Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Milton
Taylor & Francis Group
2022
|
| Schlagworte: | |
| Online-Zugang: | HWR01 |
| Beschreibung: | 1 Online-Ressource (147 Seiten) |
| ISBN: | 9781000629552 |
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| 505 | 8 | |a Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- About the authors -- 1 A comprehensive review of machine application in the oil and gas industry -- 1.1 Introduction -- 1.2 Contribution of machine learning to the oil and gas industry -- 1.2.1 Predictive maintenance -- 1.2.2 Spotting digging sites with machine learning -- 1.2.3 Machine learning in drilling operations -- 1.2.4 Problem-solving with machine learning -- 1.2.5 Replacement of manual labor with machine learning tools or automated robots -- 1.3 Machine learning in the oil and gas upstream sector -- 1.4 Machine learning in the oil and gas midstream sector -- 1.5 Machine learning in the oil and gas downstream sector -- 1.6 Challenges and future scope -- 1.7 Conclusion -- References -- 2 AI and ML applications in the upstream sector of the oil and gas industry -- 2.1 Introduction -- 2.1.1 Upstream and downstream in the oil and gas industry -- 2.2 Need for ML-based techniques in the oil and gas upstream sector -- 2.3 Operations of AI- and ML-based oil and gas fields -- 2.4 Accurate modeling and smart rig operations -- 2.5 Sensor-based models for well spotting the location -- 2.6 AI- and ML-based risk detection system and improved drilling efficiencies -- 2.7 Machine learning-based data analysis of the well location -- 2.8 Digital models for the extreme paperwork in the field -- 2.9 An effective method for providing data to the on-field engineer -- 2.10 Challenges faced now and future scope of more development -- References -- 3 One step further in upstream sector -- 3.1 Introduction -- 3.2 Digitalization and automation in exploring sector -- 3.3 Mapping and analyzing of the field digitally -- 3.4 Spotting drilling and pipeline location precisely using ML-based applications (total oil and Google cloud) -- 3.5 Digitally monitored production sites | |
| 505 | 8 | |a 3.6 Planning and commissioning the onshore and offshore production site based on the ML models -- 3.7 Digitally governed production and standardized data collecting -- 3.8 SCADA-based network for effective communication in the field -- 3.9 Robotization at the dangerous location for drilling for the betterment and safety of workforce -- 3.10 Process modeling and simulation for the offshore, onshore and hydraulic fracturing before drilling -- 3.11 Future advancements and challenges faced currently -- References -- 4 Midstream sector with ML models and techniques -- 4.1 Introduction to midstream sector and advancement with the ML techniques -- 4.2 Transportation with the pipeline and the digital monitoring system -- 4.3 Optimizing pipeline scheduling for product flows -- 4.4 Improving reliability risk modeling for refining and processing assets -- 4.5 Improved storage and processing facilities -- 4.6 Maximizing labor productivity and wrench time via employing robotization and ML techniques -- 4.7 Predicting the subsea and ground pipeline by ML to optimize lateral buckling mitigations -- 4.8 Data management for equipment and facilities along with optimization and process control with automation -- References -- 5 Downstream sector with machine learning -- 5.1 Introduction -- 5.2 Smart refining process integrated with the ML -- 5.3 Advanced modeling and simulation of the plant and process for better functioning -- 5.4 Remote systems operations -- 5.5 Risk analysis with the ML during the refining -- 5.6 Connecting IoT and other parts of the digital devices -- 5.7 Machine vision for safety -- 5.8 Energy and asset management -- 5.9 Remote operation and performance shutdown using IoT -- References -- 6 Safety and maintenance with AI and ML -- 6.1 Introduction -- 6.2 Deep learning risk detecting and predictive diagnostics | |
