Academic literature on the topic 'Analytics in oil and gas'
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Journal articles on the topic "Analytics in oil and gas"
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R Azmi, Putri Azmira, Marina Yusoff, and Mohamad Taufik Mohd Sallehud-din. "A Review of Predictive Analytics Models in the Oil and Gas Industries." Sensors 24, no. 12 (2024): 4013. http://dx.doi.org/10.3390/s24124013.
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Enhancing the management and monitoring of oil and gas processes demands the development of precise predictive analytic techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern machine learning techniques. Several review articles detail the developments in predictive maintenance and the technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sectors. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry’s predictive analytical modeling. This review covers different forms of machine learning techniques used in predictive analytical modeling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, describing the model’s categories, the data’s temporality, field, and name, the dataset’s type, predictive analytics (classification, clustering, or prediction), the models’ input and output parameters, the performance metrics, the optimal model, and the model’s benefits and drawbacks. In addition, suggestions for future research directions to provide insights into the potential applications of the associated knowledge. This review can serve as a guide to enhance the effectiveness of predictive analytics models in the oil and gas industries.
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Ogu, Elemele, Peter Ifechukwude Egbumokei, Ikiomoworio Nicholas Dienagha, and Wags Numoipiri Digitemie. "Big data analytics in environmental impact predictions: Advancing predictive assessments in oil and gas operations for future sustainability." International Journal of Multidisciplinary Research and Growth Evaluation 5, no. 1 (2024): 1203–8. https://doi.org/10.54660/.ijmrge.2024.5.1.1203-1208.
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Big Data analytics is revolutionizing environmental impact predictions in the oil and gas industry, offering enhanced capabilities for predictive assessments and sustainable operations. This Review explores the pivotal role of Big Data analytics in advancing environmental impact predictions, focusing on its implications for future sustainability in oil and gas operations. In the oil and gas industry, environmental impact assessments are crucial for ensuring sustainable operations and minimizing ecological footprints. Traditional approaches to environmental impact predictions often rely on historical data and simplistic models, leading to limited accuracy and effectiveness. However, the advent of Big Data analytics has transformed this landscape, offering unprecedented opportunities for enhancing predictive assessments. Big Data analytics enables the processing and analysis of vast amounts of data from diverse sources, including sensor data, satellite imagery, and environmental monitoring data. By leveraging advanced machine learning algorithms and predictive analytics techniques, Big Data analytics can identify complex patterns and trends in environmental data, providing more accurate and timely predictions of environmental impacts. One key application of Big Data analytics in environmental impact predictions is the modeling of air and water quality. By analyzing historical and real-time data on pollutant emissions, weather patterns, and environmental conditions, Big Data analytics can forecast changes in air and water quality, helping oil and gas companies mitigate potential impacts on ecosystems and human health. Furthermore, Big Data analytics can enhance the monitoring and management of biodiversity in oil and gas operations. By analyzing data on species distributions, habitat characteristics, and ecological interactions, Big Data analytics can help identify sensitive areas and develop targeted conservation strategies to protect biodiversity. Overall, Big Data analytics holds immense promise for advancing environmental impact predictions in oil and gas operations, offering a pathway towards future sustainability. By harnessing the power of Big Data analytics, oil and gas companies can make informed decisions, reduce environmental risks, and contribute to a more sustainable future for the industry.
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Jacobs, Trent. "Analytics Firms Explore Oil and Gas Market." Journal of Petroleum Technology 68, no. 10 (2016): 36–38. http://dx.doi.org/10.2118/1016-0036-jpt.
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Chuka Anthony Arinze, Izionworu, Vincent Onuegbu, Daniel Isong, Cosmas Dominic Daudu, and Adedayo Adefemi. "Predictive maintenance in oil and gas facilities, leveraging ai for asset integrity management." International Journal of Frontiers in Engineering and Technology Research 6, no. 1 (2024): 016–26. http://dx.doi.org/10.53294/ijfetr.2024.6.1.0026.
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This paper explores the application of AI in predictive maintenance within oil and gas facilities, discussing its benefits, challenges, and future prospects. Through the integration of AI-driven analytics and real-time data monitoring, oil and gas companies can enhance their asset integrity management practices, ultimately driving cost savings and operational excellence. Predictive maintenance has become indispensable in the oil and gas industry, serving as a pivotal strategy to uphold operational efficiency and preserve asset integrity. This paper delves into the profound impact of artificial intelligence (AI) technologies on predictive maintenance, ushering in a new era of proactive equipment management. By harnessing AI capabilities, oil and gas companies can preempt equipment failures, curtail downtime, and refine maintenance protocols, thereby optimizing overall operational performance. The integration of AI in predictive maintenance marks a paradigm shift, offering a proactive approach to asset management. Leveraging AI-driven analytics and real-time data monitoring, oil and gas facilities can fortify their asset integrity management practices. Through predictive algorithms and machine learning models, these technologies empower companies to forecast equipment malfunctions with unprecedented accuracy, allowing for timely interventions and mitigating potential risks the benefits of AI-powered predictive maintenance in the oil and gas sector are multifaceted the future of predictive maintenance in the oil and gas industry is brimming with promise. As AI technologies continue to evolve, we can anticipate further advancements in predictive analytics, fault detection, and decision support systems. By embracing innovation and collaboration, oil and gas companies can harness the full potential of AI-driven predictive maintenance, cementing their position as industry leaders in asset management and operational efficiency.
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Olusile, Akinyele Babayeju, Adefemi Adedayo, Onyedika Ekemezie Ifeanyi, and Olatoye Sofoluwe Oludayo. "Advancements in predictive maintenance for aging oil and gas infrastructure." World Journal of Advanced Research and Reviews 22, no. 3 (2024): 252–66. https://doi.org/10.5281/zenodo.14725645.
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The oil and gas industry relies heavily on aging infrastructure to extract, transport, and process hydrocarbons. As these assets age, the risk of failures and downtime increases, leading to safety hazards and costly repairs. Predictive maintenance has emerged as a valuable strategy to mitigate these risks by using data-driven insights to predict equipment failures and schedule maintenance proactively. This review highlights advancements in predictive maintenance technologies for aging oil and gas infrastructure, focusing on the benefits and challenges of implementation. Advancements in sensor technology and data analytics have significantly improved the effectiveness of predictive maintenance in the oil and gas industry. Sensors installed on critical equipment collect real-time data on temperature, pressure, vibration, and other key parameters, providing insights into equipment health and performance. Data analytics tools analyze this data to identify patterns and trends indicative of potential failures, enabling operators to take preventive action before a breakdown occurs. Machine learning algorithms have also played a crucial role in enhancing predictive maintenance capabilities. These algorithms can process large volumes of data and learn from past equipment failures to predict future issues accurately. By continuously learning from new data, machine learning algorithms can improve their predictive accuracy over time, leading to more effective maintenance strategies. Despite these advancements, implementing predictive maintenance in aging oil and gas infrastructure poses several challenges. One major challenge is integrating new sensor technology with existing equipment, which may require retrofitting or upgrading existing assets. Another challenge is managing the vast amounts of data generated by sensors and analytics tools, which can strain existing IT infrastructure and require specialized expertise to analyze effectively. In conclusion, advancements in predictive maintenance technologies offer significant benefits for aging oil and gas infrastructure. By leveraging sensor technology, data analytics, and machine learning, operators can predict equipment failures, reduce downtime, and extend the life of critical assets. However, implementing these technologies requires careful planning and investment to overcome challenges related to integration, data management, and expertise.
