تقرير
In-situ Water quality monitoring in Oil and Gas operations
| العنوان: | In-situ Water quality monitoring in Oil and Gas operations |
|---|---|
| المؤلفون: | Kumar, Satish, Kou, Rui, Hill, Henry, Lempges, Jake, Qian, Eric, Jayaram, Vikram |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Statistics - Applications, Statistics - Computation, Statistics - Methodology, G.1, I.1, I.2, I.4, J.2 |
| الوصف: | From agriculture to mining, to energy, surface water quality monitoring is an essential task. As oil and gas operators work to reduce the consumption of freshwater, it is increasingly important to actively manage fresh and non-fresh water resources over the long term. For large-scale monitoring, manual sampling at many sites has become too time-consuming and unsustainable, given the sheer number of dispersed ponds, small lakes, playas, and wetlands over a large area. Therefore, satellite-based environmental monitoring presents great potential. Many existing satellite-based monitoring studies utilize index-based methods to monitor large water bodies such as rivers and oceans. However, these existing methods fail when monitoring small ponds-the reflectance signal received from small water bodies is too weak to detect. To address this challenge, we propose a new Water Quality Enhanced Index (WQEI) Model, which is designed to enable users to determine contamination levels in water bodies with weak reflectance patterns. Our results show that 1) WQEI is a good indicator of water turbidity validated with 1200 water samples measured in the laboratory, and 2) by applying our method to commonly available satellite data (e.g. LandSat8), one can achieve high accuracy water quality monitoring efficiently in large regions. This provides a tool for operators to optimize the quality of water stored within surface storage ponds and increasing the readiness and availability of non-fresh water. Comment: 15 pages, 8 figures, SPIE Defense + Commercial: Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2301.08800 |
| رقم الأكسشن: | edsarx.2301.08800 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |