bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automated Computer Vision System for Real-Time Detection of Drilled Cuttings and Cavings
Authors: Svendsen K. E. , Kristiansen T. G. , Martin J. , Askø A. , Bjørlo J. , Khosravanian R. , Holt C. , Ruel F. 
Published: February 2025

Abstract
This paper describes the role of a computer vision system in assessing wellbore stability. It validates the application of computer vision to detect the presence of cavings at the shale shaker using a remotely monitored camera system. The main objective was the early detection of developing wellbore pressure and stability issues during drilling to avoid non-productive time (NPT), a potential boon for the industry.
Two cameras with onboard computer vision software were installed in the hazardous zone above the shale shakers to monitor the returning drilling cuttings and quantify their sizes. Images from the cameras were analyzed onsite and cutting size and volume data were delivered to the drilling team in real-time. The term Unidentified Falling Object (UFO) was introduced to account for the current state of technology and the difficulty inherent in discriminating between cavings and cuttings at the shakers. Detecting an anomalously large object is considered a potential indication of a caving.
Five wells in the Norwegian Offshore Sector were consecutively monitored over eight months across hole sizes ranging from 16½-inches to 8½-inches. The project was managed remotely via an interactive, multi- platform, open-source analytics and visualization application. We include a description of the project's implementation of new technology on an active drilling rig.
The computer vision model was validated by dropping known test rock samples into the cuttings flow, measuring cutting size distribution (CSD) on cuttings samples, and weighing drill cuttings samples. The results were used to calibrate a computer vision model that accurately measured a range of cutting sizes from 10mm to 45mm. The data showed a credible correlation to changes in wellbore formation, and cavings were successfully detected. The model improved throughout the project, alerting the drilling team to suspected issues in near-real-time. Other valuable observations included cuttings overflow, vibration performance, and the potential for time savings during hole cleaning activities. This demonstrated the versatility of the system and its potential to provide a more comprehensive understanding of the drilling process.
Wellbore instability is one of the largest contributors to drilling NPT. Early detection through the identification of cavings at the shakers is an established process but is currently manpower-intensive, intermittent, and subjective. Applying a computer vision model to monitor and analyze drill cuttings in real- time continuously—autonomously characterizing the drilled cuttings’ size, shape, and volume—will help optimize overall penetration rates and significantly improve hole-cleaning practices. This will reduce lengthy circulation times and pack-off events, enhancing the efficiency of drilling operations.
Modernizing Top Drive Monitoring and Maintenance: Efficiency-Factor-Based Imminent Failure Detection Using Common Accelerometers
Authors: Reynerson P. , Mitkus A. , Gee T. , Posada J. , Lee H. , Gupta P. , McCarthy K. , Ziehm T. , Willerth M. 
Published: February 2025

Abstract
In drilling operations, the top drive is one of the most important and expensive pieces of equipment. The majority of top drives fail close to total depth, where they are under the most physical stress and the wellbore integrity is most vulnerable. Failure during this critical point in operations may leave the rig with a reduced capacity to deal with wellbore stability issues, and a higher chance of lost in hole. A proposed real time top drive monitoring algorithm uses accelerometer data to detect imminent top drive failure on the order of hour to days before failure, aiming to provide a window to trip out, plan, and either repair or replace the top drive before serious downtime costs are incurred.
A physics-based bearing failure detection algorithm was applied to high-speed accelerometer burst data collected from 12 top drives over two years, with three bearing failures represented in the set. The approach estimates a proxy for bearing efficiency by calculating the kinetic energy required to induce the measured acceleration, normalized by output energy estimated from the torque and RPM at the burst time. To further isolate vibrations related to the bearing itself, Butterworth notch filters are applied to the accelerometer data, removing known strong vibrational frequencies unrelated to the bearing (specifically the blower motor). The algorithm classifies a potential failure using a sustained magnitude of the bearing efficiency proxy.
The detection algorithm was applied to the three failure cases along with a randomly selected set of non-failure cases, flagging the bearings more than 6 hours before the downtime occurred. False positive flags were exceedingly rare and short-lived among the non-failure set, while the correctly flagged failure cases showed large, sustained magnitudes. With additional data and maintenance workflow coordination, detection thresholds could be even further optimized.
Given the encouraging performance of the algorithm on the historical data and the small number of failure cases represented, a follow up study has been proposed to pair this accelerometer-based monitoring with top drive health rankings to drive more efficient maintenance workflows and acquire additional accelerometer data to improve the method.
In an asset-intensive industry like oil and gas, maintenance and monitoring of heavy equipment is vital. This approach modernizes prognostic workflows by integrating real-time, physics-based classification and continually monitored health metrics. While future investigations will explore more metrics in greater detail, this study introduces a novel vibration classifier that enhances traditional accelerometer analysis by focusing on physically relevant frequencies and mapping them to a wasted input energy proxy.
Insights from Process Automation: Improving Efficiency
Authors: Auld S. , McMillan A. , Whitlow Z. , Hopwood F. , Kern S. 
Published: February 2025

