Machine Learning in Oil and Gas - Thematic Intelligence

Best Price Guarantee	Length	Publisher	Published Date	SKU
from $995	83 Pages	GlobalData	May, 2023	GBDT17999673

Best Price Guarantee
Price	from $995
Length	83 Pages
Publisher	GlobalData
Published Date	May, 2023
SKU	GBDT17999673

Machine Learning in Oil and Gas - Thematic Intelligence

Summary

Machine learning is a rapidly growing field in the oil and gas industry and can potentially revolutionize how companies explore and produce oil and gas. It can be used to analyze seismic data, well logs, and other geologic data to identify potential oil and gas reservoirs. Machine learning algorithms are also capable of analyzing production data and identifying patterns that can be used to improve well performance. This can lead to increased production rates and reduced downtime. Besides, this analysis can also be used to identify potential hazards, thereby preventing any untoward incidents and boosting operational safety. Overall, machine learning has the potential to improve efficiency, increase production, and reduce costs in the oil and gas industry

Scope

This report presents an overview of growth of machine learning technologies with special focus on adoption of machine learning in oil and gas industry.
It analyses the machine learning value chain in terms of hardware, software, and services, and identifies key players across the value chain.
It evaluates the market growth trends, M&A activity, venture financing, patent, and hiring trends in the machine learning theme.
The report provides an overview of the competitive positions held by public as well as private machine learning technology vendors as well as adoption among oil and gas companies.
It also highlights machine learning use cases by the oil and gas players.

Reasons to Buy

Evaluates the machine value chain and highlights major players in each segment.
Impact analysis of machine learning on the oil and gas industry.

Executive Summary: Players; Hardware; Services; Technology Briefing; What is machine learning?; Machine learning training techniques; What are deep learning and neural networks?; How are machine learning models trained, tested, and deployed?; Data collection; Data preparation; Training; Model evaluation; Parameter tuning; Publication and monitoring; Cloud, AI chips, and edge computing; How does machine learning affect industries?; Trends; Technology trends; Macroeconomic trends; Regulatory trends; Industry trends; Impact on the Oil and Gas Industry; Aiding in the discovery and extraction of hydrocarbons; Preventing unplanned equipment failures; Negotiating supply chain disruptions; Push for tech start-ups; Case studies; TotalEnergies turns to machine learning to predict pump failures; ExxonMobil automates well drilling with machine learning; Industry Analysis; Market size and growth forecasts; Mergers and acquisitions; Venture financing; Foreign direct investment; Patent trends; Hiring trends; Timeline; Value Chain; Hardware; AI chips; Sensors; Servers; Storage; Networking equipment; Software; Big data management; Machine learning techniques; Services; Machine learning libraries; Machine learning platforms; Machine learning as a service; Companies; Public companies; Private companies; Oil and gas companies; Sector Scorecard; Integrated oil and gas sector scorecard; Who’s who; Thematic screen; Valuation screen; Risk screen; Independent oil and gas sector scorecard; Who’s who; Thematic screen; Valuation screen; Risk screen; Glossary; Further Reading; GlobalData reports; Our Thematic Research Methodology; About GlobalData; Contact Us
List of Tables: Table 1: Technology trends; Table 2: Macroeconomic trends; Table 3: Regulatory trends; Table 4: Industry trends; Table 5: Mergers and acquisitions; Table 6: key venture financing deals associated with the renewable energy theme in the last two years; Table 7: Machine learning libraries; Table 8: Public companies; Table 9: Private companies; Table 10: Oil and gas companies; Table 11: Glossary; Table 12: GlobalData reports
List of Figures: Figure 1: GlobalData estimates that the global AI markets will be worth $136B in 2026; Figure 2: Who are the leading players in machine learning hardware, and where do they sit in the value chain?; Figure 3: Who are the leading players in machine learning services, and where do they sit in the value chain?; Figure 4: Machine learning is a subset of artificial intelligence; Figure 5: Machine learning can use supervised learning, unsupervised learning, or reinforcement learning; Figure 6: Deep learning neural network structure; Figure 7: Machine learning models are trained and tested using existing data before they can make predictions; Figure 8: Machine learning is impacting every industry; Figure 9: The AI market is predicted to grow substantially from 2022 to 2026; Figure 10: The US dominates the AI market, while computer vision is the leading specialist AI application; Figure 11: After the US, China is the second largest country for machine learning-related venture finance; Figure 12: The total value of venture finance machine learning deals increased between 2020 and 2021; Figure 13: Machine learning R&D operations top FDI business functions; Figure 14: Machine learning patents in oil and gas rose steadily throughout 2020 and 2021 but declined in 2022; Figure 15: US dominates oil and gas machine learning patent registrations, led by Halliburton; Figure 16: Shell has consistently hired the most machine learning professionals in the oil and gas industry; Figure 17: The machine learning story; Figure 18: The machine learning value chain; Figure 19: The machine learning value chain – AI chips; Figure 20: The machine learning value chain – sensors; Figure 21: The machine learning value chain – servers; Figure 22: The machine learning value chain – storage; Figure 23: The machine learning value chain – networking equipment; Figure 24: The machine learning value chain – big data management; Figure 25: The machine learning value chain – machine learning techniques; Figure 26: Classification predicts the outcome of data points into pre-set categories; Figure 27: Linear regression predicts continuous numerical values based on historical training data; Figure 28: The machine learning value chain – machine learning techniques; Figure 29: K-means clustering algorithms assign data points to a predetermined number of clusters based on similarities; Figure 30: The machine learning value chain – machine learning techniques; Figure 31: The machine learning value chain – machine learning libraries; Figure 32: The machine learning value chain – machine learning platforms; Figure 33: Who does what in the integrated oil and gas space?; Figure 34: Thematic screen - Integrated oil and gas sector scorecard; Figure 35: Valuation screen - Integrated oil and gas sector scorecard; Figure 36: Risk screen - Integrated oil and gas sector scorecard; Figure 37: Who does what in the independent oil and gas space?; Figure 38: Thematic screen - Independent oil and gas sector scorecard; Figure 39: Valuation screen - Independent oil and gas sector scorecard; Figure 40: Risk screen - Independent oil and gas sector scorecard; Figure 41: Our five-step approach for generating a sector scorecard