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Akerele, Joshua Idowu, Anuoluwapo Collins, Chisom Elizabeth Alozie, Olumese Anthony Abieba, and Olanrewaju Oluwaseun Ajayi. "The Evolution and Impact of Cloud Computing on Real-Time Data Analysis in Oil and Gas Operational Efficiency." International Journal of Multidisciplinary Research and Growth Evaluation 3, no. 1 (2024): 83–89. https://doi.org/10.54660/ijmor.2024.3.1.83-89.
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In recent years, the oil and gas industry has witnessed a significant transformation fueled by the adoption of cloud computing technologies, revolutionizing the way real-time data analysis is conducted to enhance operational efficiency. This review delves into the evolution and profound impact of cloud computing on real-time data analysis within the oil and gas sector. The evolution of cloud computing in the oil and gas industry has been marked by a shift from traditional on-premises data management systems to cloud-based platforms. This transition has enabled companies to overcome the limitations of on-site infrastructure, offering scalability, flexibility, and cost-effectiveness. Cloud platforms provide the necessary computational power to handle vast amounts of real-time data generated from various sources such as sensors, IoT devices, and drilling equipment. Real-time data analysis plays a pivotal role in optimizing operational efficiency in the oil and gas sector. By harnessing cloud-based analytics tools, companies can extract actionable insights from data streams instantaneously. These insights empower decision-makers to detect anomalies, predict equipment failures, optimize production processes, and mitigate risks in real-time, leading to improved operational performance and reduced downtime. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) algorithms into cloud-based data analysis platforms has augmented the capabilities of predictive analytics in the oil and gas industry. These advanced analytics techniques enable predictive maintenance, reservoir optimization, and demand forecasting, allowing companies to streamline operations and maximize resource utilization. The impact of cloud computing on real-time data analysis extends beyond operational efficiency to encompass broader industry trends such as digital transformation, remote monitoring, and collaboration. Cloud-based solutions facilitate remote access to data and analytics tools, enabling geographically dispersed teams to collaborate seamlessly and make informed decisions in real-time. The evolution of cloud computing has revolutionized real-time data analysis in the oil and gas industry, offering unprecedented opportunities to enhance operational efficiency, optimize resource utilization, and drive innovation. Embracing cloud-based analytics platforms is crucial for oil and gas companies seeking to thrive in an increasingly competitive and dynamic market landscape.
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Ajayi, Ajibola Joshua, Experience Efeosa Akhigbe, Nnaemeka Stanley Egbuhuzor, and Oluwole Oluwadamilola Agbede. "Bridging Data and Decision-Making: AI-Enabled Analytics for Project Management in Oil and Gas Infrastructure." International Journal of Multidisciplinary Research and Growth Evaluation 2, no. 1 (2021): 567–80. https://doi.org/10.54660/.ijmrge.2021.2.1.567-580.
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The oil and gas industry is increasingly complex, requiring robust project management approaches to handle challenges such as cost overruns, delays, regulatory compliance, and risk management. Artificial Intelligence (AI)-enabled analytics has emerged as a transformative solution, offering real-time data-driven insights to enhance decision-making and improve project outcomes. This paper explores the integration of AI in project management for oil and gas infrastructure, emphasizing how predictive analytics, machine learning, and optimization algorithms bridge the gap between raw data and actionable decisions. Key challenges in oil and gas infrastructure projects include managing vast amounts of unstructured data, mitigating risks in dynamic operational environments, and aligning projects with sustainability goals. AI-enabled analytics addresses these challenges by automating data processing, identifying patterns, and generating actionable insights. This study proposes a comprehensive framework for implementing AI-driven analytics in project management, focusing on resource allocation, scheduling, and risk mitigation. The framework also incorporates predictive models to forecast potential delays, cost escalations, and equipment failures, enabling proactive interventions. Case studies highlight the successful application of AI-enabled analytics in major oil and gas projects, demonstrating significant improvements in operational efficiency, cost control, and safety compliance. The use of AI tools such as digital twins, natural language processing (NLP) for document management, and computer vision for site monitoring is discussed, showcasing tangible benefits in reducing downtime and optimizing resource utilization. This paper concludes by addressing future trends, including the integration of AI with the Internet of Things (IoT) for real-time project monitoring, the role of generative AI in designing project workflows, and advancements in autonomous decision-making systems. These developments have the potential to redefine project management in the oil and gas industry, enabling organizations to navigate challenges with greater agility and precision.
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Oruganti, Yagna. "Technology Focus: Data Analytics (October 2022)." Journal of Petroleum Technology 74, no. 10 (2022): 89–90. http://dx.doi.org/10.2118/1022-0089-jpt.
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Oil and gas companies are starting to invest more in the energy transition and establish emissions-reduction goals, such as an end to routine gas venting and flaring, with some going as far as setting net-zero goals by mid-century. A wide range of climate technologies with varying degrees of maturity levels are needed to pave the road to net zero. These include electrification and grid decarbonization, advances in battery technology, blue and green hydrogen fuels, bioenergy, carbon capture use and storage, and mitigating emissions of potent greenhouse gases such as methane. The COVID-19 pandemic has helped accelerate the pace of the digital transformation in the oil and gas industry. Digitalization is, in effect, a part of the broader energy transition that is occurring in the industry. This includes a move from on-premises data centers to the cloud, building digital twins of physical assets, process automation using the Internet of Things (IoT), leveraging reams of data from oil and gas operations for artificial intelligence/machine leaning (AI/ML) applications, cloud-based high-performance computing for applications such as seismic imaging for carbon capture and storage, and predictive maintenance to fix leaky equipment to reduce the environmental footprint of operations. These technologies serve to improve productivity, increase operational efficiency, reduce downtime, increase cost savings, and reduce the carbon intensity of operations. The International Energy Agency report “The Oil and Gas Industry in Energy Transitions” states that reducing methane leaks to the atmosphere is the single most important and cost-effective way for the industry to bring down these emissions. This is a domain that can benefit significantly from the use of AI/ML techniques for leak detection and remediation (LDAR) efforts. ML techniques such as computer vision and anomaly detection can be used to identify both large and localized methane leaks from remote-sensing data and data streaming in from IoT sensors in the field. A combination of data modalities, with varying spatiotemporal resolutions, together with appropriate AI/ML technologies, would be essential for an effective methane LDAR program. As companies look to reduce their Scope 1, 2, and 3 emissions, breaking down data silos and establishing data standardization with common data models becomes critical in systems integration to develop an optimal emissions-reduction strategy. A seamless flow of information will enable the generation of high-fidelity data sets, which can be used to lower the operational footprint and drive business effects with AI solutions. With cloud-enabled technologies, driven by the application of ML and deep learning, companies can combine speed of implementation with scalability to accelerate their energy-transition efforts. I would like to invite readers to review the selection of papers to get an idea of various applications in the upstream oil and gas space where ML methods have been used. The highlighted papers cover the use of transformer-based models to predict oil production, the use of data analytics to study parent/child well relationships in shale plays, and the use of convolutional neural networks in core analysis. Recommended additional reading at OnePetro: www.onepetro.org. SPE 207744 Accelerating Subsurface Data Processing and Interpretation With Cloud-Based Full Waveform Inversion Systems by Sirivan Chaleunxay, Amazon Web Services, et al. SPE 205443 Natural-Language Processing and Text-Mining Approaches in Production-Shortfalls Analytics: Methodology, Case Study, and Value in the North Sea by Edgar Bernier, Total Denmark, et al. URTEC 208367 Real-Time Applications for Geological Operations: Repeatable AI Use Cases by Alfio Malossi, Eni, et al.
