One of my Oil & Gas clients posed a challenge to me once to apply technology to improve oil and gas pipeline corrosion detection. At the time, I was the CTO for Oil and Gas at HP Enterprise. I accepted the challenge with a condition for the client to fly me and one of my IT engineers to visit the Oil and Gas field.
One constant problem in the oil & gas industry is the corrosion of pipes which is detected by a trained detection crew. Throughout a pipe’s lifetime, Oil & Gas and harsh climate conditions corrode steel pipes from both the inside and outside. Pipe corrosion detection is no light job, with the standard corrosion detection crew consisting of 3 people: 1 team leader in the truck, and 2 team members in the field, usually only a few hundred yards away, operating a set of heavy X-Ray equipment to scan the pipe. Until a pipe is near bursting, the cracks can only be seen by a trained professional, usually, the team member sitting in the truck, who has at least 6 years of X-ray reading experience to accurately detect pipe corrosion.
This traditional pipe corrosion process is labor-intensive and costly. That’s where our team came in to develop a technology solution to hopefully automate the process to make it more efficient and safer.
"The productivity increase allowed hundreds of miles of the pipeline network to be scanned more efficiently with better coverage and frequency, avoiding potential significant environmental accidents while extending the longevity of the pipeline, an expensive capital outlay. "
We came up with all kinds of great ideas out of our innovation lab. In the end, the solution we successfully deployed had less than 50% resemblance to our original idea. Yet, our solution worked, only because we insisted on an in-the-field Design Thinking implementation approach. Here are the 3 lessons learned:
1. Define the problem and opportunity, but don’t forget the context and constraints
I facilitated a Design Thinking workshop at corporate and in our innovation lab, following the well-good-time approach Stanford popularized through its Design Thinking classes. The opportunity is automation for productivity. We came up with ideas of flying drones and autonomous vehicles to run the X-ray machine, running the AI/ML to auto-detect the corrosion, and using the cloud to aggregate the data and findings to visualize the overall conditions of the pipeline. Once we went to the oil field in the arctic circle, we discovered that over there the pipeline can be raised 50 feet above the ground with crosswind routinely over 50 miles/hour. There is no neat service road along the pipeline as we had imagined, thus our idea to have drones and autonomous vehicles traveling along the pipeline to perform the X-ray scan would not work. As it turned out, the automation opportunity is less about the 2 crew members in the field, but rather the team leader in the truck, which our solution eliminated. For safety reasons, the field crew must be two. We did find ways to make the life of the crew easier, such as Bluetooth-enabled gloves to trigger X-rays, smartphone-integrated instant x-ray quality checks, and specialized high-precision GPS to guide inspection location. In the arctic circle, all satellite dishes face horizontally to the ground as the location is at the top of the world. A Smartphone GPS would simply not be precise enough.
2. Human connections matter, insist on going to the field
Once we had a base solution from the Corporate Design Thinking workshop, the client wondered why we still wanted to fly to the oil field, given the cost and process involved including a private jet, hazmat suits, background checks, etc. I held to my original request and insisted we facilitate the Design Thinking workshop in the oil field. In retrospect, part of the motivation was perhaps curiosity and the desire to experience the arctic circle and 24*7 daytime. I was warned that the Unions and workers there would be resistant to automation. But after the 3 days of our workshop and listening to the workers on their daily challenges, they were excited about our solution.
They told us, “Over the past 10 years corporate tried 3 times to deploy solutions that did not work. You are the first team that made it here and listen to us.”
As it turned out, the productivity increase allowed hundreds of miles of the pipeline network to be scanned more efficiently with better coverage and frequency, avoiding potential significant environmental accidents while extending the longevity of the pipeline, an expensive capital outlay.
3. Fit your solution to the operational environment
After the workshop and listening to the workers, the final technological solution we arrived at was an AI/ML TensorFlow model trained with over 6000 plus x-ray images. However, practically implementing this solution posed some technical challenges. Connectivity to the endpoint is spotty and in-field IT support is extremely limited. How do we make the deployment solution as simple as possible? We ended up using Docker container technology to wrap the entire software solution into a dock image file, so that deployment would be as simple as copying and pasting a file. This approach deviates from standard IT practices, but it works beautifully in the field. We also replaced the Wi-Fi router with a 200 ft ethernet cable to ensure a solid connection between the X-ray scanner and the truck-mounted ruggedized computer. The workers turned out to prefer this resilient solution far over Wi-Fi which depends on a steady antenna in harsh conditions and geography with many metal tubes around, sometimes over many layers.
In summary, Smart Technology can dramatically increase productivity, as we have seen in this example of reducing the number of crew members from 3 to 2. The essence of Design Thinking is not “what are your requirements”, but rather, “how can I listen better to understand the problem, the context, and the constraints to arrive at a solution?” To effectively implement smart energy solutions, IT leaders must lead from the front, listen deeply, and be flexible with solution choices beyond the usual corporate IT toolbox.
