Artificial Intelligence in Oil and Gas
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AI has great promise for improving operations in the oil and gas industry. It can provide 24/7 supervision of field assets through visual analytics, optimize plant performance using digital twins to run virtual simulations, and accelerate access to engineering information by capturing past work. AI can also improve morale by using robots to perform low-value tasks currently done by humans.
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Artificial Intelligence Application in Oil and Gas
Visit http://sparkcognition.com for more information.
To access and listen to the on-demand version of the webinar, go here:
http://sparkcognition.com/ai-oil-and-gas-webinar-video/
Learn how Artificial Intelligence and Machine Learning are being effectively applied in Oil & Gas right now, how they will become even more prevalent, and how they can impact your bottom line and transform your business.
We'll cover:
• Fundamentals of Artificial Intelligence and Machine Learning
• Understanding of why Artificial Intelligence and Machine Learning are revolutionary in how they can help the Oil & Gas industry. This technology is already being used to prevent downhole tool failures or events like stuck pipes, pinpointing the ideal drilling locations during exploration and discovery, predicting pipeline pump failures, identify frack truck pump failures, etc.
• Real world examples of how other clients are using AI/ML today
Big Data in Oil and Gas
This document discusses big data in the oil and gas industry. It defines big data as high volumes of data from various sources that comes in at a fast velocity. This data has value for oil and gas companies by enabling quicker and more accurate decisions. The document outlines sources of big data growth for oil and gas companies and how big data is driving innovation. It also discusses how the oil and gas industry represents the majority of the energy industry and how big data can provide value through business decisions, investments, production planning and safety.
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The electric grid has evolved from linear generation and delivery to a complex mix of renewables, prosumer-generated electricity, and electric vehicles (EVs). Smart meters are generating loads of data. As a result, traditional forecasting models and technologies can no longer adequately predict supply and demand. Extreme weather, an aging infrastructure, and the burgeoning worldwide population are also contributing to increased outage frequency.
In oil and gas, commodity pricing pressures, resulting workforce reductions, and the need to reduce failures, automate workflows, and increase operational efficiencies are driving operators to shift analytics initiatives to advanced data-driven applications to complement physics-based tools.
While sensored equipment and legacy surveillance applications are generating massive amounts of data, just 2% is understood and being leveraged. Operationalizing it along with external datasets enables a shift from time-based to condition-based maintenance, better forecasting and dramatic reductions in unplanned downtime.
The session includes plenty of real-world anecdotes. For example, how an electric power holding company reduced the time it took to investigate energy theft from six months to less than one hour, producing theft leads in minutes and an expected multi-million dollar ROI. How a global offshore contract drilling services provider implemented an open source IIoT solution across its fleet of assets in less than a year, enabling remote monitoring, predictive analytics and maintenance.
Key takeaways:
• How are new processes for data collection, storage and democratization making it accessible and usable at scale?
• Beyond time series data, what other data types are important to assess?
• What advantage are open source technologies providing to enterprises deploying IIoT?
• Why is collaboration important across industrial verticals to increase IIoT open source adoption?
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Visit http://sparkcognition.com for more information.
To access and listen to the on-demand version of the webinar, go here:
http://sparkcognition.com/ai-oil-and-gas-webinar-video/
Learn how Artificial Intelligence and Machine Learning are being effectively applied in Oil & Gas right now, how they will become even more prevalent, and how they can impact your bottom line and transform your business.
We'll cover:
• Fundamentals of Artificial Intelligence and Machine Learning
• Understanding of why Artificial Intelligence and Machine Learning are revolutionary in how they can help the Oil & Gas industry. This technology is already being used to prevent downhole tool failures or events like stuck pipes, pinpointing the ideal drilling locations during exploration and discovery, predicting pipeline pump failures, identify frack truck pump failures, etc.
• Real world examples of how other clients are using AI/ML today
Big Data in Oil and Gas
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IIoT + Predictive Analytics: Solving for Disruption in Oil & Gas and Energy &...
The electric grid has evolved from linear generation and delivery to a complex mix of renewables, prosumer-generated electricity, and electric vehicles (EVs). Smart meters are generating loads of data. As a result, traditional forecasting models and technologies can no longer adequately predict supply and demand. Extreme weather, an aging infrastructure, and the burgeoning worldwide population are also contributing to increased outage frequency.
In oil and gas, commodity pricing pressures, resulting workforce reductions, and the need to reduce failures, automate workflows, and increase operational efficiencies are driving operators to shift analytics initiatives to advanced data-driven applications to complement physics-based tools.
While sensored equipment and legacy surveillance applications are generating massive amounts of data, just 2% is understood and being leveraged. Operationalizing it along with external datasets enables a shift from time-based to condition-based maintenance, better forecasting and dramatic reductions in unplanned downtime.
The session includes plenty of real-world anecdotes. For example, how an electric power holding company reduced the time it took to investigate energy theft from six months to less than one hour, producing theft leads in minutes and an expected multi-million dollar ROI. How a global offshore contract drilling services provider implemented an open source IIoT solution across its fleet of assets in less than a year, enabling remote monitoring, predictive analytics and maintenance.
Key takeaways:
• How are new processes for data collection, storage and democratization making it accessible and usable at scale?
• Beyond time series data, what other data types are important to assess?
• What advantage are open source technologies providing to enterprises deploying IIoT?
• Why is collaboration important across industrial verticals to increase IIoT open source adoption?
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Kenneth Smith, General Manager, Energy, Hortonworks
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Oil and gas exploration and production activities generate large amounts of data from sensors, logistics, business operations and more. Given the data volume, variety and velocity, gaining actionable and relevant insights from the data is challenging. Learn about these challenges and how to address them by leveraging big data technologies in this webinar.
During the webinar we will dive deep into approaches for predicting drilling equipment function and failure, a key step towards zero unplanned downtime. In the process of drilling wells, non-productive time due to drilling equipment failure can be expensive. We will highlight how the Pivotal Data Labs team uses big data technologies to build models for predicting drilling equipment function and failure. Models such as these can be used to build essential early warning systems to reduce costs and minimize unplanned downtime.
Panelist:
Rashmi Raghu, Senior Data Scientist, Pivotal
Hosted by:
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Video replay is available to watch here: http://youtu.be/dhT-tjHCr9E
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oWe decided to create this point-of-view after seeing many abstract presentations and esoteric concepts on Digital Oilfield, IoT, Big Data and Analytics.
oThis is our attempt to bring a practical implementation view to IoT by combining Digital Oilfield and IoT.
oHere, we also envisage sharing our IoT experience and lessons learnt in implementing Digital Oilfield solutions around IoT.
oThe following comprise our fundamental business case for Finance:
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oFAULT COMPARTMENTS
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This brief overview of exploration includes segments on exploration processes, some historical perspective including an explanation of hydrocarbons, and finally we’ll discuss the ‘basin-play concept’.
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First is understanding and evaluating the geologic setting, called a play,
Next is obtaining access to the potential reserves usually in the form of a lease.
The third step is determining where to drill and completing a successful discovery or “wildcat” well.
Finally, additional hydrocarbon reserves can be added to the portfolio of an oil company using guidelines set by the Society of Petroleum Engineers (SPE) and the US Securities and Exchange Commission (SEC).
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