This means a major revamp of power generation portfolio for the EU as a whole. But it also means a changing landscape in electricity generation and trade in the EU as some countries are already much closer to achieving the target than others. The transition to clean energy will inevitably mean emergence of new leaders and followers in the changed European energy landscape.

Power generation intensity is a term that refers to the amount of CO2 emitted for each MWh of power generated. Intensity can be used as an indicator of the type of power plants used. Hydro dams, nuclear, wind and solar will have zero intensity. Natural gas power plants can have intensities ranging from 300kg to 800kg CO2 per MWh. The dirtiest range from 900kg and above are likely to be coal power plants. Minimizing overall intensity of country’s power generation portfolio implies significant investments into new facilities and technologies or importing power from cleaner countries.

One way of getting a birds-eye view of the current situation is to use Enipedia’s data on power plants, their intensities and power generated. The data has been adopted from the CARMA project and is currently being maintained by the Enipedia community. Plotting generation intensity vs energy production gives a good overview of the country’s portfolio and a rough indication of the competitive position of the country in the changing EU energy landscape.

The charts below plot intensity (Kg CO2 per MWh) and total annual electricity generation (MWh) at that level of intensity.

A simple shorthand to interpret these charts: the larger the green area under the curve – the cleaner the country’s generation portfolio, the better long-term competitive position.

Cleaner

France’s electricity portfolio is based on CO2-free nuclear, while Sweden relies on both nuclear and hydro power.

Dirtier

Poland and Greece charts display a typical coal based power generation portfolio, with significant amount of power generated at above 900Kg intensity threshold.

Natural gas-based

The Dutch, Portuguese, Spanish, Italian, British and Irish charts display a hump in the 400-800Kg range indicative of natural gas based generation.

EU

Power generation emissions portfolio of all EU member countries are vastly different. Countries based on CO2-intensive generation (Poland, Greece, UK, Italy) will have to revamp their infrastructure to be able to ensure sufficient supply of cleaner electricity in the future. It also means higher costs of power as countries have to invest or import power from cleaner neighbors. The costs might be high but the opportunities for the countries able to develop significant clean portfolio are huge. The transition to clean energy will inevitably mean creating new leaders and followers, exporters and importers in the new European energy landscape.

Data and methodology behind the charts

Enipedia blog tries to promote “open knowledge” by exposing the data behind the analysis and the methodologies used. The charts were generated using R open source statistical package and r-sparql – SPARQL plugin for R that allows running queries from R scripts. The script below contains everything necessary to generate the charts. The author of this article is not an R-expert (yet) and would like to encourage the readers to download the script, improve it and come up with different (better) analyses and visualizations.

library(sparql)
#Endpoint
endpoint = "http://enipedia.tudelft.nl/wiki/Special:SparqlExtension"
#Select EU Member counrtries using Category:EUMember
countries_query = "select ?country where {
    ?country a  <http://enipedia.tudelft.nl/wiki/Category:EUMember> .
}"
# Execute the query and put results into data frame
countries <- query(countries_query, endpoint)
# Convert from factors to chars
countries <- data.frame(lapply(countries, as.character), stringsAsFactors=FALSE)
# Remove the uri part
countries <- transform(countries, country = substr(country,33, nchar(country)))
# Loop through countries
for (country in countries$country) {
    # Select output (annual generation) and intensity (kg co2 per mwh) of all power plants in the country
    q = paste("select ?intensity (?output / 3.6e9 as ?outputMWh) where {
    ?plant a <http://enipedia.tudelft.nl/wiki/Category:Powerplant> ;
             <http://enipedia.tudelft.nl/wiki/Property:Energyoutput-23J> ?output ;
             <http://enipedia.tudelft.nl/wiki/Property:Intensity-23kg> ?intensity ;
             <http://enipedia.tudelft.nl/wiki/Property:Country> <http://enipedia.tudelft.nl/wiki/", country, "> .
    Filter (?output > 0) .
    } order by ?intensity", sep="")
    # Execute the query and put results into data frame
    results <- query(q, endpoint)
    # Convert from factors to numbers
    d <- transform(results, intensity = round(as.numeric(levels(intensity)[intensity]),0), outputMWh=as.numeric(levels(outputMWh)[outputMWh]))
    # Calculate cumulative sums
    d$totals <- apply(d, 2, cumsum)
    # We need only intensity vs cumulative output
    d <- data.frame(intensity=d$intensity, totaloutput=d$totals[,2])
    # Charts folder
    jpeg(paste("charts/", country, "_generation_emissions.jpg", sep=""))
    # Set line width
    par(lwd=2)
    # Plot
    plot(d$intensity, d$totaloutput, type="l", xlab="Intensity (Kg CO2 / MWh)", ylab="Electricity Production (MWh)", xaxt="n", yaxt="n", xlim=c(0,2000))
    par(ps=30)
    title(main=country, col.main="black", font.main=4)
    par(ps=12)
    # Create x axis ticks sequence - standard from 0 to 2000 kg per mwh
    xpos <- seq(0, 2000, by=400)
    # Create x axis labels
    axis(1, at=xpos, labels=sprintf("%.0f", xpos))
    # Create y axis labels
    axis(2, axTicks(2), format(axTicks(2), scientific = F))
    # Create grid
    abline(v=xpos, h=axTicks(2), lty=2, col="darkgray")
    # Color the area under the curve
    polygon(c(d$intensity,2000, 2000), c(d$totaloutput, tail(d$totaloutput,1), 0), col="green")
    # release file handle
    dev.off()
}
q()