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Kumar Sinha, Gaurav. "Data Analytics and Anomaly Detection Techniques for Identifying Fraudulent Transactions in Oil & Gas Trading." International Journal of Science and Research (IJSR) 10, no. 7 (2021): 1529–40. http://dx.doi.org/10.21275/sr24422165831.
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Park, Chankook, and Minkyu Kim. "Characteristics Influencing Digital Technology Choice in Digitalization Projects of Energy Industry." Environmental and Climate Technologies 25, no. 1 (2021): 356–66. http://dx.doi.org/10.2478/rtuect-2021-0026.
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Abstract Digitalization projects are actively underway in the energy industry, such as the power industry and oil and gas industry. However, there has been no in-depth and quantitative analysis of the relationships between the participants, industry, and technology of digital projects. Therefore, this study focused on which technologies are invested according to key characteristics such as the types of participants and industries driving digitalization projects. This study also examined whether there are differences in technology choices depending on the degree of clean energy exposure. Based on statistics from Bloomberg New Energy Finance (BNEF), a total of 711 projects were analysed using multinomial logistic regression (MNLR). As a result, the proportion of Analytics software was generally higher in the whole industry, and the energy industry was more likely to invest in Analytics software than in other industries. Comparing the power, oil and gas sectors, there was a high probability of investment in Internet of Things (IoT) in the power sector and Automation in the oil and gas sector. In the type of cooperation between energy companies and industrial companies, the probability of investing in Analytics software was significantly higher. In the case of cooperation between energy companies and information and communications technology (ICT) companies, in the oil and gas sector, Analytics software and Cloud/Data accounted for a large proportion. This study provides insight into the effect of characteristics of energy digitalization projects on the technology choice.
Dissertations / Theses on the topic "Analytics in oil and gas"
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Donnelly, G. F. "An analytical evaluation of horizontal multiphase flow." Thesis, Queen's University Belfast, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361244.
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Kovach, Jessica Lynn. "USING ANALYTICAL METHODOLOGIES TO ASSESS THE ORGANOLEPTIC CHARACTER OF CITRUS ESSENTIAL OIL." Diss., Temple University Libraries, 2019. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/590546.
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Chemistry<br>Ph.D.<br>Essential oils are natural products used to flavor food and beverages. With the increase in nutrition conscious consumers, manufacturers of food additives and food products are faced with the challenge of making healthy alternatives. In particular, food products going to market with label claims stating reductions in sugar and salt, organic certified, organic compliant, and all natural; moreover the ingredients used in flavors must meet these label claims as well. More often than not, the challenge in using ingredients that follow these requirements is the pricing, the sourcing and the variability among those sources. Variability is common in the ingredients coming from nature such as fruits and plants because the area of cultivation can vary by the soil at the sight of planting and/or the climate in the region. Pricing is also problematic in naturally grown ingredients because it is a matter of supply and demand. Stock could be depleted from natural disasters, disease carrying pest(s), pests that consume the crop, and/or other causes for scarce supply of crop(s). Essential oils are natural byproducts of fruit, peels, and leaves from plants that contribute to flavor formulae for a large variety of food products. Because the essential oils are a crop based commodity, every variety has inherent differences based on the growing conditions and their ripening stages [1]. Nevertheless, each type of oil has marker chemicals that make up the majority of its composition; these marker chemicals have the tendency to degrade over time based on their interaction with light, oxygen exposure, and temperature. For companies that manufacture flavorings, understanding the variability among sources of essential oils as well as the possible degradants of essential oils is valuable information to obtain because it is possible the variants and degradants will negatively impact the flavor profile. Flavor is without question the most important attribute of the food we consume and by default stability of said flavor(s) need to be understood [30]. The content in this dissertation involves the stability analysis of a common essential oil, Oil Mandarin Italian Select, from Citrus Reticulata Blanco. It has known off notes that form from unknown causes. Most common is the plastic note that has formed in carbonated products like soda. Studying this particular essential oil in various conditions is intended to shed light on what those degradants are and under which conditions they form to give mandarin oil an off-note when applied to high acid and carbonated beverage applications. Once the note is reproduced, a correlation between analytical data and sensory interpretation of the oil will be developed. Mandarin essential oil being in the Citrus genus is traditionally analyzed via gas chromatography (GC) because of the high quantities of volatile constituents that give an oil high aroma activity. The volatile fraction of mandarin oil to be studied includes stability of methyl-N-methylanthranilate (MNMA), a major component giving mandarin its distinct grapey character, as well as gamma terpinene, thymol, sinensal, alpha pinene, beta pinene, myrcene, para cymene, alpha terpineol, and beta caryophyllene. Each of these ten compounds contributes to the unique flavor profile of mandarin oils when compared to orange and tangerine essential oils [1]. It was the common knowledge that para cymene can be perceived as rancid in aroma and the many interconversions the terpenes make that cause para cymene formation in Citrus oils, which made monitoring the changes of this chemical in the three stability environments crucial. Attention is being paid to para-cymene, as a specific marker of degradation in Citrus. The data obtained from the applied stability studies were challenging to understand as the marker chemicals are volatile and sensitive to chemical change. In this work the chemical changes and trends were analyzed under various storage conditions. Significant statistical analyses were employed to help define criteria of usability. The analyses were required because of natural variants and apparent inconsistencies of the data. Dixon Q Test and the Z Test were applied to determine outliers. Additionally, the Bland Altman method was applied to compare storage conditions and to determine if this statistical approach could be used to define significant changes in the marker chemical stability. The Bland Altman plots suggest that each component met the statistical limits of agreement, meaning the samplings were not significantly changing, statistically speaking. A final approach to assess the analytical data of the mandarin oil for significant change was the mass balance of each marker chemical from week 0 to week 24. Instrumental fluctuations have an acceptable range of +/- 20% in the industry; hence, a significant change criterion for a chemical in the mass balance must be one that exceeded +/- 20%. Unlike classical statistic methods, the mass balance was indicative that significant change had occurred to the compounds in the three studies. Upon sensory analysis of the oil samples, display of plastic note, oxidation, and overall loss of characteristic mandarin notes, the mass balance was found to correlate best to the significant change detected by sensory evaluation of the oil samplings. Due to the inadequate number of validated methods on Citrus essential oil research and the absence of large groupings of terpenes validated in a unified methodology, reconciliation of mass balance is an underutilized method of assessment in the literature. As a final assessment of the GC method validated, a product containing the selected mandarin oil was analyzed to evaluate the ability of the method to separate the oil components within a significantly more complicated matrix than the initial samples. The method was successful though not all marker chemicals were detected due to their low formulation concentration being below the level of detection of the method. This should not be seen as a failure of the method. For the major components of the essential oil studied, the method was quantitatively successful, meeting industry requirements.<br>Temple University--Theses
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King, Sarah M. "Enhancement in Degradation of Environmental Pollutants: Fenton Degradation of 2,4,6-Trinitrotoluene and Photodegradation of Deepwater Horizon Crude Oil." ScholarWorks@UNO, 2012. http://scholarworks.uno.edu/td/1451.
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Pollution poses serious threats to both the environmental and the organisms that depend on their environment for survival. Due to the toxicity of most contaminants, there is a dire need for remediation of polluted sites. Remediation studies were conducted on two high priority pollutants: 2,4,6-trinitrotoluene (TNT) and crude oil.
