New Transient Identification Methods for Automated Pre-processing of Pressure Measurements with Permanent Well Gauges

Well monitoring with pressure and temperature gauges is a part of well and reservoir surveillance systems across various industries, including petroleum and geothermal energy production as well as carbon capture and storage. Installing permanent downhole gauges (PDG) becomes a standard in the industry, which in combination with flow rates, provides measurements for well and reservoir monitoring by using pressure transient analysis (PTA). The feasibility and accuracy of PTA are governed by proper identification of pressure transients. Transient identification is traditionally a heavy manual trial-and-error process. It often involves pre-processing PDG data by resampling, denoising or outlier removal. However, any pre-processing may have a risk of overlooking important information. In addition, lack of pressure-rate synchronization in raw data complicates further PTA applications.This paper introduces a novel methodology for automated transient identification from raw gauge data. The methodology enables identification of both shut-in and multi-rate flowing transients by using pressure data only. Moreover, the new transient identification runs on the raw data without resampling, denoising or outlier removal, which ensures keeping all the information from the measurements. The methodology is a combination of two new independent methods: Topographic Prominence Max Rotation (TPMR) and Local Minimum in Rotation (LMIR). The TPMR method utilizes the concept of prominence to identify significant shut-in transients. The LMIR method detects multi-rate flowing transients by identifying local minima in transformed pressure data via proper rotation matrix. Together, these methods provide an automated solution for dividing a pressure history into sequential flowing and shut-in transients. The new methodology has been tested and verified using real PDG datasets from the Norwegian Continental Shelf. The testing confirmed stability and accuracy of the methods, providing fast results with minimal human intervention. Then, an automated data pre-processing framework is described integrating the transient identification methodology with pressure and rate synchronization, rate reconstruction, superposition time and Bourdet derivative calculations. Finally, an integration of the framework within an automated time-lapse PTA well monitoring workflow is demonstrated

CFD simulations of running aerodynamics:Impact of computational parameters

Running is a fundamental discipline in athletics, yet its aerodynamic characteristics have not yet been intensively studied, particularly from a computational perspective. In recent years, Computational Fluid Dynamics (CFD) has become an increasingly valuable tool for advancing research in sports aerodynamics. However, the reliability of CFD predictions depends strongly on the selection of computational parameters which remains insufficiently explored in the context of human running. This paper presents a detailed study on the impact of grid resolution, computational domain size, and turbulence modelling on the computed drag area for a full-scale female runner manikin. The CFD simulations are validated by comparison with wind tunnel measurements performed in a geometrically matched test section. The sensitivity analysis provides practical guidelines for generating grids that balance accuracy and computational economy. The blockage ratio (BR) is found to be a critical parameter: values exceeding 3.5% result in drag overestimations larger than 2.8%. Among the turbulence models tested, transition-sensitive models (γ–SST and T–SST) in pseudo-transient RANS formulation and the hybrid scale-adaptive simulation (SAS) approach showed the best agreement with experimental results. Based on these findings, the study proposes a set of best-practice guidelines for reliable and cost-effective CFD simulations of running aerodynamics

3D Printing in the Construction Sector: Identification of Key Topics, Technologies, Applications and Relevant Factors Discussed in the Literature

3D printing is transforming the construction industry by reducing material waste, enhancing design flexibility, and shortening construction timelines. As a rapidly advancing technology, it offers innovative solutions for efficient, sustainable, and advanced building practices. This study aims to give an overview of the contents treated in the literature on 3D printing of concrete, with a focus on key topics, technologies, applications, and parameters influencing printability. Following a systematic review process, 1079 studies were analyzed in terms of objectives, structural applications, and printing technologies. The findings reveal a strong emphasis on parameters such as strength, interlayer bonding, and rheological properties, while durability-related aspects like freeze-thaw resistance and water absorption are explored more seldom. The study underscores the need for material optimization to balance fresh-state and hardened-state properties, ensure long-term structural performance, and incorporate sustainable materials. By addressing these gaps, this research identifies critical pathways for advancing 3D printing in construction and provides recommendations for achieving durable, efficient, and environmentally sustainable solutions

