Abstract Oil and gas industries are facing several challenges and issues in data processing and handling. Large amount of data bank is generated with various techniques and processes. The proper technical analysis of this database is to be carried out to improve performance of oil and gas industries. This paper provides a comprehensive state-of-art review in the field of machine learning and artificial intelligence to solve oil and gas industry problems. It also narrates the various types of machine learning and artificial intelligence techniques which can be used for data processing and interpretation in different sectors of upstream oil and gas industries. The achievements and developments promise the benefits of machine learning and artificial intelligence techniques towards large data storage capabilities and high efficiency of numerical calculations. In this paper a summary of various researchers work on machine learning and artificial intelligence applications and limitations is showcased for upstream and sectors of oil and gas industry. The existence of this extensive intelligent system could really eliminate the risk factor and cost of maintenance. The development and progress using this emerging technologies have become smart and makes the judgement procedure easy and straightforward. The study is useful to access intelligence of different machine learning methods to declare its application for distinct task in oil and gas sector.
Fiber–optic sensors have been widely deployed in various applications, and their use has gradually increased since the 1980 s. Distributed fiber–optic sensors, which enable continuous and real–time measurements along the entire length of an optical fiber cable, have undergone significant improvements in underlying industries. In the oil and gas industry, distributed fiber–optic sensors can provide significantly valuable information throughout the life cycle of a well and can monitor pipelines transporting hydrocarbons over great distances. Here, we review the deployment of fiber–optic Rayleigh–based distributed acoustic sensing (DAS), Raman–based distributed temperature sensing (DTS), and Brillouin–based distributed temperature and strain sensing (DTSS) in the oil and gas industry. In particular, we describe the operation principle and basic experimental setups of the DAS, DTS, and DTSS, highlighting their applications in the upstream, midstream, and downstream sectors of the oil and gas industry. We further developed a prototype of a fiber–optic hybrid DAS–DTS system that simultaneously measures vibration and temperature along a multimode fiber (MMF). The reported hybrid sensing system was tested in an operational oil well. This work also discusses the challenges that might hinder the growth of the distributed fiber–optic sensing market in the petroleum industry, and we further point out the future directions of related research.
This review presents the latest update, applications, techniques of the NMR tools in both laboratory and field scales in the oil and gas upstream industry. The applications of NMR in the laboratory scale were thoroughly reviewed and summarized such as porosity, pores size distribution, permeability, saturations, capillary pressure, and wettability. NMR is an emerging tool to evaluate the improved oil recovery techniques, and it was found to be better than the current techniques used for screening, evaluation, and assessment. For example, NMR can define the recovery of oil/gas from the different pore systems in the rocks compared to other macroscopic techniques that only assess the bulk recovery. This manuscript included different applications for the NMR in enhanced oil recovery research. Also, NMR can be used to evaluate the damage potential of drilling, completion, and production fluids laboratory and field scales. Currently, NMR is used to evaluate the emulsion droplet size and its behavior in the pore space in different applications such as enhanced oil recovery, drilling, completion, etc. NMR tools in the laboratory and field scales can be used to assess the unconventional gas resources and NMR showed a very good potential for exploration and production advancement in unconventional gas fields compared to other tools. Field applications of NMR during exploration and drilling such as logging while drilling, geosteering, etc., were reviewed as well. Finally, the future and potential research directions of NMR tools were introduced which include the application of multi-dimensional NMR and the enhancement of the signal-to-noise ratio of the collected data during the logging while drilling operations.
IntroductionEnvironmentally friendly pork production is crucial to the pig industry, where the enhancement of growth performance and feed efficiency with reduced environmental impacts is favored. This study aimed to evaluate the effect that protease supplementation in a low crude protein diet has on the growth performance, nutrient digestibility, nitrogen retention, and gut microbiome in growing pigs.MethodsEighty pigs (Landrace × Yorkshire × Duroc; 24.72 kg) were selected, and based on initial body weight and sex, randomly allocated to one of the following dietary treatments: H, 16% crude protein (CP) diet; L, 14% CP diet; L+E1, low CP diet + 0.1% protease; and L+E2, low CP diet + 0.2% protease. Each treatment comprised four replicates with five pigs per pen. ResultsPigs fed a low CP diet with protease supplementation showed a significantly higher body weight, average daily gain, and feed conversion ratio than those fed a high CP diet. In addition, ammonia emissions were lower in the L+E2 group than in the L group. Based on microbiome analysis, the L+E1 and L+E2 groups showed an increased Firmicutes-to-Bacteroidota ratio and elevated expression of pathways related to carbohydrate metabolism, coinciding with higher concentrations of short-chain fatty acids, such as butyrate and propionate, which support intestinal health. Additionally, the predicted function of the microbiota of pigs fed protease exhibited reduced nitrogen and sulfur metabolism, suggesting a potential reduction in excreted odorous compounds. DiscussionThese findings highlight the role of protease in enhancing growth performance and feed efficiency by modulating gut microbial composition and metabolic functions and reducing noxious gas emissions. Also, potential feed-cost savings are inferred from lower CP formulation.