TNT was the most common explosive used in the 20th century. Continuous contamination has resulted in an urgent need for remediation. Fenton reagent provides an advanced oxidation process that is capable of remediating recalcitrant explosives, such as TNT. One drawback of Fenton chemistry is that the reaction requires acidic pH to prevent precipitation of iron. Our studies have investigated Fenton degradation of TNT at near neutral pH with several modifiers present: β-cyclodextrin, carboxymethyl-β-cyclodextrin, alcohols, and polyethylene glycol (PEG, MW 200, 400, or 600 g/mol). Fenton degradation was also carried out on other nitroaromatics to better understand the reaction mechanism with PEG 400. Further mechanistic studies investigated the production of nitrate and ammonium with and without PEG 400.
The Deepwater Horizon oil spill devastated the Gulf of Mexico and the surrounding wetlands. There are several mechanisms for degradation of oil released into aquatic environments. Bioremediation is one of the most important remediation methods; however degradation becomes stagnant in low nutrient waters. Furthermore, larger molecular weight alkanes and polycyclic aromatic hydrocarbons (PAHs) are not readily available for biodegradation. Transformation of these molecules often requires initial photodegradation. We have investigated the photochemical transformation of oil films with and without photocatalysts present. To better understand the photochemical transformations that occur to the Deepwater Horizon oil, we have conducted additional studies with dispersants present.
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Roux, Izak Johannes. "Applying the Analytic Hierarchy Process to Oil Sands Environmental Compliance Risk Management." ScholarWorks, 2015. https://scholarworks.waldenu.edu/dissertations/164.
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Oil companies in Alberta, Canada, invested $32 billion on new oil sands projects in 2013. Despite the size of this investment, there is a demonstrable deficiency in the uniformity and understanding of environmental legislation requirements that manifest into increased project compliance risks. This descriptive study developed 2 prioritized lists of environmental regulatory compliance risks and mitigation strategies and used multi-criteria decision theory for its theoretical framework. Information from compiled lists of environmental compliance risks and mitigation strategies was used to generate a specialized pairwise survey, which was piloted by 5 subject matter experts (SMEs). The survey was validated by a sample of 16 SMEs, after which the Analytic Hierarchy Process (AHP) was used to rank a total of 33 compliance risks and 12 mitigation strategy criteria. A key finding was that the AHP is a suitable tool for ranking of compliance risks and mitigation strategies. Several working hypotheses were also tested regarding how SMEs prioritized 1 compliance risk or mitigation strategy compared to another. The AHP showed that regulatory compliance, company reputation, environmental compliance, and economics ranked the highest and that a multi criteria mitigation strategy for environmental compliance ranked the highest. The study results will inform Alberta oil sands industry leaders about the ranking and utility of specific compliance risks and mitigations strategies, enabling them to focus on actions that will generate legislative and public trust. Oil sands leaders implementing a risk management program using the risks and mitigation strategies identified in this study will contribute to environmental conservation, economic growth, and positive social change.
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Ghalebani, Alireza. "Renewable Energy Investment Planning and Policy Design." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6243.
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In this dissertation, we leverage predictive and prescriptive analytics to develop decision support systems to promote the use of renewable energy in society. Since electricity from renewable energy sources is still relatively expensive, there are variety of financial incentive programs available in different regions. Our research focuses on financial incentive programs and tackles two main problem: 1) how to optimally design and control hybrid renewable energy systems for residential and commercial buildings given the capacity based and performance based incentives, and 2) how to develop a model-based system for policy makers for designing optimal financial incentive programs to promote investment in net zero energy (NZE) buildings.
In order to customize optimal investment and operational plans for buildings, we developed a mixed integer program (MIP). The optimization model considers the load profile and specifications of the buildings, local weather data, technology specifications and pricing, electricity tariff, and most importantly, the available financial incentives to assess the financial viability of investment in renewable energy. It is shown how the MIP model can be used in developing customized incentive policy designs and controls for renewable energy system.
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Alharbi, Sami. "Electrodeposition of Hydrogen Molybdenum Bronze Films and Electrochemical Reduction of Carbon Dioxide at Low Over Potentials." Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/etd/3602.
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Hydrogen molybdenum oxide, known has molybdenum bronze, is a material of interest due to catalyzing electron transfer reactions. Specifically, molybdenum bronze is an electrocatalyst toward carbon dioxide reduction. Electrochemical deposition from a peroxymolybdic acid solution is a method for preparing molybdenum bronze films. This work demonstrates reproducible electrodeposition on indium tin oxide substrates and conductive carbon paper. Film thickness depends on concentration, time and pH. After characterization by film thickness, resistance, XRD and XPS, the as deposited films served as the working electrode for electrochemical reduction of carbon dioxide in 0.1 M NaHCO3. Ion chromatography determined formate resulting in 8% faradaic efficiency at an applied potential of -0.4 V. Interestingly, this potential is similar to use of formate dehydrogenase as an electrocatalyst. Carbon monoxide levels were attempted to be determined by GC in the headspace of an H type electrochemical cell. Results show that these films are applicable towards electrochemical CO2 reduction to formate when supported on carbon.
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Орфанова, М. М. "Еколого-технологічні принципи поводження з відходами нафтогазового комплексу". Thesis, Івано-Франківський національний технічний університет нафти і газу, 2006. http://elar.nung.edu.ua/handle/123456789/4128.
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Дисертація присвячена вирішенню проблеми поводження з відходами нафтогазового комплексу на основі визначення еколого-технологічних принципів поводження з ними та створення інформаційно-аналітичної системи поводження з відходами галузі. Науково обгрунтовані еколого-технологічні принципи поводження з відходами нафтогазового виробництва. Розроблена інформаційно-аналітична система поводження з відходами нафтогазової галузі дозволить проводити вибір оптимальних природоохоронних заходів. Доводиться, що для оптимізації вибору природоохоронних заходів доцільно використати метод визначення області еколого-економічного оптимуму, результати порівняльного еколого-економічного аналізу з розрахунком групи еколого-економічних показників та їх порівняння за визначеними критеріями оптимальності. Метод вибору оптимального варіанту поводження з відходами відпрацьований на прикладі нафтошламів НГВУ “Надвірнанафтогаз”. Розроблений спосіб переробки нафтошламів на мінеральний порошок для дорожнього будівництва. Розроблена довідкова інформаційно-комп’ютерна система обліку відходів для підприємств ВАТ “Укрнафта” як перший етап створення інформаційно-аналітичної системи поводження з відходами нафтогазового комплексу.<br>В работе рассмотрены пути решения проблемы обращения с отходами нефтегазового комплекса. Установлено, что современное решение экологических проблем отрасли в отношении отходов сводится, как правило, к решению проблемы уменьшения отрицательного влияния отдельных загрязняющих веществ на окружающую среду, что не решает проблемы в целом. На всех технологических стадиях нефтегазового производства образуются значительные объемы сложных по составу разнообразных отходов, что усложняет процессы их утилизации, транспортирования и захоронения. Создавшаяся ситуация приводит к постоянному накоплению значительных масс отходов, что выдвигает задачу уменьшения объемов их образования и накопления в одну из актуальных экологических проблем современности. Доказывается, что данная задача может быть решена только на основе проведения многовариантного анализа по выбору природоохранных мероприятий на основе создания информационно-аналитической системы обращения с отходами нефтегазовой промышленности. Использование данной системы позволит получать общую и детальную экологическую информацию по любому производственному объекту или нефтегазопромысловому району, выбирать и принимать оптимальные природоохранные решения в конкретной экологической ситуации. Научно обоснованы эколого-технологические принципы обращения с отходами нефтегазового комплекса. Усовершенствован метод выбора оптимального варианта обращения с отходами при последовательном выполнении следующих