"5 Reasons Why AI Won’t Take Your Job but Make You Faster, Safer, and Better" Our CEO, Diti Sood, wrote a guest editorial for this month's JPT, the flagship magazine of the Society of Petroleum Engineers International (SPE). Special thanks to Trent Jacobs and Nii Ahele Nunoo for making this happen! https://lnkd.in/gKyctNMr #JPT75 #JPE #AIinEnergy #AIinOilandGas #DigitalTransformation
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Don't miss out! Brandon M. Myers, our Head of Research, is taking the stage at SPE Calgary on May 9th. 🚀 Discover how we're using AI to transform the upstream industry. Register today and secure your spot! (Link in the comments👇) Society of Petroleum Engineers (SPE) Calgary Section #oott #oilgas #spe #specalgary #ai #upstream #machinelearning
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Enabling Enterprises with Digital Transformation, IoT/sensor integration, and AI deployment for data & AI-driven decision support systems to improve operational and worker productivity, sustainability, and AI Governance.
SPE (Society of Petroleum Engineers International) Forum on AI last week (Feb 5 - 9, 2024, in Dallas, TX) was a learning goldmine, or “oilfield” (no pun intended)! Thanks to the amazing AI practitioners and leaders from the world’s largest E&P companies, academicians, and industry analysts. I gained valuable insights from sessions “AI-Based Reservoir Modeling - The Future” (led by Omer Gurpinar at SLB and Jessica Iriarte at Corva, “Future of Data Analyses and Interpretation” (led by Xioa-Hui Wu at ExxonMobil and Dingzhou Cao at Devon Energy), “AI and Automation - The Future of Drilling and Completions” (led by Prof. iraj ershaghi at USC and Matt Maguire at Diamondback Energy) and “Future of Decision Support and Decision making and the Role of Human Knowledge (led by Stanislas Jayr at Chevron and Sebastien Matringe at Hess Corporation). The discussion on Explainability for E&P AI deployment mirrored my previous experiences, highlighting its critical role in addressing industry-specific concerns about transparency and regulatory compliance. Explainability is also vital for our DoD customers; patent-backed explainability features of Falkonry AI helped us win customers' trust like the US Navy. This Sunday evening, I am writing with a heavy heart since I am based in the San Francisco Bay Area. As you all know, Google's Gemini used AI to predict the winner before the game. I would say Gemini did use Explainable AI: "Important Note: I'll use recent data and trends, but things can change rapidly in the NFL. Kansas City Chiefs. The Mahomes factor: Patrick Mahomes is an exceptional quarterback. ....." 🤒 #explainableai
🚀 Falkonry is gearing up for the SPE Forum on Artificial Intelligence in Upstream E&P 2030. 💡 We are honored and excited to join discussions with industry leaders about the rapid advancements in Artificial Intelligence set to revolutionize the E&P sector. 🫱🏼🫲🏼 If you're attending the #event between 4-9 Feb in Dallas, Texas, be sure to connect with Ashis Khan to learn more about how Falkonry is playing a pivotal role in shaping the future of #AI adoption. See you there! 🔗 https://lnkd.in/dK2hjiPh #oilandgas #operationalexcellence | Society of Petroleum Engineers International
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Enabling Enterprises with Digital Transformation, IoT/sensor integration, and AI deployment for data & AI-driven decision support systems to improve operational and worker productivity, sustainability, and AI Governance.
I will attend the SPE Forum (an invitation-only event) on AI in Upstream E&P 2030 from February 4 to 9 in Dallas, TX. Please DM me if you want to discuss how Falkonry's Time Series AI Cloud is helping Oil and Gas producers to deploy AI (fully automated) without needing data scientists, data architects, AI model builders, algorithm developers, MLOPs engineers, etc. The AI in Oil And Gas Market is estimated to be valued at $3.5B in 2024 and is expected to reach $13B by 2034 (source: Future Market Insights, report published on Feb 1, 2024). AI has been used for decades to analyze seismic data, identify potential reserves, and optimize drilling strategies. Oil and Gas manufacturers now use AI in pipeline and asset management; AI-powered real-time monitoring systems detect anomalies, preventing costly downtime and improving safety. AI helps detect and respond to oil spills and leaks more quickly, minimizing environmental damage. It can also optimize processes to reduce greenhouse gas emissions. AI can optimize Overall Maintenance Effectiveness and schedule shutdowns optimally. If you want to learn how Chesapeake Energy, the largest gas E&P, is deploying AI and data-driven decision system, you can attend the 28th Annual ARC Industry Leadership Forum in Orlando, Florida, on Feb 6, 2024. Ryan Goltz of Chesapeake Energy will share the E&P enterprise data architecture. By combining Falkonry, Salesforce, Snowflake, MuleSoft, and ServiceNow, Chesapeake seeks to establish a competitive IT advantage. https://lnkd.in/gXSYn_Vf
🚀 Falkonry is gearing up for the SPE Forum on Artificial Intelligence in Upstream E&P 2030. 💡 We are honored and excited to join discussions with industry leaders about the rapid advancements in Artificial Intelligence set to revolutionize the E&P sector. 🫱🏼🫲🏼 If you're attending the #event between 4-9 Feb in Dallas, Texas, be sure to connect with Ashis Khan to learn more about how Falkonry is playing a pivotal role in shaping the future of #AI adoption. See you there! 🔗 https://lnkd.in/dK2hjiPh #oilandgas #operationalexcellence | Society of Petroleum Engineers International
SPE Forum: Artificial Intelligence in Upstream E&P 2030
spe.org
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#generativeai can improve the Oil and Gas industry’s operational efficiency, overall productivity, and profitability by creating accurate reservoir models and analyzing geophysical data that provides insights into reservoir porosity. 🖇 Here's all you need to know https://lnkd.in/dycWPXNF #generativeai #dataanalytics #supplychainoptimization #xenonstack
