Is Your Oil and Gas Field Data Helping or Hurting Your Bottom Line?04/29/2016
If data quality isn’t made the priority for E&P companies, the decision-making process could be undermined
“Is our field data helping or hurting our bottom line?”
It’s a question that’s rarely posed by data managers at any size oil and gas operator. The consensus in modern business, including upstream oil and gas, is that every bit of data gathered has a positive value. But is this an oversimplification of proper data management?
In theory, all data has value and can help organizations make better decisions. But what about when data is flat-out wrong? When poor data is used to make decisions, poor decisions will be made.
The vast majority of oil and gas data originates in the field; this field data must be validated before it enters a company data stream, or else errors will quickly magnify as data is distributed throughout the organization. In simple terms, an organization can spend all the time and money in the world on data acquisition and management systems, but if data quality isn’t made the priority, these systems could actually undermine the decision-making process.
How do we account for data quality in our daily and long-term decision-making processes? Data must be categorized by the level of accuracy and therefore by the amount of confidence we have in the numbers collected. Now the question becomes, “How do we go about categorizing data into different levels of confidence?” To answer that question, we’re going to use a data quality control method called the “Four I’s”: Inspect, Interpret, Identify, Indicate.
To begin this method, the database must include a source type for each piece of data. This source type could include acquisition methods such as SCADA or manual readings from field personnel. Once this database is ready, the information should be examined using the Inspect-Interpret-Identify-Indicate approach. Now let’s take a look at each one of these individually...
Operators must begin by assessing each way data is collected in their operations and how accurate these collection systems are. Operators should also consider possible sources of error that could be affecting data quality.
The second step in the process can be easily overlooked, but the way data is interpreted and the assumptions made have an effect on data quality. What was the weather and time of day? Did these have an effect on the data that was collected? Certain measurement devices have built-in assumptions as well. For example, many Coriolis flow meters use a constant oil and water density to determine volumetric flow. How accurate is this assumption? The accuracy of these assumptions must also be factored in to the confidence level.
Next, operators must identify which sources of data are the most reliable. Would you trust the diagnoses of a first-year med student or a 30-year medical professional? The same goes for field data. In some cases, SCADA data is preferred over manual gauge readings and vice versa. It is vital for companies to include all data sources in their evaluation to determine the confidence level of each source in any given situation.
Now, operators can indicate a final level of confidence for the specific data set or data source. The best part is that much of this process can be automated.
Data quality control is not just a supplement to a modern data management system but a requirement. The step-by-step approach presented in this post provides a guide for oil and gas operators looking to layer in data validation into their platform. A data validation system helps ensure that data-driven decisions are made with confidence. This simple guide, paired with a powerful data platform, creates a dynamic data validation system that delivers value every day it’s used.
We'll be talking a lot more about data and how it can be used to optimize every facet of an E&P enterprise in upcoming blog posts. In the meantime, check out this customer-success webinar to see how Energen Resources, a leading producer in the Permian Basin, is using real-time production data to boost its production volumes and run more efficient operations.