Over the last decade CAS Group has been actively involved in developing environmental criteria in support of a wide range of projects worldwide, including numerous LNG terminals. In addition, we are often asked to review existing or in progress siting studies. During our review of such siting studies, we have identified several issues which we believe could have been avoided if the considerations listed above were considered in the development of the environmental parameters for the siting studies. The two case studies shown below provide illustrate, in practical terms, the importance of proper analysis of environmental data.
Case Study 1
CAS group was asked to review the siting study for a proposed LNG terminal in the United States as detailed vapor dispersion modeling being performed post-FID were showing larger than previously anticipated hazard distances.
The terminal is located along the coast. Based on a review of the siting study we determined following an analysis of long-term wind speeds acquired at an airport about 15 km inland from the project site.
After independently securing raw time-series from the airport station, the frequency distribution of the wind speeds was plotted (see Figure 1 below). An inspection of the Figure 1 shows a rather unusual distribution of the wind speeds. Indeed, the record did not contain any wind speeds greater than 0 and less than 3 mph. After further investigation, we determined that station considered all wind speeds less than 3 mph were considered as calm and were assigned a speed of 0 mph. Considering that this assumption resulted in over 20% of the wind speed observations being considered clam, thus the 10th percentile wind speed was determined to be zero. Consequently, a 1 m/s was used for the vapor dispersion modeling resulting in a very significant hazard zone and necessitating significant layout changes and incorporation of unanticipated mitigation measures.
Figure 1 – Distribution of Wind Speed at Originally Selected Meteorological Station.
Another observed idiosyncrasy was the fact that wind speeds were recorded in mph (not knots or m/s) and the wind speeds were rounded to the nearest integer. Finally, it was established that the wind speed represented 5-min average wind speed collected once every hour.
This station was not only closer to the project site, but climatic conditions at the new station appear to be more representative of those observed at the project site. This was established after a short-term comparison of the prevalent meteorological conditions at the site against those at the coastal meteorological station.
Based on our findings, it was evident that the meteorological station that was selected is not suitable for the purposes of a siting study.
CAS Group performed an audit of the available meteorological stations in proximity to the project site and we identified a coastal metrological station located about 10 km from the project site. This station was not only closer to the project site, but climatic conditions at the new station appear to be more representative of those observed at the project site. This was established after a short-term comparison of the prevalent meteorological conditions at the site against those at the coastal meteorological station. Therefore, it was agreed to rely on the long-term meteorological time-series acquired at the coastal station.
During our diligence, we observed the anemometer at the new stations was located 5.5 m above mean sea level and the anemometer height is represented to be 5.7 m above site elevation. This meaning that the wind measurements were collected at an elevation of 5.7 m above ground. Our investigation showed that the height or type of anemometer had not varied during the course of the observations. Furthermore, it was established that the wind speeds were 6-min wind speeds collected once every 10 minutes.
The long-term wind speed time-series underwent a through quality control process. The resulting dataset was quite complete the percentage of missing data being within the acceptable range (mostly due to gaps in the original dataset). Identified gaps appear to be random and no systematic, seasonal, gaps were observed.
The quality checked wind speeds were adjusted to a 10-m anemometer height. Figure 2 shows the distribution of wind speeds at the new meteorological station. It is important to note that the distribution of the recorded wind speeds matches well the expected theoretical distribution of environmental data (e.g., Weibull distribution).
The quality checked and anemometer height corrected data was then analyzed and the 10th percentile wind speed.
Figure 2 - The Frequency Distribution of Wind Speed at Alternative Meteorological Station.
Figure 3 – Probability of Exceedance Wind Speed at Alternative Meteorological Station.
Case Study 2
CAS Group was retained as technical advisor by a fund pursuing an investment in a proposed LNG Export Terminal to be located in the United States. As part of the diligence effort, CAS Group was asked to review the siting study for a proposed LNG facility.
The proposed site is along the coast. We were advised that the 10th and 90th percentile wind speeds, 2.1 m/s and 8.8 m/s, respectively, were developed based on analysis of a 60-year wind speed record collected from a nearby meteorological station.
Based on CAS Group’s prior experience at other sites near the project site, concerns about the selected wind speed were identified as a result of which the raw wind data for the meteorological station used in the initial analysis was secured and re-analyzed by CAS Group. Our re-analysis showed that the correct 10th and 90th percentile wind speed to be 1.4 m/s and 9.0 m/s, respectively. A comparison between the probability of exceedance curves is shown in Figure 1.
We identified several issues with the original analysis of wind data. These included the use of wind speeds that were not adjusted to 10 m anemometer height; inexplicable use of subsets of the available record as opposed to the full record; and, incorrect quality assurance and interpolation of the recorded wind speed time-series resulting in the introduction of an artificial bias in the time-series.
As a result of the reduction in the wind speed for dispersion modeling, several changes to the projects were necessitated to bring the project into compliance.
Figure 4 – Comparison Between the Original Probability of Exceedance Curve (red) and the One Prepared by CAS Group shown in black.
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