This study offered a detailed review of data sciences and machine learning (ML) roles in different petroleum engineering and geosciences segments such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, emphasizing the newly emerging field of unconventional reservoirs. The future of data science and ML in the oil and gas industry, highlighting what is required from ML for better prediction, is also discussed. This study also provides a comprehensive comparison of different ML techniques used in the oil and gas industry. With the arrival of powerful computers, advanced ML algorithms, and extensive data generation from different industry tools, we see a bright future in developing solutions to the complex problems in the oil and gas industry that were previously beyond the grip of analytical solutions or numerical simulation. ML tools can incorporate every detail in the log data and every information connected to the target data. Despite their limitations, they are not constrained by limiting assumptions of analytical solutions or by particular data and/or power processing requirements of numerical simulators. This detailed and comprehensive study can serve as an exclusive reference for ML applications in the industry. Based on the review conducted, it was found that ML techniques offer a great potential in solving problems in almost all areas of the oil and gas industry involving prediction, classification, and clustering. With the generation of huge data in everyday oil and gas industry activates, machine learning and big data handling techniques are becoming a necessity toward a more efficient industry.
Adindu Donatus, Adindu Donatus Ogbu, Williams Ozowe
et al.
In the highly complex and dynamic landscape of the oil and gas industry, efficient procurement processes are crucial for maintaining operational excellence and cost-effectiveness. However, traditional procurement methods often suffer from inefficiencies, leading to delays, redundancies, and increased expenses. Leveraging innovative approaches to SAP Ariba implementation presents a promising solution to address these challenges and streamline procurement in oil and gas industry logistics. SAP Ariba is a cloud-based procurement platform that offers end-to-end procurement solutions, including strategic sourcing, contract management, supplier management, and procurement analytics. By adopting SAP Ariba, oil and gas companies can optimize their procurement processes, enhance visibility and control over their supply chain, and drive cost savings. One innovative approach to SAP Ariba implementation in the oil and gas industry logistics involves the utilization of advanced data analytics and artificial intelligence (AI) technologies. These technologies enable companies to analyze vast amounts of procurement data, identify patterns and trends, and make data-driven decisions to optimize procurement strategies and supplier relationships. Another innovative approach is the integration of blockchain technology into SAP Ariba, which enhances transparency, security, and traceability in procurement transactions. Blockchain ensures the immutability and integrity of procurement records, reducing the risk of fraud and errors while increasing trust and accountability among stakeholders. Furthermore, adopting a collaborative procurement model through SAP Ariba enables oil and gas companies to collaborate closely with suppliers and partners, driving innovation, and fostering strategic partnerships. By leveraging SAP Ariba's collaborative features, such as supplier networks and sourcing events, companies can streamline communication, negotiate better deals, and ensure compliance with industry regulations and standards. In conclusion, innovative approaches to SAP Ariba implementation offer a transformative solution to solve procurement inefficiencies in the oil and gas industry logistics. By harnessing advanced technologies, embracing collaborative procurement models, and integrating blockchain into their procurement processes, companies can achieve greater efficiency, agility, and competitiveness in their procurement operations, ultimately driving sustainable growth and success in the challenging oil and gas industry landscape.
Abstract Volcanic eruptions produce plumes of ash, gas and aerosols that present a risk to aviation at all standard flight levels. Here, we investigate atmospheric dispersal of volcanic emissions, whether and how they infiltrate aircraft, and whether ground-level public health exposure thresholds can be related to the pressurised cabin environment. We then review the limited evidence for physical and mental health, and behavioural impacts, resulting from volcanic emissions entering aircraft. Serious health risks are considered low for healthy individuals, but respiratory irritation is likely for a high exposure scenario to sulfur dioxide (SO2). Asthmatics are particularly sensitive to SO2, with even relatively low, short exposures, potentially resulting in severe respiratory impacts. Negative group behaviours are not expected but individual distress is possible. Communicating this evidence to the aviation industry may result in more informed decision-making on flightpath alterations and triggering of emergency protocols, both before and during volcanic emission encounters.
Environmental protection, Disasters and engineering
Abstract The 10 000‐m ultradeep dolomite reservoir holds significant potential as a successor field for future oil and gas exploration in China's marine craton basin. However, major challenges such as the genesis of dolomite, the formation time of high‐quality reservoirs, and the preservation mechanism of reservoirs have always limited exploration decision‐making. This research systematically elaborates on the genesis and reservoir‐forming mechanisms of Sinian–Cambrian dolomite, discussing the ancient marine environment where microorganisms and dolomite develop, which controls the formation of large‐scale Precambrian–Cambrian dolomite. The periodic changes in Mg isotopes and sedimentary cycles show that the thick‐layered dolomite is the result of different dolomitization processes superimposed on a spatiotemporal scale. Lattice defects and dolomite embryos can promote dolomitization. By simulating the dissolution of typical calcite and dolomite crystal faces in different solution systems and calculating their molecular weights, the essence of heterogeneous dissolution and pore formation on typical calcite and dolomite crystal faces was revealed, and the mechanism of dolomitization was also demonstrated. The properties of calcite and dolomite (104)/(110) grain boundaries and their dissolution mechanism in carbonate solution were revealed, showing the limiting factors of the dolomitization process and the preservation mechanism of deep buried dolomite reservoirs. The in situ laser U‐Pb isotope dating technique has demonstrated the timing of dolomitization and pore formation in ancient carbonate rocks. This research also proposed that dolomitization occurred during the quasi‐contemporaneous or shallow‐burial periods within 50 Ma after deposition and pores formed during the quasi‐contemporaneous to the early diagenetic periods. And it was clear that the quasi‐contemporaneous dolomitization was the key period for reservoir formation. The systematic characterization of the spatial distribution of the deepest dolomite reservoirs in multiple sets of the Sinian and the Cambrian in the Chinese craton basins provides an important basis for the distribution prediction of large‐scale dolomite reservoirs. It clarifies the targets for oil and gas exploration at depths over 10 000 m. The research on dolomite in this study will greatly promote China's ultradeep oil and gas exploration and lead the Chinese petroleum industry into a new era of 10 000‐m deep oil exploration.
Engineering geology. Rock mechanics. Soil mechanics. Underground construction
In response to the global challenge of climate change, financial institutions are increasingly called upon to assess and disclose their carbon emissions. Various global carbon quantification and reporting standards were developed, such as the Greenhouse Gas (GHG) Protocol, Task Force on Climate-related Financial Disclosures (TCFD), Partnership for Carbon Accounting Financials (PCAF) and others. Unfortunately, the now diverse landscape of standards increases the complexity for institutions seeking to develop voluntary carbon quantification and reporting. This study addresses the complexity issue by developing a criteria-based tool that summarizes the various components and requirements of the carbon standards. We propose eight criteria that summarize the standards’ key elements, requirements and relevance to the financial industry. We analyze seven major carbon quantification and reporting standards, systematically evaluating them against our tool. By doing so, we provide financial institutions with valuable insights in selecting appropriate standards to inform their emissions quantification and reporting decisions.
Machine learning is an essential tool for optimizing industrial quality control processes. However, the complexity of machine learning models often limits their practical applicability due to a lack of interpretability. Additionally, many industrial machines lack comprehensive sensor technology, making data acquisition incomplete and challenging. Explainable Artificial Intelligence offers a solution by providing insights into model decision-making and identifying the most relevant features for classification. In this paper, we investigate the impact of feature reduction using XAI techniques on the quality classification of injection-molded parts. We apply SHAP, Grad-CAM, and LIME to analyze feature importance in a Long Short-Term Memory model trained on real production data. By reducing the original 19 input features to 9 and 6, we evaluate the trade-off between model accuracy, inference speed, and interpretability. Our results show that reducing features can improve generalization while maintaining high classification performance, with an small increase in inference speed. This approach enhances the feasibility of AI-driven quality control, particularly for industrial settings with limited sensor capabilities, and paves the way for more efficient and interpretable machine learning applications in manufacturing.
Mari Ashiga, Vardan Voskanyan, Fateme Dinmohammadi
et al.
