Large Pipeline Research Articles

Asymmetric fluid flow in helical pipes inspired by shark intestines

Unlike human intestines, which are long, hollow tubes, the intestines of sharks and rays contain interior helical structures surrounding a cylindrical hole. One function of these structures may be to create asymmetric flow, favoring passage of fluid down the digestive tract, from anterior to posterior. Here, we design and 3D print biomimetic models of shark intestines, in both rigid and deformable materials. We use the rigid models to test which physical parameters of the interior helices (the pitch, the hole radius, the tilt angle, and the number of turns) yield the largest flow asymmetries. These asymmetries exceed those of traditional Tesla valves, structures specifically designed to create flow asymmetry without any moving parts. When we print the biomimetic models in elastomeric materials so that flow can couple to the structure's shape, flow asymmetry is significantly amplified; it is sevenfold larger in deformable structures than in rigid structures. Last, we 3D-print deformable versions of the intestine of a dogfish shark, based on a tomogram of a biological sample. This biomimic produces flow asymmetry comparable to traditional Tesla valves. The ability to influence the direction of a flow through a structure has applications in biological tissues and artificial devices across many scales, from large industrial pipelines to small microfluidic devices.

Determining onshore or offshore hydrogen storage for large offshore wind parks: The North Sea Wind Power Hub case

The large-scale integration of renewable energy sources leads to daily and seasonal mismatches between supply and demand and the curtailment of wind power. Hydrogen produced from surplus wind power offers an attractive solution to these challenges. In this paper, we consider a large offshore wind park and analyze the need for hydrogen storage at the onshore and offshore sides of a large transportation pipeline that connects the wind park to the mainland. The results show that the pipeline with line pack storage, though important for day-to-day fluctuations, will not offer sufficient storage capacity to bridge seasonal differences. Furthermore, the results show that if the pipeline is sufficiently sized, additional storage is only needed on one side of the pipeline, which would limit the needed investments. Results show that the policy which determines what part of the wind power is fed into the electricity grid and what part is converted into hydrogen has a significant influence on these seasonal storage needs. Therefore, investment decisions for hydrogen systems should be made by considering both the onshore and offshore storage requirements in combination with electricity transport to the mainland.

Spade: Synthesizing Data Quality Assertions for Large Language Model Pipelines

Large language models (LLMs) are being increasingly deployed as part of pipelines that repeatedly process or generate data of some sort. However, a common barrier to deployment are the frequent and often unpredictable errors that plague LLMs. Acknowledging the inevitability of these errors, we propose data quality assertions to identify when LLMs may be making mistakes. We present spade, a method for automatically synthesizing data quality assertions that identify bad LLM outputs. We make the observation that developers often identify data quality issues during prototyping prior to deployment, and attempt to address them by adding instructions to the LLM prompt over time. spade therefore analyzes histories of prompt versions over time to create candidate assertion functions and then selects a minimal set that fulfills both coverage and accuracy requirements. In testing across nine different real-world LLM pipelines, spade efficiently reduces the number of assertions by 14% and decreases false failures by 21% when compared to simpler baselines. spade has been deployed as an offering within LangSmith, LangChain's LLM pipeline hub, and has been used to generate data quality assertions for over 2000 pipelines across a spectrum of industries.

Accelerating real-time deterministic discovery through single instruction multiple data graphical processor unit for executing distributed event logs

With the rapid expansion of process mining implementation in global enterprises distributed across numerous branches, there is a critical requirement to develop an application qualified for real-time operation with fast and precise data integration. To address this challenge, computational parallelism emerges as a feasible solution to accelerate data analytics, with graphical processor unit (GPU) computing currently trending for achieving parallelism acceleration. In this study, we developed a process mining application to optimize parallel and distributed process discovery through a combination of central processing unit (CPU) and GPU computing. The use of this computing combination is leveraged for executing multi-windowing threads within multi-instruction, multiple data (MIMD) in the CPU for streaming distributed event logs, using multi-instruction, single data (MISD) within the CPU to deploy a large footprint pipeline to the GPU, and then utilizing single instruction, multiple data (SIMD) to execute global thread discovery within the GPU. This method significantly accelerates performance in real-time distributed discovery. By reducing branch divergence in SIMD on the global thread GPU parallelism, it outperformed local-thread CPU execution in deterministic discovery, speeding up from 10 to 40 times under specific conditions using a novel min-max flag algorithm implemented within the main steps of the process discovery.