| 505 | 8 | |a 6.3 Boosting productivity with predictive maintenance -- 6.4 Digital pre-fire alarming sensor mode -- 6.5 Motor vehicle safety and in-vehicle monitoring system -- 6.6 Clear vision communication and monitoring model on sites and exploring sites -- 6.7 Smart helmets and other safety equipment for the workers -- 6.8 Safety with the security of the data with ML models -- 6.9 Pipeline leakage monitoring sensor-based system -- References -- 7 Finance with ML and AI -- 7.1 General introduction -- 7.2 Factors affecting the finance cost of the industry, end product, and processing -- 7.3 Forecasting growth, trends, and market with AI models -- 7.4 Price prediction models based on data analysis and ML -- 7.5 Financial modeling with the current data for better performance -- 7.6 Digitalizing the distribution channel and the end-customer things -- 7.7 Smart supply chain management -- 7.8 Demand management with computational power -- 7.9 Managing the virtual agents and supplier selection with ML and data science -- References -- 8 Market and trading in oil and gas (petroleum) industry -- 8.1 Introduction -- 8.2 Oil and gas (petroleum) industry market dynamics -- 8.3 Data analysis and market forecasting of prices from the raw material produced in the oil and gas (petroleum) industry -- 8.4 Valuation of derivatives or assets of oil and gas (petroleum) industry -- References -- 9 Future of oil and gas (petroleum) industry with AI -- 9.1 Introduction -- 9.2 AI in reservoir management -- 9.3 AI in drilling -- 9.4 AI in exploration -- 9.5 AI in production -- References -- Index | |
| 653 | 6 | |a Electronic books | |
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| 700 | 1 | |a Panchal, Jainam |e Sonstige |4 oth | |
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| contents | Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- About the authors -- 1 A comprehensive review of machine application in the oil and gas industry -- 1.1 Introduction -- 1.2 Contribution of machine learning to the oil and gas industry -- 1.2.1 Predictive maintenance -- 1.2.2 Spotting digging sites with machine learning -- 1.2.3 Machine learning in drilling operations -- 1.2.4 Problem-solving with machine learning -- 1.2.5 Replacement of manual labor with machine learning tools or automated robots -- 1.3 Machine learning in the oil and gas upstream sector -- 1.4 Machine learning in the oil and gas midstream sector -- 1.5 Machine learning in the oil and gas downstream sector -- 1.6 Challenges and future scope -- 1.7 Conclusion -- References -- 2 AI and ML applications in the upstream sector of the oil and gas industry -- 2.1 Introduction -- 2.1.1 Upstream and downstream in the oil and gas industry -- 2.2 Need for ML-based techniques in the oil and gas upstream sector -- 2.3 Operations of AI- and ML-based oil and gas fields -- 2.4 Accurate modeling and smart rig operations -- 2.5 Sensor-based models for well spotting the location -- 2.6 AI- and ML-based risk detection system and improved drilling efficiencies -- 2.7 Machine learning-based data analysis of the well location -- 2.8 Digital models for the extreme paperwork in the field -- 2.9 An effective method for providing data to the on-field engineer -- 2.10 Challenges faced now and future scope of more development -- References -- 3 One step further in upstream sector -- 3.1 Introduction -- 3.2 Digitalization and automation in exploring sector -- 3.3 Mapping and analyzing of the field digitally -- 3.4 Spotting drilling and pipeline location precisely using ML-based applications (total oil and Google cloud) -- 3.5 Digitally monitored production sites 3.6 Planning and commissioning the onshore and offshore production site based on the ML models -- 3.7 Digitally governed production and standardized data collecting -- 3.8 SCADA-based network for effective communication in the field -- 3.9 Robotization at the dangerous location for drilling for the betterment and safety of workforce -- 3.10 Process modeling and simulation for the offshore, onshore and hydraulic fracturing before drilling -- 3.11 Future advancements and challenges faced currently -- References -- 4 Midstream sector with ML models and techniques -- 4.1 Introduction to midstream sector and advancement with the ML techniques -- 4.2 Transportation with the pipeline and the digital monitoring system -- 4.3 Optimizing pipeline scheduling for product flows -- 4.4 Improving reliability risk modeling for refining and processing assets -- 4.5 Improved storage and processing facilities -- 4.6 Maximizing labor productivity and wrench time via employing robotization and ML techniques -- 4.7 Predicting the subsea and ground pipeline by ML to optimize lateral buckling mitigations -- 4.8 Data management for equipment and facilities along with optimization and process control with automation -- References -- 5 Downstream sector with machine learning -- 5.1 Introduction -- 5.2 Smart refining process integrated with the ML -- 5.3 Advanced modeling and simulation of the plant and process for better functioning -- 5.4 Remote systems operations -- 5.5 Risk analysis with the ML during the refining -- 5.6 Connecting IoT and other parts of the digital devices -- 5.7 Machine vision for safety -- 5.8 Energy and asset management -- 5.9 Remote operation and performance shutdown using IoT -- References -- 6 Safety