Published on 12/19/2016 | Strategy
The Oil & Gas industry consists of a vast ocean of collaborative players working together to squeeze as much value out of every drop of oil as possible. It's a highly mature industry, with structures and processes in play to ensure smooth operations and business continuity. However, the current structures, processes, investments, returns, etc. are based on a crude price of about USD 100 per barrel. The collapse of crude oil prices to a new equilibrium of about US$50 per barrel has shaken up the industry across the width and breath of its value chain. While technology, such as enhanced recovery, shale oil, etc. are partially responsible for this new equilibrium, the vast array of new data exploitation technologies is a most important lever for the industry to turn this new equilibrium into an opportunity.
The data exploitation technologies, commonly known as “Big Data” and “(Industrial) Internet of Things”, will play a major role in differentiating between winners and loser in the near future. Businesses exploiting data better than its peer will have competitive advantage through better cost performance and innovations.
I’m planning to write a 5-part series that sheds some light on the impact that IoT/Big Data is going to have on the Hydrocarbon Industry. Here is how I plan on structuring it:
1) Wind of Change – Opportunities, and New Technologies
2) Big Data & IoT – an end to end explanation
3) IT & OT Challenges and Solutions
4) Key Use Cases for Refiners
5) Benefits in the Entire Value Chain – One Key Example, and Concluding Thoughts
Truth is, while Big Data may be new to many industries, the Oil & Gas industry has been dealing with enormous amounts and varieties of data for a long time. However, due to the low digital maturity of the industry, the numerous technologies that have been adopted by the industry are often confined to the "Asset", "Function", "Process" or to the “Equipment”. It has not only created data silos around the assets, functions and processes, it also created data silos and power centers around the people. As such, only 1% of critical information reaches top-level decision makers in the industry. Far less reach the decision makers within the opportunity window and most of the 1% critical information that reach the decision makers is only good for postmortem. If one can make this data work for the business in a much better way than it is currently done, it can make industry much more profitable, and safer. However, it will take approaches different from the traditional business process re-engineering wizardry and IT approaches such as “Enterprise Application Integration (EAI)”, “Data Warehousing” or “Business Intelligence” to unleash the data for enterprise wide empowerment, lost profit opportunity (LPO) minimization and new value creation.
Truth is, while Big Data may be new to many industries, the Oil & Gas industry has been dealing with enormous amounts and varieties of data for a long time. However, due to the low digital maturity of the industry, the numerous technologies that have been adopted by the industry are often confined to the "Asset", "Function", "Process" or to the “Equipment”. It has not only created data silos around the assets, functions and processes, it also created data silos and power centers around the people. As such, only 1% of critical information reaches top-level decision makers in the industry. Far less reach the decision makers within the opportunity window and most of the 1% critical information that reach the decision makers is only good for postmortem. If one can make this data work for the business in a much better way than it is currently done, it can make industry much more profitable, and safer. However, it will take approaches different from the traditional business process re-engineering wizardry and IT approaches such as “Enterprise Application Integration (EAI)”, “Data Warehousing” or “Business Intelligence” to unleash the data for enterprise wide empowerment, lost profit opportunity (LPO) minimization and new value creation.
In this context I would like to mention a few common LPO areas that are so typical, they have almost been accepted as part of life. A study of 143 US refineries published by Solomon Associates* in 2008 pointed out that only 3, had world class EII (Energy Intensity Index) of about 73, the average being 99. Please note that just 1% improvement is worth USD 4.05M /year for a 300KBPD deep conversion refinery @ USD 50/barrel crude cost (assuming 9% Fuel & Loss (F&L) including COGEN plant, 300 stream days). This is indicative of a major improvement opportunity to bring down the EII by a few points through the effective use of data. I shall elaborate on this with a few use-cases from petroleum refining operations in later sections.
In the recent past, a major refinery in Southeast Asia dispatched a very large off-spec parcel to Africa. Another customer found Naphtha in Diesel received from a refiner in South Asia. These two incidents are results of the information throttle, i.e. 99% of critical information that do not reach decision makers within the opportunity window. These are expensive blunders that can be easily avoided by making use of data. The best part is – it costs almost nothing to put a proper (Big Data / IOT) data infrastructure in place compared to the cost of just one such incident and resulting LPO.
That’s all for now folks. In my next post, I’ll provide a detailed summary of what Big Data/IoT consists of and how it is applicable in the industry. I will essentially talk about 5 key elements:
1) Sensing
2) Networking
3) Cyber Physical Systems
4) Data Leveraging Technologies
5) (Enterprise) Cloud Computing
Mobility
Thank you for your time.
This article was originally posted on LinkedIn.
Truth is, while Big Data may be new to many industries, the Oil & Gas industry has been dealing with enormous amounts and varieties of data for a long time. However, due to the low digital maturity of the industry, the numerous technologies that have been adopted by the industry are often confined to the "Asset", "Function", "Process" or to the “Equipment”. It has not only created data silos around the assets, functions and processes, it also created data silos and power centers around the people. As such, only 1% of critical information reaches top-level decision makers in the industry. Far less reach the decision makers within the opportunity window and most of the 1% critical information that reach the decision makers is only good for postmortem. If one can make this data work for the business in a much better way than it is currently done, it can make industry much more profitable, and safer. However, it will take approaches different from the traditional business process re-engineering wizardry and IT approaches such as “Enterprise Application Integration (EAI)”, “Data Warehousing” or “Business Intelligence” to unleash the data for enterprise wide empowerment, lost profit opportunity (LPO) minimization and new value creation.
In this context I would like to mention a few common LPO areas that are so typical, they have almost been accepted as part of life. A study of 143 US refineries published by Solomon Associates* in 2008 pointed out that only 3, had world class EII (Energy Intensity Index) of about 73, the average being 99. Please note that just 1% improvement is worth USD 4.05M /year for a 300KBPD deep conversion refinery @ USD 50/barrel crude cost (assuming 9% Fuel & Loss (F&L) including COGEN plant, 300 stream days). This is indicative of a major improvement opportunity to bring down the EII by a few points through the effective use of data. I shall elaborate on this with a few use-cases from petroleum refining operations in later sections.
In the recent past, a major refinery in Southeast Asia dispatched a very large off-spec parcel to Africa. Another customer found Naphtha in Diesel received from a refiner in South Asia. These two incidents are results of the information throttle, i.e. 99% of critical information that do not reach decision makers within the opportunity window. These are expensive blunders that can be easily avoided by making use of data. The best part is – it costs almost nothing to put a proper (Big Data / IOT) data infrastructure in place compared to the cost of just one such incident and resulting LPO.
That’s all for now folks. In my next post, I’ll provide a detailed summary of what Big Data/IoT consists of and how it is applicable in the industry. I will essentially talk about 5 key elements:
1) Sensing
2) Networking
3) Cyber Physical Systems
4) Data Leveraging Technologies
5) (Enterprise) Cloud Computing
Mobility
Thank you for your time.
This article was originally posted on LinkedIn.
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