What You Need to Know About Digitalisation for Gas Fields
By Maria Garcia Alvarez
September 21, 2020
A drop in demand due to COVID-19 lockdown measures has struck natural gas markets which are already suffering from low commodity prices. Even though it is expected demand will return once lockdown restricted are eased, the impact on the global economy may result in continued low gas prices. As a result, gas companies need, more than ever, to improve their production systems and reduce costs.
Digitalisation is a powerful tool for mitigating inefficiencies related to Coal Seam Gas (CSG) production environments and field locations. Being dependent on human labour for data collection, maintenance and operations, the Oil and Gas industry can obtain immediate benefit from digitalisation through the use of wireless sensors, video monitoring and broadband wireless connections. These are some of the technologies enabling use cases such as remote monitoring and connected workers.
Although digitalisation is not new in the Gas industry, the analysis of upstream production processes reveals areas for improvement in daily maintenance and operation. The key benefits being:
Reduction of kilometres driven
On a daily basis, lone workers can drive hundreds of kilometres of back roads to inspect wells for operation, maintenance and manual data collection. Video analytics can reduce the frequency of inspections by detecting water leaks, presence of wildlife, signs of erosion and theft. Remote monitoring through wireless sensors can reduce or completely replace manual data collection.
Optimisation of operability and plant maintenance
Pervasive wireless access for workers allows full digital traceability of operations and provides easy access to historical data, procedures and safety applications. Additionally, faster sharing of information between technicians and engineers sharpens decision-making processes, which can help to shorten maintenance shutdowns.
Increased well site reliability
An improved supervision system combined with cloud-based computing and machine learning turns real-time data into information that can predict failures and change maintenance activities from reactive to proactive.