Hasil untuk "Gas industry"

Thumeera R. Wanasinghe, Leah Wroblewski, Búi K. Petersen et al.

With the emergence of industry 4.0, the oil and gas (O&G) industry is now considering a range of digital technologies to enhance productivity, efficiency, and safety of their operations while minimizing capital and operating costs, health and environment risks, and variability in the O&G project life cycles. The deployment of emerging technologies allows O&G companies to construct digital twins (DT) of their assets. Considering DT adoption, the O&G industry is still at an early stage with implementations limited to isolated and selective applications instead of industry-wide implementation, limiting the benefits from DT implementation. To gain the full potential of DT and related technological adoption, a comprehensive understanding of DT technology, the current status of O&G-related DT research activities, and the opportunities and challenges associated with the deployment of DT in the O&G industry are of paramount importance. In order to develop this understanding, this paper presents a literature review of DT within the context of the O&G industry. The paper follows a systematic approach to select articles for the literature review. First, a keywords-based publication search was performed on the scientific databases such as Elsevier, IEEE Xplore, OnePetro, Scopus, and Springer. The filtered articles were then analyzed using online text analytic software (Voyant Tools) followed by a manual review of the abstract, introduction and conclusion sections to select the most relevant articles for our study. These articles and the industrial publications cited by them were thoroughly reviewed to present a comprehensive overview of DT technology and to identify current research status, opportunities and challenges of DT deployment in the O&G industry. From this literature review, it was found that asset integrity monitoring, project planning, and life cycle management are the key application areas of digital twin in the O&G industry while cyber security, lack of standardization, and uncertainty in scope and focus are the key challenges of DT deployment in the O&G industry. When considering the geographical distribution for the DT related research in the O&G industry, the United States (US) is the leading country, followed by Norway, United Kingdom (UK), Canada, China, Italy, Netherland, Brazil, Germany, and Saudi Arabia. The overall publication rate was less than ten articles (approximately) per year until 2017, and a significant increase occurred in 2018 and 2019. The number of journal publications was noticeably lower than the number of conference publications, and the majority of the publications presented theoretical concepts rather than the industrial implementations. Both these observations suggest that the DT implementation in the O&G industry is still at an early stage.

Hongfang Lu, Kun Huang, Mohammadamin Azimi et al.

Blockchain technology has been developed for more than ten years and has become a trend in various industries. As the oil and gas industry is gradually shifting toward intelligence and digitalization, many large oil and gas companies were working on blockchain technology in the past two years because of it can significantly improve the management level, efficiency, and data security of the oil and gas industry. This paper aims to let more people in the oil and gas industry understand the blockchain and lead more thinking about how to apply the blockchain technology. To the best of our knowledge, this is one of the earliest papers on the review of the blockchain system in the oil and gas industry. This paper first presents the relevant theories and core technologies of the blockchain, and then describes how the blockchain is applied to the oil and gas industry from four aspects: trading, management and decision making, supervision, and cyber security. Finally, the application status, the understanding level of the blockchain in the oil and gas industry, opportunities, challenges, and risks and development trends are analyzed. The main conclusions are as follows: 1) at present, Europe and Asia have the fastest pace of developing the application of blockchain in the oil and gas industry, but there are still few oil and gas blockchain projects in operation or testing worldwide; 2) nowadays, the understanding of blockchain in the oil and gas industry is not sufficiently enough, the application is still in the experimental stage, and the investment is not enough; and (3) blockchain can bring many opportunities to the oil and gas industry, such as reducing transaction costs and improving transparency and efficiency. However, since it is still in the early stage of the application, there are still many challenges, primarily technological, and regulatory and system transformation. The development of blockchains in the oil and gas industry will move toward hybrid blockchain architecture, multi-technology combination, cross-chain, hybrid consensus mechanisms, and more interdisciplinary professionals.

Wanying Zhang, Jing Wang, Geng Qin et al.

