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After reading an interesting article discussing how academics and upstream oil & gas companies have been relying on big data and analytics to improve their operational performance in the oil patch, it reinforced ARC’s belief that there is tremendous value for oil & gas companies by shedding their conservative mindsets, cultural inertia and mainly irrational concerns over data security and fully embracing IIoT, Big Data and analytics.
The hero in this article is West Virginia University professor Shahab Mohaghegh who became obsessed with the industry’s big data well before it become cool to use terms like “machine learning”. He started studying how machines could process data generated during oil and gas production, and learn to spot patterns. Those patterns could form the basis of predictive models that could help operators narrow down where to drill and estimate how much a well will produce.
When shale development started to bubble up in the early 2000s, Professor Mohaghegh figured it would be the perfect candidate for machine learning. It had the right ingredients, including advanced instruments at each well site producing monstrous amounts of data. Shale was also new geology, in the sense that while its properties have been known for years, the industry couldn’t produce large quantities of oil and gas from it before the marriage of horizontal drilling and high-volume hydraulic fracturing.
“Shale has been incredibly nice and incredibly generous to our operators by providing so much oil and gas regardless of what we do to it,” Mr. Mohaghegh said. But what comes out of the well is still a small fraction of what’s in the ground. As much as 40 percent of the fractures blasted in the rock from a well bore don’t produce any gas and recovery rates in many formations are in the teens percentage wise.
Back in 2011, when Texas-based oil and gas service firm Baker Hughes opened its Palo Alto Innovation Center, machine learning wasn’t really on the radar of most industry players. Last year, Baker Hughes rolled out software that, among other things, analyzes conditions around electric submersible pumps helping lift oil out of wells and alerts well operators in advance of a potential problem. The software crunches data on historical pump performance and other variables, like mud pressure and temperature. Learning from that, the program can say what kinds of conditions resulted in the pump breaking a high percent of the time. Landmark, a Halliburton business line, builds machine learning concepts into software products that help operators collect and sort data. But it doesn’t aggregate data from different operators — in fact it doesn’t analyze any data collected through its apps. Baker Hughes and Halliburton, among many others, were both covered in ARC’s Artificial Lift Optimization study.
Over the years, Mr. Mohaghegh’s work has gained traction in the oil and gas industry. He helped Anadarko Petroleum analyze data to determine how to coax more oil out of its wells. Working with data from an international oil and gas firm, Mr. Mohaghegh determined which oil service contractor, such as Halliburton or Baker Hughes, did the best job at fracking its wells.
At Southwestern Energy Corp., a Texas-based operator that cut its shale teeth on the Fayetteville Shale in Arkansas before shifting most of its focus to the Marcellus, bid data analytics is ramping up across divisions even as drilling has been suspended until it is more economical. Southwestern started taking stock of the data it has in 2010. Two years later, it began collecting information with the express purpose of running it through machine learning algorithms.
“Now, we have enough wells, enough geologic information, that we can begin to put those together to discover what we don’t know,” said Mark Reynolds, senior solutions architect and knowledge engineer, who pioneered the effort.
For Mr. Reynolds, it became obvious looking at the data that the mechanics of shale didn’t yield to traditional models. In 20 years, he said, a different oilfield boom may render obsolete or, at best, incomplete all the groundbreaking insights being gathered today. “When you talk to the generals, they say they’re always prepared to fight the last war,” he said. ”That’s just human nature.” We at ARC believe that it is that very human nature that typically stands in the way of companies adopting IIoT enabled solutions as well other technologies that will enhance operational performance and profitability.
The hero in this article is West Virginia University professor Shahab Mohaghegh who became obsessed with the industry’s big data well before it become cool to use terms like “machine learning”. He started studying how machines could process data generated during oil and gas production, and learn to spot patterns. Those patterns could form the basis of predictive models that could help operators narrow down where to drill and estimate how much a well will produce.
When shale development started to bubble up in the early 2000s, Professor Mohaghegh figured it would be the perfect candidate for machine learning. It had the right ingredients, including advanced instruments at each well site producing monstrous amounts of data. Shale was also new geology, in the sense that while its properties have been known for years, the industry couldn’t produce large quantities of oil and gas from it before the marriage of horizontal drilling and high-volume hydraulic fracturing.
“Shale has been incredibly nice and incredibly generous to our operators by providing so much oil and gas regardless of what we do to it,” Mr. Mohaghegh said. But what comes out of the well is still a small fraction of what’s in the ground. As much as 40 percent of the fractures blasted in the rock from a well bore don’t produce any gas and recovery rates in many formations are in the teens percentage wise.
Back in 2011, when Texas-based oil and gas service firm Baker Hughes opened its Palo Alto Innovation Center, machine learning wasn’t really on the radar of most industry players. Last year, Baker Hughes rolled out software that, among other things, analyzes conditions around electric submersible pumps helping lift oil out of wells and alerts well operators in advance of a potential problem. The software crunches data on historical pump performance and other variables, like mud pressure and temperature. Learning from that, the program can say what kinds of conditions resulted in the pump breaking a high percent of the time. Landmark, a Halliburton business line, builds machine learning concepts into software products that help operators collect and sort data. But it doesn’t aggregate data from different operators — in fact it doesn’t analyze any data collected through its apps. Baker Hughes and Halliburton, among many others, were both covered in ARC’s Artificial Lift Optimization study.
Over the years, Mr. Mohaghegh’s work has gained traction in the oil and gas industry. He helped Anadarko Petroleum analyze data to determine how to coax more oil out of its wells. Working with data from an international oil and gas firm, Mr. Mohaghegh determined which oil service contractor, such as Halliburton or Baker Hughes, did the best job at fracking its wells.
At Southwestern Energy Corp., a Texas-based operator that cut its shale teeth on the Fayetteville Shale in Arkansas before shifting most of its focus to the Marcellus, bid data analytics is ramping up across divisions even as drilling has been suspended until it is more economical. Southwestern started taking stock of the data it has in 2010. Two years later, it began collecting information with the express purpose of running it through machine learning algorithms.
“Now, we have enough wells, enough geologic information, that we can begin to put those together to discover what we don’t know,” said Mark Reynolds, senior solutions architect and knowledge engineer, who pioneered the effort.
For Mr. Reynolds, it became obvious looking at the data that the mechanics of shale didn’t yield to traditional models. In 20 years, he said, a different oilfield boom may render obsolete or, at best, incomplete all the groundbreaking insights being gathered today. “When you talk to the generals, they say they’re always prepared to fight the last war,” he said. ”That’s just human nature.” We at ARC believe that it is that very human nature that typically stands in the way of companies adopting IIoT enabled solutions as well other technologies that will enhance operational performance and profitability.