Arctic Transportation Network

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Project Personel

Main Contact: Dr. Ronald P. Daanen
Scientific Personel: Michael Lilly (GWS), Horacio Toniolo (WERC), Yuri Shur (INE), Billy Connor (AUTC), Chien-Lu Ping (PRS), Vladimir Romanovsky (GI), Ronald Daanen (WERC), Tom Heinrichs (GINA), Gerald Sehlke (INL)
Collaborators:
Partner Organizations: Visit the original ATN website

Project duration: 
3 years

Project Summary

 

The objective of this project is to improve oil and gas transportation operations on the North Slope, Alaska. This includes providing methods and tools to develop Nowcast and Forecast models for environmental conditions related to tundra travel and management and water use associated with ice roads and pads and other industry operations.


The Problem

Oil and Gas Development on Alaska's North Slope is a critical part of the US energy supplies and is facing a period of new growth to meet the increasing energy needs of the nation. A majority of all oil and gas-related activities (exploration, development, maintenance) take place in the middle of winter, when the tundra surface is stable. However, the window for the critical oil and gas operational season has been steadily decreasing over time, as the number of companies working on the North Slope is increasing.

 

CONDITIONS REQUIRED FOR OPENING OF WINTER SEASON TUNDRA TRAVEL

Currently there are no methods to forecast the opening date for tundra travel (dependent on snow pack and soil temperature profiles), so field mobilization efforts are dependent on agencies to visit field sites and test field conditions. Weeks can be easily lost in the winter operating season due to delays in field verification of tundra conditions and the resulting mobilization.

 

WATER RESOURCES CONSIDERATIONS FOR ICE ROAD NETWORKS

Once the season is open, work does not proceed until ice roads and pads can be built. This effort is dependent on lake ice and under-ice water. Ice chipping is a common road construction technique used to build faster ice roads. Knowing when and how much water to be used is a constant constraint to industry. At the end of the winter season, projects depending on ice road networks are often faced with ending operations early or risking being caught out on the ice roads with flood stream crossings, or unusable section of ice road due to local melt.

All of these challenges result in high support costs due to shorter operational seasons, high uncertainty in timing mobilization of field efforts, and in finding and using water resources.

 

Proposed Solution

A set of tools will be developed as a useful solution for industry and management agencies. The proposed approach is based on an understanding of the physical conditions to ensure protection of fisheries and other natural resources on the sensitive tundra landscape. These solutions have the ability to not only describe current conditions, but to forecast conditions so that management agencies can respond to snow cover and soil temperature audits more effectively, and industry can plan the significant project mobilization efforts that take place every transportation season.

  • Nowcast and Forecast tools for snow conditions and soil temperatures using weather stations and existing forecast models to provide snow distribution and soil conditions for short-term forecasts.
  • Measure the relationships between soil strength and temperatures for freezing soils typical of field operational areas.
  • Develop Nowcast and Forecast tools for predicting lake ice growth and potential recharge for lakes
  • Develop water management and permitting approaches that correspond with the natural hydrologic cycle in arctic lakes to allow adaptive management approaches.
  • These tools have a high likelihood of improving the transportation logistics on the North Slope for winter operations. This will result in significant cost savings to industry and allow resource managers to better reduce risks do to development activities by having both more timely and accurate data.
  • Nowcast and forecast predictive models for soil temperature conditions at remote data sites
  • Development of weather forecast models (WRF) to provide short term forecasts of snow cover and soil temperature

 

Objectives

The objective of this project is to improve oil and gas transportation operations on the North Slope, Alaska. This includes providing methods and tools to develop Nowcast and Forecast models for environmental conditions related to tundra travel and management and water use associated with ice roads and pads and other industry operations.

 

Scope of Work

We will collect data from environmental stations and study lakes, including snow depth distributions and soil samples for soil-strength studies. This information will be used to develop forecast of environmental conditions used by industry and management for tundra travel and lake water use for ice roads and pads. The project team will develop near-real-time reporting products for both data stations and Nowcast/Forecast spatial products to help show current and projected conditions to help environmental managers manage increasingly complex field activities and industry improve their mobilization efforts, as well as increase the winter operational season.

 

Expected Impacts and Benefits

The cost savings to industry in a single season of building and operating a winter transportation system will result in economic returns greater than the project cost within the life of the project. Analysis of varying the soil temperature limit for opening tundra travel from -5C to -3C alone could result in an additional two weeks of tundra travel access. For exploration activities, the value of this time is approximately equal to half an exploration well. The development of short term forecasting of these environmental management conditions will have an even greater effect in allowing industry to better plan and mobilize project efforts. The transferability of these project efforts to future development needs for water addresses potential enhanced oil recovery methods, development of new facilities requiring water sources, and the development of future reservoirs in areas with inadequate natural water supplies for oil and gas development.

Measurements: 

30cm Ground Temperature at 4 North Slope sites, 2010 to 2011.

30cm Ground Temperature at 4 North Slope Sites

This figure shows 30cm ground temperature data from 4 of our North Slope Sites, West Dock, Deadhorse, Franklin Bluffs Dry, and Franklin Bluffs Wet.  Snow cover at the sites is quite variable, at West Dock snow cover ranges from 20 to 30cm due to the windy conditions.  At Deadhorse and Franklin Bluffs Wet the snow cover can be anywhere from 50 to 70cm and at Franklin Bluffs Dry the snow cover is anywhere from 20 to 40cm.

