Scholarly article on topic 'GIS based CCS source-sink matching models and decision support system'

GIS based CCS source-sink matching models and decision support system Academic research paper on "Earth and related environmental sciences"

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Abstract of research paper on Earth and related environmental sciences, author of scientific article — Wenying Chen, Lingyan Huang, Xing Xiang, Jiyong Chen, Liang Sun

Abstract We developed two models for source-sink matching, a vector data based multi- source and sink matching model and a raster data based single source and sink matching model. Based on the models, a decision support system (DSS) is developed on ArcGIS, which considers the influence of complex terrain factors such as the slope of the terrain, the bypass of protected areas like urban areas and national parks and the crossing of rivers, railways or highways on the transportation cost to find the least cost pathway between source and sink. Three provinces, Shandong, Jilin and Hebei (including Beijing and Tianjin), are selected as case studies. Firstly, we estimated the main emission sources from power sector, iron & steel, cement, ammonia, and oil refinery in these areas, including around 900 emission sources. Secondly, the storage potentials in oil/gas fields and aquifers are evaluated. Finally, source-sink matching cost curves for the three areas are obtained and sensitivity analysis for the impact of source-sink distance and N-year rule on both the cost and matched storage capacity is carried out with application of the DSS. In addition, least cost pathways between the GreenGen, the first near-zero-carbon-emission integrated gasification combined cycle (IGCC) power plant in China, and the three neighbouring oilfields, Dagang oilfields in Tianjin, Renqiu/Huabei oilfields in Hebei province, and Gudao/Shengli oilfields in Shandong province are found.

Academic research paper on topic "GIS based CCS source-sink matching models and decision support system"

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Procedia

Energy Procedia 4 (2011)5999-6006 ;

www.elsevier.com/locate/procedia

GHGT-10

GIS based CCS Source-Sink Matching Models and Decision

Support System

Wenying CHEN*, Lingyan HUANG, Xing XIANG, Jiyong CHEN, Liang SUN

Energy Environment and Economy (3E) Research Institute, Tsinghua University, Beijing 100084, China

Abstract

We developed two models for source-sink matching, a vector data based multi- source and sink matching model and a raster data based single source and sink matching model. Based on the models, a decision support system (DSS) is developed on ArcGIS, which considers the influence of complex terrain factors such as the slope of the terrain, the bypass of protected areas like urban areas and national parks and the crossing of rivers, railways or highways on the transportation cost to find the least cost pathway between source and sink. Three provinces, Shandong, Jilin and Hebei (including Beijing and Tianjin), are selected as case studies. Firstly, we estimated the main emission sources from power sector, iron & steel, cement, ammonia, and oil refinery in these areas, including around 900 emission sources. Secondly, the storage potentials in oil/gas fields and aquifers are evaluated. Finally, source-sink matching cost curves for the three areas are obtained and sensitivity analysis for the impact of source-sink distance and N-year rule on both the cost and matched storage capacity is carried out with application of the DSS. In addition, least cost pathways between the GreenGen, the first near-zero-carbon-emission integrated gasification combined cycle (IGCC) power plant in China, and the three neighbouring oilfields, Dagang oilfields in Tianjin, Renqiu/Huabei oilfields in Hebei province, and Gudao/Shengli oilfields in Shandong province are found.

©^ 200111 Published by Elsevier Ltd.

Key words: Carbon capture and storage; source-sink matching; Decision Support System; China

1. Introduction

COACH (Cooperation Action within CCS China-EU) was a project funded by the European Commission with overall objective to evaluate the feasibility of the deployment of CO2 Capture and Storage (CCS) in China. NZEC (China-UK Near Zero Emissions Coal) Initiative also examined the merits of various options for carbon dioxide capture, transport and geological storage in China. GeoCapacity project is also a project funded by the European Commission with China is one of the regions for storage capacity assessment. All these three projects were successfully conducted between 2006 and 2009. We are involved into all these three projects and are responsible for emission sources assessment, storage potential assessments and development of ArcGIS based decision support system for source-sink mapping (Chen et al, 2009a [2]; Chen et al, 2009b [3]; Chen et al, 2009c [4]; Chen et al, 2010 [5]). In this paper, we update emission sources and sinks data for the three provinces, that is, Hebei, Shandong,