операций: 1) отбор возможных вариантов при помощи области эколого-экономического оптимума, 2) расчет группы эколого-экономических показателей для каждого варианта, 3) выбор оптимального варианта на основе критериев оптимальности для показателей группы. Предлагаемый метод отработан на примере выбора оптимального варианта обращения с нефтешламами НГДУ “Надвирнанафтогаз”. Проанализированы возможные варианты уменьшения количества отходов с учетом объемов их образования на предприятии. Как один из вариантов рассматривался разработанный способ переработки нефтешламов в минеральный порошок для дорожного строительства. По результатам проведенных исследований были определенны оптимальные условия подготовки минерального порошка на основе нефтешламов и механоактивированного песка при соотношении компонентов 1:1. По заключению спецлаборатории Ивано-Франковского облавтодора полученный минеральный порошок соответствует требования ГОСТ 9128-84. На основе полученных результатов предложена технологическая схема переработки нефтешламов на компонент для дорожного строительства с техническим решением предложения. Апробация предложенного метода выбора оптимального варианта обращения с отходами показала, что для НГДУ “Надвирнанафтогаз” в данных экономических условиях оптимальным вариантом является вариант их продажи заинтересованным предприятиям, в частности ОАО “Ивано-Франковскцемент”. Разработана информационно-аналитическая система обращения с отходами отрасли, которая позволит принимать решения по уменьшению объемов отходов и с учетом значительного количества взаимосвязанных экологических, технических, технологических и экономических показателей. Определен комплекс задач системы, обоснована и разработана общая ее структура и принципы функционирования ее блоков. Разработана справочная информационно-компьютерная система учета отходов для предприятий ОАО “Укрнафта”.<br>Thesis is devoted to the solution of treatment’s problem with wastes of oil and gas complex on the basis of definition of ecological and technological principles of treatment with wastes and creation of information and analytical system of treatment with wastes in this industry. Principles of treatment with industry’s wastes are scientifically grounded. Developed information-analytical system of treatment with this waste will allow to carry out selection of optimal nature protection measures. It is confirmed that for optimal selection of decision it is useful to apply comparison results of ecological and economic analysis on the basis of Automatic Decision Making System with the calculation of the ecological and economic group and technical and technological indices, on ecological and economic optimum method. The method has been developed to optimize the choice of the variant of oil waste treatment “Nadvirnanaftogaz”. The way of oil wastes recycling into the mineral powder to be used for further road construction has been developed. Inquiry information system of wastes’ accounting for enterprises of Company “Ukrnafta” is developed as the first stage of formation of information-analytical system of treatment with industry’s wastes.
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Kosmidis, Vasileios. "Integrated oil and gas production." Thesis, Imperial College London, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.407995.
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Xu, He Kensinger John W. "Crude oil and crude oil derivatives transactions by oil and gas producers." [Denton, Tex.] : University of North Texas, 2007. http://digital.library.unt.edu/permalink/meta-dc-5106.
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Xu, He. "Crude Oil and Crude Oil Derivatives Transactions by Oil and Gas Producers." Thesis, University of North Texas, 2007. https://digital.library.unt.edu/ark:/67531/metadc5106/.
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This study attempts to resolve two important issues. First, it investigates the diversification benefit of crude oil for equities. Second, it examines whether or not crude oil derivatives transactions by oil and gas producers can change shareholders' wealth. With these two major goals in mind, I study the risk and return profile of crude oil, the value effect of crude oil derivatives transactions, and the systematic risk exposure effect of crude oil derivatives transactions. In contrast with previous studies, this study applies the Goldman Sachs Commodity Index (GSCI) methodology to measure the risk and return profile of crude oil. The results show that crude oil is negatively correlated with stocks so adding crude oil into a portfolio with equities can provide significant diversification benefits for the portfolio. Given the diversification benefit of crude oil mixed with equities, this study then examines the value effect of crude oil derivatives transactions by oil and gas producers. Differing from traditional corporate risk management literature, this study examines corporate derivatives transactions from the shareholders' portfolio perspective. The results show that crude oil derivatives transactions by oil and gas producers do impact value. If oil and gas producing companies stop shorting crude oil derivatives contracts, company stock prices increase significantly. In contrast, if oil and gas producing companies start shorting crude oil derivatives contracts, stock prices drop marginally significantly. Thus, hedging by producers is not necessarily good. This paper, however, finds that changes in policy regarding crude oil derivatives transactions cannot significantly affect the beta of shareholders' portfolios. The value effect, therefore, cannot be attributed to any systematic risk exposure change of shareholders' portfolios. Market completeness, transaction costs, and economies of scale are identified as possible sources of value effect. The following conclusions have been obtained in this study. Crude oil provides significant diversification benefits for equities. In the presence of market imperfections, crude oil derivatives transactions by oil and gas producers may change shareholders' wealth, even though crude oil derivatives transactions by oil and gas producers do not have significant effect on the systematic risk exposures of companies.
Books on the topic "Analytics in oil and gas"
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Srivastava, Kingshuk, Thipendra P. Singh, Manas Ranjan Pradhan, and Vinit Kumar Gunjan. Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry. CRC Press, 2023. http://dx.doi.org/10.1201/9781003357872.
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Comfort, G. Analytical modelling of oil and gas spreading under ice. Arctec Canada Limited, 1987.
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Wood, David A. Analytical methods in petroleum property valuation: Integrating probabilistic and deterministic techniques. PennWell Corp., 1999.
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Crovelli, Robert A. An analytic probabilistic methodology for resource appraisal of undiscovered oil and gas resources in play analysis. U.S. Dept. of the Interior, Geological Survey, 1985.
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Crovelli, Robert A. An analytic probabilistic methodology for resource appraisal of undiscovered oil and gas resources in play analysis. U.S. Dept. of the Interior, Geological Survey, 1985.
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Madakor, Nnamdi. Analytical fate and mass transport modeling of Harbor Island tank farms: ARCO Oil, GATX (former Shell Oil) and TEXACO Oil : a decision making tool in the cleanup action plan. Dept. of Ecology, Toxics Cleanup Program, Northwest Regional Office, 1997.
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Madakor, Nnamdi. Analytical fate and mass transport modeling of Harbor Island tank farms: ARCO Oil, GATX (former Shell Oil) and TEXACO Oil : a decision making tool in the cleanup action plan. Dept. of Ecology, Toxics Cleanup Program, Northwest Regional Office, 1997.
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P, Lloyd John. The Weeks royalty: An historical and analytical account of the world's premier petroleum royalty granted for personal services. Fortis Pub., 1993.
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Ramazanov, Ayrat, I. Habibullin, and V. N. Fedorov. Analytical models in borehole thermometry. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1568658.
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The manual discusses the issues of modeling thermohydrodynamic processes in oil reservoirs and in the borehole, which constitute the theoretical foundations of the method of borehole thermometry. At the same time, preference is given to the study of analytical models.
 Meets the requirements of the federal state educational standards of higher education of the latest generation.
 The manual is intended for students of higher educational institutions studying in oil and gas specialties. It will also be useful for graduate students and specialists dealing with issues of geophysical control of the development of oil and gas fields.
Book chapters on the topic "Analytics in oil and gas"
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Vaz Jr, Silvio. "Oil and Gas." In Applications of Analytical Chemistry in Industry. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-38952-8_8.
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Priyadarshy, Satyam. "IoT REVOLUTION IN OIL AND GAS INDUSTRY." In Internet of Things and Data Analytics Handbook. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119173601.ch31.