Recent advancements in Large Language Models (LLMs) for code optimization have enabled industrial platforms to automate software performance engineering at unprecedented scale and speed. Yet, organizations in regulated industries face strict constraints on which LLMs they can use - many cannot utilize commercial models due to data privacy regulations and compliance requirements, creating a significant challenge for achieving high-quality code optimization while maintaining cost-effectiveness. We address this by implementing a Mixture-of-Agents (MoA) approach that directly synthesizes code from multiple specialized LLMs, comparing it against TurinTech AI's vanilla Genetic Algorithm (GA)-based ensemble system and individual LLM optimizers using real-world industrial codebases. Our key contributions include: (1) First MoA application to industrial code optimization using real-world codebases; (2) Empirical evidence that MoA excels with open-source models, achieving 14.3% to 22.2% cost savings and 28.6% to 32.2% faster optimization times for regulated environments; (3) Deployment guidelines demonstrating GA's advantage with commercial models while both ensembles outperform individual LLMs; and (4) Real-world validation across 50 code snippets and seven LLM combinations, generating over 8,700 variants, addresses gaps in industrial LLM ensemble evaluation. This provides actionable guidance for organizations balancing regulatory compliance with optimization performance in production environments.
Andrew Emuobosa Esiri, Olusile Akinyele Babayeju, Ifeanyi Onyedika Ekemezie
Methane emissions from the oil and gas industry are a major contributor to climate change due to their high global warming potential. Accurate and standardized monitoring of these emissions is essential for effective mitigation. This review explores the current state of methane emission monitoring technologies, highlighting the strengths and limitations of direct measurement, remote sensing, and modeling approaches. It also examines the diverse regulatory frameworks and industry practices, identifying key challenges such as accuracy, consistency, and economic barriers. The paper proposes strategies for harmonizing monitoring standards globally, including adopting international guidelines, certification programs, and centralized reporting platforms. Additionally, it advocates for innovative regulatory approaches that incentivize better monitoring and reporting practices and emphasizes the need for international cooperation through data sharing and capacity building. The review concludes by discussing the potential impact of standardized monitoring on the industry, outlining future research and development directions, and calling for proactive steps by all stakeholders to achieve effective methane emission reduction. Keywords: Methane Emissions, Oil and Gas Industry, Monitoring Technologies and Regulatory Frameworks.
The oil and gas industry is renowned for its inherent operational risks and complex safety challenges, necessitating robust Human Resource Management (HRM) strategies for effective safety measures and risk mitigation. This comprehensive review explores the evolving landscape of HRM practices within the oil and gas sector, focusing on their pivotal role in enhancing workplace safety and reducing operational risks. The review delves into the dynamic nature of the oil and gas industry, characterized by hazardous work environments, intricate technological processes, and a global workforce. Analyzing existing literature and case studies, the paper underscores the critical need for HRM strategies that prioritize safety culture, employee training, and proactive risk management. Key aspects include recruitment and selection processes tailored to identify candidates with a strong safety mindset, ongoing training programs to enhance competencies and awareness, and the establishment of a safety-centric organizational culture. Furthermore, the review examines the integration of technology and data analytics in HRM practices within the oil and gas sector. The utilization of advanced technologies for personnel training, real-time monitoring, and predictive analytics is discussed as a means to pre-emptively identify potential safety risks and proactively address them. Additionally, the paper highlights the importance of fostering communication and collaboration among employees, emphasizing the role of HRM in facilitating a transparent and open reporting culture. The findings of this review contribute to a deeper understanding of the multifaceted role played by HRM in promoting safety and mitigating risks within the oil and gas industry. As the industry continues to evolve, the adoption of innovative HRM strategies becomes imperative for organizations seeking to maintain a secure and resilient operational environment while safeguarding the well-being of their workforce. Keywords: HR, Management, Safety, Risk Mitigation, Oil and Gas, Industry, Review.
V. Solovyeva, Khaled H. Almuhammadi, W. Badeghaish
In the oil and gas industry, the presence of aggressive fluids and gases can cause serious corrosion problems. Multiple solutions have been introduced to the industry to minimize corrosion occurrence probability in recent years. They include cathodic protection, utilization of advanced metallic grades, injection of corrosion inhibitors, replacement of the metal parts with composite solutions, and deposition of protective coatings. This paper will review the advances and developments in the design of corrosion protection solutions. The publication highlights crucial challenges in the oil and gas industry to be solved upon the development of corrosion protection methods. According to the stated challenges, existing protective systems are summarized with emphasis on the features that are essential for oil and gas production. Qualification of corrosion protection performance based on international industrial standards will be depicted in detail for each type of corrosion protection system. Forthcoming challenges for the engineering of next-generation materials for corrosion mitigation are discussed to highlight the trends and forecasts of emerging technology development. We will also discuss the advances in nanomaterial and smart material development, enhanced ecological regulations, and applications of complex multifunctional solutions for corrosion mitigation which have become of great importance in recent decades.