* Corresponding author. Tel.: + 86 10 62772756; fax: +86 10 62771150 E-mail address: chenwy@tsinghua.edu.cn

ELSEVIER

doi:10.1016/j.egypro.2011.02.603

and Jilin which were selected as case study areas in the three above-mentioned projects. Since Hebei province encompass Beijing and Tianjin, the emission sources in Beijing and Tianjin are also estimated. And we simulate source-sink matching for these areas with application of the Arc-GIS based Decision Support System.

2. Carbon emissions estimation for major point sources

The focus within this study is on large stationary source CO2 emitters to which CCS might be applied, such as power plants, iron & steel, cement, ammonia, and oil refineries. In general, CO2 emissions are calculated by multiplying fuel consumption by the corresponding emission factor. The emission factor for coal, oil, and natural gas is estimated to be 0.715 tC/tce, 0.548 tC/tce, and 0.409 tC/tce respectively (Wu and Chen, 2001 [15]). Fuel consumption for each carbon emission source is calculated by multiplying its production by its fuel consumption per unit production. For power generation plants, the production is calculated by multiplying their capacity by operation hours. For cement, carbon emission from production process is also estimated as 0.371 t CO2/t cement with the assumption that clinker consumption for producing one ton of cement is about 0.7 t clinker/t cement and average carbon emission factor for clinker is about 0.53 t CO2/t clinker (Research Team of China Climate Change Country Study, 1999 [12]).

Data such as plant name, location, capacity, operation hour, production, fuel consumption per unit of production, fuel type and share of different fuel, and etc. for each carbon emission source are collected and stored in the carbon emission source database which can be easily accessed by Microsoft Excel, Microsoft Access, and ArcGIS. Carbon emissions for each source can be easily calculated for each source in the database using above-mentioned approaches. For Hebei, Beijing and Tianjin, totally 314 sources (218 power plants, 16 iron& steel plants, 18 cement plants, 56 ammonia plants, and 6 oil refinery plants) are estimated and their emissions amount to around 357 MtCO2/yr, with power, iron & steel, cement, ammonia, and oil refinery sharing 55.8%, 30.4%, 7.2%, 4.8%, and 1.8% respectively, as detailed in Table 1. There are 243 sources with annual carbon emission larger than 100 KtCO2/yr and their emissions amount to around 354 MtCO2/yr, sharing 99.4% of the total. For Shandong province, totally 494 sources (389 power plants, 5 iron& steel plants, 21 cement plants, 63 ammonia plants, and 16 oil refinery plants) are estimated and their emissions amount to around 354 MtCO2/yr, with power, iron & steel, cement, ammonia, and oil refinery sharing 65.7%, 12.9%, 10.8%, 8.8%, and 1.8% respectively, as detailed in Table 2. And around 97.6% of the total emissions produced by the 324 sources whose annual emissions are all larger than 100 KtCO2/yr. For Jilin province, totally 78 sources (55 power plants, 4iron& steel plants, 13 cement plants, 4 ammonia plants, and 2 oil refinery plants) are estimated and their emissions amount to around 70.5 MtCO2/yr, with power, iron & steel, cement, ammonia, and oil refinery sharing 67.6%, 14.6, 12.9%, 2.5%, and 2.4% respectively, as detailed in Table 3. And around 98.5% of the total emissions produced by the 53 sources whose annual emissions are all above 100 KtCO2/yr.

3. CO2 storage potential

3.1 Approach to estimate CO2 storage potentials

For calculation of CO2 storage capacity in hydrocarbon field, the methodology described in the CSLF paper applies the following formulas for gas and oil fields respectively (Bachu et al, 2007 [1]):

MCO2 =pCO2 x RF_G x (1 - Fig) x OGIP x FVF_G MCO2 =pCO2 x (RF Ox OOIP xFVF O - Viw + Vpw)

where MCO2 is hydrocarbon field storage capacity; pCO2 is CO2 density at reservoir conditions (best estimate); RF_O or RF_G is recovery factor for oil or gas; Fig is fraction of injected gas; OGIP is original gas in place (at surface conditions); FVF G is gas formation volume factor; OOIP is original oil in place (at surface conditions); FVF O is oil formation volume factor; Viw is volume of injected water; Vpw is volume of injected water.

