Digging Deeper into your Data
Digging Deeper into Your Data
By Robert C. White, Jr.
Explorationists have a tendency to take critical exploration data at face value. Of course, you want your well locations to show up in the right place on your map. What measurable confidence do you have that this location is indeed correct? An exploration project will probably not get very far if a critical pipeline doesn’t show up or you’re missing a well or two on your map.
There are many other aspects of your data you might not have thought about if you collect your data in a non-rigorous, haphazard way. You may have experienced using raw data from the Internet only to discover a confusing variety of formats, coordinate systems, undefined accuracies, and missing or invalid attributes. If your AOI is big enough, merging data sets together from a variety of different sources also becomes a problem not only the first time you do it but each time you attempt to update. The trick is to solve these problems quickly and then move on to your analysis.
Data can be enhanced and improved to address these problems in unexpected ways, particularly if you have access to a Geographic Information System (GIS). Data that has been enhanced can then be converted to geospatial formats that work with popular exploration software programs.
Consider the common problem of missing or obviously incorrect well elevations in your data. An incorrect well elevation will create a data bust on a contour map. An elevation bust will also result if the well is in the wrong location, one reason you need to use an accurate land grid. These problems will require manual cleaning before you can perform interpretations.
In what ways can the data be enhanced, improved, or processed? What if instead, you established a workflow to compare the well locations to a high-resolution digital elevation model before you began to use the data to identify busts? At the very least, you would be alerted to suspect elevations. Taking steps like this can provide you an estimate or baseline of the well’s elevation. In addition, you could use digital contour lines of sufficient accuracy to estimate the well elevation.
The strategy is to have these data sets at your fingertips. Some methods can be automated for improving your data while other methods are simply improvements in workflows. Taking advantage of such methods increases your efficiencies and lets you get on to more important things. There are numerous strategies to improve information about almost every data element in your database.
Another overlooked possibility is comparing one database to another. For example, when a well is permitted, wouldn’t you want an immediate email if that well is near your pipeline if you are a pipeline company? Would you want to be alerted internally if the permitted well has special considerations such as steep ground or is near a wildlife area?
Upon cleaning your data, what other things can your data then reveal? Data can reveal interesting patterns that may prove critical to your exploration efforts. Good data sets can establish the framework for additional computations, provided we have some confidence in their collection and processing history. For instance, adding lot and tract data to a land grid database allows for more precise mapping of legal descriptions and by extension better analysis of competitive lease positions.
Have you considered querying your data and looking for rates of drilling activity in different areas? Which areas are heating up? Which areas are companies moving away from? What are the effects of legislation on the oil and gas industry? Quantifying trends like this and publicizing them can have a huge impact on our policy and decision makers. How many wells are producing in a given area? How many have been shut in? How many wells have been plugged? What are the net changers in the past quarter? The past year? The analysis potential is limitless.