To see the charts for the rest of the world see Enipedia entry on Power generation intensity.

For more information on decarbonizing European power generation see:

• Roadmap 2050

• rapid demand growth in BRIC countries, especially China;
• mothballed nuclear (immediate in Japan and Germany);
• production of unconventional gas in the US and China;
• growing LNG market;
• lower (than projected before) gas prices due more aggressive conventional and shale resource development

Detailed scenarios and convincing projections are IEA speciality. It would be interesting though to try to recreate some of the analyses done by IEA using SPARQL and Enipedia’s data on natural gas and see if we can arrive at similar intuitions as detailed in the IEA report.

Note that Enipedia’s data on natural gas is obtained mostly from BP’s World Statistical Energy Review but also includes numerous other sources, such as CIA Factbook, Wikipedia, IEA, US EIA and others.

China

Most of the Chinese power generation is coal based, due to abundance of local resources. Currently Chinese produce around 3’000Mt annually from their proven reserves of 115’000Mt ensuring almost 40 years of future consumption. The low prices of coal in China make natural gas a poor competitor in the power generation area. The query below aggregating Chinese power plant data in Enipedia indicates that around 75% of power generation emits more than 600Kg of CO2 per MWh produced. The 600kg/MWh benchmark can be used to indicate a coal based generator, inferring around 80% of generation is coal based. The CIA factbook also confirms that 80% of power generation on China is in fact fossil fuel based.

select ?Category (sum(?out / 3.6e15) as ?TotalAnnualOutputTWh) where {
{
    select * where {
    ?plant rdf:type cat:Powerplant;
       prop:Country a:China ;
       prop:Energyoutput-23J ?out ;
       prop:Intensity-23kg ?IntensityKgMWh .
    LET (?Category := "MoreThan600KgCO2perMWh")
    Filter(?IntensityKgMWh > 600)
    }
} UNION {
    select * where {
    ?plant rdf:type cat:Powerplant;
       prop:Country a:China ;
       prop:Energyoutput-23J ?out ;
       prop:Intensity-23kg ?IntensityKgMWh .
    LET (?Category := "LessThan600KgCO2perMWh")
    Filter(?IntensityKgMWh < 600)
    }
}
} group by ?Category

Result

The IEA scenario justifies gas growth in China by assuming a proactive government policy to diversify fuel sources and clean up power generation portfolio. Interestingly, given the premium gas prices in Asia, gas based generation becomes attractive (on the basis of levelized cost of electricity) only at CO2 cost of around 100 USD/T. It could be interesting to increase the weight of nuclear and wind alternatives in China too. But given the rapid expansion of Chinese urban sprawl, it is reasonable to expect gas growth due to factors besides cost: quick installation (2 years for CCGT), flexibility of cycle making gas attractive in combination with wind, and relatively low emissions.

Japan

A more immediate effect on the natural gas demand comes from Japan, where 30% of power generation is nuclear and due to recent unfortunate events might have to be replaced by gas based generation. (Try to run the first query by replacing China with Japan in lines 6 and 15.) Currently Japan imports around 100Bcm of natural gas annually. The following query calculates the required amount of gas to replace the current 30% of nuclear capacity by CCGT, assuming 6000BTU/kWh heat rate.

select
(sum(?out) as ?OutputMWh)
(sum(?out) * 0.3 as ?NuclearOutputMwh)
(sum(?out) * 0.3 * 6e6 as ?NuclearOutputReplacedByGasBtu)
(sum(?out) * 0.3 * 6e6 / 35e12 as ?GasBcm)
where {
?plant rdf:type cat:Powerplant;
       prop:Country a:Japan ;
       prop:Annual_Energyoutput_MWh ?out .
}

Result
The result is around 53Bcm of natural gas annually or 50% of current Japanese imports and 18% of worlds total LNG market. Of course the nuclear generation will not be phased out completely but it is valuable to see the extent of such decision.

