The Pro: Digital field data capture is an improvement.
Transitioning from grease books to digital field data capture (FDC) provides visibility into daily work. The transparency generates insights, field aggregations and informs decisions. As a result, field data capture has changed the data availability game in oil and gas operations.
The Con: We don’t use it effectively.
However, the technology has now reached a tipping point causing ineffective work, misinformed decisions, and workarounds. Companies have prioritized data availability rather than utility. I call this the “give me everything so that I can do nothing” approach. Does this sound like your business?
Seven signs of a field data capture problem
#1 You add things to capture but never remove any.
As you learn what data is beneficial to capture, you should logically eliminate inputs that are not useful. Data utility is a word we need to start using in O&G. Stop wasting time capturing data that does not help you automate a problem or inform decisions.
#2 You only capture failures, not actions.
There is too much focus on what went wrong instead of what went right to get a well back to normal production. How do you expect to solve a problem more efficiently if you only capture what failed? Start capturing corrective actions that solve a problem so that when you find similar issues, you have a starting point of effective steps to take.
#3 You aren’t trying to automate data capture.
In 10 years, do you see yourself manually capturing more or less data than today? Your answer should be less. Businesses should use data to train models to remove the need for manual entries. For example, why does someone need to input a deferral (downtime) code for a down compressor when you have SCADA that tells you when & why the compressor failed? Design models to remove 80% of the need for human entry.
#4 People enter information differently.
Inconsistency in data entry creates inaccuracy in decisions made on the data. Data entry becomes ineffective when operations cannot use it to improve. Technology like AppCues can help drive consistent field data capture or create simple, thoughtful flow diagrams for when operations should use each form.
#5 Despite capturing data, the problem is still unclear.
You may find yourself calling/emailing the operator to understand the problem. If the data doesn’t speak for itself, the low utility data causes workarounds. Start with what the calls/emails are about, then adjust the current forms to prevent the need for the calls & emails.
#6 Field staff says that entering all of the data is useless…
First, they are probably right, and second, you have a communication problem. Data entry isn’t useless when it helps people do their job more effectively. But, unfortunately, the data either isn’t available to them or is actually meaningless. When the field believes the data entered is useless, you get lower quality entries creating low-quality decisions made on those poor entries. Understand what data they are entering that they define as useless. Then, either communicate why it is necessary, remove it, or adjust it to create higher utility data.
#7 …and they do not have a say in the data collected.
Data capture forms are used but rarely revised. Create a feedback loop within each form to get stakeholder feedback so that you can increase the quality of the data captured. Keep it simple & open-ended: such as “if you think this form is crap, tell us why.”
Our approach to data capture
At Tasq, we believe all data should have high utility. Our platform only requires data entry that either helps automate the process by training a model or improves an operator’s effectiveness.
Simple is still thoughtful. As Mark Twain once said:
“I apologize for such a long letter — I didn’t have time to write a short one.”
As a parting question, ask yourself how much data you are capturing and how useful is it to your business?