and maintenance with AI and ML -- 6.1 Introduction -- 6.2 Deep learning risk detecting and predictive diagnostics 6.3 Boosting productivity with predictive maintenance -- 6.4 Digital pre-fire alarming sensor mode -- 6.5 Motor vehicle safety and in-vehicle monitoring system -- 6.6 Clear vision communication and monitoring model on sites and exploring sites -- 6.7 Smart helmets and other safety equipment for the workers -- 6.8 Safety with the security of the data with ML models -- 6.9 Pipeline leakage monitoring sensor-based system -- References -- 7 Finance with ML and AI -- 7.1 General introduction -- 7.2 Factors affecting the finance cost of the industry, end product, and processing -- 7.3 Forecasting growth, trends, and market with AI models -- 7.4 Price prediction models based on data analysis and ML -- 7.5 Financial modeling with the current data for better performance -- 7.6 Digitalizing the distribution channel and the end-customer things -- 7.7 Smart supply chain management -- 7.8 Demand management with computational power -- 7.9 Managing the virtual agents and supplier selection with ML and data science -- References -- 8 Market and trading in oil and gas (petroleum) industry -- 8.1 Introduction -- 8.2 Oil and gas (petroleum) industry market dynamics -- 8.3 Data analysis and market forecasting of prices from the raw material produced in the oil and gas (petroleum) industry -- 8.4 Valuation of derivatives or assets of oil and gas (petroleum) industry -- References -- 9 Future of oil and gas (petroleum) industry with AI -- 9.1 Introduction -- 9.2 AI in reservoir management -- 9.3 AI in drilling -- 9.4 AI in exploration -- 9.5 AI in production -- References -- Index |
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| id | DE-604.BV048523523 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T20:50:26Z |
| indexdate | 2024-07-10T09:40:30Z |
| institution | BVB |
| isbn | 9781000629552 |
| language | English |
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| spelling | Shah, Manan Verfasser aut Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry Milton Taylor & Francis Group 2022 ©2023 1 Online-Ressource (147 Seiten) txt rdacontent c rdamedia cr rdacarrier Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- About the authors -- 1 A comprehensive review of machine application in the oil and gas industry -- 1.1 Introduction -- 1.2 Contribution of machine learning to the oil and gas industry -- 1.2.1 Predictive maintenance -- 1.2.2 Spotting digging sites with machine learning -- 1.2.3 Machine learning in drilling operations -- 1.2.4 Problem-solving with machine learning -- 1.2.5 Replacement of manual labor with machine learning tools or automated robots -- 1.3 Machine learning in the oil and gas upstream sector -- 1.4 Machine learning in the oil and gas midstream sector -- 1.5 Machine learning in the oil and gas downstream sector -- 1.6 Challenges and future scope -- 1.7 Conclusion -- References -- 2 AI and ML applications in the upstream sector of the oil and gas industry -- 2.1 Introduction -- 2.1.1 Upstream and downstream in the oil and gas industry -- 2.2 Need for ML-based techniques in the oil and gas upstream sector -- 2.3 Operations of AI- and ML-based oil and gas fields -- 2.4 Accurate modeling and smart rig operations -- 2.5 Sensor-based models for well spotting the location -- 2.6 AI- and ML-based risk detection system and improved drilling efficiencies -- 2.7 Machine learning-based data analysis of the well location -- 2.8 Digital models for the extreme paperwork in the field -- 2.9 An effective method for providing data to the on-field engineer -- 2.10 Challenges faced now and future scope of more development -- References -- 3 One step further in upstream sector -- 3.1 Introduction -- 3.2 Digitalization and automation in exploring sector -- 3.3 Mapping and analyzing of the field digitally -- 3.4 Spotting drilling and pipeline location precisely using ML-based applications (total oil and Google cloud) -- 3.5 Digitally monitored production sites 3.6 Planning and commissioning the onshore and offshore production site based on the ML models -- 3.7 Digitally governed production and standardized data collecting -- 3.8 SCADA-based network for effective communication in the field -- 3.9 Robotization at the dangerous location for drilling for the betterment and safety of workforce -- 3.10 Process modeling and simulation for the offshore, onshore and hydraulic fracturing before drilling -- 3.11 Future advancements and challenges faced currently -- References -- 4 Midstream sector with ML models and techniques -- 4.1 Introduction to midstream sector and advancement with