Unconventional Gas

select
    (sum(?consumption) / 1e9 as ?ConsumptionBcm)
    (sum(?production) / 1e9 as ?ProductionBcm)
    (sum(?conventionalResources) / 1e12 as ?ConventionalResourcesTcm)
    (sum(?shaleResources) / 1e12 as ?ShaleResourcesTcm)
    (?ConventionalResourcesTcm + ?ShaleResourcesTcm as ?TotalResourcesTcm)
    (?ConventionalResourcesTcm * 1e3 / ?ProductionBcm as ?ConventionalProductionYears)
    (?ShaleResourcesTcm * 1e3 / ?ProductionBcm as ?ShaleProductionYears)
    (?ConventionalProductionYears + ?ShaleProductionYears as ?TotalProductionYears)
where {
    ?country prop:NaturalGasConsumption-23m3 ?consumption;
         prop:NaturalGasProduction-23m3 ?production;
         prop:NaturalGasProvenResources-23m3 ?conventionalResources;
         prop:TechnicallyRecoverableShaleGasResources-23m3 ?shaleResources ;
}

Result
Natural gas resource calculations (based primarily on BP data) above reveal that the shale gas resources available worldwide already match the conventional resources. Both amount to around 190Tcm and provide 60 years of production at current rates (R/P ratio). The interesting property of shale gas resources is their distribution. While conventional gas resources are concentrated and stranded in remote locations far away from the end consumer (Siberia or Sahara), shale resources are distributed relatively more evenly and closer to the growing markets.

The following query reveals the top 20 countries with most conventional resources:

select ?name
    (?conventionalResources / 1e12 as ?conventionalResourcesTcm)
where  {
    ?country rdfs:label ?name;
                prop:NaturalGasProvenResources-23m3 ?conventionalResources .
} order by desc(?conventionalResourcesTcm) limit 20

Result

The chart puts Russia, Iran, Qatar, Turkmenistan and Saudi Arabia as the primary donors of natural gas reserves. Comparing that to the distribution of shale gas reserves (see query and chart below) - the owners here are the primary consumers too. China, Argentina, Mexico, Brazil and South Africa are the growing economies soon to benefit from the improvements in shale gas technology and resulting domestic resources.

select
    ?name
    (?shaleResources / 1e12 as ?shaleResourcesTcm)
where  {
    ?country rdfs:label ?name;
                prop:TechnicallyRecoverableShaleGasResources-23m3 ?shaleResources .
} order by desc(?shaleResourcesTcm) limit 20

Result

For US - the owner of second largest shale resource - it could mean turning into natural gas exporter.
For countries like France, Poland and the Eastern European block it will mean more diversified supply and lower import costs from Russia.

Despite the environmental concerns, for the EU, shale gas could translate into more diversified and secure supply of natural gas.

Pricing

As much as shale gas could impact the pricing mechanisms leading to more gas-indexed prices, the current natural gas market is still primarily tied to oil prices. Globally, around one-fifth of gas supply is oil-linked - primarily in South Korea, Japan and continental Europe (IEA). A third of world's gas supply is based on gas-index prices (called gas-to-gas), mechanism prevalent primarity in the US, UK and Australia. In order to be able to index long-term contract prices to gas futures prices, a liquid gas futures market is required. Henry Hub in the US and National Balancing Point in the UK provide pricing benchmarks for the two markets.

The price charts below indicate that a stronger relation between more liquid US and UK gas market prices. The continental European and Japanese prices (indicative of South-East Asian market) display a stronger link to rising oil prices.

Despite that in 2010 the UK-US price spreads widened as the natural gas from UK was redirected to higher price areas in the continental Europe. It is likely that in the medium term a similar situation could turn the US into gas exporter to South-East Asia, as the price spread has recently shot up to 6-7USD/MMBtu. IEA explains that economic slowdown and local shale discoveries in the US have a temporary glut that already redirected US destined LNG to Europe and Asia. Even the destination-restricted LNG cargoes were unloaded in the US just to be loaded back again and sent to higher price destinations.