Automatic response framework for large complex natural gas pipeline operation optimization based on data-mechanism hybrid-driven

In recent years, the scale of gas transmission networks has been continuously expanding, and the operational conditions are becoming increasingly complex. This poses higher requirements for the centralized control of the pipeline network operation. Relying solely on the experience of scheduling personnel may not comprehensively address the operational issues within the network. There is an urgent need for efficient automatic response methods (ARM) to assist in formulating operation schemes and ensuring the safe and stable operation of the pipeline network. Therefore, this paper proposes an automatic response framework of large complex natural gas pipeline operation optimization based on data-mechanism coupling to provide operation schemes that ensure safety, stability, and economic benefits. First, two ARM is proposed, namely the Data-Based ARM using operation scheme database, and the Opti-Model ARM based on optimization modeling. Subsequently, the rapid response features of Data-Based ARM and the optimal response characteristics of Opti-Model ARM are combined to establish the Integrated ARM. Finally, these three ARMs are compared and analyzed through a regional natural gas pipeline network in China. The result indicates that the Data-Based ARM can quickly produce a variety of matched solutions but cannot ensure economic optimality. Response solutions obtained through Opti-Model ARM reduce the compressor energy costs by 4.6–10.8 % compared to on-site operational schemes, but they take longer in response time. In contrast, the economic attributes of solutions derived from the Integrated ARM are on par with Opti-Model ARM, but with a 58.7 % improvement in response speed. The proposed Integrated ARM can swiftly and accurately offer economically viable operation schemes tailored to the varying needs of pipeline operators, which can help to address the energy demand challenges of the future, fostering sustainable development.

Dynamical states associated with the shift in whistling frequency in aeroacoustic system

Self-sustained aeroacoustic oscillations arising from the interactions between the hydrodynamic and acoustic fields are perceived as a whistle. Such whistling can lead to large amplitude acoustic oscillations that have disastrous consequences for engineering systems such as large segmented solid rocket motors and large gas pipelines. The whistling corresponds to the state of limit cycle oscillations (LCO) in dynamical systems theory. An aeroacoustic system exhibits different dynamical states when the bulk flow velocity is varied as a control parameter. Understanding the dynamical states and the transitions between them, as the control parameter is varied, is crucial in designing control strategies for such aeroacoustic oscillations. Previous studies have shown that as the control parameter varies, in an aeroacoustic system that has a flow through orifices, the whistling frequency shifts. We show that such a change in frequency occurs via three different scenarios— (1) direct transition between the two LCOs as an abrupt transition, (2) via a state of intermittency, and (3) via a state of aperiodicity. In the current aeroacoustic system, the abrupt transition between the LCOs is manifested as a bursting behaviour where the amplitude of the acoustic pressure fluctuations abruptly switches between the high and low-amplitude LCOs. Further, we show that the dynamical state and the transition between them during the frequency shift have a correlation with the magnitude of the frequency shift. Using recurrence theory we show that there is a change in the dynamical state of the system during the frequency shift. Further, we use synchronisation theory to investigate the coupled behaviour of the velocity (u′) and the acoustic pressure (p′) fluctuations during the different dynamical states. Our findings imply that u′ and p′ exhibit phase synchronisation (PS) during the state of LCO, corresponding to whistling. In contrast, u′ and p′ are desynchronised during the state of aperiodicity, corresponding to stable operation. Furthermore, the bursts of periodic oscillations during intermittency correspond to the phase-synchronised epochs of periodic u′ and p′, and the aperiodic epochs correspond to the desynchronised aperiodic u′ and p′.