Modeling: 

Permafrost Laboratory task: Ground Temperature Forecast Modeling 

 

The simulation model from the Geophysical Institute Permafrost Laboratory (GIPL) used in this study was first described in Tipenko and Romanovsky (2001) and used for “permafrost temperature reanalysis” by Romanovsky et al. (2002). GIPL is a sophisticated numerical model (Tipenko and Romanovsky, 2001; Sergueev et al., 2003), which takes into account the temperature-dependent latent heat effects. The model is capable of modeling spatially distributed permafrost temperatures as described in Sazonova and Romanovsky (2003) and Marchenko et al. (2008). The structure of the model is illustrated in Figure 1.

The model needs to be calibrated using available data for air temperature, snow depth and density, vegetation, soil properties, and soil temperatures. The first step in that calibration process we use a few years of soil surface temperatures to calculate and subsequently calibrate the ground temperature calculations including permafrost temperatures. When a match is found between measure and calculated soil temperatures we start the procedure on using the air temperature as an input variable. The air temperature is more variable and the ground is insulated with snow. The snow thermal conductivity is variable based on snow density and snow cover extend. The first step is assuming a constant snow density over the winter, this can be modified for a more detailed simulation during projection runs. 

 

For ground temperature projections we can optimize parameters to match observed and predicted ground temperatures as freezing progresses. There are likely small differences in soil moisture from year to year that have an effect on the thermal properties. The optimization will need to be done manually.

 

An example for Climate Station DFM4 is show in Figure 2. 


Figure 2.  Soil-temperature comparisons between measured values at 20 cm depth for Climate Station DFM4 and simulated values after model calibration.

The next phase of the project included running the model tool using air temperature and snow data. The model performance is reduced due to the complex nature of snow insulation, but the overall match between observed and simulated soil temperatures is still good, see Figure 3.


Figure 3.  Soil-temperature comparisons between measured values at 5 cm depth for Climate Station DFM4 and simulated values after model calibration using measured air temperature and snow depth, with the later illustrated in the graph.

The project team has tested different forward simulation techniques to produce 14-day forecast models of soil temperatures at 20-cm depths. Figure 4 illustrates a forecast simulation using simulations starting from a current day and using a fixed forward air temperature and an adjusted snow boundary condition.

 

 

Figure 4.  Soil-temperature predictive simulations showing one example of using a running 5-day average air temperature as input showing the 20 cm depths simulation, compared with the measured temperature at 20 cm depth for Climate Station DFM4.

The individual lines show 14 day predictions on that day. The initial conditions are always updated as starting temperatures in the model. They are then calculated forward using the fixed average daily air temperature of the past 5 days.

 

Similarly we have calibrated the model for a second site called DFM3. Figure 5 shows the calibration step using soil surface temperature as the input for the model.

 

Figure 5. Soil-temperature comparisons between measured values at 20 cm depth for Climate Station DFM3 and simulated values after model calibration.

 

The model calibration run with air temperature and snow depth is illustrated in Figure 6.

 

Figure 6. Soil-temperature comparisons between measured values at 5 cm depth for Climate Station DFM3 and simulated values after model calibration using measured air temperature and snow depth, with the later illustrated in the graph.

The predictions for 2007 for the DFM3 site are presented in Figure 7. 

 

Figure 7 – Soil-temperature predictive simulations showing one example of using a running 5-day average air temperature as input showing the 20 cm depths simulation, compared with the measured temperature at 20 cm depth for Climate Station DFM3.

 

The goal of our part in the ATN project is to predict soil temperatures automatically. Therefore we have to implement a routine that will run the soil temperature forecast tools for index sites.  Currently we are in the process of implementing this procedure on a Supper Computer at ARSC. Data communication has been setup to download field data on a daily basis. Data will feed into the model through a set of input files. These files are currently generated automatically.


 

 

References

 

Marchenko, S.S., Romanovsky, V.E. and Tipenko, G.S.: Numerical Modeling of Spatial Permafrost Dynamics in Alaska. Editor(s): D.L. Kane and K.M. Hinkel Collection: Proceedings of the Ninth International Conference on Permafrost, June 29-July 3, Fairbanks, Alaska, 2008 Bibliography: Vol.2: pp. 1125-1130, (2008).

Romanovsky, V.E., Burgess, M., Smith, S.L., Yoshikawa, K., and Brown, J.: Permafrost temperature records: indicators of climate change. EOS, AGU Transactions, 83: 589-594, (2002).

Sazonova, T.S. and Romanovsky, V.E.: A model for regional-scale estimation of temporal and spatial variability of active layer thickness and mean annual ground temperatures. Permafrost and Periglacial Processes, 14: 125-139, (2003).

Sergueev, D., Tipenko, G., Romanovsky, V., and Romanovskii, N.: Mountain permafrost thickness evolution under influence of long-term climate fluctuations (results of numerical simulation). In: Proceedings of the VII International Permafrost Conference, Switzerland, July 21-25, pp. 1017-1021, (2003).

Tipenko, G.S. and Romanovsky, V.E.: Simulation of Soil Freezing and Thawing: Direct and Inverse Problems, EOSTrans. AGU, 82 (47), Fall Meet. Suppl., Abstract, F551, (2001).

Results: 

 

Real Time Measured Ground Temperature and Predictions at 30cm

Figure 1 - Hourly measured ground temperature at 30cm for the past month and daily predicted ground temperature (30cm) for the next 14 days at our Deadhorse site.