In this paper, a simplified formula from the GESTCO project was used to estimate carbon storage potentials in oil/gas fields not considering CO2 enhance oil recovery (EOR), that is, the estimation of the CO2 storage capacity was performed by using the following formula with the optimistic assumption that all recovered hydrocarbon could

Table 1 Large CO2 Sources and Emissions by sector in Hebei, Beijing and Tianjin

Enterprises estimated Number Capacity (GW) or production _(Mt/yr)_

Enterprises estimated >100 KtCO^yr CO2 estimated Number Capacity (GW) (Mt CO2/yr) or production _(Mt/yr)_

CO2 estimated (Mt CO2/yr)

Power Plant Iron & Steel Cement Ammonia Oil refinery Total

218 16 18 56 6

39.64 GW

199.33

108.52

356.92

38.85 GW

196.37

108.52

353.69

Table 2 Large CO2 Sources and Emissions by sector in Shandong Province

Type Enterprises estimated Enterprises estimated >100 KtCO2/yr

Number Capacity (GW) CO2 estimated Number Capacity (GW) CO2 estimated

or production (Mt CO2/yr) or production (Mt CO2/yr)

(Mt/yr) (Mt/yr)

Power Plant 389 52.24 GW 232.60 228 49.83 GW 224.55

Iron & Steel 5 24.66 45.54 4 24.62 45.46

Cement 21 45.04 38.06 21 45.04 38.06

Ammonia 63 6.41 31.24 55 6.31 30.79

Oil refinery 16 31.09 6.51 16 31.09 6.51

Total 494 -- 353.95 324 -- 345.37

Table 3 Large CO2 Sources and Emissions by sector in Jilin Province

Enterprises estimated Number Capacity (GW)

or production (Mt/yr)

Enterprises estimated >100 KtCO2/yr CO2 estimated Number Capacity (GW) (Mt CO2/yr) or production _(Mt/yr)_

CO2 estimated (Mt CO2/yr)

Power Plant Iron & Steel Cement Ammonia Oil refinery Total

55 4 13 4 2

9.15 GW 5.17 10.79 0.54 8.21

8.83 GW 5.16 10.79 0.53 8.21

be replaced by an equivalent volume of CO2 at reservoir conditions (Schuppers et al, 2003 [13]):

MCO2depie,ed = OGIP x RF_G x FVF_G x pCÜ2+ OOIP x RF_O x FVF_O x pCO2

For CO2 enhance oil recovery, the estimation method taken from literature (Dahowski et al, 2004 [6]) is explained as followings:

EOR = OOIPc x % EXTRA f5.3% (API < 31)

%EXTRA = \ (1.3 x API - 3 5)% (31 < API < 41) [l8.3% (API > 41)

API = 1415 -131.5 S

OOIPC = OOIP x C

Where EOR is total incremental volume of oil produced as a result of CO2-driven enhanced oil recovery; OOIPc is original in-place reserves that could be contacted by CO2 flood within the reservoir; C is contact factor assumed as 75%; %EXTRA is proportion of extra recovery to OOIP; S is oil relative density.

The amount of CO2 required to produce the estimated available EOR is given by the following:

MCO2eor - EOR x Rcoi

Where RCO2 is assumed as in the range of 2.47 to 4.12 ton CO2/ton oil, in this paper 3.18 ton CO2/ton oil is applied.

3.2 CO2 storage potentials in the three areas

Huabei oilfield in Hebei province, Shengli oilfield in Shandong province, and Jilin oilfield in Jilin province are selected for storage potentials assessment. Data needed for storage potentials assessment are from literature [11].

