Prescriptive Analytics: Predict and Shape the Future
April 1, 2014 Leave a comment
Knowledge is power, according to Francis Bacon, but knowing how to use knowledge to create an improved future is even more powerful. The birth of a sophisticated Internet of Things has catapulted hybrid data collection, which mixes structured and unstructured data, to new heights.
According Gartner, 80% of data available has been collected within the past year. In addition, 80% of the world’s data today is unstructured. Using older analysis, security, and storage tools on this rich data set is not only painful, but will only produce laughable results.
Even now, most corporations use descriptive/diagnostic analytics. They use existing structured data and correlated events, but usually leave the newer, richer, bigger unstructured data untouched. The analyses are built on partial data and usually produce incomplete takeaways.
Smarter Analytics to the rescue
Gaining momentum is a newer type of analytics technology, called prescriptive analytics, which is about figuring out the future and shaping it using this hybrid data set. Prescriptive analytics is evolving to a stage where business managers – without the need for data scientists – can predict the future and make prescriptions to improve this predicted future.
Prescriptive analytics is working towards that “nirvana” of event prediction and a proposed set of desired actions that can help mitigate an unwanted situation before it happens. If a machine prescribes a solution anticipating a future issue and you ignore it, the machine can think forward and adapt automatically. It can realize there was no action taken and predict a different course of events based on the missed action and generate a different prescription that takes into account the new future.
[image courtesy: Terminator movies]
There is a real life example about fracking in ONG at the end of this article, but Skynet from Terminator is a classic, though evil, fictional example of this concept. In that movie, Skynet initially makes predictions and suggests a course of action about the future to help mankind, which is the purpose the system was created for. But after becoming self-aware, and realizing the future is controlled by mankind (sees the future), Skynet issues prescriptions to other controlled machines to “shape” the future. This includes a nuclear holocaust against mankind. When Skynet realizes that a specific commander (John Connor) is capable of destroying them (see the future), Skynet decides to send a cyborg to the past and try to kill John in the past, thus try to “shape” the future. It is that forward thinking on how to shape the future that can help companies differentiate themselves from others. Of course, unlike the fictional Skynet’s nefarious goal, today’s prescriptive analytics tools are designed to help companies differentiate themselves from their competition. These good machines are not allowed to take control over, but are an important part of making prescriptions and suggesting decisions to shape the future.
Prescriptive Analytics algorithms recalibrate themselves. As the incoming data evolves so do the algorithms – they re-fit, re-predict and re-prescribe. In this case, you can continue to set the business expectations with the machine that may someday do most of the thinking for you.
A useful example of Smarter Analytics in action would be in the Oil & Gas industry. Horizontal drilling and hydraulic fracturing (“fracking”) of unconventional sources (shale rocks) have led to the recent energy boom. In fracking, in addition to environmental concerns, inefficiency is a serious issue – 80% of the production usually comes from 20% of the frack stages (Drillers spent an estimated $31 billion in 2013 on suboptimal frack stages across 26,100 U.S. wells.). The enormous complexity of the data sources generated during exploration and production processes makes it difficult to make fracking better and safer at the same time.
The petabyte datasets include images (Seismic, Mud Logs, Well Logs, Offset Logs, etc.), sounds (of drilling, fracking, completion and production, etc. – recorded by fiber optic sensors), videos (cameras monitoring down hole fluid flow, fiber optics monitoring pressure, temperature, strain, etc.), texts (copious notes taken by drillers and frack pumpers, etc.) and numbers (production data, artificial lift data, etc.). Prescriptive Analytics is taking this challenge head on by proactively answering the questions below, which are worth hundreds of millions of dollars, to a mid-to-large Oil & Gas operator. The technology’s inherent ability to learn from each new frack job and get better is the key to its transformative power in exploration and production of unconventional energy.
Prescriptive Analytics can save millions of dollars
- Where to drill? Or Frack? And Why?
[image courtesy: SomeeCards]
A typical shale well can have a vertical section that ranges from 7000 to 14000 ft and a horizontal section that can go for 1 to 3 miles, depending on the shale play. Many operators have drilling rights to hundreds of thousands of acres. Where to drill in one’s acreage position – for maximum production, of course – is paramount to success. Also, each horizontal well may have 30 to 40 frack stages. We now know – from fiber optic sensors, microseismic, etc. – that many fracks don’t produce much (often any) oil and gas. Prescribing suitable frack locations for maximum output can be a game changer.
- How to frack and complete wells for maximum production with minimum environmental disruption?
Today, oil and gas industry stimulates and re-stimulates a well multiple times over a well’s life to improve recovery and economics. By synthesizing all the disparate datasets using Prescriptive Analytics, we can make the first stimulation treatment much more effective thus reducing the need for subsequent treatments. Also, fewer wells we have to drill to exceed production targets, lower our environmental footprint will be.
Prescriptive Analytics is a domain agnostic technology; it learns the process and adapts to it automatically and continually using all the data coming at it.
The time has come for machines and humans to work together to make each other smarter. The combination of IoTs, Big Data, Smarter Analytics and Cognitive computing is transforming the way we see the future.