The oil industry has undergone significant transformations in drilling, containment, control, and distribution. However, the holistic system perspective has only recently gained prominence among technologists. While there have been pockets of progress and some commendable efforts in centralized data solutions, many of these solutions primarily focus on data acquisition, often overlooking the underlying purpose of this data.
"To put it simply, Digital Energy or Digital Oilfields revolve around aligning information technology with the core objectives of the petroleum business." (www.istore.com, January 17, 2011)
Steve Hinchman, Senior VP of World Wide Production at Marathon, emphasized in a 2006 speech at the Digital Oil Conference that "Quality, timely information leads to better decisions and productivity gains," and he further stressed that "Better decisions lead to better results, greater credibility, more opportunities, and enhanced shareholder value."
"Petroleum information technology (IT), real-time digitized downhole data, and computer-aided practices are experiencing a surge, providing new momentum to the industry. The frustrations and hesitations prevalent in the 1990s are giving way to practical solutions, driven in part by improved, cost-effective, and secure data transmission via the Internet." (The Digital Oilfield, Oil and Gas Investor, 2004).
The future development of Digital Oilfields will entail integrating drilling data into the engineering and decision-making processes. This integration involves:
- Aggregating Data: Consolidating data from all stages of the drilling operation, including historical and future data, into a master data store architecture, which incorporates the Professional Petroleum Data Model (www.ppdm.org), various legacy commercial systems, and internal custom data stores.
- Real-Time Data Processing: Implementing a systematic real-time data approach encompassing data processing, analysis, proactive decision-making, and integrated presentations. This involves tasks such as collision avoidance, pay-zone tracking and analysis, rig performance optimization, and the incorporation of analytical analysis and recommendations for optimal rig configurations and performance.
- Post-Drill Data Analysis: Establishing a systematic post-drill data analysis and centralized data retrieval system for field analysis, offset well comparison, and new well engineering decisions. This effort will involve data analysis, data mining, and data-centric decision-making processes.
"The promise of enhanced operational performance through a seamlessly integrated digital oil field is captivating, but the tasks required for a realistic implementation demand specialized expertise." (http://www.epmag.com/archives/digitalOilField/1936.htm)
Achieving seamless integrated operations necessitates a comprehensive view of the entire exploration process. Notably, the drilling operation generates a wealth of diverse operational and performance data, often exceeding other processes in downstream data production. Furthermore, drilling is one of the most legally sensitive processes in the energy production sector, as exemplified by the BP Gulf disaster.
The seamless, integrated digital oilfield places data at the heart of its operations. Data is both the starting point and the endpoint of the process. However, data itself is not the ultimate goal; rather, it can be a hindrance to achieving information and knowledge. Nevertheless, data serves as the foundation of the information and knowledge tree, with data leading to information, information leading to knowledge, and knowledge eventually leading to wisdom. Elevating data to the levels of information and knowledge necessitates a systematic and profound understanding of the data within an organization, the data that should be available, and its significance.
Data mining is the overarching term used to describe the process of transforming data into information and knowledge. Specifically, data mining is the process of elevating data to the level of knowledge, extracting operational metrics' patterns and trends to forecast outcomes.
Fortunately, there are notable examples of forward-thinking entrepreneurs advancing data-to-knowledge conversion. One such example is Verdande's DrillEdge product (http://www.verdandetechnology.com/products-a-services/drilledge.html). While this blog does not endorse this technology as a matter of policy, it serves as a compelling illustration of forward-thinking and systematic data-to-knowledge development.
Another notable example is PetroLink's modular data acquisition and processing model (http://www.petrolink.com). This product leverages modular, vendor-agnostic data accumulation tools (particularly interfacing with Pason and MD-TOTCO), modular data repositories, modular equation processing, and modular displays—all achieved through the utilization of the WITSML standards (http://en.wikipedia.org/wiki/WITSML).
In future blogs, we will delve into topics such as data movement, latency, reliability, and synchronization, offering valuable insights for technologists navigating the evolving landscape of digital oilfields.