Twenty three oil fields have been selected for estimation in Huabei oilfield (Chen et al, 2009a [2]; Chen et al., 2010 [5]). The total of Original Oil In Place is estimated at 768 Mton, of which 406 Mton is for the giant Renqiu alone. Table 4 provides the estimation results of CO2 storage potentials for these twenty three oil fields in Hebei province. The total storage potentials are estimated to 207 MtCO2 if not considering EOR. Around 42 Mton oil would be enhanced produced with CO2 storage of 132 MtCO2 if EOR considered.

Table 4 CO2 storage potential assessment for the 23 selected oil fields in Hebei

OOIP (Mton) Depth (m) Pressure (Mpa) Temperture (°C) Density (ton/m3) CO2depeted (Mton) CO2EOR (Mton)

Renqiu 406.52 1500-3750 32.5 120 0.617 82.78 51.39

Yanling 16.95 3000 30.1 118 0.594 2.33 2.14

Longhuzhuang 11.5 2121-2504 21.6 82 0.617 2.25 3.36

Liubei 31.36 2016-3335 32.9 123 0.612 5.13 9.93

Dawangzhuang 37.9 3200 33.5 112 0.657 11.56 4.79

Suning 21.27 2700-3200 32 104 0.670 6.83 2.87

Mozhou 18.36 4200 41 144 0.632 4.27 4.13

Chahejji 50.28 2677 27.8 82 0.713 20.67 12.24

Liuquan 25.74 1858-2554 25.4 61 0.786 11.52 8.05

Bieguzhuang 22 1464 15.1 54 0.666 7.09 4.30

Wenan 12.26 1540-2052 27.9 89 0.682 3.77 2.69

Hexiwu 16.43 2770 46.5 95 0.814 7.76 2.63

Suqiao 20.63 3700-4500 41.6 138 0.654 22.95 3.06

Gaoyang 14.35 2510-2560 24 95 0.594 2.74 3.15

Liuchu 14.19 2500-3000 25.8 103 0.589 3.76 3.11

Nanmeng 11.5 2190 19.5 74 0.621 2.27 4.42

Balizhuangxi 12 4050 35.5 135 0.603 2.43 2.75

Xuezhuang 2.72 3000 27.9 108 0.601 0.50 0.34

Balizhuang 4.63 2750 25.8 103 0.589 0.86 0.61

Hejian 4.37 2450 22.9 83 0.637 0.87 0.97

Hezhuangxi 3.85 3770 36.5 110 0.694 1.69 1.68

Hezhuang 1.16 3300 31.6 110 0.642 0.28 0.35

Shenxi 8.5 4100 40.1 133 0.655 2.61 3.15

There are seven main oilfields in Shengli selected for storage potentials assessment (Zeng et al, 2009 [16]). Their total Original Oil In Place is estimated at 1544 Mton. The total storage potentials are estimated to 536 MtCO2 if not considering EOR. Around 61 Mton oil would be enhanced produced with CO2 storage of 195 MtCO2 if EOR considered. Detail results are shown in Table 5 for the seven oil fields estimated in Shandong province.

Table 5 CO2 storage potential assessment for the 7 selected oil/gas fields in Shandong

OOIP (Mton) Depth (m) Pressure (Mpa) Temperture (°C) Density (ton/m3) CO2depeted (Mton) CO2EOR (Mton)

Chengdong 60.42 1175-1200 69 12.3 0.935 16.01 7.64

Gudao 387.52 1200-1335 70 12.3 0.94 102.16 48.98

Gudong 263.86 1000-2000 59 12.4 0.953 69.17 33.35

Shengtuo 478.51 970-2960 80 20.6 0.915 217.05 60.49

Shanjiashi 27 1000-1200 59 11.4 0.969 6.40 3.41

Dongxin 241.99 1200-3250 82 19.7 0.92 104.40 30.59

Lean 84.63 880-960 55 11.9 0.98 20.70 10.70

Five main oilfields in Jilin oilfields have been selected for estimation (Pearce et al, 2009 [10]). The total of Original Oil In Place is estimated at 307 Mton. Table 6 provides the estimation results of CO2 storage potentials for

these five oil fields in Jilin province. The total storage potentials are estimated to 92 MtCO2 if not considering EOR. Around 18 Mton oil would be enhanced produced with CO2 storage of 58 MtCO2 if EOR considered.