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Shah, Vrutang, Jaimin Shah, Kaushalkumar Dudhat, Payal Mehta, and Manan Shah. "Big Data Analytics in Oil and Gas Industry." In Emerging Technologies for Sustainable and Smart Energy. CRC Press, 2022. http://dx.doi.org/10.1201/b23013-3.
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Chhotaray, Gitanjali, and Anupam Kulshreshtha. "Defect Detection in Oil and Gas Pipeline: A Machine Learning Application." In Data Management, Analytics and Innovation. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1274-8_14.
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Bedrikovetsky, Pavel, and Gren Rowan. "Analytical Water-Alternate-Gas Modelling." In Mathematical Theory of Oil and Gas Recovery. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-017-2205-6_18.
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Patidar, Atul Kumar, Utkkarsh Agarwal, Utkarsh Das, and Tanupriya Choudhury. "Understanding the Oil and Gas Sector and Its Processes." In Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry. CRC Press, 2023. http://dx.doi.org/10.1201/9781003357872-1.
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Ningombam, Devarani Devi, and Venkata Sravan Telu. "Supply Chain Management in the Oil and Gas Business." In Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry. CRC Press, 2023. http://dx.doi.org/10.1201/9781003357872-5.
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Shakya, Achala, and Gaurav Tripathi. "Oil and Gas Industry in Context of Industry 4.0." In Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry. CRC Press, 2023. http://dx.doi.org/10.1201/9781003357872-8.
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Aziz, Norshakirah, Mohd Hafizul Afifi Abdullah, Nurul Aida Osman, Muhamad Nabil Musa, and Emelia Akashah Patah Akhir. "Predictive Analytics for Oil and Gas Asset Maintenance Using XGBoost Algorithm." In Lecture Notes in Networks and Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20429-6_11.
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Gurzhiy, Anastasia, Klara Paardenkooper, and Alexandra Borremans. "Predictive Analytics at an Oil and Gas Company: The Rosneft Case." In Algorithms and Solutions Based on Computer Technology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93872-7_25.
Full textConference papers on the topic "Analytics in oil and gas"
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Amale, Ashvin B., I. K. Kantharaj, Bhandari Mahesh Ashok, A. Amudha, Lakshay Bareja, and Priya M. Raut. "Integrating Real-Time Automation and Big Data Analytics in the Oil and Gas Industry." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10723854.
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Qurashi, Abdulaziz. "NGL Operation Strategy Using Predictive Analytics." In Middle East Oil, Gas and Geosciences Show. SPE, 2023. http://dx.doi.org/10.2118/213290-ms.
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Abstract Oil & Gas is a data-rich industry which is prime for data-driven and decision making. The significant growth witnessed in the digital transformation field and the new era of the industrial revolution 4 (IR 4.0), is a direct result of the need within the industry to utilize the large amount of data to make better decisions, improve operation strategy, plan better for preventive maintenance (PM), and process improvement. The uncertainty associated with estimating the incoming feed gas rate to NGL plants has resulted in deviation from optimal compressor recycle rate, missed opportunities of meeting planned PM and imposed urgency during operation. Utilizing machine learning algorithms, namely regression and decision tree model, the incoming feed gas can be predicted which result in the machine learning algorithms which results in the identification of the optimum number of running trains and recycle rate required for efficient operation. NGL-Operation Planner (NGL-OP) is the outcome of utilizing ML algorithms which provides the ability of predicting incoming feed gas, identifying optimum number of running trains required as well as estimating the optimal recycle rate. Adopting this approach is a new and strategical way to plan NGL operation. The developed tool also has the ability to advise whether to shut down, maintain existing operation or starting-up a new train. The implementation of the model resulted in a significant improvement in NGL operation. The improvement includes fuel gas consumption reduction of around 449 MMSCF/Year which resulted in a significant cost saving, reduction in emissions around 27 M tons/year, and 10% reduction in operating unnecessary running trains. These savings have been achieved through the utilization of the NGL-OP to minimize uncertainty and improve our planning strategy.
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Al Ady, Ahmad, Nata Franco, Mauricio Corona, and Arnott Dorantes. "Operation Efficiency and Rig Performance Improvements through Data Analytics." In Gas & Oil Technology Showcase and Conference. SPE, 2023. http://dx.doi.org/10.2118/213977-ms.
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Abstract This paper is focus on the performance benefits when drilling data is streamed from rig sensors (high-frequency) and is integrated with daily drilling reports and well plans (low-frequency). The purpose is to systematically monitor and evaluate the performance of the entire rig fleet across the well construction process in the 20 land rigs drilling and completing oil and gas wells. The first step is to segregate each activity in the well construction process in main categories to allow the system to recognize the operational sequence from all the historical wells. A set of KPIs are defined for each one of the activities and the benchmark is set. Both high-frequency and low-frequency data are quality checked and computed into the pre-defined KPIs. Through the systematic analysis approach, the indicators are reviewed and in-deep understanding of rig capabilities, crew performance, operational constrains and drilling tools efficiency is made available to the team members, accessed via web-based application or automated daily report. With the help of the data coming from the rig sensors and the data collected from the daily drilling reports a perfect match result in a reliable source for the KPI generation is done. The procession of all the historical data provides a good insight to support the benchmark and proceed with the next step, which is the computation of the Invisible Lost Time, measuring the inefficiency of each one of the well construction activities. The drilling team takes appropriate actions using continuous improvement principles to identify waste and performance improvement opportunities. This also involves implementing all the best practices and introduce changes on the way we perform operations. Improvement plans can be prepared for achieving greater operational efficiency by evaluating everyday performance against agreed benchmarks and extend technical limits of established well plans, generating automated best composite times for future wells. Additionally, cost saving initiatives are identified in underperforming areas, non-productive time, and invisible lost time operations. This performance-based approach along with the multi-rig analysis platform has been a tremendous improving tool in the project and greatly enhanced rig performance, and some of the cases and insights to be shared might mutually benefit other operators and service companies in the region.
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Reddicharla, Nagaraju, Mayada Ali Sultan Ali, Rachelle Cornwall, et al. "Next-Generation Data-Driven Analytics- Leveraging Diagnostic Analytics In Model Based Production Workflows." In SPE Middle East Oil and Gas Show and Conference. Society of Petroleum Engineers, 2019. http://dx.doi.org/10.2118/195014-ms.
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Clayton, Luke Michael, Ming Hwa Lee, and Alireza Salmachi. "Alleviating Directional Well Trajectory Problems via Data Analytics." In SPE Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210766-ms.