IEA also questions the emergence of gas-to-gas based pricing mechanisms in the continental Europe, where 75% of natural gas is still linked to high oil prices. The query below lists the EU's gas suppliers and annual volumes.

select ?from (sum(?volume  / 1e9) as ?ImportVolumeBcm) where {
   ?flow prop:GasFlowTo ?to ;
         prop:GasFlowVolume-23m3 ?volume ;
         prop:GasFlowFrom ?from .
   ?to a cat:EUMember .
   Filter(Not exists {?from a cat:EUMember} ) .
} group by ?from order by desc(?ImportVolumeBcm)

Result

Russia dominates EU's natural gas imports, which to a great extent explains the oil-linked pricing that is beneficial to the Russian national oil and gas companies. We will look at the security of gas supply in relation to pricing mechanisms in more detail next.

Security of Supply

Asia

Asia has relatively diversified supply but the significantly higher natural gas prices reflect higher costs of transport and the monopoly situation in downstream (non-liberalised market) where the gas distribution companies can pass the high gas prices to consumers. For example, government monopoly KOGAS in South Korea controls all LNG imports and thus - prices. LNG terminals in Japan are owned by utilities and power generators. The query below reveals that 40% of natural gas based generation is owned by Tepco who is also perhaps able to pass the higher costs to the end customer. In Japan heating is frequently oil-based or electrical, thus oil price is the real opportunity cost for the gas consumer (which is NOT the case in Europe).

select ?company ?totalCompanyOutputMWh ?totalGasOutputMWh (?totalCompanyOutputMWh / ?totalGasOutputMWh as ?pctOutput) where {
{
select ?company (sum(?output) / 3.6e9 as ?totalCompanyOutputMWh) where {
?plant a cat:Powerplant ;
   prop:Country a:Japan ;
   prop:Intensity-23kg ?intensity ;
   prop:Energyoutput-23J ?output ;
   prop:Ownercompany ?company .
Filter (?intensity < 600 && ?intensity > 200) .
} group by ?company order by desc(?totalOutputMWh)
}
{
select (sum(?output) / 3.6e9 as ?totalGasOutputMWh) where {
?plant a cat:Powerplant ;
   prop:Country a:Japan ;
   prop:Intensity-23kg ?intensity ;
   prop:Energyoutput-23J ?output ;
   prop:Ownercompany ?company .
Filter (?intensity < 600 && ?intensity > 200) .
}
}
} order by desc(?pctOutput) limit 10

Result

Europe

As mentioned before, 3/4 of imported natural gas volume in the EU is also oil-price linked, while - unlike in Japan - the gas competition in Europe is not oil but mostly coal, nuclear and European gas. Gradual liberalisation of European gas markets has pushed the gas consumers to demand more flexibility and gas-to-gas based contracts (indexed to gas prices observed at European gas hubs). Despite that major exporters (Russia) refuse to change their lucrative pricing schemes. Herfindahl–Hirschman index calculated on the exporter shares can be used to assess the bargaining power of the importing countries and explain the pricing mechanisms.

Herfindahl–Hirschman Index as Proxy for Security Of Supply

The Herfindahl–Hirschman Index (HHI) is a measure of the size of firms in relation to the industry and an indicator of the amount of competition among them. Named after economists Orris C. Herfindahl and Albert O. Hirschman, it is an economic concept widely applied in competition law, antitrust and also technology management. It is defined as the sum of the squares of the market shares of the largest firms within the industry, where the market shares are expressed as fractions. The result is proportional to the average market share, weighted by market share. As such, it can range from 0 to 1.0, moving from a huge number of very small firms to a single monopolistic producer. Increases in the Herfindahl index generally indicate a decrease in competition and an increase of market power, whereas decreases indicate the opposite.

Here we're using HHI as a proxy to measure security of natural gas supply. Instead of calculating the market shares of companies, we calculate the shares of gas suppliers to a country. The higher the HHI score, the more the importing country is dependent on its suppliers, and the higher the monopoly power of the suppliers.