Table 6 CO2 storage potential assessment for the 5 selected oil/gas fields in Jilin

OOIP (Mton) Depth (m) Pressure (Mpa) Temperture (°C) Density (ton/m3) CO2depeted (Mton) CO2EOR (Mton)

Honggang 17.54 1200 12 55 0.885 4.96 2.22

Xinli 49.36 1400 12.2 66 0.863 14.44 8.41

Qianan 121.39 1600-1700 19.3 76 0.857 37.22 25.03

Yingtai 100.17 1400-1690 15 65 0.874 30.52 19.58

Mutou 18.21 600 6.8 40 0.891 5.12 2.30

CO2 storage potentials in aquifer in Hebei province, Shandong province, and Jilin province are roughly estimated to be 3.9, 1.4, and 1.2 GtCO2 respectively based on the literature (Xiang, 2007 [14]; Huang, 2009 [8]; Li et al, 2006 [9]).

4. Cost estimation for CO2 storage

Storage cost estimated in this paper consists of transport cost, injection cost and monitoring cost.

The transport cost estimated includes the cost of pipeline construction and maintenance & operation (O&M) cost which is assumed as a factor of annualized pipeline construction cost. The cost of pipeline construction is calculated as the sum of a basic cost of construction and an additional cost due to terrain conditions / obstacles (Chen et al, 2009a [2]; Chen et al, 2010 [5]). The basic cost of construction in RMB/km varies with the pipe diameter which depends on CO2 flow rate. For the construction cost for different diameter pipeline, we refer the data for natural gas pipeline provided by Chinese experts. For CO2 flow rate less than 3 MtCO2/y, the construction cost is estimated to about 1500 thousand RMB/km.

Calculation of additional costs due to the slope of the terrain, the bypass of urban areas and the crossing of rivers, highways and railways are made with ArcGIS (Chen et al, 2009a [2]; Chen et al, 2010 [5]). The slope of the terrain and the rivers are calculated using USGS (United States Geological Survey) 90 m digital elevation data. Data on urban area, railways and highways and etc. are from ESRI (Environmental Systems Research Institute, Inc.) and China National Basic Geography Information Center. We sum all these grids according to the equation: Total Cost = Basic Cost x (1 + a x Slope + b x Rivers + c x Cities + d x Highway + e x Railway). And the cost factor for base case, slope 10%-20%, slope 20-30%, slope>30%, waterway crossing, populated place, highway, and railway is 1, 0.1, 0.4, 0.8, 10, 15, 3 and 3 respectively (Herzog, 2006 [7]).

Injection cost includes the construction cost for both injection wells and production wells, and O&M cost for these wells. The revenue from EOR is also considered. The unit construction cost of injection well and production well in RMB/km is assumed as the same for each oilfield. The unit construction costs of injection wells in different geographical conditions are different and we refer the data provided by Chinese experts. The number of the production wells is assumed as 5.7 times of that of the injection wells for EOR, while the number of injection wells is determined by the amount of CO2 sequestrated and oil fields' injectivity which is assumed as 61 KtCO2/yr (Dahowski et al, 2004 [6]).

5. Decision support system for matching of sources and sinks

A decision support system for matching of sources and sinks based on ArcGIS is developed with two models, that is, vector data based multi source-sink matching model, and raster data based single source-sink matching model, as illustrated in Figure 1. For pipeline construction cost estimation, both of these two models applies the above-mentioned approach to take into account the additional costs due to the slope of the terrain, the bypass of protected areas such as urban areas and national parks and the crossing of rivers, railways or highways. The former provides straight pathway between matched sources and sinks, while the latter provides more realistic pathway.

