Background:

Oil and gas companies continue to pursue new ways of incorporating advanced analytics into day to day operations. As many companies have learned, the data acquisition process from the field is a critical component required to generate enough quality data to run many of the sophisticated models available today.

Acquiring and processing data from the field comes with many challenges. Although the industry has been at the forefront of developments in SCADA and distributed control, there has been significant influx of software and hardware innovations to acquire entirely new sets of data, increase the fidelity of the data, and distribute the processing load of the collected data, among other areas.

As these innovations come online across the industry, there will continue to be a considerable amount of information asymmetry in the market as to new best practices and emerging vendors. The impact is also felt beyond the producer, and through each fragment of the supplying chain, including oilfield service companies, equipment manufacturers, rig contractors and high-tech companies who are all preparing to augment their business models to accommodate this shift in the way we acquire and analyze data.

Our scouting and research efforts are focused on exposing only the most talented Internet-of-Things firms that possess unique solutions to the exclusive problems facing the oil and gas industry.

Study Scope:

The study will be focused on advanced analytics and machine learning technologies to uncover solutions specifically applicable to:

HARDWARE

• Sensors: new sensor platforms to complement or displace traditional measurement devices.
• Connected Devices: new connective devices such as wearables and robotic devices

CONNECTIVITY

• Communication Hardware: smart controllers and telemetry units to enable edge analytics.
• Networks: new topologies for balancing transmission load and connecting devices

SOFTWARE

• Integration platforms that enables users to identify and manage devices across different protocols and network infrastructures.
• Edge Analytics specialists to enable users to push computations to the device level and enable more automation.
• Universal Platforms for application development and data management.