Abstract
Invisible lost time (ILT) is a measure of inefficiency. Though not immediately apparent, it can be significant over the course of a drilling campaign. Much effort is dedicated to reducing ILT throughout the well construction process. Well construction is comprised of many subprocesses, each of which is an opportunity to reduce ILT with process optimization that can be realized with automation. Consistency through automation often provides insight to further refine and automate drilling processes.
Well construction subprocesses are created by the drilling contractor and/or the operator, usually involving a detailed review. Processes such as applying or removing weight on bit (WOB), or zeroing the WOB, are critical to improving drilling performance and preserving the life of the bottom hole assembly (BHA). Once defined in detail, process automation facilitates precise and consistent execution. As drillers are trained, they transition from manual process execution to automation oversight, with insights from this automation utilization identify further opportunities for improvements.
Applying, removing, or zeroing WOB are detailed processes with multiple machines moving simultaneously, occurring frequently during the well construction process. Because of this frequency, even seconds saved per event can potentially add up to days saved over the course of a well program.
Consider the process of zeroing WOB in the lateral section. Often conducted at the end of a stand, it involves hoisting up a certain distance and rate, followed by lowering at a certain distance and rate, before deciding to zero the WOB. The specifics of how far and fast to hoist and lower are usually prescribed by the operator’s drilling personnel and were likely created before the process was automated. Manual execution of this process provides significant variability, leading to conservative process recipes.
This process was automated, with a manual zero selection required for process completion. Opportunities to refine and improve the process became visible once replicated through automation. The most apparent observation was the lowering parameters could be optimized to reduce process time by 29%, as drillers are trained to make similar observations to improve efficiency of this process. A secondary observation is the optimized process can be automated to completely remove the manual zero selection step.
This work will review the next phase of process automation and is complementary to prior work (SPE-217751-MS and SPE-222152-MS) that present the value of consistency and the role of digital transformation to improve process automation (Auld et al., 2024).
Hydraulic Sealability of Geopolymers for Shallow Depth Cementing – A Comparative Study
Authors: Gomado Foster Dodzi , Agista Madhan Nur , Khalifeh Mahmoud 
Published: February 2025

Abstract
Shallow-depth well cementing can sometimes be challenging due to low temperatures and weak formation pressure. Also, the bonding of the cementitious materials used is the key to ensuring lifelong sealability. In reducing carbon footprint related to oil and gas drilling operations, different variations of low-density geopolymers are compared against the gas-tight cement in terms of its bonding integrity, that is, the shear bond strength, hydraulic sealability as its strength development at curing conditions of 25℃ and 14 bar.
Three different kinds of rock-based geopolymers were prepared. Namely, Neat low-dense Geopolymer, glass-bead low-dense geopolymers, and expansive low-density Geopolymer and their performance was compared to the industrially used gas-tight cement. The bonding criteria were accessed using the linear expansion, hydraulic sealability and compressive strength. The low-dense granite-based geopolymers examined in this study experience chemical shrinkage and could not provide sufficient hydraulic sealability. When expansive agent is used in the water-extended geopolymers, the shrinkage is compensated and hydraulic sealability obtained is higher than that of the commerically available gas-tight cement.
Enhancing Lost Circulation Management by Data-Driven Agile Methods
Authors: Converset J. , Sharma S. , Blay Florian Le 
Published: February 2025

