(originally posted on blogspot January 18, 2010)
“The improved operational performance promised by a seamless digital oil field is alluring, and the tasks required to arrive at a realistic implementation are more specialized than might be expected.” (http://www.epmag.com/archives/digitalOilField/1936.htm)
Seamless integrated operations requires a systematic view of the entire exploration process. But the drilling operation may be the largest generator of diverse operational and performance data and may produce more downstream data information than any other process. Additionally, the drilling process is one of the most legally exposing processes performed in energy production – BP’s recent Gulf disaster is an excellent example.
The seamless, integrated, digital oilfield is data-centric. Data is at the start for the process, and data is at the end of the process. But data is not the objective. In fact, data is an impediment to information and knowledge. But data is the base of the information and knowledge tree – data begets information, information begets knowledge, knowledge begets wisdom. Bringing data up to the next level (information) or the subsequent level (knowledge) requires a systematic and root knowledge of the data available withing an organization, the data which should be available within an organization, and the meaning of that data.
Data mining is the overarching term used in many circles to define the process of developing information and knowledge. In particular, data mining is taking the data to the level of knowledge. Converting data to information is often no more complex that producing a pie chart or an x-y scatter chart. But that information requires extensive operational experience to analyze and understand. Data mining takes data into knowledge tier. Data mining will extract the tendencies of operational metrics to fortell an outcome.
Fortunately, there are several bright and shinning examples of entrepreneurs developing the data-to-knowledge conversion. One bright and promising star is Verdande’s DrillEdge product (http://www.verdandetechnology.com/products-a-services/drilledge.html). Although this blog does not support or advocate this technology as a matter of policy, this technology does illustrate an example of forward thinking and systematic data-to-knowledge development.
A second example is PetroLink’s modular data acquisition and processing model (http://www.petrolink.com). This product utilizes modular vendor-agnostic data accumulation tools (in particular interfacing to Pason and MD-TOTCO), modular data repositories, modular equation processing, and modular displays. All of this is accomplished through the WITSML standards (http://en.wikipedia.org/wiki/WITSML).
Future blogs will consider the movement of data, latency, reliability, and synchronization.