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AI Coworker

With everyone talking about Big Data, Advanced Analytics and the Industrial Internet of Things, I have been trying to look beyond the hype, think about what's next and the adoption challenges all this brings. These new technologies are coming at a time when the oil and gas industry is trying to see if it is safe to lift their heads out of the bunker they have been in for the last several years due to low commodity prices. Some, especially those invested in unconventional plays in the Permian Basin, have already left the bunker and are charging ahead with lease acquisitions, mergers and new drilling programs. Oil production is going up, inventories are going up, more pipelines are getting built, oil field service rates are rising on higher demand, but their prices are staying down.

Others are more cautious and many are still trying to "fix" their asset portfolios, selling properties to repay debt, (many majors reducing downstream assets for a few examples), cutting capital budgets and projects from their annual plans, leaning on suppliers to keep prices low and some are even trimming staff a little more. Exploration is down and close attention to the operating budget is still essential.

But the technology advances are not waiting for commodity prices to rebound. The cry for digitization and new business models ring from consultants' speeches at every conference. Are we looking at these new technology advances in the right way? Are they just new and more capable tools or is there another way to think about adoption and transformation?

My thought process runs to a convergence rather than to the new capabilities themselves. I am more of a broad integrator then a deep specialist anyway. Here's my logic. What if we are able to integrate the new analytics capabilities, the new data lake capabilities, the cloud computing infrastructure, the new robotic process automation advances, with new interfaces enabled by mobile and voice technologies? Instead of buying a bunch of new tools could you think about Artificial Intelligence (AI) as a new coworker?

With the demand for increases in productivity (doing more with less), your new coworker, let's call him, Buddy, can interface with you by voice command and response (just like Apple's Siri®, Microsoft's Cortana®, Amazon's Echo®, IBM's Watson® and others). Unlike these others, Buddy knows the oil business (domain knowledge) and speaks the language that your job responsibilities require. Buddy knows how to navigate the diverse data environment, understands the business processes and authority chain unique to your company. Buddy is enabled by process automation and data discovery technology. At the appropriate stages, Buddy can answer queries with data from relevant sources and perform requested data processing, analysis and display steps. Buddy can even complete standard reports, escalate requests and recommendations to the proper level of authority.

Most importantly of all, Buddy saves time. Think about what else you could do while Buddy performs your time-consuming tasks of finding the right "trusted" data, performing data quality checks and updates, complying with standard work processes, processing data through sophisticated analytics tools and procedures. With all that happening behind the scenes, you are free to evaluate, to prioritize and just to think about what your data-driven world has for you.

You may think that this is a science fiction vision. You are right, but new projects like IBM Watson® working to understand stuck pipe, Amelia® from IPSoft, working on back office accounting tasks for Shell and Baker Hughes and others that haven't yet come out of the R&D labs, we may not be as far from this vision as you think. I recently worked with my team at Noah/Infosys developing an initial pilot of a "Buddy" like AI co-worker using Amazon's Alexa as the user interaction platform. It was both a great manifestation of the AI co-worker idea, but also served taught us several lessons on the complex challenges associated with deploying this type of technology. For example, in order for Buddy to know where to go get clean data, you need to have clean data, meaning many of the age-old data management challenges will still need to be addressed for AI co-workers to be implementable. Next step is to further leverage Infosys' Big Data and AI platform, Nia, to take the Buddy concept to the next level.

If all this new technology is going to truly transform our industry instead of just adding more tools to our already overwhelming collection of applications, we need to challenge our view of new technology. The use of automation has to take care of routine tasks, not just pile more data and more email requests on our desks (maybe they are text messages and tweets instead of emails these days). We already have more data than we can cope with and more is coming. We need this technology to find, sort, filter, process, model and simulate and then display the interesting stuff.

I am not suggesting that your company start a robotics resources group to complement your human resources group just yet. I am asking you to think about all this new technology in a different way. It may be science fiction right now and many early pilots will probably fail to some degree, but things are changing fast. Self-driving cars are an example of technology potentially changing the future of transportation. Drones are changing methods of visual inspection across industry (Cyberhawk) and even how countries fight wars with more uses being discovered each day. Drones are controlling themselves as remote operated vehicles (ROV) and autonomous underwater vehicles (AUV). Gamification is using Virtual Reality (VR) and Augmented Reality (AR) to turn training sessions into video "games" with scoring and even competitions.

There are new "smart technologies" transforming the oil field at an increasing rate of speed. Some of these technologies will help with the daily challenge of trying to find data to make better decisions and "do more with less".  Unlike the movie, "2001, A Space Odyssey", the AI Hal, might be a glimpse of how it might look, but I hope it isn't trying to get rid of you somewhere in the plot.

Ok, Buddy, let's get to work.

 

Comments

A vision very well explained. I really believe that sooner than later we will have an AI Assistant for many routine and even not routine tasks. I would like to add:
1 - Although Buddy might be real soon, he/she has to have a Master orchestrator/coordinator which will also likely be of course also an AI agent subject to a real human-being. In the future, I still see human beings not relegating fully autonomy to AI agents.
2 - The scenario you have described looks to me completely feasible, but in stages, due to the natural resistance of human beings to see their domain of action invaded (my expertise, my education, my years of dealing with this kind of problems, etc.), human pride, selfishness, etc.
A 1st Stage looks to me looks like individual equipments (speaking of Oil & Gas, like ESP pumps) actioned by intelligent AI Agents in situ, speaking to other similars, and its controller, to optimize production across the field, sending request to other agents to increase gas injection, for a specific and particular need (maybe on a GasLift installation), monitoring potentials faults in the equipment and requesting some other agents to perform economics on replacement of parts and equipments. Therefore on this 1st stage is all about IoT with governance plus intelligent and autonomous operation.
In a 2nd Stage, looks to me as a second level of abstraction, similar to the human-being (petroleum engineer) proceeding with higher levels of analysis, abstraction, modeling, data requests, and asset optimization and able to relate and most probably by AI learning to other areas of field development, and in doing so, increasing potential and book value of the investment for the shareholder. ... We can continue to dream this possible dream but I think we are in the right track. There are numerous brilliant minds in Computer Science, AI, and Statistics, presenting novel ideas to resolve the main draw back and challenges in Machine Learning and AI.

Thanks for your comment and your support for the vision. I agree that implementation will be in stages as the human gets more comfortable with the capabilities of the AI agent. It is a matter of trust. Does the agent understand what I am asking for? Putting a natural language speech interface on the AI system is a start. This requires a common language, or semantic ontology, that covers even very specific terms and acronyms (get the buzzwords down). Starting with specific pieces of equipment (stuck pipe, or ESP installation, or pumping unit performance for example) is a good first step. As the comfort and trust level expands and the actions of the AI agent (Buddy) perform according to expectations, then more of the capability of the AI will be implemented including the closed loop automation of some field processes.

This will be a journey marked more by the change management acceptance by the human operator for the AI coworker's capabilities and not on the advances in the algorithms (they will proceed faster in the data science team than in the operations center). It will take several steps, over several years, and proceed at different paces, in different operators and oilfield service companies, but I think the transformation has already started (resistance ifs futile using a Star Trek analogy). I agree the human does not get cut out of the picture, but her ability to monitor more wells and optimize complex operations, will only grow as Buddy becomes a trusted coworker. Her productivity will rise as Buddy helps her to shift through more data, faster and analyze exceptions to predicted behavior.

Thanks for you comment. I am writing another article on some of the barriers to adoption to AI (Summoning the Demon). I look forward to your comments on that blog.

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