The Internet of Things (IoT) addresses an old and continuing problem: the lack of access to adequate and timely data with new and more capable lower cost solutions. The IoT first solves the data sparsity problem by increasing the volume and usefulness of data. Then, IoT solves the data accessibility problem by making it available almost everywhere to almost everyone at an exciting lower price-point.
Applying these IoT advances to a range of energy industry problems will be a major factor in helping the industry return to profitability in the new, lower commodity price environment through innovation based on better data.
Here are some examples of how the IoT enables innovation in the upstream energy industry:
1. Data collection frequency
Pre-IoT, the energy industry was hampered by the sparse collection of data points — about one data point per day — and long elapsed time to move the data due to high capital and operating costs.
With an IoT solution, the flow rate, pressure, and temperature data collection frequency can now be every minute, or even every second, with near real-time data availability. The higher frequency data enables optimizing the operation of facilities such as gas plants and pipelines.
2. End-point sensor cost
Pre-IoT, the high end-point sensor cost of over $1,000 each made their widespread use in the energy industry uneconomic. This high capital cost of sensors restricted their use to expensive processing facilities such as SAGD production sites and heavy oil upgraders.
With new low cost IoT end-point sensors costing less than $10 each, multiple sensors can be affordably implemented more widely to increase performance even at lower value locations such as wells and compressor stations.
3. Ubiquitous data networks
Pre-IoT, high-cost proprietary or non-standard data networks precluded gathering sensor data in the energy industry. Much of the data was collected using the tedious sneaker network.
IoT devices at remote field locations can now collect data about well and pipeline performance. With Internet ubiquity, the data is sent to a central data centre through an affordable, standardized network for remote monitoring. The near real-time data availability leads to higher production volumes, better operating cost control and reduced impact of equipment failures.
4. Cloud computing
Pre-IoT, the high capital cost of computing hardware together with the high operating cost for the computing environment hampered the widespread application of near real-time computing in the energy industry.
Lower costs for IoT hardware, integration software, data centre management software, and reduced electrical consumption created cloud-based computing. The shared operating cost model for cloud-based computing now enables the use of data analytics for a variety of applications including seismic data processing and reservoir modelling.
5. Software for data visualization
Pre-IoT, primitive and simplistic software for data graphing precluded achieving much business value from the sensor data in the energy industry.
New advanced data visualization software, developed with higher-productivity developer tools, now enables the next generation of visual analytics for energy trading and subsurface modelling.
6. Software for data management
Pre-IoT, simple yet expensive software for data storage and data management meant that an enormous cost and effort was required to access the sensor data in the energy industry.
New advanced management software now enables better IoT data management for simplified data integration for applications such as cost reduction and improved forecasting.
To learn more about what is required to position IoT projects for success, please read Critical success factors for IoT projects.
Can you share any experiences using IoT computing technology to solve business problems more innovatively?