The shipping industry, responsible for transporting 90% of global goods, is a major source of pollution and greenhouse gas (GHGs) emissions. In response to the increasingly stricter global and regional emission control regulations, the maritime industry has adopted various operational and technical measures to improve vessel energy efficiency so as to reduce emissions. However, these measures might not be able to effectively address the core issue of emissions, which arises from a heavy reliance on carbon-intensive energy sources. To reduce the emissions from the whole shipping industry more fundamentally, this review evaluates the viability of five alternative marine fuels — liquefied natural gas (LNG), methanol, ammonia, biofuel, and hydrogen — as potential solutions for maritime decarbonization. This review adopts the systematic search flow (SSF) approach, using iterative search refinement and thematic analysis for a structured synthesis of maritime alternative fuel literature. It first introduces each type of alternative fuel with an emphasis on production methods and sources, which are distinctively categorized by “color.” Following this, a comprehensive comparison of the fuels is presented, focusing on technical feasibility, economic viability, emission reduction capabilities, availability, and safety considerations. The practical application of these fuels is further explored through an analysis of their adoption in operational fleets and new orders, as well as the readiness of port infrastructure to support these changes. This review also examines the role of alternative fuels in the development of green shipping corridors, underscored by an analysis of green shipping finance initiatives. The findings provide valuable insights into the viability of these fuels, supporting the International Maritime Organization (IMO)’s 2050 decarbonization goals and paving the way towards zero emissions in global shipping.

M. A. Vishnyakova, R. A. Shaukharov, N. V. Kocherina et al.

Background. Guar (Cyamopsis tetragonoloba (L.) Taub.) is a leguminous crop plant of tropical origin that has gained unprecedented popularity in recent years due to the presence of gum in its seeds. The use of guar gum in the oil and gas industry gives the crop strategic importance. This was the reason for its introduction to the Russian Federation (RF) at the beginning of the 21st century and for the active breeding of domestic cultivars. The demand for the guar collection has increased dramatically, serving as an impetus for its active study.Materials and methods. The materials of the study were guar accessions from the VIR collection: 50 accessions in 2023, and 30 most productive of them in 2024. The accessions were phenotyped for 13 traits important for breeding at Volgograd Experiment Station of VIR. The indicator of early maturity was assessed by indirect methods. Statistical processing of the research results was performed using the Statistica 13.3 software package.Results. Seed productivity of guar accessions was analyzed, its structure and interrelations among its defining characteristics. Differentiation of the guar gene pool for the studied traits was revealed. The most productive accessions were identified. Previously obtained data on the relationship of guar collection accessions to the photoperiod served as a basis for proposing a modified algorithm for determining the photoperiod sensitivity of accessions as an indirect indicator of their earliness.Conclusion. The data obtained for guar accessions from the VIR collection under the conditions of the Russian Federation will make it possible to use this germplasm effectively as source material for breeding domestic cultivars.

Alexander V. Prokopev, Nadezhda V. Puchkova, Natalya V. Timofeeva et al.

This study examines the labor market of the Khanty-Mansi Autonomous Okrug–Yugra. The relevance of the research stems from the region’s strong dependence on natural resource reserves and production volumes, driven by the dominance of the mining and processing industries and the resulting unique employment conditions. These structural features create specific demands for workforce qualifications and competencies. The study aims to analyze labor market supply and demand while considering employer requirements and the professional qualifications of workers. The analysis draws on statistical data, job postings, and résumés from the HeadHunter online recruitment platform. Approximately 7,500 job descriptions and 21,600 résumé entries were collected for the period June–July 2024. The methodological framework combines natural language processing techniques with neural network models. The scientific novelty of the study lies in identifying qualitative correspondences and discrepancies between the competencies of job seekers and the requirements of employers within a resource-based regional economy. The results confirm the high level of resource dependence in the Khanty-Mansi Autonomous Okrug–Yugra, classifying its economy as highly dependent on resources. An analysis of employer job postings and job seeker résumés reveals that universal and general professional skills are prioritized over specialized competencies. Overall, both labor supply and demand are dominated by industry-specific professions. Job postings with clear sectoral specialization—such as those in the oil and gas industry—tend to emphasize general skills, personal qualities, and relevant education, followed by specialized skills that can be developed through work experience. The practical significance of the study lies in its potential to inform labor demand forecasting, optimize vocational training, and support the development of effective regional human resource policies.

Nicola Zanini, Alessio Suman, Mattia Piovan et al.

