Remote Sensing for Leak Detection and Quantification
- Laith Amin (Worley Advisian Digital)
- Document ID
- Society of Petroleum Engineers
- SPE Offshore Europe Conference and Exhibition, 3-6 September, Aberdeen, UK
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- carbon reduction, fugitive emissions, leak quantification, leak detection, greenhouse gas
- 1 in the last 30 days
- 190 since 2007
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|SPE Member Price:||USD 5.00|
|SPE Non-Member Price:||USD 28.00|
Offshore oil and gas installations are (by their nature) located in remote locations that are both difficult and costly to access. While such challenges exist, the operate & maintain requirements associated with such assets are consistent and must be addressed, requiring operators to identify the most efficient form of service to reduce staffing levels, risk and cost.
Offshore hydrocarbon production assets commonly incorporate equipment and processes that can lead to significant (fugitive) gas emissions. The consequences are both economic and social (environmental) in nature, requiring operators to perform emissions surveys with the objective of leak identification and remediation within the shortest possible timeframe. The frequency of this activity is naturally limited and must be balanced with the staffing and operating needs of the broader facility, which in-turn can lead to sub-optimal leak detection to fix timing and reliability.
Addressing the three key challenges of access productivity, detection reliability and results quantification, Worley has developed a remote sensing platform that incorporates the use of productive remote access equipment such as unmanned aerial vehicles (UAV) and in-situ monitoring, with machine based emissions detection and algorithmic quantification to provide a solution that allows the operator to increase survey frequency, obtain more reliable results at lower cost, and perform the work in a manner consistent with safe and low-risk operations.
In both testing and field deployments, the results have provided for significant reductions in both false positive and negatives and have produced datasets that allow for accurate indications of greenhouse gas reduction via comparison of volumetric emissions before and after leak repair activity has taken place.
The technology is largely mathematical, utilizing coded routines for machine learning to perform gas detection under (initially) supervised modeling conditions, and algorithmic gas dispersion models for further emission quantification. The performance of the survey is typically carried out through the integration of existing, proven manufactured sensing equipment across several types of UAV or in-situ monitors which collect field data for transmission to a cloud-based portal which further processes the results.
The approach has been shown effective in accessing hard or costly to reach areas, improving survey productivities, while the data processing and quantification allows the operator to benefit from improved measurability and prioritize leak repair accordingly.
|File Size||1 MB||Number of Pages||10|