Abstract
This paper presents a method using surface and downhole data to enhance responses to lost circulation events in drilling operations, which are significant challenges leading to nonproductive time and financial losses. Lost circulation costs the oil and gas industry approximately USD 1 billion per year in rig time, materials, and other financial resources (Ferron et al. 2014). Accurately detecting lost circulation in the oil and gas industry presents several challenges. The lost-circulation process relies heavily on real-time data from various sensors, which can be incomplete, or noisy, complicating detection and analysis. Current methods for lost circulation management often require frequent manual adjustments and monitoring, introducing human error and inconsistencies. Additionally, the diagnosis of lost circulation types often depends on the experience and judgment of experts, leading to biases and inconsistencies. Technological constraints also play a role because integrating new technologies with existing systems can be challenging and costly. Operational challenges, such as changes in pump rates and pressure during drilling, further affect detection accuracy. Addressing these challenges requires a combination of advanced technologies, improved data analytics, and enhanced training for personnel to reduce reliance on manual processes and expert judgment.
The novel method this paper presents addresses the variability in success rates of traditional solutions by providing real-time, automated insights into the loss zone, considering dynamic changes in loss rates, and wellbore pressures. The proposed solution integrates multiple tools, such as mud logging, annular pressure while drilling (APWD), and geomechanics, into a single platform to improve the detection and response to loss events.
Traditional response plans, which categorize loss rates as seepage, partial, or total losses, often overlook critical factors such as the types of losses and their dynamic nature. The development process revealed complexities in fully automating the integration of these data sources, leading to the adoption of a human-in-the-loop approach. This paper presents unique workflows developed, the methodology for workflow validation using a labeled database, and the results of real-time applications, highlighting the success of this integrated approach.
Application of Machine Learning for Bit-Formation Matching in drilling operations
Authors: Igemhokhai Shedrach , Olaoye Abiodun , Bello Kelani , Momodu Ehigie , Isuk Nsisong , Adejumo Abayomi 
Published: February 2025

Abstract

Efficient bit formation matching is imperative for the success and cost-effectiveness of drilling operations. At present, drill bit selection predominantly depends on historical data and experiential knowledge. While machine learning, particularly Artificial Neural Networks (ANNs), has gained prominence in bit selection, other diverse and impactful algorithms such as XGBOOST and Random Forest (RF), are often overlooked. This paper involves the systematic application and comparative analysis of XGBOOST, RF, and ANN, alongside an optimization approach using Genetic Algorithm. The study comprehensively considers various influential factors including formation properties, drilling fluid characteristics, bit design, and operational parameters. In this study, we achieved promising results with the highest classification accuracy for bit selection recorded at 0.97 using the XGBOOST model, while RF and ANN yielded accuracies of 0.91 and 0.93 respectively. Additionally, we obtained impressive R squared values of 0.991, 0.975, and 0.953 for predicting the Rate of Penetration (ROP) using the XGBOOST, ANN, and RF models respectively. These algorithms, coupled with the optimization techniques, aims to establish a robust framework for nuanced and accurate bit-formation matching. The results obtained hold significant potential for minimizing costs and optimizing resource allocation & utilization during the planning and execution of drilling projects in the oil and gas industry.
Snow- and ice-ecosystem cleaning capability of the pucciniomycotinous yeast Phenoliferia psychrophenolica
Authors: Marechal Eric , Ezzedine Jade , Guenzi-Tiberi Pierre , Villain Gaelle , Cigliano Riccardo Aiese , Diagne Yacine , Franceschi Enzo , Drula Elodie , Foret Jerome , Valay Jean-Gabriel , Procházková Lenka , Remias Daniel , Terrapon Nicolas , Amato Alberto 
Published: February 2025