Prediction of slug length distribution in horizontal large-diameter gas/liquid pipeline systems

Large-diameter (ID ≥ 0.1524 m) pipeline network systems are widely used in the food industry for grain transportation, in the chemical and nuclear industries for steam/water thermal distribution systems, in the oil and gas industry for crude oil transportation and exploitation, and in urban water supply and drainage systems. Due to the favorable operational and geometrical conditions in surface gas/liquid pipelines, slug flow often prevails, dominating the horizontal and slightly inclined flow pattern map. Formed liquid slugs at large-diameter pipeline entrances and dip can grow, accelerate, and overtake other slugs, resulting in high-momentum and long slugs ranging in length from a few meters to a few hundred meters. The discrepancy of current mechanistic models to predict slug length in large diameter pipes, and the scarcity of large diameter slug length data motivated this work to propose a combined mechanistic and empirical approach to predict full slug length distribution in large diameter surface pipeline systems. A first-principle mechanistic mean slug length model is improved by optimizing its closure relationships using a large-diameter lab and field slug length database. Furthermore, an empirical large-diameter slug length standard deviation dimensionless model is proposed and statistically validated. The proposed mean and standard deviation slug length models are combined to construct a probability distribution and maximum slug length models. Validation of the proposed model revealed an absolute average error of 27% and 31% for the mean and maximum slug lengths, respectively, vastly outperforming all existing models.

Determining a Reliability Centered Maintenance (RCM) analysis model for large diameter prestressed concrete water pipelines

Which maintenance task to perform on an asset is generally known but the schedule of when to perform the task may not be well defined. If the task is performed early, while it may benefit the asset, it may also be expending resources that could be better used elsewhere. If the task is not performed soon enough, the asset may fail, or the asset may require more extensive maintenance be performed. Reliability Centered Maintenance (RCM) is an analysis process used to determine the most effective maintenance strategies. RCM analysis can help an agency establish the most cost effective maintenance strategies for an asset and help establish the best schedule for implementing those strategies. The ultimate goal of an RCM is to determine the required function of an asset with the required reliability at the lowest operation and maintenance costs by identifying revealing indicators of potential failures to establish a condition based strategy; essentially, predicting failures and identifying procedures to minimize the risk of the failure. This paper reviews potential models for predicting the progression of distress of prestressed concrete pipe segments for use in an RCM analysis and proposes a regression model operators can use to forecast when to perform maintenance activities. The paper further considers the condition of the drinking water infrastructure in the United States, developing the case for evaluating predictive models to forecast when a pipeline may reach a specified limit state. The results of this study suggest that RCM analyses for large diameter water pipelines can improve reliability, reduce maintenance costs, and extend the useful life of a pipeline regardless of age or material. Further, using a regression style model with gathered data can be used to forecast when specific maintenance thresholds may be reached, prompting predictive maintenance actions.

Experimental study of leakage characteristics and risk prediction of N2-containing dense-phase CO2 pipelines in real transportation conditions

In the background of pipelines being widely used as a critical transportation chain for the carbon capture, utilization and storage (CCUS) process, studying the leak diffusion characteristics and risks of high-pressure impure CO2 pipelines in real transportation conditions is critical for process safety. Due to the limitations of the capture process and economic cost, the CO2 used for sequestration contains impurities in the range of 3% molar ratio, mainly N2. In this paper, the first leakage experiments involving dense-phase CO2 with a 3% molar ratio of N2 impurity were conducted on a large CO2 pipeline (258 m long and 233 mm inner diameter) using initial leakage orifices of 50 mm and 100 mm. Data concerning the thermodynamic properties of the impurity-containing leakage source were gathered for the analysis of phase transitions and flow alterations within the pipeline, as well as near-field temperature and the progression of the far-field concentration. The pressure-temperature curve in the pipe shifted along the dew line and then slowly shifted to the saturation line of pure CO2 with the influence of N2 impurities during the multiphase leakage phase. In the near-field region, cryogenic diffusion was governed by the boundary of the jet, with the control distance decreasing as the leakage orifice increased. In addition, an accurate prediction model of CO2 concentration on the leak axis was established based on the virtual source theory and the concentration decay law, and the predicted locations of CO2 concentrations at the 5% were 169 m and 210 m. As the leak orifice increases, the risk of low temperatures and high concentrations escalates, albeit with a sharp decrease in duration. These data provide accurate leakage characteristics and risk assessment for safely operating high-pressure CO2 processes under real conditions with impurities.

