DRA injection.

ABSTRACT One of the urgent and time-consuming problems in the area of oil and oil products transportation is the problem of oil cargo flows scheduling in trunk pipeline systems. The problem is complicated by the necessity to take into account both the characteristics of the oil pipelines themselves, the properties of oil and capacity of tank terminals. The relevance of this topic is due to the deterioration in the composition and properties of incoming oil to the pipeline systems from suppliers, the increase in the share of high-sulfur oils and the need to supply low-sulfur oil to consumers. The article considers a new approach to oil transportation scheduling in branched oil pipeline systems. A new approach based on mathematical model that solves the optimization transport problem. The transport problem is formalized as a system of objective function and constraints then this system is solved using linear and sequential quadratic programming methods. The system can be varied depending on the needs of a given pipeline system. The approach allows to compute oil flow distribution during the certain time period (day, week, month, etc.) with given time sampling (hour, day, week, etc.) considering pipeline characteristics (flow capacity, technological regimes, etc.), oil properties (mass sulfur fraction, density, etc.) and capacity of tank terminals. Also, the approach allows to optimize oil transportation by energy consumption. The possibilities of the proposed approach are shown using a system of 10 oil pipelines, 4 transitional tank terminals, 3 oil suppliers and 6 oil consumers. The result of flow distribution calculation in a branched system is the schedule of cargo flows for each pipeline in a whole pipeline system with all constraints satisfied and optimized objective function.

ABSTRACT The Austin-Palfrey equation could be directly applied to a pipeline where both the Reynolds number and the pipe diameter are not varied along the entire length and where there is no initial mixing volume at the start point of the pipeline. If the Reynolds number, or the pipe diameter, or both are varied along a pipeline, the pipeline should be divided into segments based on the point along the pipeline where Reynolds number or the diameter change. The Austin-Palfrey equation should be revised to include effects of the initial mixing volumes in subsequent segments. In this study, the Austin-Palfrey equations were revised by introducing an initial pipe length or an initial length factor to incorporate the initial mixing volumes at the start point of each pipe segment. The revised equations could predict the mixing volume along the entire pipeline length. The initial length factor has an impact on the incremental mixing length in subsequent segments. When the initial length factor is larger than one, the mixing length in subsequent pipe segment tends to decrease. When the initial length factor is less than one, the mixing length in subsequent pipe segment tends to increase.

ABSTRACT One of the most important aspects when transporting multiple different liquid products within the same pipeline, is being able to know where the head and tail of each batch is at any given time based on the current operation to provide an accurate Estimated Time of Arrival (ETA) at upcoming stations. With this information, pipeline operators can be ready to execute valve swings at intermediate delivery and injecting stations helping minimize product contamination. It is relatively simple to track these batches and their interfaces when the pipeline has a uniform and gradual elevation profile. However, when pipelines travel through mountainous regions with high and drastic elevation changes, the ability to track the head and tail of these batches become far more complex due to the presence of slack. When slack is present, the common assumption that the volume of liquid equals the physical volume of the pipe is no longer valid. The fraction of the pipe filled with liquid, called the "liquid holdup", has a large impact on the position of the batch head and tail. As a result, the accuracy of the volume contained within the region and the corresponding Estimated Times of Arrival at upcoming stations are directly affected. An empirical approach using volume accumulation at a pipeline injections and deliveries has been successfully implemented with an end user to account for the liquid volume within a specific region known to have slack during normal operations. However, this approach does not provide an accurate solution for the exact location of where the slack occurs, nor the size of the slack itself, which has a direct effect on the location estimate for when the head or tail is passing through the area. With the current empirical approach, Estimated and Actual Times of Arrival are within a 15-minute time window after a batch has traveled a total distance of 1,200 km (746 mile) with a drastic slack region along the route. This paper provides an analysis on the current empirical approach used in the daily operation by the existing end user, and how the results compared to a full scientific approach running a slack model based on the field instrumentation, and the physical properties of the pipeline.

ABSTRACT Presently one of the methods of increasing pipeline capacity is using the drag reducing agents (DRA). DRA are typically high molecular mass polymers that are added at very low concentrations to reduce the pressure drop necessary to generate a given flow rate in a turbulent flow. They can be used in case if building of extra loops or pumping stations is impossible or the need to increase pipeline capacity is seasonal. Scheduling pumping regimes and calculating the amount of drag reducing additive necessary to achieve the specified pumping parameters, requires a mathematical model. This paper proposes a method that allows to integrate DRA into a mathematical model of viscous fluid motion in a pipeline. The model takes into account the degradation of DRA as the agents travel forward the pipeline. This article focuses on the question of theoretical model adjustment to the characteristics of a certain pipeline. Using the nominal information about DRA (provided by manufacturers) generally leads to a strong error and some information needed for the modeling might not be provided at all. Thus, the model needs to be tuned to the real DRA characteristics and the main source of data are real measurements of flow parameters (pressures, flow rates, etc.). Methods of using operational pipeline data for identifying DRA characteristics are considered. The issues of data collection and further data processing are discussed. The results of comparing modeling computations with real data from operating pipelines are presented. The characteristics of these pipelines are very diverse: internal diameters vary from 0.4 m to 1 m, different DRAs are used, and different types of liquid (oils, oil products and gas-condensates) are pumped.

