| Popis: |
Although the oil and gas industry continues to progress with drilling automation, technical challenges remain and must be addressed before this vision can become a reality. One of these challenges is the data aggregation between operators, service companies, drilling contractors and equipment manufacturers. Automation requires that the system not only control sub processes, it must also enable more complex intelligent systems to plan and react to real-time evaluation criteria and respond to predictive intelligence in real time. Data from control systems such as top drives, and evaluation systems such as downhole logging while drilling (LWD) sensors, must be aggregated in a meaningful way so it is portable, contextual, and actionable. Consider controlling the rate of penetration (ROP) based on predictive analytics assessed from formation evaluation (FE) data provided by an LWD sensor. To achieve this, intelligent control and evaluation systems must monitor and exchange information and events within a single, shared perspective. In this paper, we review several technologies currently in use throughout the drilling industry— WITS, WITSML, OPC and PROFIBUS—and present case studies describing the current roles these technologies play and the problems they solve. The authors then analyze the limitations between these interoperable systems and associated barriers in achieving the drilling automation vision. This paper recommends defined goals for the data aggregator and identifies existing challenges surrounding its deployment in current drilling automation environments. The authors also provide recommendations for how the industry should proceed with the implementation of data aggregation for automation. These recommendations ensure drilling automation becomes more than simple process mechanization, but advances towards intelligent systems that transform plans into reality and optimize performance and cost. |
