An offshore oil and gas platform in the Middle East has been experiencing significantly elevated levels of gas flaring. Instead of the usual 2.00MMSCFD the average flaring was 3.26MMSCFD. This increased flaring caused substantial economic losses estimated around USD 2.26 million annually.
In addition to direct financial losses due to wasted hydrocarbon resources, the company also experienced operational inefficiencies in gas handling, compression, reinjection and processing system.
The elevated gas emission also caused environmental and regulatory concerns for the operator.
In response, client’s multidisciplinary team launched an internal initiative, leveraging operational data and advance analytics through VROC's machine learning platform, OPUS.
The approach included utilizing historical and real-time data, to uncovered the root causes of flaring inefficiencies. The team then trained AI models using OPUS to monitor and analyze critical systems including the Flash Gas Compressor, Nitrogen Rotameter and the Fuel Gas System. The objective of the models was to identify operational patterns and parameters contributing to excessive flaring and discover optimization levers to reduce waste and improve efficiency.
Utilizing insights from the AI models, the team implemented the following strategic interventions:
Flash Gas Compressor Optimization: AI insights revealed opportunities to adjust operating parameters such as pressure and flow rates. These adjustments minimized wear and tear, leading to reduced downtime and improved compressor efficiency.
Nitrogen Rotameter Calibration: The Rotameter was found to be transmitting inaccurate readings to the DCS. After calibration and reconnection, accurate data flow was restored, eliminating flow calculation errors and improving downstream consumption control.
Fuel Gas System – GTPOGT Insight: Analysis identified excessive flaring originating from the OGT Expansion Area. This led to a Fuel Gas Commissioning of the GTPOGT Expansion, optimizing fuel distribution and reducing flaring while improving power utilization
The interventions resulted in the following outcomes for the oil and gas operator:
Flaring Reduction:
A 40% decrease in flaring was achieved, from 3.26 MMSCFD to 1.90 MMSCFD
Financial Impact:
The effort resulted in an annual savings of USD 527,000
Operational Benefits:
-Reduced equipment stress and downtime
-Improved system control and efficiency
-Enhanced fuel and power utilization
Environmental Benefits:
-Significant reduction in greenhouse gas emissions
-Support for ESG and sustainability goals
This initiative is a strong example of how AI and machine learning can empower operational teams, enabling data-driven decisions that lead to tangible business savings and environmental improvements.
The success highlights the power of cross-functional collaboration, real-time analytics, and digital innovation in the oil and gas sector.
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