Theoretical research of impact vibration of multi-section serial pipeline inspection gauge in large drop pipeline

When operating in large drop pipelines, pipeline inspection gauge may experience severe impact vibration at elbows, potentially leading to its failure. This paper focuses on serial pipeline inspection gauges with multiple sections in large drop pipelines, analyzes the force acting on the pipeline inspection gauge during the descent phase, and establishes a multi-body dynamics model of the gauge under pipe-soil coupling conditions. In addition, a correction formula is developed for the pressure changes in the medium at the front and rear of the pipeline inspection gauge. To solve the model, MSC/ADAMS and MATLAB/Simulink are used for the bi-directional fluid–structure-interaction joint simulation. Moreover, the results are compared with the pipeline inspection gauge motion model established by OLGA. The study investigates the dynamic responses, vibration superposition and laws of dynamic evolution at the upper and lower elbows of the large drop pipeline. The results show that the speed of the gauge increases suddenly during the descent in large drop sections and then stabilizes, with different effects of the fluid medium pressure difference at various stages. The vibration responses along the axial, horizontal radial, and vertical directions at the elbows vary, with the second section experiencing stronger vibrations due to superposition effects. Compared to fixed boundaries, the acceleration extremes of the gauge are lower under the constraints of pipe-soil model, indicating significant soil buffering and vibration damping effects.

SeqKit2: A Swiss army knife for sequence and alignment processing.

In the era of ubiquitous high-throughput sequencing studies, there is a growing need for analysis tools that are not just performant but also comprehensive and user-friendly enough to cater to both novice and advanced users. This article introduces SeqKit2, the next iteration of the widely used sequence analysis tool SeqKit, featuring expanded functionality, performance optimizations, and support for additional compression methods. Retaining a pragmatic subcommand architecture, SeqKit2 represents substantial enhancement through the inclusion of 19 additional subcommands, expanding its overall repertoire to a total of 38 in eight categories. The new subcommands add functionality such as amplicon processing and robust, error-tolerant parsing of sequence records. In addition, three subcommands designed for real-time analysis are added for periodic monitoring of properties of FASTQ and Binary Alignment/Map alignment records and real-time streaming from multiple sequence files. The performance of SeqKit2 is benchmarked against the old version of SeqKit, Bioawk, Seqtk, and SeqFu tools. SeqKit2 consistently outperforms its predecessor, albeit with marginally higher memory usage, while maintaining competitive runtimes against other tools. With its broad functionality, proven usability, and ongoing development driven by user feedback, we hope that bioinformaticians will find SeqKit2 useful as a "Swiss army knife" of sequence and alignment processing-equally adept at facilitating ad hoc analyses and seamlessly integrating into larger pipelines.

Reinforcement Learning as a Parsimonious Alternative to Prediction Cascades: A Case Study on Image Segmentation