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Abstract A consistent leading cause of drilling non-productive time (NPT) is the inability to steer the planned well trajectory trouble-free. Separate from downhole tool and drill bit failures, an unplanned trip to change the Bottom Hole Assembly (BHA) is required for up to one in every seven drilling runs. Root cause analyses indicate potentially a quarter of all drilling NPT has poor planning or field execution as the failure mechanism, signifying scope for improvement. This paper aims to help guide optimal selection of RSS/motor and bit, to ensure challenging wellpaths will be achieved with minimal NPT associated with BHA trips. Directional drilling analysis typically compares dogleg severity (DLS) for planned and actual trajectory. This metric is fundamentally direction-blind; absolute tortuosity is represented whether planned or unintentional. Without full context, DLS analysis can mask many steering issues. Typically, industry software does not measure how closely the steering inputs match their anticipated responses during a run. Strategic management and identification of zones with erratic toolface control, or strong formation/BHA tendencies is critical. The proposed ‘derived steering’ analytics method was applied to plan demanding 3D trajectories for an Extended Reach offshore campaign in Australia. Existing minimum curvature equations were repurposed to plot previous runs steering inputs and then infer efficiencies for each formation. Supervision was essential to counteract strong consistent right-hand BHA walk tendency for all the variety of wells studied. Multiple NPT events on previous campaigns had resulted from poor steering response in the shallow interbedded geology. In view of quantifiable field-specific risks, wellplans were refined to minimize tortuosity and maximize the design safety factor. The combination of highest anticipated dogleg response rotary steerable technology and bit selection was selected for steering assurance. Modelled tendencies per lithology were shared with wellsite supervisors, and recent drilling results essentially mimicked data analytics. Others operating in this field in the 21st century had drilled total meterage of 36,740m MD from 83 runs. Bit Gradings showed two ‘Lost in Holes’, one ‘Drill String Failure’, six trips for ‘Downhole Tool Failures’, seven for ‘Penetration Rate’, six to ‘Change BHA’, two for ‘Hole Problems’ and one for ‘Downhole Motor Failure’. The current campaign's improved directional drilling offset analysis contributed towards significant avoidance of well delivery NPT to drill 28,061m in 34 runs. No trips were required to change BHA or bit because of inability to follow the trajectory, and field teams were able to pre-empt lithology-specific challenges.
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Copping, Simon, Jason Payne, and Ronan O'Malley. "Leveraging Data Analytics to Optimise Production Performance." In SPE Asia Pacific Oil and Gas Conference and Exhibition. Society of Petroleum Engineers, 2018. http://dx.doi.org/10.2118/191909-ms.
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Saghir, Fahd, Helenio Gilabert, and Matthieu Boujonnier. "Edge Analytics and Future of Upstream Automation." In SPE Asia Pacific Oil and Gas Conference and Exhibition. Society of Petroleum Engineers, 2018. http://dx.doi.org/10.2118/192019-ms.
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Djanuar, Yanfidra, Qingfeng Huang, Jimmy Thatcher, and Morgan Eldred. "Integrated Field Development Plan for Reliable Production Forecast Using Data Analytics and Artificial Intelligence." In Gas & Oil Technology Showcase and Conference. SPE, 2023. http://dx.doi.org/10.2118/214021-ms.
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Abstract Having a robust field development plan (FDP) for mid-size mature oil fields generally poses considerable challenges in the context of the integrational elements of production forecast, operational environment, projects and surface facilities. An integrated FDP combined with data analytics and artificial intelligence (AI) has been introduced and deployed in a heavily compartmentalized offshore field of Turkmenistan. An integrated approach through data-centric analytics and AI has been proposed for an optimal FDP. It consists of four aspects: model integration, time-series forecast (TSF) of production, AI-assisted operation-schedule generation, and evaluation and selection of scenarios. Firstly, model integration is performed as bringing together both multi-discipline raw data from field measurement and their interpretations that change non-linearly. Secondly, model integration aids in the application of AI for production forecast. A unique AI technique was built to allow raw data and interpretation. Illustratively, the model is capable of forecasting decline curves matching the history production. Meanwhile, engineers’ production forecast inheriting from simulation, machine learning or type curves is also constructed by understanding how/why human-driven forecasts differ from the measured decline and incorporating those insights. In addition, AI-assisted scheduler efficiently allocates resources for operational activities, considering the well planning nature, intrinsic operation properties, project planning process, surface facilities and expenditures. Resources are thus utilized for optimal schedules. Finally, evaluation and selection of FDP scenarios take place by considering the multidimensional matrix of factors. Multiple scenarios are generated and scored, reacting to the change of factors. AI-powered optimization is availed to recommend the most efficient tradeoffs between production and carbon generation. The implementation of the integrated FDP approach has been successfully applied for the generation of production profiles and operation schedules, which reduces the time by 80% and increasing accuracy by 55%. Production forecast for existing wells and future wells proved to be reliable. It achieved the production targets with proper allocation of schedules, by considering multi-discipline constraints. Through AI-assisted scheduler, different types of rigs were properly assigned to the planned wells, which requires additional rigs based on the outcome. The model was agile to the change and sensitivities of wells requirement, projects uncertainties and cost changes. The optimum FDP scenario was recommended for the business decision, operation guide and execution. This approach represents a novel and innovative means of integrating and optimizing FDP considering complex factors using AI methods. It is efficient in merging raw data and interpretations for model integration. It accommodates changes and uncertainties from multiple aspects and efficiently generates optimum FDP in a few days rather than months for giant fields. It is the first robust tool that unites subsurface properties, reservoir engineering, production, drilling, projects, engineering and finance for the corporate FDP.
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Zhao, Wenyang, Lamya Humaid Al Jneibi, Saif Muaaded Al Mashghouni, and Omar Obaid Almheiri. "Data Analytical Thinking: The New Booster to Petroleum Industry and Foundation of Data Driven Organization." In Gas & Oil Technology Showcase and Conference. SPE, 2023. http://dx.doi.org/10.2118/213992-ms.
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Abstract Data is everywhere. Data is undeniable the foundation to the Oil and Gas 4.0 in the petroleum industry. However, data is not equal to value. The true business lies in extracting and maximizing value out of the available and required data. Data analytical thinking is the key to identify and define the problems, extract values, and develop solutions. This is also the key to transform to a data-driven organization. Being data driven involves a combination of people, process and technology. The structure has to be built based on a solid foundation of data driven culture, which requires both technical and soft skills updates. Being data-driven is a teamwork requiring the collaboration between domain experts and data scientists to maximize their strengths. Identifying and defining problems by domain experts is crucial. The traditional working approaches will no longer be sufficient in a data-driven culture. Becoming data sensitive with data analytical thinking will be a necessity especially during the transformation of the traditional petroleum industry. The paper illustrates the theoretical concepts of data analytical thinking and discusses the real cases exploiting the dark data and enhancing data collection. The data-driven organization is based on massive data. Fundamentally, the data flows through the process of data collection, quality assurance, data access, data security, and analytics. Every organization is collecting an amount of data much more than ever before. Unfortunately, the collected data could become dark data without proper analytics and utilization. In order to shed light on the importance of data analytical thinking, the paper utilizes the most frequently gathered data, including field production flow testing data and daily field production operational data. Real cases of utilizing eight offshore fields’ Business Plan data are also illustrated. Customized automation data analytical processes with significant boost of working efficiency are also shared to highlight the importance of integrating domain experts and data scientists. A data-driven organization makes effective decisions based on the values extracted from massive data. The smooth collaboration between domain experts and data scientists is fundamental to minimize the communication cost. Data analytical thinking bridges the gap and smoothens the problem solving process. The collaborative and data-analytical-thinking culture lays the foundation to the data-driven organization.
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Gryzlov, Anton, Liliya Mironova, Sergey Safonov, and Muhammad Arsalan. "Artificial Intelligence and Data Analytics for Virtual Flow Metering." In SPE Middle East Oil & Gas Show and Conference. SPE, 2021. http://dx.doi.org/10.2118/204662-ms.
