In our last blog, we covered the importance of quality data across well cohorts. In this last blog of our series on data quality, we will address why data quality matters. For production, if an organization is consistently using allocated production, actual production may vary by roughly 10%. At OspreyData, we have seen much higher rates when the allocated wells have a number of up and down cycles on production. This can cause problems with a true-up of the actual decline in the well and can ultimately lead to inaccurate reserve determinations. The downside to this scenario is that reserves drive a company valuation and ensure ongoing employment for all of the teams.
In another scenario, there may be an electrical submersible pump (ESP) that sees spikes in overheating, which may not be seen without adequate frequency in the polling of data. This will most certainly shorten the life of those very expensive pumps. Similarly, on a rod pump well, it is easy to see “pounding” on Dynacards, but the identification of “friction” is not always as noticeable. Quality data can show “friction” and help to avoid some of the issues that friction causes. Repairing holes in tubing or casing can be very expensive, but quality data leads to faster response times, and time is money.
The conclusion is that effective and accurate data quality can lead to early detection of problems, issues, or suboptimal states that will aid in improving production levels, reducing well downtime or deferred production. Establishing a set of guidelines or criteria for evaluating the collected data enables teams to more directly determine what data can assist in their overall artificial intelligence journey. The goal is to derive enhanced and efficient management of the field operations. This allows for increased capacity to handle more activities and make more efficient, effective, and pro table oil fields.
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