The following (more advanced) query will return a list of countries that have gas import HHI over 0.7, which is a good cut-off point for assuming high monopoly power of the supplier.

select ?to ?from ((?volume / 1e9) as ?volumeBcm) ?HHI  where {
?flow prop:GasFlowTo ?to ;
       prop:GasFlowFrom ?from ;
       prop:GasFlowVolume-23m3 ?volume .
{ select ?to (sum(?shareOfSupplySqr) as ?HHI) where {
{ select ?to ?from ((?volume  / 1e9) as ?volumeBcm) ?totalVolumeBcm (?volumeBcm / ?totalVolumeBcm as ?shareOfSupply) (?shareOfSupply * ?shareOfSupply as ?shareOfSupplySqr) where {
 ?flow prop:GasFlowTo ?to ;
       prop:GasFlowFrom ?from ;
       prop:GasFlowVolume-23m3 ?volume .
 ?from rdfs:label ?fromName .
 ?to rdfs:label ?toName .
 {
  select ?to (sum(?volume  / 1e9) as ?totalVolumeBcm) where {
   ?flow prop:GasFlowTo ?to ;
         prop:GasFlowVolume-23m3 ?volume .
   } group by ?to
 }
}
}
} group by ?to
}
filter (?HHI > 0.7)
} order by desc(?HHI) ?to ?from

Result

The most dependent seem to be the Eastern European countries from the ex-Soviet block, plus Finland having Russia as the sole supplier of natural gas. This explains the fact that a big share of European gas still is and probably will be linked to high oil prices, unless the sources of supply are diversified. Growth in shale gas and LNG expansion seem to be reasonable to expect in the immediate future as Europe diversifies its suppliers in order to move towards more competitive gas prices.

Another group of countries in a similar situation are Syria, Lebanon, Israel and Jordan. They have Egypt as their sole supplier.

The following query will return a list of countries with HHI less than 0.3 - meaning a well diversified supply:

select ?to ?from ((?volume / 1e9) as ?volumeBcm) ?HHI  where {
?flow prop:GasFlowTo ?to ;
       prop:GasFlowFrom ?from ;
       prop:GasFlowVolume-23m3 ?volume .
{ select ?to (sum(?shareOfSupplySqr) as ?HHI) where {
{ select ?to ?from ((?volume  / 1e9) as ?volumeBcm) ?totalVolumeBcm (?volumeBcm / ?totalVolumeBcm as ?shareOfSupply) (?shareOfSupply * ?shareOfSupply as ?shareOfSupplySqr) where {
 ?flow prop:GasFlowTo ?to ;
       prop:GasFlowFrom ?from ;
       prop:GasFlowVolume-23m3 ?volume .
 ?from rdfs:label ?fromName .
 ?to rdfs:label ?toName .
 {
  select ?to (sum(?volume  / 1e9) as ?totalVolumeBcm) where {
   ?flow prop:GasFlowTo ?to ;
         prop:GasFlowVolume-23m3 ?volume .
   } group by ?to
 }
}
}
} group by ?to
}
filter (?HHI < 0.3)
} order by desc(?HHI) ?to ?from

Result

Japan, South Korea in Asia top the list, having the most diversified sources of supply, and perhaps the highest security of supply. Despite that their gas prices are significantly higher, which as already discussed is more due to non-liberalized national gas markets, rather than un-diversified supply.

In Europe countries with low HHI are countries with LNG terminals (Belgium, France, Spain) or geographically situated closer to suppliers (Italy). HHI can be interpreted as a measure of diversification, it can also be used as a heuristic for country's trading potential - potential to become a gas trading hub. In conjunction with storage, other infrastructure investments and the right policies countries like Belgium, Netherlands or Austria have the potential to become the trading hubs of continental Europe such as HenryHub in the US, or NBP in the UK.

Hopefully this rather quick glimpse at the world's gas markets and the arguments used in the new IEA's report also managed to demonstrate how to use SPARQL to analyse Enipedia's energy data.

P.S Enipedia's public SPARQL endpoint is available at: http://enipedia.tudelft.nl/wiki/Special:SparqlExtension

Happy Sparqling!

Many Western European countries (Netherlands, Belgium, Germany, Italy) have already been relying on natural gas as primary source for heating and power generation for a few decades now. The indigenous European resources have been gradually depleting giving way to imports from Russia and North Africa. With growing importance of natural gas in the energy landscape of the EU but also the emerging energy markets such as the BRIC economies, the sources of supply of natural gas and the emerging dynamics of the (global) gas market is questioned.

One of the goals of Enipedia is to enable a collaborative analysis (or more humbly: collaborative curiosity) of such issues by aggregating and publishing relevant data. We also belief that data is not enough. It is as important to provide context to the data, give examples and write stories about data.

In the next few paragraphs I will try to do exactly that – write a short story of how data describing major natural gas pipelines came to be and give a couple examples how it can be used.

