Abstract
Drilling operators worldwide face increasingly complex and costly drilling challenges. In addition to difficult and sometimes harsh downhole environments, the driller is also faced with a dramatic increase in the quantity and quality of information available to optimize recovery. In many of these situations, there is little or no room for error and the cost of nonproductive time (NPT) due to a poor or less informed decision can significantly increase the final cost per barrel to the consumer.
Advances in cost-effective satellite communication and the application of compression technology have removed the bandwidth limitation, and Internet transfer of continuous high-volume real-time data between the offshore installation and shore-based operations is almost seamless.
With the current shortage of skilled personnel, most major oil and gas companies are exploring remote Operation Support Centers to support their real-time E&P business processes. With a combination of infrastructure, technology, services and processes, these support centers are consolidating their expertise in a collaborative environment so that informed real-time decisions can be made to improve drilling processes and reduce costs.
The economic justification to management and partners for investing and building support centers is clear, but it is necessary to scale the effort and capital expenditure for support centers to each specific drilling operation. This paper describes the concept of low-cost remote support centers that have been used successfully in the Gulf of Mexico, and land drilling projects on the Alaska North Slope and in northern Mexico.
A successful support center must quickly turn real-time data into useful information displayed with minimum latency in a format benefiting decision makers. This depends on the skill and experience of each participating engineer and the quality of the software answer products in this surveillance environment. This paper describes advanced real-time software answer products that take raw drilling data and filter it into different rig states, automatically generating logical outputs.