the ML techniques -- 4.2 Transportation with the pipeline and the digital monitoring system -- 4.3 Optimizing pipeline scheduling for product flows -- 4.4 Improving reliability risk modeling for refining and processing assets -- 4.5 Improved storage and processing facilities -- 4.6 Maximizing labor productivity and wrench time via employing robotization and ML techniques -- 4.7 Predicting the subsea and ground pipeline by ML to optimize lateral buckling mitigations -- 4.8 Data management for equipment and facilities along with optimization and process control with automation -- References -- 5 Downstream sector with machine learning -- 5.1 Introduction -- 5.2 Smart refining process integrated with the ML -- 5.3 Advanced modeling and simulation of the plant and process for better functioning -- 5.4 Remote systems operations -- 5.5 Risk analysis with the ML during the refining -- 5.6 Connecting IoT and other parts of the digital devices -- 5.7 Machine vision for safety -- 5.8 Energy and asset management -- 5.9 Remote operation and performance shutdown using IoT -- References -- 6 Safety and maintenance with AI and ML -- 6.1 Introduction -- 6.2 Deep learning risk detecting and predictive diagnostics 6.3 Boosting productivity with predictive maintenance -- 6.4 Digital pre-fire alarming sensor mode -- 6.5 Motor vehicle safety and in-vehicle monitoring system -- 6.6 Clear vision communication and monitoring model on sites and exploring sites -- 6.7 Smart helmets and other safety equipment for the workers -- 6.8 Safety with the security of the data with ML models -- 6.9 Pipeline leakage monitoring sensor-based system -- References -- 7 Finance with ML and AI -- 7.1 General introduction -- 7.2 Factors affecting the finance cost of the industry, end product, and processing -- 7.3 Forecasting growth, trends, and market with AI models -- 7.4 Price prediction models based on data analysis and ML -- 7.5 Financial modeling with the current data for better performance -- 7.6 Digitalizing the distribution channel and the end-customer things -- 7.7 Smart supply chain management -- 7.8 Demand management with computational power -- 7.9 Managing the virtual agents and supplier selection with ML and data science -- References -- 8 Market and trading in oil and gas (petroleum) industry -- 8.1 Introduction -- 8.2 Oil and gas (petroleum) industry market dynamics -- 8.3 Data analysis and market forecasting of prices from the raw material produced in the oil and gas (petroleum) industry -- 8.4 Valuation of derivatives or assets of oil and gas (petroleum) industry -- References -- 9 Future of oil and gas (petroleum) industry with AI -- 9.1 Introduction -- 9.2 AI in reservoir management -- 9.3 AI in drilling -- 9.4 AI in exploration -- 9.5 AI in production -- References -- Index Electronic books Kshirsagar, Ameya Sonstige oth Panchal, Jainam Sonstige oth Erscheint auch als Druck-Ausgabe Shah, Manan Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry Milton : Taylor & Francis Group,c2022 9781032245652 |
| spellingShingle | Shah, Manan Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- About the authors -- 1 A comprehensive review of machine application in the oil and gas industry -- 1.1 Introduction -- 1.2 Contribution of machine learning to the oil and gas industry -- 1.2.1 Predictive maintenance -- 1.2.2 Spotting digging sites with machine learning -- 1.2.3 Machine learning in drilling operations -- 1.2.4 Problem-solving with machine learning -- 1.2.5 Replacement of manual labor with machine learning tools or automated robots -- 1.3 Machine learning in the oil and gas upstream sector -- 1.4 Machine learning in the oil and gas midstream sector -- 1.5 Machine learning in the oil and gas downstream sector -- 1.6 Challenges and future scope -- 1.7 Conclusion -- References -- 2 AI and ML applications in the upstream sector of the oil and gas industry -- 2.1 Introduction -- 2.1.1 Upstream and downstream in the oil and gas industry -- 2.2 Need for ML-based techniques in the oil and gas upstream sector -- 2.3 Operations of AI- and ML-based oil and gas fields -- 2.4 Accurate modeling and smart rig operations -- 2.5 Sensor-based models for well spotting the location -- 2.6 AI- and ML-based risk detection system and improved drilling efficiencies -- 2.7 Machine learning-based data analysis of the well location -- 2.8 Digital models for the extreme paperwork in the field -- 2.9 An effective method for providing data to the on-field engineer -- 2.10 Challenges faced now and future scope of more development -- References -- 3 One step further in upstream sector -- 3.1 Introduction -- 3.2 Digitalization and automation in exploring