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On September 7, 2023, NVI Solutions presented its video analytics solution owl.Guard at the online conference "Digital Oil & Gas". The company shared its experience in implementing promising solutions for the oil and gas industry based on digital technologies. A significant interest among listeners was the issue of data management for training neural networks, since access to them is especially difficult to organize due to data protection policies. For some scenarios, such as fire work, it is almost impossible to find data at all due to the relatively rare occurrence of real events and the difficulty of staging them. For such cases, the NVI team has created a tool for generating fully synthetic data for training, which has already proven itself well in real-world implementations. So, on some of them, it was possible to achieve extremely high recognition accuracy without any access to real training data. The number of participants exceeded 1,000 people and 300 companies. Among the invited speakers and experts were representatives of companies such as Gazprom Neft, Lukoil, SIBUR, Gazprom, Rosneft, Novatek, Tatneft, Transneft, Zarubezhneft, and Surgutneftegaz, among others. As a result of the presentation, more than 30 major companies showed their interest. #NVISolutions #owlGUARD #videoanalytics #AI
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Talk about a #Holiday Surprise! Impossible Sensing Energy Inc. is proud to be featured in this month's edition of JPT Journal of Petroleum Technology, published by the Society of Petroleum Engineers International. The feature on #Aerospace and the #OilField by Blake Wright does a great job highlighting how #technologies designed for different sectors complement each other, ultimately leading to novel solutions across multiple value chains. In discussing what drives our #innovation, IS.Energy CEO Ariel Torre says: "Our initial objective was to eliminate test separation equipment. Separators are a known source of greenhouse gases because they contain valves and actuators. Those valves and actuators require either compressed air to actuate them, or you need to generate electricity to make them work. But the easiest way is to use natural gas from the well itself, which is readily available at the wellhead, and which ends up being vented into the atmosphere every time a valve is open or closed. That was the problem. We wanted to eliminate these valves and actuators by eliminating the test separators completely.” The article goes on to describe several use cases for our suite of #DeepTech solutions, including improving operating #efficiencyby identifying and #recycling solvents, as well as airborne #sensing for the detection of fugitive #emissions. Interested in learning more about how IS.Energy can help your company increase revenue while reducing its environmental footprint? Reach out directly at info@isenergy.ca
JPT | LinkedIn
linkedin.com
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Time-consuming reservoir simulations require several hours to complete, especially when the water alternating gas (WAG) production scenario is involved. Data-driven proxy models are alternatives to reservoir simulators as they (1) can replicate the simulators in outputs prediction, (2) are easier to manipulate (fewer parameters) in history matching and optimization processes and (3) are much faster (less computional time). We recently published a paper in which a hybrid ANN-NSGA-II (Artificial Neural Network coupled with NSGA-II) was used to predict (with ANN) reservoir simulator outputs (cumulative oil recovery factor and net present value) first and then perform an optimization (with NSGA-II) in which the Pareto front of non-dominated solutions has been constructed. The following link allows you to read and download the aforementioned paper: https://lnkd.in/g_cq6iZ4
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Full-Professor and Scientist @ Recod.ai, University of Campinas, LinkedIn Top Voice in Artificial Intelligence
Show me the CAUSE! I don't want correlations only; I want to understand causal links! Thinking about the above, we have been investigating ways of discovering causal links directly from data. It is a dantesque task but we have managed to touch the tip of the iceberg. Take a look at our most recent paper at Nature Scientific Reports where we design causal links discovery for the oil & gas industry and for climatology. Paper: https://lnkd.in/etHcHqZ9 #ai #causality #causallinks #machinelearning #why #bookofwhy #artificialintelligence #datascienceacademy Data Science Academy Shell Petrobras Equinor TotalEnergies Schlumberger Oil Field Svcs PETRONAS Chevron ExxonMobil
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Imagine a task that used to take 11 minutes now taking less time than the blink of an eye. Couple that speed increase with 97% accuracy, and these are the results researchers at Texas A&M University achieved when combining machine learning, neural networks and novel compression tactics in a new project advancing reservoir production forecasts. Why is the research important? Because advancing forecasts improves the availability and accuracy of information oil and gas companies use to make sound financial and operating decisions. "Production forecasting can be done several ways, but everything depends on it," said researcher Mohammad Elkady. "Any decision – if you want to take out a loan, do an economic study, or make a development phase decision – depends on the forecast because it tells you how much oil, gas or water you're going to produce." Read more: https://lnkd.in/ghPmn32N #TAMUpete
Ultrafast Reservoir Forecasting Boasts Speed and Accuracy
engineering.tamu.edu
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https://lnkd.in/gFHSskxF The Dallas Fed Energy Survey is out! This quarter we have a set of interesting questions on AI, where we found out that half of our respondents have used or are planning to use AI in their business. Executives report using AI for a number of purposes, including predicative analytics, process automation and geology or reservoir engineering, with most reporting some type of benefit. We also have special questions on the potential impact of future consolidation on U.S. oil production, the effect of low Waha natural gas prices, and some questions on extracting lithium from oil field brine. Please check out the website for more details and feel free to reach out to me or Kunal Patel if you have any questions.
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