Andrew Emuobosa Esiri, Oludayo Olatoye Sofoluwe, Ayemere Ukato
The alignment of oil and gas industry practices with Sustainable Development Goals (SDGs) is imperative for fostering a sustainable future. This abstract provides an overview of the strategies and challenges associated with this alignment. The oil and gas industry plays a significant role in global energy supply, economic development, and geopolitical dynamics. However, its operations often have adverse environmental, social, and economic impacts, making alignment with SDGs essential. Challenges in aligning industry practices with SDGs include the environmental degradation caused by extraction activities, social and economic disparities in oil-producing regions, and regulatory complexities. To address these challenges, strategies such as reducing carbon footprints, transitioning to renewable energy sources, engaging with local communities, protecting human and indigenous rights, and fostering economic diversification are crucial. Case studies of companies successfully aligning with SDGs highlight best practices and lessons learned. Impact assessments demonstrate the positive outcomes of aligned practices on environmental conservation, social well-being, and economic development. Recommendations include policy reforms, industry guidelines, and stakeholder collaboration to facilitate broader adoption of sustainable practices. In conclusion, aligning oil and gas industry practices with SDGs is essential for achieving sustainable development goals globally. This abstract calls for concerted efforts from industry stakeholders, policymakers, and civil society to create a more sustainable future. Keywords: Oil, Gas, Industry Practices, Sustainable Development Goals (SDGs).
As a rapidly evolving technology, carbon capture and storage (CCS) can potentially lower the levels of greenhouse gas emissions from the oil and gas industry. This paper provides a comprehensive review of different aspects of CCS technology, including its key components, the methods and stages of carbon storage, implied environmental effects, and its pros and cons. This paper also investigates the utilization of CCS as an alternative method to water injection into oil reservoirs. It also probes the technical and operational challenges of implementing CCS technology in the oil and gas industry. Additionally, this paper examines the regulatory and policy issues associated with CCS, including incentives and frameworks for promoting the deployment of CCS technology. Finally, in this paper the potential benefits of CCS are discussed, including reducing the carbon footprint of the oil and gas industry, enhancing energy security, and supporting the transition to a low-carbon economy.
Onoriode Reginald, Aziza, Onoriode Reginald Aziza
et al.
Artificial Intelligence (AI) is increasingly transforming the regulatory compliance landscape in the oil and gas industry. This paper examines the profound impact of AI on ensuring adherence to complex regulatory frameworks governing this sector. Regulatory compliance in the oil and gas industry involves adhering to a myriad of environmental, safety, and operational regulations, often posing significant challenges due to the volume and complexity of data involved. AI technologies, including machine learning, natural language processing, and predictive analytics, offer innovative solutions to these challenges. AI enhances data management and analysis by automating data collection, processing, and reporting, thereby increasing accuracy and efficiency. Predictive maintenance and risk assessment tools powered by AI can identify potential compliance issues before they arise, allowing for proactive measures. Moreover, AI-driven compliance monitoring systems enable real-time tracking of regulatory adherence, reducing the risk of non-compliance and associated penalties. Automated auditing and inspection processes further streamline compliance checks, ensuring thorough and consistent evaluations. Case studies demonstrate successful AI implementations in regulatory compliance, such as automated reporting systems in offshore drilling and predictive maintenance in pipeline management, which have resulted in improved compliance rates and reduced operational risks. However, the adoption of AI is not without challenges. Issues related to data quality and integration, cybersecurity, and regulatory acceptance pose significant hurdles. Additionally, ethical and legal considerations surrounding AI deployment must be addressed to ensure responsible use. AI holds substantial potential to revolutionize regulatory compliance in the oil and gas industry by enhancing efficiency, accuracy, and proactive risk management. As AI technologies continue to evolve, their integration into compliance processes will likely become more sophisticated, offering greater benefits and addressing current limitations. The future of regulatory compliance in the oil and gas sector will be increasingly shaped by the advancements in AI, driving both operational excellence and adherence to stringent regulatory standards.