Non-Newtonian fluids are widespread in industry, e.g., biomedical, food, and oil and gas, and their rheology plays a fundamental role in choosing the processing parameters. Centrifugal pumps are widely employed to ensure the displacement of a huge amount of fluids due to their robustness and reliability. Since the pump performance is usually provided by manufacturers only for water, the selection of a proper pump to handle non-Newtonian fluids may prove very tricky. On-field experiences in pump operations with non-Newtonian slurries report severe head and efficiency drops, especially in part-load operations, whose causes are still not fully understood. Several models are found in the literature to predict the performance of centrifugal pumps with this type of fluids, but a lack of reliability and generality emerges. In this work, an extensive experimental campaign is carried out with an on-purpose test bench to investigate the effect of non-Newtonian shear-thinning fluids on the performance of a small commercial centrifugal pump. A dedicated experimental campaign is conducted to study the causes of performance drops. The results allow to establish a relationship between head and efficiency drops with solid content in the mixture. Sudden performance drops and unstable operating points are detected in part-load operations and the most severe drops are detected with the higher kaolin content in the mixture. Performance drop investigation allows to ascribe performance drop to gas-locking phenomena. Finally, a critical analysis is proposed to relate the resulting performance with both fluids’ rheology and the gas fraction trapped in the fluid. The results here presented can be useful for future numerical validation and predicting performance models.

GUAN Wenjie, PENG Xiaolong, ZHU Suyang, YANG Chen, PENG Zhen, MA Xiaoran

During the development of middle and deep gas reservoirs in South Sichuan, conventional reservoir engineering methods—such as fracture propagation, stress-induced analysis, and numerical simulation—render productivity prediction of infilling wells laborious and ineffective in addressing variations in production capacity across different production stages, with stringent application conditions. In order to quickly and accurately predict the production capacity of infilling wells, this study classifies the “three-stage” declining trend observed in the production pressure curves of existing wells into: (1) A drastic decline period, regarded as the initial water production stage; (2) a rapid decline period; and (3) a slow decline period, both considered part of the later gas production stage. The Grey Wolf Optimizer(GWO) algorithm, a fast optimization algorithm with adaptive capabilities and an information feedback mechanism, is applied for hyperparameter optimization of the Long Short-term Memory (LSTM) neural network. Two stage-specific models were constructed, with the number of hidden layer neurons, dropout rate, and batch size determined by the optimal solutions obtained via GWO. The number of iterations was selected based on the loss curve and performance metric curve, while a linear warm-up strategy was used to dynamically adjust the learning rate, facilitating high-speed training and the formation of a staged productivity prediction model. Example studies show that the GWO-optimised LSTM neural network model achieves rapid convergence with a preset learning rate of 0.002 and 450 iterations, ultimately reaching a performance index of 0.923. Compared to the conventional LSTM neural network model, the average absolute errors during the early and later stages are reduced by 1.290 m3/d and 0.213 × 104 m3/d, respectively. Compared with numerical simulation fitting results, the average absolute error in gas production prediction is reduced by 0.24 × 104 m3/d. Therefore, the improved LSTM neural network model demonstrates excellent performance in capacity prediction across different production stages, and the stage-specific productivity variations in infilling wells within middle and deep shale gas reservoirs in South Sichuan. This provides a theoretical foundation for productivity prediction methods of infilling wells.

Ningyu He, Shangtong Cao, Haoyu Wang et al.

As JavaScript has been criticized for performance and security issues in web applications, WebAssembly (Wasm) was proposed in 2017 and is regarded as the complementation for JavaScript. Due to its advantages like compact-size, native-like speed, and portability, Wasm binaries are gradually used as the compilation target for industrial projects in other high-level programming languages and are responsible for computation-intensive tasks in browsers, e.g., 3D graphic rendering and video decoding. Intuitively, characterizing in-the-wild adopted Wasm binaries from different perspectives, like their metadata, relation with source programming language, existence of security threats, and practical purpose, is the prerequisite before delving deeper into the Wasm ecosystem and beneficial to its roadmap selection. However, currently, there is no work that conducts a large-scale measurement study on in-the-wild adopted Wasm binaries. To fill this gap, we collect the largest-ever dataset to the best of our knowledge, and characterize the status quo of them from industry perspectives. According to the different roles of people engaging in the community, i.e., web developers, Wasm maintainers, and researchers, we reorganized our findings to suggestions and best practices for them accordingly. We believe this work can shed light on the future direction of the web and Wasm.

Anastasia Zhukova, Jonas Lührs, Christian E. Lobmüller et al.

