Structuring data and generating caverns

Functions to structure data and generate caverns with constraints.

References

[1]

Brennan, J. (2020). ‘Fast and easy gridding of point data with geopandas’, 16 March. Available at: https://james-brennan.github.io/posts/fast_gridding_geopandas/ (Accessed: 1 January 2024).

[2]

Williams, J. D. O., Williamson, J. P., Parkes, D., Evans, D. J., Kirk, K. L., Sunny, N., Hough, E., Vosper, H., and Akhurst, M. C. (2022). ‘Does the United Kingdom have sufficient geological storage capacity to support a hydrogen economy? Estimating the salt cavern storage potential of bedded halite formations’, Journal of Energy Storage, 53, p. 105109. https://doi.org/10.1016/j.est.2022.105109.

[3]

Chan, S. (2019). ‘Left Join – Building a hexagonal cartogram with Python’, 7 September. Available at: https://sabrinadchan.github.io/data-blog/building-a-hexagonal-cartogram.html (Accessed: 1 January 2024).

[4] (1,2,3,4)

Department of the Environment, Climate and Communications (2023). Draft Offshore Renewable Energy Development Plan II (OREDP II). Government of Ireland. Available at: https://www.gov.ie/en/publication/71e36-offshore-renewable-energy-development-plan-ii-oredp-ii/ (Accessed: 1 October 2023).

[5]

National Institute of Standards and Technology (2016). ‘Units Outside the SI’, in NIST Guide to the SI. (Special Publication, 811). Available at: https://www.nist.gov/pml/special-publication-811/nist-guide-si-chapter-5-units-outside-si (Accessed: 30 November 2023).

[6]

Renewables Ireland (2021). Dublin Array Offshore Wind Farm: Supporting Information Report - Annex D: Marine Archaeology Assessment. Available at: https://assets.gov.ie/202763/9160dd51-b119-44cf-a224-8b5302347e7d.pdf (Accessed: 10 November 2023).

h2ss.functions.net_to_gross(dat_xr, slope=0.0009251759226446605, intercept=0.2616769604617021, max_ntg=0.75)

Estimate the net-to-gross for a given halite thickness.

Parameters

dat_xrxarray.Dataset

Xarray dataset of the halite data

slopefloat

Slope of the net-to-gross linear regression

interceptfloat

y-intercept of the net-to-gross linear regression

max_ntgfloat

Maximum allowed value for the net-to-gross

Returns

xarray.Dataset

Xarray dataset of the halite with net-to-gross information

h2ss.functions.zones_of_interest(dat_xr, constraints, roof_thickness=80, floor_thickness=10, max_ntg=0.75)

Generate zones of interest by applying thickness and depth constraints.

Parameters

dat_xrxarray.Dataset

Xarray dataset of the halite data

constraintsdict[str, float]

Dictionary containing the following: "net_height": net cavern height [m]; "min_depth": minimum cavern depth [m]; "max_depth": maximum cavern depth [m]

roof_thicknessfloat

Salt roof thickness [m]

floor_thicknessfloat

Minimum salt floor thickness [m]

max_ntgfloat

Maximum allowed value for the net-to-gross

Returns

tuple[geopandas.GeoDataFrame, xarray.Dataset]

A (multi)polygon geodataframe of the zones of interest and an Xarray dataset of the zones of interest

h2ss.functions.generate_caverns_square_grid(dat_extent, zones_df, diameter=85, separation=340)

Generate salt caverns using a regular square grid.

Parameters

dat_extentgeopandas.GeoSeries

Extent of the data

zones_dfgeopandas.GeoDataFrame

Zones of interest

diameterfloat

Diameter of the cavern [m]

separationfloat

Cavern separation distance [m]

Returns

geopandas.GeoDataFrame

A polygon geodataframe of potential caverns in the zone of interest

Notes

Gridding method based on [1].

h2ss.functions.hexgrid_init(dat_extent, separation)

Initialise variables for generate_caverns_hexagonal_grid.

Parameters

dat_extentgeopandas.GeoSeries

Extent of the data

separationfloat

Cavern separation distance [m]

Returns

tuple[float, list[int], list[int]]

a, cols, and rows

h2ss.functions.generate_caverns_hexagonal_grid(dat_extent, zones_df, diameter=85, separation=340)

Generate caverns in a regular hexagonal grid.

Parameters

dat_extentgeopandas.GeoSeries

Extent of the data

zones_dfgeopandas.GeoDataFrame

Zones of interest

diameterfloat

Diameter of the cavern [m]

separationfloat

Cavern separation distance [m]

Returns

geopandas.GeoDataFrame

A polygon geodataframe of potential caverns

Notes

The close-packed hexagonal grid configuration was proposed by [2]; this configuration provides around 15% more caverns compared to a square grid configuration. Hexagonal gridding method based on [3].

h2ss.functions.cavern_dataframe(dat_zone, cavern_df, depths, roof_thickness=80)

Merge halite data for each cavern location and create a dataframe.

Parameters

dat_zonexarray.Dataset

Xarray dataset for the zone of interest

cavern_dfgeopandas.GeoDataFrame

Geodataframe of caverns within the zone of interest

depthsdict[str, float]

Dictionary of cavern top depth ranges [m] for labelling: "min": minimum depth; "min_opt": minimum optimal depth; "max_opt": maximum optimal depth; "max": maximum depth

roof_thicknessfloat

Salt roof thickness [m]

Returns

geopandas.GeoDataFrame

The cavern geodataframe with halite height and depth data for only the thickest halite layer at each given point

h2ss.functions.label_caverns(cavern_df, depths, heights=None, roof_thickness=80, floor_thickness=10)

Label cavern dataframe by height and depth.