Road to Bio-polymer Flooding in Carbonate Reservoirs: Numerical Modelling

Polymer flooding can be of pivotal importance in improving oil recovery factors from carbonate reservoirs. Bio-based polymer floods have displayed excellent and cost-effective enhanced oil recovery (EOR) performance at laboratory-scale experiments. Nevertheless, despite these promising performance signs, many bio-based-polymer flooding options fail to progress outside the experimental stages. This paper’s thrust is to numerically assess the performance of an okra-based solution for polymer flooding across different scales. The numerical models aim to reproduce the results from laboratory core-flooding experiments that were previously published. Furthermore, this study conducts a sensitivity assessment examining the factors that significantly impact the flood’s outcome. The models are also used to explore oil displacement and the propagation of the okra-based polymer. The numerical model was built on CMG STARS using a one-dimensional (1D) approach to match lab-scale flooding experiments. The laboratory examination was designed to be applied at reservoir conditions in western Kazakhstan. CMOST was used for history matching the simulation outputs with the lab-scale oil production volumes. The procedure honored the upper and lower range limits of the following parameters: polymer viscosity and concentration, water and polymer injection rates, along with a relative permeability model for both for water flooding (WF) and polymer flooding (PF) conditions. A sub-model experiment design (DoE) was generated explicitly using the Latin hypercube sampling (LHS) method. The numerical simulation results showed that the laboratory-obtained final oil recovery numbers for WF exceeded the corresponding numerically-obtained recovery. However, in terms of the overall performance, the difference between the cumulative oil production between the laboratory experiments and numerical model is only four percent. This is due to the rate alteration required under laboratory conditions. The sensitivity assessment indicated that the relative permeability curvature interpolation and the polymer concentration are the most dominant independent parameters impacting the final oil recovery (in the homogeneous model). For a 2D model, the propagation of the polymer front remains uniform compared to the more pointed water fronts in the WF. A significant reduction in the total mobility from 10 mD/cp to 5.2 mD/cp confirms the sweep efficiency enhancement. The history-matching results suggest that the saturation endpoints have been altered during PF in comparison to the WF. This behavior was reported as inconclusive for several polymer-flooding options. The meta-model’s sensitivity assessment highlights a strong relationship between the polymer concentration and the final oil recovery for the okra-based polymer flooding

Interaction Design Strategies for ADHD Learning Attention - A Review

Interaction design and development strategies contain the proficiency to mimic user attention and engagement. These strategies are proven to be beneficial for learning attention while employing various types of digital learning platforms. However, special education need (SEN) learners possess special needs due to their neurodevelopmental disorder and deficiency of proper brain functioning and there-fore giving rise to malfunctioning of inhibitory control, sustained attention, and working memory. Hence, there is a need to develop user-centric interaction design and development strategies and implications to increase the attention span of ADHD (attention deficit hyperactivity disrorder) while learning. In this literature review, we aim to highlight the attention problem of ADHD due to malfunctioning of executive functioning and working memory. We have summarized the existing IxD (interaction design)based solutions for ADHD learning attention. The related limitations, chal-lenges and findings of the literature review are presented along with the future possi-bilities. This paper highlights the need to develop user-centric solutions for ADHD attention improvement during learning and the incorporation of the machine learning and artificial intellegnce based interfaces for the advance user-centric solutions

Characterizing few-cycle UV resonant dispersive waves through direct field sampling

We demonstrate compression of few-cycle ultraviolet (UV) resonant dispersive waves (RDWs) generated in a cascaded hollow capillary fiber setup using a Yb laser system. Temporal characterization is performed using both tunneling ionization with a perturbation for the time-domain observation of an electric field (TIPTOE) and self-diffraction frequency-resolved optical gating (SD-FROG), which show good agreement. Through careful dispersion management, we compress the RDW pulse to 6.9 fs at a ∼390-nm central wavelength. This is the first, to our knowledge, measurement of an RDW using the TIPTOE method and demonstrates the viability of this technique to reliably characterize few-cycle UV pulses with μJ pulse energies

Electronic and structural properties of magnesium-doped platinum clusters: superatomic features of the MgPt₉ complex