sector -- 3.3 Mapping and analyzing of the field digitally -- 3.4 Spotting drilling and pipeline location precisely using ML-based applications (total oil and Google cloud) -- 3.5 Digitally monitored production sites 3.6 Planning and commissioning the onshore and offshore production site based on the ML models -- 3.7 Digitally governed production and standardized data collecting -- 3.8 SCADA-based network for effective communication in the field -- 3.9 Robotization at the dangerous location for drilling for the betterment and safety of workforce -- 3.10 Process modeling and simulation for the offshore, onshore and hydraulic fracturing before drilling -- 3.11 Future advancements and challenges faced currently -- References -- 4 Midstream sector with ML models and techniques -- 4.1 Introduction to midstream sector and advancement with the ML techniques -- 4.2 Transportation with the pipeline and the digital monitoring system -- 4.3 Optimizing pipeline scheduling for product flows -- 4.4 Improving reliability risk modeling for refining and processing assets -- 4.5 Improved storage and processing facilities -- 4.6 Maximizing labor productivity and wrench time via employing robotization and ML techniques -- 4.7 Predicting the subsea and ground pipeline by ML to optimize lateral buckling mitigations -- 4.8 Data management for equipment and facilities along with optimization and process control with automation -- References -- 5 Downstream sector with machine learning -- 5.1 Introduction -- 5.2 Smart refining process integrated with the ML -- 5.3 Advanced modeling and simulation of the plant and process for better functioning -- 5.4 Remote systems operations -- 5.5 Risk analysis with the ML during the refining -- 5.6 Connecting IoT and other parts of the digital devices -- 5.7 Machine vision for safety -- 5.8 Energy and asset management -- 5.9 Remote operation and performance shutdown using IoT -- References -- 6 Safety and maintenance with AI and ML -- 6.1 Introduction -- 6.2 Deep learning risk detecting and predictive diagnostics 6.3 Boosting productivity with predictive maintenance -- 6.4 Digital pre-fire alarming sensor mode -- 6.5 Motor vehicle safety and in-vehicle monitoring system -- 6.6 Clear vision communication and monitoring model on sites and exploring sites -- 6.7 Smart helmets and other safety equipment for the workers -- 6.8 Safety with the security of the data with ML models -- 6.9 Pipeline leakage monitoring sensor-based system -- References -- 7 Finance with ML and AI -- 7.1 General introduction -- 7.2 Factors affecting the finance cost of the industry, end product, and processing -- 7.3 Forecasting growth, trends, and market with AI models -- 7.4 Price prediction models based on data analysis and ML -- 7.5 Financial modeling with the current data for better performance -- 7.6 Digitalizing the distribution channel and the end-customer things -- 7.7 Smart supply chain management -- 7.8 Demand management with computational power -- 7.9 Managing the virtual agents and supplier selection with ML and data science -- References -- 8 Market and trading in oil and gas (petroleum) industry -- 8.1 Introduction -- 8.2 Oil and gas (petroleum) industry market dynamics -- 8.3 Data analysis and market forecasting of prices from the raw material produced in the oil and gas (petroleum) industry -- 8.4 Valuation of derivatives or assets of oil and gas (petroleum) industry -- References -- 9 Future of oil and gas (petroleum) industry with AI -- 9.1 Introduction -- 9.2 AI in reservoir management -- 9.3 AI in drilling -- 9.4 AI in exploration -- 9.5 AI in production -- References -- Index |
| title | Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry |
| title_auth | Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry |
| title_exact_search | Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry |
| title_exact_search_txtP | Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry |
| title_full | Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry |
| title_fullStr | Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry |
| title_full_unstemmed | Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry |
| title_short | Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry |
| title_sort | applications of artificial intelligence ai and machine learning ml in the petroleum industry |
| work_keys_str_mv | AT shahmanan applicationsofartificialintelligenceaiandmachinelearningmlinthepetroleumindustry AT kshirsagarameya applicationsofartificialintelligenceaiandmachinelearningmlinthepetroleumindustry AT panchaljainam applicationsofartificialintelligenceaiandmachinelearningmlinthepetroleumindustry |