Deep learning architectures have achieved state-of-the-art (SOTA) performance on computer vision tasks such as object detection and image segmentation. This may be attributed to the use of over-parameterized, monolithic deep learning architectures executed on large datasets. Although such large architectures lead to increased accuracy, this is usually accompanied by a larger increase in computation and memory requirements during inference. While this is a non-issue in traditional machine learning (ML) pipelines, the recent confluence of machine learning and fields like the Internet of Things (IoT) has rendered such large architectures infeasible for execution in low-resource settings. For some datasets, large monolithic pipelines may be overkill for simpler inputs. To address this problem, previous efforts have proposed decision cascades where inputs are passed through models of increasing complexity until desired performance is achieved. However, we argue that cascaded prediction leads to sub-optimal throughput and increased computational cost due to wasteful intermediate computations. To address this, we propose PaSeR (Parsimonious Segmentation with Reinforcement Learning) a non-cascading, cost-aware learning pipeline as an efficient alternative to cascaded decision architectures. Through experimental evaluation on both real-world and standard datasets, we demonstrate that PaSeR achieves better accuracy while minimizing computational cost relative to cascaded models. Further, we introduce a new metric IoU/GigaFlop to evaluate the balance between cost and performance. On the real-world task of battery material phase segmentation, PaSeR yields a minimum performance improvement of 174% on the IoU/GigaFlop metric with respect to baselines. We also demonstrate PaSeR's adaptability to complementary models trained on a noisy MNIST dataset, where it achieved a minimum performance improvement on IoU/GigaFlop of 13.4% over SOTA models. Code and data are available at github.com/scailab/paser.

Detecting wire breaks in prestressed concrete pipes: an easy-to-install distributed fibre acoustic sensing approach

The escalating water stress resulting from drought conditions in certain global regions underscores the imperative to minimize water losses, particularly within drinking water supply networks. One way to achieve this is by improving pipe monitoring systems to allow the early detection of possible structural collapse of the pipes. One type of pipe widely used in water mains is the prestressed concrete pipe, whose main cause of structural failure is the breakage of prestressing wires. This research paper analyses the ability of an easy-to-install distributed acoustic sensing (DAS) monitoring system using fibre optics to identify and locate the acoustic signal produced by the wire breaks in prestressed concrete pipes to make early detection of possible structural failures. For this purpose, a large experimental pipeline stretch was built (approximately 1 m in diameter and 40 m long) where wire breaks were simulated. Several variables were studied: the origin of the signal (to distinguish wire breaks from events of a similar nature), the location of the event in the pipe, the presence of background noise, the internal water pressure, the length of the prestressed wire not subject to bonding with the concrete and the presence of water in the pipe. The results showed that the DAS system could detect almost all events. In addition, two of the multiple parameters measured in the signals, the zero-crossing rate and the short-time energy, made it possible to precisely determine the signal’s origin and the event’s location. Another parameter measured, the duration of the signal in this case, made it possible to differentiate whether the events had occurred when the pipe was empty or full of water. These and other results in this paper present a highly promising perspective on using this DAS system in water main monitoring.

Корпоративное волонтерство как инструмент внутренних и внешних коммуникаций организации

Nowadays, companies are actively introducing corporate volunteering programs at their enterprises. In this re-gard, there is a growing interest in this topic in scientific circles: there are more and more works devoted to the study of this phenomenon as a form of corporate social responsibility. However, more attention is often paid to the social and economic effects of corporate volunteering, and an equally important communication aspect remains undisclosed. This article is aimed at studying the phenomenon of corporate volunteering as a tool of corporate communications. It analyzes the image and reputation effects from the implementation of volunteer programs at Russian enterprises. The empirical basis of the analysis was the results of a study of the nature of information coverage of volunteer activities of a large regional pipeline company in the field of oil and petrole-um products transportation. Conclusion dwells upon the fact that corporate volunteering contributes to strength-ening the information presence of the organization and reinforcing the status of a socially responsible enter-prise.

Leakage Diffusion Modeling of Key Nodes of Gas Pipeline Network Based on Leakage Concentration

In order to achieve the prediction and early warning of city gas pipe network leakage accidents, as well as to provide rapid and precise support for emergency response to such accidents, this study focuses on a Gaussian diffusion model applied to a large urban gas pipeline network. Specifically, it investigates the gas gate wells, which are key nodes in the pipeline network, to develop a leakage model. The objective is to analyze the variation in internal gas concentration in the gate wells and determine the range of danger posed by external gas diffusion from the gate wells. In addition, Fluent simulation is utilized to compare the accuracy of the model’s calculations. The findings of this study indicate that the gas concentration inside the gate well, as predicted by the model fitting results and Fluent simulation, exhibit a high level of agreement, with coefficient of determination (R2) values exceeding 0.99. Moreover, when predicting the hazardous distance of gas leakage outside the gate well, the model’s results show an average relative error of 0.15 compared to the Fluent simulation results. This demonstrates that the model is highly accurate and meets the practical application requirements.