Recent trends in NLP utilize knowledge graphs (KGs) to enhance pretrained language models by incorporating additional knowledge from the graph structures to learn domain-specific terminology or relationships between documents that might otherwise be overlooked. This paper explores how SciNCL, a graph-aware neighborhood contrastive learning methodology originally designed for scientific publications, can be applied to the process industry domain, where text logs contain crucial information about daily operations and are often structured as sparse KGs. Our experiments demonstrate that language models fine-tuned with triplets derived from graph embeddings (GE) outperform a state-of-the-art mE5-large text encoder by 9.8-14.3% (5.45-7.96p) on the proprietary process industry text embedding benchmark (PITEB) while having 3 times fewer parameters.

Chaozheng Wang, Zezhou Yang, Shuzheng Gao et al.

Code completion, a crucial practice in industrial settings, helps developers improve programming efficiency by automatically suggesting code snippets during development. With the emergence of Large Code Models (LCMs), this field has witnessed significant advancements. Due to the natural differences between open-source and industrial codebases, such as coding patterns and unique internal dependencies, it is a common practice for developers to conduct domain adaptation when adopting LCMs in industry. There exist multiple adaptation approaches, among which retrieval-augmented generation (RAG) and fine-tuning are the two most popular paradigms. However, no prior research has explored the trade-off of the two approaches in industrial scenarios. To mitigate the gap, we comprehensively compare the two paradigms including Retrieval-Augmented Generation (RAG) and Fine-tuning (FT), for industrial code completion in this paper. In collaboration with Tencent's WXG department, we collect over 160,000 internal C++ files as our codebase. We then compare the two types of adaptation approaches from three dimensions that are concerned by industrial practitioners, including effectiveness, efficiency, and parameter sensitivity, using six LCMs. Our findings reveal that RAG, when implemented with appropriate embedding models that map code snippets into dense vector representations, can achieve higher accuracy than fine-tuning alone. Specifically, BM25 presents superior retrieval effectiveness and efficiency among studied RAG methods. Moreover, RAG and fine-tuning are orthogonal and their combination leads to further improvement. We also observe that RAG demonstrates better scalability than FT, showing more sustained performance gains with larger scales of codebase.

Marcos Lima Romero, Ricardo Suyama

The recent development of Agentic AI systems, empowered by autonomous large language models (LLMs) agents with planning and tool-usage capabilities, enables new possibilities for the evolution of industrial automation and reduces the complexity introduced by Industry 4.0. This work proposes a conceptual framework that integrates Agentic AI with the intent-based paradigm, originally developed in network research, to simplify human-machine interaction (HMI) and better align automation systems with the human-centric, sustainable, and resilient principles of Industry 5.0. Based on the intent-based processing, the framework allows human operators to express high-level business or operational goals in natural language, which are decomposed into actionable components. These intents are broken into expectations, conditions, targets, context, and information that guide sub-agents equipped with specialized tools to execute domain-specific tasks. A proof of concept was implemented using the CMAPSS dataset and Google Agent Developer Kit (ADK), demonstrating the feasibility of intent decomposition, agent orchestration, and autonomous decision-making in predictive maintenance scenarios. The results confirm the potential of this approach to reduce technical barriers and enable scalable, intent-driven automation, despite data quality and explainability concerns.

Suman Kumar Das

India's growing population and economy have significantly increased the demand and consumption of natural resources. As a result, the potential benefits of transitioning to a circular economic model have been extensively discussed and debated among various Indian stakeholders, including policymakers, industry leaders, and environmental advocates. Despite the numerous initiatives, policies, and transnational strategic partnerships of the Indian government, most small and medium enterprises in India face significant challenges in implementing circular economy practices. This is due to the lack of a clear pathway to measure the current state of the circular economy in Indian industries and the absence of a framework to address these challenges. This paper examines the circularity of the 93-textile industry in India using the C-Readiness Tool. The analysis comprehensively identified 9 categories with 34 barriers to adopting circular economy principles in the textile sector through a narrative literature review. The identified barriers were further compared against the findings from a C-readiness tool assessment, which revealed prominent challenges related to supply chain coordination, consumer engagement, and regulatory compliance within the industry's circularity efforts. In response to these challenges, the article proposes a strategic roadmap that leverages digital technologies to drive the textile industry towards a more sustainable and resilient industrial model.

Jingzhi Gong, Rafail Giavrimis, Paul Brookes et al.

