The Internet of Things for the Oil and Gas Industry: Why Quality Assurance is Critical
With its machine to machine (M2M) communication capabilities, the Internet of Things (IoT) is evolving as a potential solution, which can ensure correct, complete, and optimal performance of IoT enabled devices.
To gain actionable business insights in the Oil & Gas industry, the production data needs to be analyzed across upstream, midstream, and downstream processes. However this three step process chain requires smart solutions that extend beyond automation and drive not just core business processes but also ensure regulatory compliance and business results.
With its exponential growth rate, IoT offers great potential for organizations worldwide to unlock value and benefits from devices, systems and services communicating with each other, using the internet as a backbone.
Hereâ€™s how IoT enabled sensors can drive production efficiencies for O&G companies:
- Sensors on drilling machines
- Interconnected devices and data
- Sensor data, linked with Enterprise Resource Planning (ERP) systems
- IoT enabled sensors not only facilitate an efficient supply chain, but also accelerate go to market.
- Safety data from sensors
- Sensors mounted on equipment
IoT enabled devices and systems can facilitate efficient O&G operations, with minimum human intervention, providing higher value than traditional technology and automation. Correctly implemented IoT solutions hold the potential to introduce efficiencies in upstream, midstream, and downstream operations. IoT sensors deployed across the value chain â€“ from exploration and production, to transportation of crude and refined oil – can bring in the much needed visibility and workplace safety, with better control and maintenance of assets. Many organizations are using IoT enabled sensors and devices to remotely monitor operations, and improve end-to-end processes. While such IoT initiatives hold potential to drive business bottom-line, the industryâ€™s high risk production operations cannot be left dependent on untested and immature technologies. It is pivotal to assure these IoT initiatives â€“ so that they meet their intended purpose. It is here that assurance plays a critical role in validating IoT performance and verifying its correct deployment in production.
IoT enabled devices and sensors generate astronomical volumes of data, which is used to analyze and improve operations. These devices, sensors, and associated applications and underlying data need to be tested for accuracy and correctness. Generating testing data of such a large magnitude is difficult and time consuming. In such a situation, an automated data generator tool should be considered to ensure the effective functionality and performance of these devices. Some important features that should be considered when selecting an effective test data management tool are:
- Quality: It should generate realistic test data that not only maintains referential integrity and business rules, but also seamlessly preserves intricate data patterns.
- Usability: The tool should automatically read database constraints and include user friendly interfaces for specifying transformations.
- Data on demand: Data should be readily generated on demand and as per requirements, thereby reducing storage requirements.
- Data integration: The tools should be equipped to handle large volumes of data from varied geographies, applications and devices, including sensors, handheld equipment, web sources, and social media feeds.
- Data security: Security measures should be available to prevent unauthorized access, fix vulnerabilities, and mitigate risks and threats from all devices and systems, towards ensuring overall compliance
- Data validation: Data should be validated for its correctness as per business validation rules.