Abstract

Psychrophilic pucciniomycotinous yeasts inhabit snowfields and glacial ecosystems worldwide, yet their ecological role remains unclear. We isolated a clonal strain of Phenoliferia psychrophenolica (LCC-F-001-001) from an alpine red snowfield. Its 42-Mbp genome contains 11,523 genes, including 37 ice-binding protein genes, the highest number recorded in fungi, mainly acquired through horizontal transfers. This yeast tolerates freezing, grows optimally at 10°C and forms pseudohyphae above 15°C. Known to assimilate phenol and small metabolites, we found LCC-F-001-001 also hydrolyzes carotenoid and phenolic pigments from snow and glacier ice algae. LCC-F-001-001 genome encodes ~ 500 carbohydrate-active enzymes, ranking among the most catalytically versatile sequenced Microbotryomycetes, alongside a closely related permafrost species. P. psychrophenolica and related fungi are therefore key players in snow and ice ecosystems, capable of degrading organic molecules in snowfields and glaciers, likely during algal bloom declines, and connecting the carbon cycle between cryospheric environments and soils through their exceptional metabolic adaptability.
Metaheuristic Optimization based Coordinated Electric Ferry Charging Impacts on Distribution Network
Authors: Roy Rajib Baran , Alahakoon Sanath , Rensburg Piet Janse Van , Arachchillage Shantha Jayasinghe 
Published: February 2025

Abstract

The global maritime industry significantly contributes to greenhouse gas emissions in marine environments. To address this, there is a growing global initiative to adopt renewable-powered electric marine vessels, where storage plays a crucial role. This study delves into the potential impacts of charging electric ferries in coordinated mode on local distribution network by metaheuristic optimization. Using Gladstone Marina in Queensland, Australia, as a test case, the research employs DIgSILENT PowerFactory for power flow analysis, based on actual load data. The simulated network includes four BESSs (Battery Energy Storage Systems) representing proposed charging stations. For analysis, MATLAB Simulink based BESS’s dynamic model is included in the simulated network of DIgSILENT. A novel control algorithm is used for controlling and optimizing the operation of BESSs according to load demand and status of network’s system parameters. Python based control algorithm implements a balanced hybrid GA-PSO-BFO (Genetic Algorithm-Particle Swarm Optimization-Bacterial Foraging Optimization) metaheuristic optimization which ensures sequential operation of BESSs according to their SOC (State of Charge) in coordinated mode. Initially, power flow analysis is conducted without BESS integration, termed as the base case, at 50% and 80% load capacities of transformers. For impact analysis, power flow analysis is performed by integrating BESSs to simulate fully utilized charging stations at 50% and 80% load increment in coordinated charge-discharge and only charge coordinated modes. Results show a 1%-1.5% increase in bus voltages in coordinated modes as load escalates. Transformer loading decreases by 3%-4% in coordinated charge-discharge mode, while line loading drops by 2.5%-3.5%, contributing to reduced system current and power. The transformer loading and line loading remain same to base case in only charge coordinated mode. The findings from the time-based quasi-dynamic mode in DigSilent suggest that coordinated charge-discharge imposes beneficial effect on the system parameters of the test network. By aligning charge-discharge times with load demand, coordinated mode enables BESSs to participate in peak shaving of the test distribution network. This peak shaving strategy indicates that electric ferries at dockyards can serve as a spinning reserve for the shore-side distribution network.
Chatbot for Electrical Appliances Troubleshooting With Nlp and Python
Authors: V Thangapandi , S Thousiha , Raja Vignesh , G Kiruthiga 
Published: February 2025

Abstract

The creation of an intelligent chatbot system that can aid with electrical circuit diagnosis is described in this document. The chatbot, which uses Python and Natural Language Processing (NLP), attempts to close the gap between technical knowledge and regular users by providing instant assistance to both technicians and non-technicians. The difficulties in locating electricians in the area and the high expenses related to little electrical issues are addressed by this approach. In addition to saving needless callouts and costs, the chatbot also helps technicians by providing consumers with information and confidence. The way experts and individuals approach electrical circuit diagnosis might be completely changed by this creative method, which would also make technical information more affordable and accessible.
Experimental evidence of Tc enhancement above 50 K and diode and paramagnetic-Meissner effects, in Nickelate films on highly reduced SrTiO3
Authors: Koren Gad , Eyal Anna 
Published: February 2025