Optimization effect of propylene oxide (PO) on evaporation, combustion, and pollutant emissions of high-energy–density JP-10 fuel

JP-10 is a high-energy–density fuel that is widely used in aerospace propulsion systems due to its wide range of synthetic raw materials and low production costs. However, it faces challenges related to difficult ignition, low combustion efficiency and high pollutant emissions. To address this, blending JP-10 with propylene oxide (PO), a highly volatile substance, provides a practical solution. This study investigates the propagation and evolution characteristics of JP-10/PO binary fuel in large horizontal pipelines. The results demonstrate a synergistic enhancement effect achieved by the JP-10/PO hybrid fuel. PO, with its lower boiling point, quickly evaporates from the mixed droplets, creating a continuous primary flame. As the droplet temperature increases, JP-10, known for its high combustion heat, starts to evaporate, actively participating in the explosion process and forming a secondary flame that spreads in two directions. The combustion of the JP-10/PO mixed droplets involves both physical and chemical processes, including droplet evaporation and gaseous combustion. A binary droplet evaporation model is developed to quantitatively assess the impact of PO fuel ratio on spray evaporation. Additionally, the study examines steam explosion parameters and reaction product characteristics under different PO fuel ratios. It is observed that the evaporation of JP-10/PO mixed droplets occurs in three stages: transient heating, binary equilibrium evaporation, and single component equilibrium evaporation. Increasing the PO fuel ratio leads to a higher temperature rise rate of the droplets and an increased evaporation reaction rate constant. Moreover, the ignition delay time of JP-10/PO gas-phase mixture fuel decreases, while the adiabatic flame temperature gradually reduces. Furthermore, higher PO fuel ratios help reduce the emissions of unstable products in both mixed fuels due to the inherent oxygen content of PO molecules. The current research results can provide scientific references for the improvement of high-energy density fuels and the development of new propellants.

Study on physical explosion equivalent of large diameter and high pressure natural gas pipeline

High pressure natural gas pipelines are prone to material failure and explosion due to various reasons. Over the years, scholars have done in-depth research on the process of chemical explosion. In this paper, the energy components of high-pressure trunk natural gas pipeline under normal operation state were derived. Based on the assumption of adiabatic non isentropic, the pressure distribution in the pipeline within a period of time after the pipeline cracks was derived, and the energy released by the physical explosion of the pipeline was further derived. To verify the theoretical results, an original size pipe explosion experiment was organized, and the pressure distribution obtained from the experiment was found to be in good agreement with the theoretical calculation results. Through the crack propagation time and the duration of ground motion obtained from the experiment, the range of the duration of the pipeline burst crack impact was obtained, and the equivalent range of the pipeline explosion was further calculated. At the same time, through the regression analysis of ground motion, the equivalent of the pipe explosion in this experiment was about 177 kg TNT, which was consistent with the theoretical results. Further, the theoretical model and experimental results were verified by numerical simulation, and deeply studied the influence factors of buried depth on the explosion equivalent, as well as the relationship between the environment of the pipeline and the explosion damage mode and hazard range of the pipeline. The research results of this paper give the calculation method and magnitude range of physical explosion equivalent of high-pressure trunk natural gas pipeline. It provides a reference for the formulation of safety standards for high-pressure trunk natural gas pipelines and inert gas pressure vessels.

