(originally posted on blogspot January 17, 2010)
The oil business has evolved in drilling, containment, control, and distribution. But the top-level system view has only recently become paramount. Certainly there are pockets of progress. And certainly there are several quality companies producing centralized data solutions. But even these solutions focus on the acquisition of the data while ignoring the reason for the data.
“Simply put Digital Energy or Digital Oilfields are about focusing information technology on the objectives of the petroleum business.” (www.istore.com, January 17, 2011)
Steve Hinchman, Marathon’s Senior VP of World Wide Production, in a speech to 2006 Digital Oil Conference says “Quality, timely information leads to better decisions and productivity gains.” and “Better decisions lead to better results, greater credibility, more opportunities, greater shareholder value.”
“Petroleum information technology (IT), digitized real-time downhole data and computer–aided practices are exploding, giving new impetus to the industry. The frustrations and hesitancy common in the 1990s are giving way to practical solutions and more widespread use by the oil industry. Better, cheaper and more secure data transmission through the Internet is one reason why.” (The Digital Oilfield, Oil and Gas Investor, 2004).
Future Digital Oilfield development will include efforts to integrate drilling data into its engineering and decision making. This integration consists of:
- Developing and integrating the acquisition of data from all phases of the drilling operation. The currently dis-joint data will be brought together (historical and future) into a master data store architecture consisting of a Professional Petroleum Data Model (www.ppdm.org), various legacy commercial systems, and various internal custom data stores.
- Developing a systematic real-time data approach including data processing, analysis, proactive actioning, and integrated presentations. Such proactive, real-time processing includes collision avoidance, pay-zone tracking and analysis, and rig performance. Included is a new technology we a pushing for analytical analysis and recommendations for the best rig configuration and performance.
- Developing a systematic post-drill data analysis and centralized data recall for field analysis, offset well comparison, and new well engineering decisions. Central to this effort will include data analysis, data mining, and systematic data-centric decision making.
“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.