Abstract

Since the discovery of superconductivity in nickelate thin films in 2019, the quest for enhancing their Tc has been ongoing. Here we provide experimental evidence for Tc enhancement in oxygen deficient films on highly reduced and conducting SrT iO3 substrates. Tc onset of 50-70 K was found in Meissner and transport measurements, which indicates superconductivity in islands or domains in our films, where Tc of zero resistance is obtained at 20-25 K. In addition, we observed a giant paramagnetic-Meissner effect peak at about 48 K, which further supports the existence of a superconductive transition just above it. Furthermore, an asymmetric or nonreciprocal and non-hysteretic superconductive diode effect was observed. The later could be fully polarized, and its polarity could be reversed. The specific phase responsible for these notable effects was not yet identified, and further research is needed in order to identify and isolate the relevant phase.
Open and closed photobioreactors for microalgal cultivation and wastewater treatment
Authors: Laizu S. A. , Hassan L. H. S. , Ahmad I. 
Published: February 2025

Abstract
The utilization of microalgae in wastewater treatment (WWT) witnesses a global interest. From this sector, there are two main cultivation systems that can be used for microalgae cultivation, either open or closed systems; the choice of which of these cultivation systems to be used depends on the purpose of the produced biomass. Use of closed photobioreactors (PBRs) is better than open systems because they can provide uncontaminated monoalgal culture. Design of PBR allows controlling light intensity, light penetration ratio, with good gas exchange sectors, air/carbon dioxide supply, and mixing strategy. Different configurations of PBRs, including aerobic, horizontal tube, moving tank, and tubular aerobic PBRs, are studied in terms of their design, operation, and performance, with emphasis on their efficiency in generating microalgae biomass. Airlift pressure-feedback PBRs are energy-efficient and improve mass transfer in both internal and external loop configurations. Horizontal tube PBRs maximize light exposure but face biofouling and uneven distribution. Stirred tank PBRs have good mixing efficiency but limited light penetration and high energy requirements. PBRs are preferred for continuous cultivation systems due to their superior light penetration and high biomass productivity. The most common PBRs are flat panel reactors, tubular, and bubble column PBRs. Microalgae cultivation technology using PBRs depends on cultivation mode, factors that control the optimal growth, and harvesting techniques. PBRs are used in WWT, with suspended systems offering economical alternatives. Fixed systems, such as PBRs, improve separation but struggle with accurate biomass measurement. Hence, this chapter emphasizes the need for engineering improvements and optimization techniques to overcome operational and economic limitations of open and closed PBRs. This provides an overview of commercial WWT using microalgae, highlighting growth factors, different types and designs, and their applications.
LE POUVOIR MÉMORIEL DU MUR OU L’IMPOSSIBLE TRÊVE DE L’OUBLI DANS L’OCCUPPATION DES SOLS DE JEAN ECHENOZ
Authors: Meftahi Majda 
Published: February 2025

Notre lecture de L’occupation des sols a pour sujet l’exploration du rapport perceptif qu’entretiennent l’architecture et l’oubli avec le deuil. L'objet de notre analyse est donc double : il consiste à montrer comment l'oubli éclaire notre réflexion sur le deuil, et comment le deuil enrichit l'oubli à travers l'expérience de l'effacement. Nous avons préféré partir des perceptions suscitées par l’oubli afin de creuser le déploiement de ses résonances narratives, temporelles et affectives chez Fabre et son fils Paul interrogeant la restitution de « l’union perdue » au portrait de la défunte. L’oubli transparaît, notamment, dans sa représentation du mur qui s’affirme dans les limites symboliques du geste se situant entre vision et action. Partant de cette consubstantialité du regard et du geste nous verrons en quoi et comment l’acte de gratter le mur, à la fin du récit, y est perçu tel un dilemme moral qui force à penser et à voir dans la mémoire un consentement à l’oubli.