Uncertainty in discrete-time integration — The case of static gas meters

We consider the evaluation of uncertainty in a particular case of discrete-time integration, i.e., that of static gas meters. We show that the current approach, which does not take correlations into account, can lead to underrating of the uncertainty associated with the estimate of the delivered volume. Now, a correct evaluation of uncertainty in the case e.g. of online measurements along large offshore pipelines is important in order to remove technical barriers to international trade.We focus our discussion on the practical example of gas meters, but the framework of uncertainty evaluation we provide is valid in general for all those measurements involving discrete-time integration, and has therefore a wide applicability.

Intermittent CO2 injection: injectivity and capacity

Сarbon capture and storage (CCS), especially offshore, involves a chain of complex and expensive infrastructure connecting emitters to the disposal site. The classic example of an industrial cluster sending CO2 by a large pipeline to a nearby storage site is considered the most favorable solution in term of techno-economics. However, many emitters are located either too far from suitable offshore geology or are dispersed in harder to reach locations, making pipeline transport uneconomical. In these instances, ship transport is a viable option for shuttling CO2 from source to sink. The Northern Lights project in Norway will implement this approach, using shuttle tankers to deliver CO2 to an onshore receiving terminal. One should note that onshore terminals add significant cost to CCS, and their permanence can hinder flexibility and delay future expansion to new regions. High costs can also hinder small emitters to embark on CCS journey until the larger infrastructure is in place and the price for joining the value chain drops. Direct injection from ships can be a good supplement to the offshore transport portfolio, allowing ships to offload CO2 directly to the injection well on a periodic basis. While direct ship injection introduces a planned intermittency into the CCS chain, intermittency can also be caused by planned maintenance and technical issues along the value chain; energy supply and demand (where either less emissions are available due to, for example, higher renewables production or less energy is available for injection, in, for example, offshore renewable energy driven case); seasonal variations (part of CO2 used in agriculture or seasonal variation of injection temperature). The effect of intermittency, in general, is not fully understood. Part 1: aspects of intermittency on the storage reservoirLittle is known about the impact of injectivity CO2 injection on storage performance, i.e. injectivity and capacity. Recent studies indicate that cycling injection can delay bottom-hole pressure build-up, thus increasing capacity of the reservoir. On the other hand, evidence from field tests show that pressure relief can cause dissolved CO2 to exsolve into bubbles that block pores and reduce injectivity. Salt precipitation is another aspect that can be either positively or negatively impacted by flow cycling. In this case, repeated drainage-imbibition cycles may dissolve salt crystals formed in a previous cycle, improving injectivity, or it may continue to feed the system with new saltwater, thus impairing injectivity. The topic of salt precipitation is an active area of research.Part 2: how to deal with itWe present results of the recent study down for NEMO Maritime AS in a research council of Norway sponsored NEMO project. The talk will briefly highlight simulation outcomes on the near wellbore and field scale.Part 3: where do we go from hereFinally, we shortly introduce a recently funded CTS project which will focus on several aspects of direct injection from ships, including full-chain LCA/TEA based on Strategy CCUS H2020 project approach and scenarios. The project focuses on four different regions of Europe, including Baltics.

3D laser point cloud-based geometric digital twin for condition assessment of large diameter pipelines

The increasing aging of underground pipe networks and the lack of effective inspection technologies present considerable challenges for whole life-cycle management of these infrastructures. Modern laser scanning technology offers a cost-effective and safe means to obtain dense and accurate 3D topographic data of the inner surface of pipelines. However, laser scanning point clouds contain substantial noise and outliers, and efficiently extracting valuable information for structural and functional mapping remains in its infancy. This paper presents an innovative method for fast processing point clouds data of large-diameter pipelines, enabling the accurate extraction of geometric features and efficient establishment of geometric digital twin using density-based clustering, fitting and region growing algorithm. Experimental tests were conducted to evaluate the accuracy, efficiency, and feasibility of the proposed method. The results demonstrate that the proposed approach not only robustly achieves high accuracy but also maintains high computational efficiency. Additionally, the geometric digital twin shows promise as tools for quantitatively assessing structural deformation and blockage defects.