Our industry faces the challenges of achieving an actual “digital field” and of using intelligent-well technologies. What is a “smart” or “intelligent” well? One definition is “a well that has measurement and/or control capabilities in the region of its completion(s).” This can be improved to indicate that “a well system could be called intelligent if, and only if, it adds value to the project during its life cycle.”
To add value to a project, reservoir management must improve beyond the reservoir-engineering discipline, thus becoming a multidisciplinary, integrated team effort. Key project decisions must encompass all disciplines.
The role of intelligent technology, in this scenario, is to gather data from sensors and transform those data into valuable information. Taking advantage of the “digital era,” the oil and gas industry uses more-reliable and more-cost-effective sensors. These sensors significantly increase the amount of collected data, the number of points for the same variable, and even the number of variables measured.
To deal with the enormous amount of data properly, a useful technique is “data validation and reconciliation” of an industrial process. In essence, this technology couples two different worlds: the data (reality) with the model (desire) of a system. Although one may think that data are the real and unique representation of a system, raw data are never fully reliable for reasons such as sensor accuracy and systematic errors. Sometimes data redundancy occurs, and a simple mass balance cannot be satisfied. So, it is nearly mandatory to construct a mathematical model that wraps up all the information into a harmonic and consistent balance of mass, momentum, and energy to represent an entire process for an operating system effectively.
In the years to come, one must seek to incorporate most processes in a modern simulator for reservoir management.
Intelligent Fields Technology additional reading available at OnePetro: www.onepetro.org
SPE 136634 • “How a Smart Completion Can Maximize Oil Production and Recovery Factor in a Stacked Marginal Reservoir” by Gangsar Naroso, ADCO, et al.
SPE 129042 • “Digital-Oilfield Workflows for Increased Automation” by Anil Pande, Infosys Consulting.
SPE 128837 • “Mega I-Field Application in the World’s Largest Oil Increment Development” by Waleed A. Al-Mulhim, Saudi Aramco, et al.