There is a growing interest in leveraging multiple large language models (LLMs) for automated code optimization. However, industrial platforms deploying multiple LLMs face a critical challenge: prompts optimized for one LLM often fail with others, requiring expensive model-specific prompt engineering. This cross-model prompt engineering bottleneck severely limits the practical deployment of multi-LLM systems in production environments. We introduce Meta-Prompted Code Optimization (MPCO), a framework that automatically generates high-quality, task-specific prompts across diverse LLMs while maintaining industrial efficiency requirements. MPCO leverages metaprompting to dynamically synthesize context-aware optimization prompts by integrating project metadata, task requirements, and LLM-specific contexts. It is an essential part of the ARTEMIS code optimization platform for automated validation and scaling. Our comprehensive evaluation on five real-world codebases with 366 hours of runtime benchmarking demonstrates MPCO's effectiveness: it achieves overall performance improvements up to 19.06% with the best statistical rank across all systems compared to baseline methods. Analysis shows that 96% of the top-performing optimizations stem from meaningful edits. Through systematic ablation studies and meta-prompter sensitivity analysis, we identify that comprehensive context integration is essential for effective meta-prompting and that major LLMs can serve effectively as meta-prompters, providing actionable insights for industrial practitioners.

Qinwen Chen, Wenbiao Tao, Zhiwei Zhu et al.

Community Question Answering (CQA) platforms can be deemed as important knowledge bases in community, but effectively leveraging historical interactions and domain knowledge in real-time remains a challenge. Existing methods often underutilize external knowledge, fail to incorporate dynamic historical QA context, or lack memory mechanisms suited for industrial deployment. We propose ComRAG, a retrieval-augmented generation framework for real-time industrial CQA that integrates static knowledge with dynamic historical QA pairs via a centroid-based memory mechanism designed for retrieval, generation, and efficient storage. Evaluated on three industrial CQA datasets, ComRAG consistently outperforms all baselines--achieving up to 25.9% improvement in vector similarity, reducing latency by 8.7% to 23.3%, and lowering chunk growth from 20.23% to 2.06% over iterations.

Christina Nikolova, T. Gutierrez

Surfactants are a group of amphiphilic chemical compounds (i.e., having both hydrophobic and hydrophilic domains) that form an indispensable component in almost every sector of modern industry. Their significance is evidenced from the enormous volumes that are used and wide diversity of applications they are used in, ranging from food and beverage, agriculture, public health, healthcare/medicine, textiles, and bioremediation. A major drive in recent decades has been toward the discovery of surfactants from biological/natural sources—namely bio-surfactants—as most surfactants that are used today for industrial applications are synthetically-manufactured via organo-chemical synthesis using petrochemicals as precursors. This is problematic, not only because they are derived from non-renewable resources, but also because of their environmental incompatibility and potential toxicological effects to humans and other organisms. This is timely as one of today's key challenges is to reduce our reliance on fossil fuels (oil, coal, gas) and to move toward using renewable and sustainable sources. Considering the enormous genetic diversity that microorganisms possess, they offer considerable promise in producing novel types of biosurfactants for replacing those that are produced from organo-chemical synthesis, and the marine environment offers enormous potential in this respect. In this review, we begin with an overview of the different types of microbial-produced biosurfactants and their applications. The remainder of this review discusses the current state of knowledge and trends in the usage of biosurfactants by the Oil and Gas industry for enhancing oil recovery from exhausted oil fields and as dispersants for combatting oil spills.

P. Orrù, Andrea Zoccheddu, L. Sassu et al.

The demand for cost-effective, reliable and safe machinery operation requires accurate fault detection and classification to achieve an efficient maintenance strategy and increase performance. Furthermore, in strategic sectors such as the oil and gas industry, fault prediction plays a key role to extend component lifetime and reduce unplanned equipment thus preventing costly breakdowns and plant shutdowns. This paper presents the preliminary development of a simple and easy to implement machine learning (ML) model for early fault prediction of a centrifugal pump in the oil and gas industry. The data analysis is based on real-life historical data from process and equipment sensors mounted on the selected machinery. The raw sensor data, mainly from temperature, pressure and vibrations probes, are denoised, pre-processed and successively coded to train the model. To validate the learning capabilities of the ML model, two different algorithms—the Support Vector Machine (SVM) and the Multilayer Perceptron (MLP)—are implemented in KNIME platform. Based on these algorithms, potential faults are successfully recognized and classified ensuring good prediction accuracy. Indeed, results from this preliminary work show that the model allows us to properly detect the trends of system deviations from normal operation behavior and generate fault prediction alerts as a maintenance decision support system for operatives, aiming at avoiding possible incoming failures.