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Abstract Modern challenges in reservoir management have recently faced new opportunities in production control and optimization strategies. These strategies in turn rely on the availability of monitoring equipment, which is used to obtain production rates in real-time with sufficient accuracy. In particular, a multiphase flow meter is a device for measuring the individual rates of oil, gas and water from a well in real-time without separating fluid phases. Currently, there are several technologies available on the market but multiphase flow meters generally incapable to handle all ranges of operating conditions with satisfactory accuracy in addition to being expensive to maintain. Virtual Flow Metering (VFM) is a mathematical technique for the indirect estimation of oil, gas and water flowrates produced from a well. This method uses more readily available data from conventional sensors, such as downhole pressure and temperature gauges, and calculates the multiphase rates by combining physical multiphase models, various measurement data and an optimization algorithm. In this work, a brief overview of the virtual metering methods is presented, which is followed by the application of several advanced machine-learning techniques for a specific case of multiphase production monitoring in a highly dynamic wellbore. The predictive capabilities of different types of machine learning instruments are explored using a model simulated production data. Also, the effect of measurement noise on the quality of estimates is considered. The presented results demonstrate that the data-driven methods are very capable to predict multiphase flow rates with sufficient accuracy and can be considered as a back-up solution for a conventional multiphase meter.
Reports on the topic "Analytics in oil and gas"
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Bernstein. PR-275-09207-R01 Method and Procedure for Remaining Life Assessment of the Combustion Turbine Disks. Pipeline Research Council International, Inc. (PRCI), 2012. http://dx.doi.org/10.55274/r0010044.
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A study of the remaining rotor life of gas turbines (or combustion turbines) used in the oil and gas industry was performed by Gas Turbine Materials Associates (GTMA). The study focused upon analytical methods of determining the remaining rotor life using a reduced data set, where not all of the operating data is known. As part of this study, a survey of oil and gas users was performed on their rotor life. An analytical model and methodology for determining the remaining rotor life was developed. This model and methodology was applied to the GE MS5002 gas turbine. By using this model for a PRCI member, the life of a rotor was safely extended for 100,000 hours beyond its design life. GTMA estimated that this PRCI member saved approximately $1,000,000 from this one application, and that repeated applications of the model for this, and other, PRCI members would save well over $10,000,000. The results of this work should also be applicable to other gas turbines, including those used by electric utilities, IPP�s, and other industrial operators.
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Honegger and Nyman. L51927 Guidelines for the Seismic Design and Assessment of Natural Gas and Liquid Hydrocarbon Pipelines. Pipeline Research Council International, Inc. (PRCI), 2004. http://dx.doi.org/10.55274/r0010350.
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Much of the current seismic practice can be traced to research conducted in the 1970s to develop design criteria and procedures for the Trans-Alaska oil pipeline. The major natural gas pipeline operators in California have implemented many of the procedures in these guidelines, in one form or another. The recognition of credible seismic hazards in other parts of the United States has been the primary driver for extending current practices in California to the rest of the United States. The experience in designing pipelines in seismically active regions of the United States has also provided a basis for specifying pipeline seismic design practices in other parts of the world. This project was initiated to provide current seismic guidelines for the design and assessment of natural gas transmission pipelines. These guidelines were refined using two rounds of review by outside technical experts in 1999 and 2000. The PRCI ad hoc steering group for the project also provided regular input regarding the scope and technical content for these guidelines. A decision was made in late 2000 to expand the scope to liquid hydrocarbon pipelines (crude oil and refined products) based upon the identical analytical treatment of seismic design and assessment of these types of pipelines.
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Vikara, Derek, Kolawole Bello, Nur Wijaya, Travis Warner, Alana Sheriff, and Donald Remson. Evaluating the Impact of Proprietary Oil & Gas Data on Machine Learning Model Performance Using a Quasi-Experimental Analytical Approach. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1855950.
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Christensen, Earl, Jack Ferrell, Mariefel V. Olarte, and Asanga B. Padmaperuma. Quantification of Semi-Volatile Oxygenated Components of Pyrolysis Bio-Oil by Gas Chromatography/Mass Spectrometry (GC/MS). Laboratory Analytical Procedure (LAP). Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1241093.
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Esparza and Westine. L51482 Well Casing Response to Buried Explosive Detonations. Pipeline Research Council International, Inc. (PRCI), 1985. http://dx.doi.org/10.55274/r0010272.
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Occasionally, buried explosives are used within proximity of producing oil and gas wells which increases the stresses in the casing near the explosion which may result in failure of the well. A procedure was needed for predicting the maximum stresses in producing oil and gas wells, specifically the well casing, induced by nearby, buried, explosive detonations. An extensive experimental and analytical program were funded and performed over a six (6) year period 1975-1981. The program was divided into two (2) parts: In the first part, similitude theory, empirical analyses and test data were used to derive equations for estimating maximum ground displacement and particle velocity. The ground motions provided the forcing function imparted to a buried pipeline. In the second part, similitude theory, conservation of mass and momentum, and approximate energy methods were used to derive functional relationships for the maximum pipe strains and stresses. Experimental data from more than sixty (60) field tests ere used to develop equations for estimating maximum pipe stresses induced by point and parallel line explosive sources buried in homogeneous soil media. The pipe stress and ground motion data from these experiments were used to develop an equation for computing an effective standoff distance so that the point source soil equations could be used to approximate the casing response. The large amount of data used and the wide range of these data make the solutions applicable to most blasting situations near producing oil and gas wells. This report provides comprehensive and detailed information for pipeline as well as oil and gas operators to predict the effect of buried explosives and thus the safety of a well(s) while in-service through proper assessment of stresses and guidelines for the appropriate selection of explosive charges, techniques and methods. This will avoid unexpected damages, operational costs, provide guidance for \operator qualification\" for blasting near in-service wells and minimize liabilities to the operator.
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Johnson, K. J., Ramil Ahmadov, Shuvajit Bhattacharya, et al. GMC technical breakout session: Examination of new analytic technologies with results of investigation on drill samples, North Slope, Alaska (presentation): 2019 Alaska Oil & Gas Association Conference North Slope Technical Session, May 31, 2019. Alaska Division of Geological & Geophysical Surveys, 2019. http://dx.doi.org/10.14509/30850.
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Rimpel, Aaron. PR-316-17200-R03 A Study of the Effects of Liquid Contamination on Seal Performance. Pipeline Research Council International, Inc. (PRCI), 2021. http://dx.doi.org/10.55274/r0012015.
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This project is a continuation of research to enhance dry gas seal (DGS) reliability. Previous work reviewed failures from literature and experience of manufacturers and end-users and identified that liquid contamination was the most common cause, but it was concluded there was insufficient quantitative data to base recommendations on for further DGS reliability enhancements. Therefore, experimental and analytical investigations were pursued to fill the void. The ultimate objective was to be able to predict DGS failures due to liquid contamination, which could lead to greater DGS reliability through improvements in design, instrumentation, and monitoring. From the previous project phase, testing had demonstrated that the introduction of small quantities of oil (liquid mass fraction up to 3%) produced a slight increase in torque but impacts on temperatures and leakage were negligible. Previous simulations demonstrated converged two-phase computational fluid dynamics (CFD) with conjugate heat transfer (CHT) solutions of the seal and reasonable trends, but the agreement with test data was lower than desired. The current project phase made significant improvements to the single- and two-phase CFD simulation of the DGS, lowering the discrepancy of all previously reported performance parameters. The current simulations were performed only at the 700 psi supply pressure case. Ideal gas was used, and CHT coupling was used to predict temperatures of the primary ring. The previous wall thermal boundary conditions were not well understood, so the current work focused on establishing performance with adiabatic walls.
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Morrell, G. R. Oil and gas discoveries. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1996. http://dx.doi.org/10.4095/207707.
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Sheng, James, Lei Li, Yang Yu, et al. Maximize Liquid Oil Production from Shale Oil and Gas Condensate Reservoirs by Cyclic Gas Injection. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1427584.
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Wade, J. A. Oil and gas occurrences and geochemistry. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1991. http://dx.doi.org/10.4095/210688.
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