ABSTRACT Heavy oil accounts for more than half the word's total oil reserves. However, heavy oil as extracted from the reservoir has an extremely high viscosity and the transportation of these fluids in a pipeline requires the use of technologies to lower the viscosities of this oil. Among these technologies are heating or dilution of the heavy oil and sometimes a mixture of both of these methods. The majority of oil storage terminals contain pipes that are installed above ground and in northern countries are prone to experiencing cold temperatures during winter months. In part of the terminal where the fluid stays stagnant in the pipes (such as relief piping and manifold pipes) the cold weather can impact the viscosity of the fluid, even after it has been diluted. When the pumping system has to move fluid that has been stagnant in the pipe and has become colder, the pressure required to achieve this is very high. The transient surge pressures created in these cases can become extreme to the point of exceeding the maximum operating pressures of the pipe. When performing transient simulations, it is important to consider the fluid with higher viscosities that have been kept in the pipes and that will require higher pressures to pump them down the line, so that pressure surges can be more accurately calculated and mitigated. This paper will present comparisons of how the transient pressures change between cases of heavy oils at different temperatures and viscosities. It will show that if heavy oil is kept stagnant in a pipe that is subject to cold temperatures, the pressures required to push this fluid down the pipe increases as the viscosity becomes higher. IN particular, it will present the case where the relief piping has been installed above ground and when the fluid needs to be displaced, the surge pressures can become significantly higher than expected.

ABSTRACT A novel model is used to predict the inception moment and location of a phenomenon known as the column separation or slack line. The existing models for prediction of the cavitation phenomenon in internal flow systems in general are primarily formulated for the capture of the inception moment and initial location of the event only. Hence questions such as where the incepted cavities and bubbles tend to go, or the detailed state of a slack line in time should be addressed using a different approach. The model presented in this paper is validated and verified against experimental data available in the literature before being applied to a 53 Km (33 miles) industrial pipeline. Imposing conditions at the injection and delivery ends of the line tests various scenarios causing column separation. Hydraulic parameters such as pressure head and flowrate as well as interfacial mass transfer rate of the incepting and collapsing bubbles and cavity zones are predicted in real time over the entire domain of space and time. The results predicted the fate of the separated column of the fluid, whether they rejoin or continue to change the size with different rates or even if they become stabilized stationary cavity pockets after passage of minutes or hours. Results are also compared based on the initial cause of the column separation such as huge transients. Also, accurate prediction of the whole column separation event, distinguishes it from other events, which would commonly mimic or mask the column separation by exhibiting identical hydraulic footprints.

ABSTRACT Many gas compressor stations use multiple gas turbine driven centrifugal compressors. In many instances, the units are not identical. The challenge is to control the units such, that certain operational parameters, for example fuel consumption, pipeline capacity or maintenance requirements, are optimized. The discussion includes the use and sizing of multiple units, the arrangement of units, the arrangement as push or pull compression, as well as examples of optimization strategies at various levels of sophistication, up to and including the integration of hydraulic and turbomachinery optimization. The benefits of methods that control compressors for the same turndown, or to control all gas turbines for the same load. This methodology can be derived and discussed for the case of identical machines, and will be expanded for the case where the compressors and their drivers are not identical. The control system has to rely on measurable parameters, but the system has to be reliable even if parameters that are not directly measured change during operation. The paper describes a variety of optimization methods, starting with a simple algorithm, then comparing equalization methods, and finally illustrating the capability of methods that combine turbomachinery optimization and the optimization of pipeline hydraulics. An example demonstrating the potential fuel savings of such an optimization is given.

ABSTRACT Upstream gas supply planning and allocation contains 3 cycles namely long term (20 years - end of life of a field) mid-term (up to 1 year) and short term (72 hours). Since the upstream assets are owned/operated by different companies under various production sharing contracts, each of the operating companies prefer to maximize their production and recover the investment costs at the earliest. A regulator acts as a custodian to ensure that all stakeholder's entitlements are protected. The custodian relies on economic and hydraulic modelling to analyse the production plateau's shared by various operating companies together with the demand profiles for viability. The commercial planning team of the custodian works closely with flow assurance engineers before confirming the production numbers as the planners are unaware of any capacity constraints in the physical network for flow. In the short term, an integrated network operator breaks the long term demand numbers and controls the network for seamless delivery of gas from upstream to LNG plants (mostly). The purpose of the paper is to highlight the challenges faced by planners and engineers during the planning cycles and to make appropriate recommendations.