Figure 1 Framework of the ArcGIS-based decision support system for matching of sources and sinks

In the multi source-sink matching model an algorithm is designed to best match sources and sinks depending on the cost of transport, injection and monitoring. The algorithm first calculates the cost for each possible combination of sources and sinks. To reduce the possibilities, it considers only the pairs in which the source and the sink are distant by less than a chosen radius. And it checks whether the sink can accommodate N years of the source emissions. If not, the algorithm moves on to the next pair. Then the storage cost (including transportation cost, injection cost, and monitoring cost) of source-sink pairs are calculated and ranked by increasing cost. Finally, starting with the cheapest pair, the algorithm checks whether the source has already been matched. If not, the source is marked as "matched" and the N-year emissions of the source are deduced from the sink capacity. Then the algorithm moves on to the next pair.

The raster data based single source-sink matching model uses ArcGIS Spatial Analyst tools: Cost Distance, Cost Backlink and Cost Path, presented in other published literatures (Chen et al, 2009a [2]; Chen et al, 2010 [5]).

6. Simulation results

6.1 Storage cost curves and sensitivity analysis for the three areas

With application of the multi-source and multi-sink matching model in the decision support system and the database of sources and sinks in the three selected areas, the storage cost curves and their sensitivity analysis (source-sink distance threshold and N-year rule) results are displayed in Figure 1. The source-sink distance threshold in the range of 50 km to 200 km and N in the range of 10 years to 25 years are simulated. In Hebei, Beijing, and Tianjin, the number of sources matched, the amount of CO2 sequestrated, and pipeline length are in the range of 323, 1.1-9.1 MtCO2, and 92-1708 km for EOR while in the range of 41-220, 157-318 MtCO2, and 198-5122 km for non EOR. In Shandong, the number of sources matched, the amount of CO2 sequestrated, and pipeline length are in the range of 13-22, 7.9-20.3 MtCO2, and 511-940 km for EOR while in the range of 26-106, 73-184 MtCO2, and 1392-7731 km for non EOR. In Jinlin, the number of sources matched, the amount of CO2 sequestrated, and pipeline length are in the range of 1-7, 0.7-4.9 MtCO2, and 11-729 km for EOR while in the range of 27-32, 30-38 MtCO2, and 142-1076 km for non EOR.

Figure 1 Storage cost curves and sensitivity analysis for the three areas

6.2 Least cost path between GreenGen and neighbouring oil fields

The Huaneng Group launched the GreenGen project in Tianjin, China, the first near-zero-carbon-emission IGCC (integrated gasification combined cycle) power plant in China. The expected CO2 emissions from GreenGen with 400 MW is estimated to be around 2.2 MtCO2/yr. least cost pathways between the GreenGen, and the three neighbouring oilfields, Dagang oilfields in Tianjin, Renqiu/Huabei oilfields in Hebei province, and Gudao/Shengli oilfields in Shandong province are found, shown in Figure 2. The investment cost for pipeline to Dagang is the lowest, varying from 83-250 million RMB, to Gudao is the highest (540 million RMB), detailed in Table 7.

Figure 2 Potential pathways between GreenGen power plant and Renqiu, Dagang and Gudao oilfield Table 9 List of matching result for GreenGen

Source

Pipeline distance (km) | Pipeline diameter (mm) | Investment for pipeline (100,000 RMB)

GreenGen Renqiu 170 300 440

GreenGen Dagang 30~90 300 83~250

GreenGen Gudao 200 300 540

7. Conclusions

The decision support system for CCS sources and sinks matching developed in this paper has friendly interfaces with the flexibility for users to input different values for the key cost impact parameters depending on specific conditions/requirements. In addition, the decision support system with two matching models allows users to carry out two levels assessment to not only provide macro pictures of sources and sinks matching and order the matched pairs by reducing cost for a given region, but also to provide more realistic pipeline pathways between the sources and sinks interested. This useful system is expected to be further updated, for example, consideration of capture cost, and expansion of the database to include sources and sinks in other areas in China.

Acknowledgements

This study was supported by the Chinese special S&T fund on Sino-European cooperation 0808, GeoCapacity, COACH, and NZEC.

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