Natural gas pipelines are the primary transport for delivering gas from its sources to the consumers. It is usually the case that the conventional natural gas fields are stranded far away from the markets and the pipelines require significant investments and long term country level commitments. It can be said that the natural gas pipeline network is essential to understand the current gas market and its the future development options.

Easier said than done. One would expect that such an essential piece of information would be publicly available from numerous energy agencies, consultants, government data streams. That is not the case. Closest to providing this data in the EU is the ENTSOG. They have a beautiful picture with all the pipes, cross-border connections and trading points in the EU. They also provide an interface allowing to query transport options on the EU gas grid. Despite that it still does not allow to export the data or explore it with a different query. But the ENTSOG picture and data provided a good starting point to scrape up the necessary data.

The description below assume one is familiar with a whole range of open source tools. If not, the links provided could be used as interesting starting points.

The following steps detail the process of creating, cross-referencing and importing gas pipeline data into Enipedia:

1. Collected bitmap pictures found on the ENTSOG and other websites (such as Theodora)
2. Chopped up the images into smaller tiles using imagemagick
3. Using the tiles created Google Earth overlays transforming the images to match the country borders and major cities. The previous step was necessary to be able to stretch the images on to the map.

4. Redrew the pipelines by hand (around 300 of them) using GE path tool
5. Drew interconnector points (where lines meet or border points) and LNG terminals using GE polygon tool
6. Exported the KMZ
7. Downloaded the html files accompanying the images (see step 1)
8. Used tidy to convert the html into valid xhtml and xpath to extract the pieces into a table (CSV)
9. Imported the CSV into Google Refine to fix up the messy pipeline start and end location names; gave names to pipelines.
10. Used Google Geocode API within Google Refine to retrieve coordinates for pipeline start and end locations
11. Used sed magic to cross-reference KML map data with the refined CSV
12. Defined a number of semantic properties, such as name, length, diameter, start, path and end points of the pipeline; defined a category “Pipeline” for all pipeline pages
13. Created a template to contain the properties, record and standardise the pipeline data; this is a good way to consistently map the CSV data structure with the one in your wiki. The template also defined the mechanics of correcting for missing data. For example the missing pipeline lengths were approximated by taking the start and end coordinates and using the {{#geodistance}} function.
14. Used the Java Wiki Bot Framework to import the CSV file into a wiki creating a page per row writing into the template
15. Uploaded the KML onto the wiki and used Google Maps extension to display the uploaded file in a wiki; Google Maps extension is also used in all pipeline pages to display maps of individual pipelines

Final result:

• The big picture
• Individual pipe: Urengoy – Budapest

When the data got written into the wiki, each pipeline property was automatically imported into a SPARQL endpoint using the SparqlExtension functionality. Now the public SPARQL endpoint allows to execute arbitrary queries on the data imported.

• Count the number of pipelines imported:

select count(?pipe) where {
   ?pipe a cat:Pipeline
}

Result

• List the pipelines in (from and to) Algeria:

select distinct ?pipe where {
   ?pipe prop:ToCountry | prop:FromCountry <Algeria> .
}

Result

• List the pipelines importing natural gas into the EU:

select * where {
   ?pipeline prop:ToCountry ?to;
                prop:FromCountry ?from .
   ?to a cat:EUMember .
   Filter (Not Exists {?from a cat:EUMember}) .
} order by ?from

Result

• List top 5 locations with most incoming gas pipelines – used to find where are the consumers and big distribution centers:

select ?consumer (count(?pipe) as ?incoming) where {
   ?pipe prop:PipelineTo ?consumer .
   Filter( Not Exists {?consumer a cat:Country} ) .
} group by (?consumer) order by desc(?incoming) limit 5

Result

• List top 5 locations with most outgoing gas pipelines; used to find where are the producers and big distribution centers:

select ?producer (count(?pipe) as ?outgoing) where {
   ?pipe prop:PipelineFrom ?producer .
   Filter( Not Exists {?producer a cat:Country} ) .
} group by (?producer) order by desc(?outgoing) limit 5

Result (Groningen in the Netherlands tops the list)

These are some basic queries that pop up immediately. The more interesting (and complicated) queries could reveal which points connect the most countries or the most gas fields with most diverse customers. That is where gas trading hubs will eventually emerge following the liberalisation of the EUs gas market.

For more information on natural gas economics and infrastructure see:

• Natural gas resources overview
• International natural gas flows network