We address herein a theoretical study of gas phase magnesium-doped platinum clusters (MgPtn, n = 2–12) using density functional theory, genetic algorithms and the quantum theory of atoms in molecules method of wavefunction analyses. The Mg atom consistently donates electron density to the Pt framework. This electronic charge depletion increases with size before it reaches an asymptotic limit. Among the series, MgPt9 exhibits enhanced stability, a large HOMO–LUMO gap (1.30 eV), a high adiabatic ionisation potential (6.94 eV) and a filled 1S2 1P6 shell, features which indicate a superatomic character of this species. Structural analysis reveals that MgPt9 forms gradually from MgPt6 and persists as a core in larger capped clusters. Spin multiplicities vary irregularly, reflecting changes in coordination and electronic degeneracy. Electrostatic potential analysis reveals the presence of σ-holes at low-coordinated Pt sites and at the Mg centre, and thereby a potential catalytic activity. These findings identify MgPt9 as a candidate superatomic cluster and suggest broader design strategies for bimetallic nanostructures with tunable electronic and chemical properties

A simplified numerical simulation of circular CFDST short column with NC, HPC and UHPC under compression

In this study, axisymmetric models to predict the ultimate strength of circular concrete-filled double-skin steel tube (CFDST) short columns containing Normal Concrete (NC), High-Performance Concrete (HPC), or Ultra-High-Performance Concrete (UHPC) under axial compression are developed. A simplified concrete material model is proposed for these axisymmetric models, offering more convenience compared to the previous axisymmetric model, which was validated only for NC, HPC, and concrete-filled steel tube (CFST) columns. The reliability and accuracy of the new model are verified using experimental data. This study demonstrates that the combination of the axisymmetric model and the simplified concrete model significantly reduces computational time while maintaining acceptable accuracy. The proposed method can generate extensive numerical databases for structural optimization or machine learning-based strength prediction. The reduced computational effort of axisymmetric models, compared to 3D models, allows for a comprehensive parametric study of axial load-displacement curves in circular CFDST short columns, exploring various influencing factors. Additionally, the study evaluates established design codes, including Eurocode 4 (EC4), American Concrete Institute (ACI), and American Institute of Steel Construction (AISC), along with analytical models from the literature, thereby enhancing the understanding of circular CFDST short columns under compression.</p

Storage v. production: challenges for reservoir modelling and simulation practitioners

The rising interest in subsurface CO2 storage makes new calls on reservoir modelling skills, most of which have been developed for hydrocarbon production scenarios. The question for practitioners is: to what extent can the familiar production tools be transferred to the world of storage? In this paper, areas requiring attention are highlighted and high-resolution models are used to compare the behaviour of simulators for production v. storage for two reservoir analogue examples. It is concluded that modelling for storage makes a significant call on multi-scale modelling, to a much greater extent than in production scenarios, and the simplification or omission of reservoir heterogeneities (sometimes tolerable in production scenarios) are much less tolerable when modelling storage. Key static model heterogeneities include the modelling of faults as 3D features, the inclusion of fine-scale reservoir permeability contrasts and the avoidance of net reservoir cut-offs. For dynamic models, use of equation of state is necessary for storage in depleted fields, and correct representation of hysteretic effects of plume migration are a requirement for modelling in aquifers (always) and depleted fields (usually). Modelling for storage, especially for saline aquifers, sets the challenge of modelling volumes previously considered to be at exploration scale, but with an effective resolution more typical of production scales

Enhancing the Solubility of Dihomo-gamma-Linolenic Acid Using Natural Deep Eutectic Solvents for Human Tumors Therapy Development

This study investigated the potential of natural deep eutectic solvents (NADES) for dihomo-γ-linolenic acid (DGLA) solubility. The conductor-like screening model for real solvents (COSMO-RS) was used in this study as a computational software tool to predict the relative solubilization ability of 50 different NADES for DGLA. Furthermore, the solubilization mechanisms were examined by analyzing the activity coefficients, sigma profile (σ-profile), and the excess Gibbs free energy. The results reveal that NADES, consisting of menthol and camphor, Men/Cam (1:1), exhibited the highest capacity (6.352) for DGLA capacity, highlighting its potential for enhanced delivery and application. Furthermore, the σ-profile analysis and excess Gibbs free energy calculations confirm strong interaction and miscibility of Men/Cam (1:1) with DGLA. This computational approach offers valuable guidance for selecting the optimal NADES compositions to improve DGLA solubility, potentially advancing cancer therapy strategies and contributing to pharmaceutical science and drug delivery. This is the first study to apply COSMO-RS in designing and developing a promising therapeutic agent for human tumor treatment.</p