During the PNEC 2015 conference last week, we managed to entice some of the attendees, passing by our booth, to take part in a short survey. As an incentive we offered a chance to win a prize and made the survey brief, we could’t make it too long and risk getting little or no intelligence.
I’m not sure if any of you will find the results to be a revelation or offer anything new that you already did not know anecdotally. But if nothing else, they may substantiate “feelings” with some numbers.
You will be pleased to know that more than 60% of the replies are from operators or NOC. In this week’s blog I share the results and offer my thoughts on the first survey question.
In the above question and graph “Which data projects are of high priority in your mind?”, it appears the industry continues to pursue data integration projects and the majority of the participants (73%) consider them to be the highest priority. Followed closely on the priority list were “data quality” projects (data governance and legacy data cleaning), 65% consider these a priority.
Integration will always be at the top of the priority list in the E&P world until we truly connect the surface measurements with the subsurface data in real time. Also, given that data integration cannot be achieved without pristine data, it is no surprise that data quality follows integration as a close second.
Because many “data cleaning” projects are driven by the need to integrate, data quality efforts are still focused on incoming data and mostly on “identification” data, such is the case in MDM projects.
Nonetheless, how a well was configured 20 years earlier and what failures (or not) were encountered during those 20 years are telling facts to engineers. Therefore, the quality of “legacy” technical data is just as important as of new incoming data.
Reaching deeper than identification and header data to ensure technical information is complete and accurate is not only important for decision making, but as my friend at a major company would say: it is important firstly for safety reasons, then for removing waste (lean principle) and then for decisions. Of course chipping away slowly at the large mountain of data is a grueling task and can be demotivating if there are only limited results.
To get them done right with impactful E&P business results, these projects should be tackled with a clear vision and a holistic approach. As an industry we need to think about legacy data preparation strategically, do them once and be done with it.
Legacy data cleanup projects are temporary (with a start and an end date), experience tells me they are best accomplished by outsourcing them to professional data cleaning firms that fully understand E&P data.
This blog is getting too long, I’d better cover the results of the rest of the survey in the next one.
Please share your thoughts and correct me where you feel I got it wrong….