UK-based Gastech News interviewed me in 2015 for a short 5-part Q&A series on how land management and information technologies can be used to maximize activities in the gas industry.
- Part 1: What are your main considerations on land management and information technologies to maximise activities in the natural gas industry?
- Part 2: What are the key challenges to evolve land data management?
- Part 3: How are gas companies transforming their land departments?
- Part 4: What is the most successful data integration approach?
- Part 5: How can companies perform a successful integration between activities related to the volume of land and lease data?
Although the questions focused on the gas industry, the answers apply to the entire oil & gas industry.
Here is Part 5….
How can companies perform a successful integration between activities related to the volume of land and lease data?
Everybody knows that “a picture is worth a thousand words.” Can you imagine a geologist scouting a potential well or a reservoir engineer analyzing a field solely based on numbers in a database……no graphs, no maps, no simulations? They have to sift through more data than anyone.
But when that high volume of raw data is processed into a visual, then these geologists and engineers can successfully derive meaningful insight because that information is much more useful to them now. It’s simple, it’s organized, it’s understandable, and now it’s actionable.
Land administration does this too using maps but often not to its full potential. Why? Sometimes it’s a lack of knowledge or resources, often it’s because of the difficulty of translating data that does not reflect the “single version of the truth.”
See how trust comes back to haunt us?
Thankfully land organizations are making progress in managing their land information assets more efficiently and effectively as they move from legacy ‘silos’ to enterprise solutions. Integrated geodatabases and mapping software applications support the land administration life cycle by capturing/managing/processing the lease data, but often it can only disseminate that information in a grid associated to that spatial representation of the leased lands. While this can be caused by limitations in the geographic information system (GIS), often it stems from the complexity of the lease data itself.
And now here is where the lack of simplicity can come back to haunt us.
Take for example the many obligations (aka commitments), provisions (aka requirements) and depth limitations (aka restrictions) set forth by a lease. One could easily use spatial analysis to display upcoming drilling obligations, critical shut-in provisions and retained depth formations by representing each as a ‘layer’ on the map. Define some business rules and develop some filtering criteria, now you can visually see which drilling obligations are due in 3/6/9 months, what leases have shut-in periods of 60/90/120 days, and where shallow and deep rights are being maintained.
Simple sells, simple also translates very well.
But, what if your data is not that simple? Many companies quickly grew not only by leasing properties themselves but by obtaining thousands of leases through a myriad of acquisitions. The lease data converted from these other land management systems may not reflect the same standards as your own. With limited resources and the law of diminishing returns always in effect, it would be difficult to see value in fixing every disparity.
So companies which did not initially develop procedures to spatially capture these obligations, provisions or depth limitations may end up with complex data which is very intensive to process and difficult to represent visually.
For example, a shut-in provision with 3 data values (60/90/120 days) could be a single mapping layer with each data value represented by different color. But instead of using data values, what if each criteria was given its own code (SI60, SI90, SI120). While each can still be a different color, they are captured on separate mapping layers so now the system must process 3 layers instead of 1.
Depth restrictions by their very nature can become quite complex. Depth intervals may be represented in units of measure or named formation (e.g. 15,000 feet vs. Queenston Formation), they may involve variances (100 feet above the top of…, 250 feet below the base of….), stratigraphic equivalents, etc. So instead of 30 layers, 30 formations in a field may be reflected in the database with hundreds of distinct codes (aka keys) to accommodate for all the variations of depth restrictions on a large volume of leases.
It’s easy to see how keeping something simple is itself a very complex thing to do.
What’s your opinion? How does the large volume of land and lease data affect managing your assets successfully?