Parameters

cavern_dfgeopandas.GeoDataFrame

Dataframe of potential caverns

depthsdict[str, float]

Dictionary of cavern top depth ranges [m] for labelling: "min": minimum depth; "min_opt": minimum optimal depth; "max_opt": maximum optimal depth; "max": maximum depth

heightslist[float] or None

List of fixed caverns heights [m] for labelling; if None, the actual height is used

roof_thicknessfloat

Salt roof thickness [m]

floor_thicknessfloat

Minimum salt floor thickness [m]

Returns

geopandas.GeoDataFrame

A dataframe of potential caverns labelled by cavern height and top depth ranges

h2ss.functions.constraint_halite_edge(dat_xr, buffer=255)

The edge of each halite member as a constraint.

Parameters

dat_xrxarray.Dataset

Xarray dataset of the halite data

bufferfloat

Buffer [m]

Returns

dict[str, geopandas.GeoDataFrame]

Dictionary of GeoPandas geodataframes of the halite edge constraint for each halite member

Notes

Set the buffer to 3 times the cavern diameter, i.e. the pillar width.

h2ss.functions.constraint_exploration_well(data_path, buffer=500)

Read exploration well data and generate constraint.

Parameters

data_pathstr

Path to the Zip file

bufferfloat

Buffer [m]

Returns

tuple[geopandas.GeoDataFrame, geopandas.GeoDataFrame]

Geodataframes of the dataset and buffer

Notes

500 m buffer - suggested in the draft OREDP II p. 108 [4].

h2ss.functions.constraint_wind_farm(data_path)

Read data for wind farms.

Parameters

data_pathstr

Path to the Zip file

dat_extentgeopandas.GeoSeries

Extent of the data

Returns

geopandas.GeoDataFrame

Geodataframe of the dataset

Notes

The shapes are used as is without a buffer - suggested for renewable energy test site areas in the draft OREDP II p. 109 [4].

h2ss.functions.constraint_shipping_routes(data_path, dat_extent, buffer=1852)

Read frequent shipping route data and generate constraint.

Parameters

data_pathstr

Path to the Zip file

dat_extentgeopandas.GeoSeries

Extent of the data

bufferfloat

Buffer [m]

Returns

tuple[geopandas.GeoDataFrame, geopandas.GeoDataFrame]

Geodataframes of the dataset and buffer

Notes

1 NM (nautical mile) buffer - suggested in the draft OREDP II p. 108 [4]. 1 NM is equivalent to 1,852 m [5].

h2ss.functions.constraint_shipwrecks(data_path, dat_extent, buffer=100)

Read shipwreck data and generate constraint.

Parameters

data_pathstr

Path to the Zip file

dat_extentgeopandas.GeoSeries

Extent of the data

bufferfloat

Buffer [m]

Returns

tuple[geopandas.GeoDataFrame, geopandas.GeoDataFrame]

Geodataframes of the dataset and buffer

Notes

Archaeological Exclusion Zones recommendation - 100 m buffer [6].

h2ss.functions.constraint_subsea_cables(data_path, dat_extent, buffer=750)

Read subsea cable data and generate constraint.

Parameters

data_pathstr

Path to the GPKG file

dat_extentgeopandas.GeoSeries

Extent of the data

bufferfloat

Buffer [m]

Returns

tuple[geopandas.GeoDataFrame, geopandas.GeoDataFrame]

Geodataframes of the dataset and buffer

Notes

750 m buffer - suggested in the draft OREDP II p. 109-111 [4].

h2ss.functions.exclude_constraint(cavern_df, cavern_all, exclusions, key)

Exclude constraint by their dictionary key.

Parameters

cavern_dfgeopandas.GeoDataFrame

Dataframe of available caverns

cavern_allgeopandas.GeoDataFrame

Dataframe of all caverns, i.e. available and excluded

exclusionsdict[str, geopandas.GeoDataFrame]

Dictionary of exclusions data

keystr

Key for the constraint in the dictionary; one of the following: "shipping": frequent shipping routes; "cables": subsea cables; "wind_farms": offshore wind farms; "wells": exporation wells; "shipwrecks": shipwrecks

Returns

geopandas.GeoDataFrame

Dataframe of available caverns

h2ss.functions.generate_caverns_with_constraints(cavern_df, exclusions)

Add constraints to cavern configuration.

Parameters

cavern_dfgeopandas.GeoDataFrame

Dataframe of available caverns

exclusionsdict[str, geopandas.GeoDataFrame]

Dictionary of exclusions data; "edge" must be present in the dictionary, but if any other of the following keys do not exist in the dictionary, the exclusion will be skipped: "edge": halite edge, dict[str, geopandas.GeoDataFrame]; "shipping": frequent shipping routes; "cables": subsea cables; "wind_farms": offshore wind farms; "wells": exporation wells; "shipwrecks": shipwrecks

Returns

tuple[geopandas.GeoDataFrame, geopandas.GeoDataFrame]

Dataframe of available and excluded caverns

h2ss.functions.read_weibull_data(data_path_weibull, data_path_wind_farms)

Extract mean, max, and min Weibull parameters of wind speeds.

Parameters

data_path_weibullstr

Path to the Weibull parameter data Zip file

data_path_wind_farmsstr

Path to the wind farm data Zip file

Returns

pandas.DataFrame

Dataframe of k and C values for each wind farm

Notes

Data extracted for each wind farm in the area of interest, i.e. Kish Basin: Codling, Dublin Array, and North Irish Sea Array.