Oil Industry Data – We need it to do our work, we often have it, but can’t get our hands on it – Still after all these years, has there been progress? Or do we just have a lot more complexity?
We are in the business of drilling holes and tunneling down into various types of rock, in various environments, both from points on land and the seafloor; some being unstable, sometimes containing powerful pressure pockets that can ignite and explode at the surface, to find the target “reservoir” from which to extract volatile resources – liquids and gasses- from under the surface of the earth at various depths, depending on where you are in the world.I love watching sci-fi shows, so let me use a few parallels with our GIS issues: just imagine that in the movie, “Armageddon”, they called a petroleum drilling crew to come to the rescue. Yep, it’s one way of looking at our work – it takes a lot of true grit, and also, it takes the right combination of skill sets to be successful. How many times have you heard this? In the petroleum business, it is certainly true.
In addition to the mechanics of manipulating the environment to extract and produce resources, not unlike science fiction, the dynamic personalities and the politics must be managed, and the risk factors are diverse like no other industry. Oh, and did we mention the regulations and the law? The aspects of health, safety and the environment? – oh those… What if the price goes way up, then drops like a rock? Oh, that too.!
Oil can bring down or it can send up the world economy. Being in the business is quite interesting and can be a “white-knuckle ride” when you have been in it for years, quite like riding a roller-coaster. Bottom line, we must have “good data” to do our work – and what is “good data”? How much do you need? How do you use it? Should you share it? What can’t you share? Good questions. It depends. Depends on a number of variables that can change at any given time.
The future of data.
I watch this one show about “the future” that involves multi-planet, multi-space-station, multi-space vehicle scenarios, with beings from all of the above, most of them having more of a “human” form, somewhat like “Star Wars” or “Star Trek”, but focused more about resource scarcity and power-grabs, politically, on an inter-galactic scale. Data, or lack thereof, seems to be the center of everything.
In a recent episode, detectives had to extract from a bionic man, a chip, which promised to “reveal all”. This was a fairly gory process, as they kept having to stab around to find the target with some sort of large syringe…and it took several attempts –- not unlike searching for the “right information” in some of our work that we do, which, in the end, consumes most of the work project.
The detectives eventually got the chip out of this guy, and immediately, with this fancy mobile all-purpose device, they viewed the contents, and realized it was just a bunch of uninteresting data files — nothing very revealing in terms of secret stuff or help in solving his murder. Upon this discovery, they exclaimed, “This guy’s just a data broker!”
In the next moments, they concluded that this fact had something to do with his eventual demise. Now that is a scary thought! Wow. Is this “The wave of the future?”
Data in GIS
In our geospatial work, we need the right data at the right time to do the analysis to solve the business problems at hand. This is the crux of what we do using GIS tools with geospatial data.
Technology has come a long way since the 1980’s when computer mapping began to creep its way into the “petroleum portfolio of tools”. Now mainstream, mapping and GIS are parts of the greater toolbox used in petroleum. Of late, “big data”, and sensor technology (what we now call the Internet of Things or IoT) – the use of imaging and remote surveying data are reporting for duty in solving problems in petroleum.
New technologies present themselves as the do-all, end-all, usually by someone who has something to sell you. More data faster is not necessarily the answer, as we all know. We need to know HOW to use these data most effectively to solve our problems at hand. “What is the problem definition that requires the new technology and data types?” we ask.
We still have the same problems that presented themselves in the 1980’s. Many solutions have been built. Large portions of the industry’s expenditures have been spent on solving data issues and making solutions to help people get their work done in this complex industry.
How about that simple, popular application?
One person in a recent conversation offered up that Microsoft Excel spreadsheets are likely the most popular data tool, still in wide use, by engineers, geoscientists, financial folks, and everyone else doing business in petroleum. I can’t argue that fact.
However, information and data management/technology folks believe that Microsoft Excel and Access are the bane of their existence, especially those who have to keep control of information that ends up on reports and maps. Using Microsoft Excel for everything can introduce risk if the use of it is not governed by some rules that promote discipline.
Using Microsoft Excel with GIS can be a blessing and a curse. Excel is great for making handy rows and columns of data available to visualize in a variety of ways, but maintaining geodetic parameters is not its forte and mixed with an untrained or unaware user, this can yield wrong results or even worse, disastrous consequences. Positional accuracy is incredibly important. Without it you can imagine the negative implications of drilling in the wrong location.
What about all of the solutions that have come about since the 1960’s?
The conversations about how to solve these problems with data often move into all sorts of “IT-ish” subjects that make the eyes of business people glaze over – and you know that look. These business people are often decision makers, not trained in IT, or fond of listening to “IT speak”. If you are an “IT” person, and suddenly you get “that look”, you realize immediately that you should have stopped talking ten minutes ago. There is no going back.
In a price-pressured environment, scrutiny will be placed on any new solutions or projects that fall under IT expenditures, at least until the market balances and stabilizes. Proven solutions that are truly proven and available are few and far between.
What about “Big Data”?
And no, “big data” is not necessarily the answer, despite suggestions that it will solve your problems – in some cases, it might just make problems worse and even more complicated. You might want to look, but not touch – at least for a while.
One of our GIS colleagues recently was successful in testing the use of “big data” hooked up to GIS. But, it was not easy and took an experienced data scientist to help engineer the solution. And then what? More work to do. This is still experimental and these guys are pioneers in getting to the solution that everyone wants – but, the solution is not cheap, or cheerful, just yet.
OK. Is this where we stop trying?
No. The reality is that we are “data dependent” – addicted to data, as it were. We cannot make decisions without data. We have those gnarly sorts of business problems that require analysis, interpretation, and visualization of the data upon which we will make decisions that involve lots of money, in high-risk environments, maintaining the health and safety of our workers, following the letters of the laws along every step. All of this is in order to drill those holes that are so expensive, but could be so lucrative in the right market environment.
Excel and GIS may be the tools of choice, and easy to use, but the data is still the question, and actually, if it is not “good data”, it creates uncertainty, which creates more risk. Where did the data come from? How old is the data? How many times has it changed hands? Are the locations the right ones? Which Excel workbook can be shared and used by others? Are the formulas correct? Can I bring this one into my GIS project? On and on…
How do we understand “good” vs. “not so good” data? These are key questions that are at the heart of every analysis, prior to making those expensive decisions. The quest for the “holy grail of data”
What is a sensible approach to data use in decision making?
Everyone is (still) looking for a silver-bullet – the magic ball for helping enable good decision making with efficiency and effective results.
Back to our question, “Still, after all these years, has there been progress? Or do we just have a lot more complexity?”