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Petroleum Science

, Volume 13, Issue 4, pp 788–804 | Cite as

The potential of domestic production and imports of oil and gas in China: an energy return on investment perspective

  • Zhao-Yang Kong
  • Xiu-Cheng DongEmail author
  • Qian Shao
  • Xin Wan
  • Da-Lin Tang
  • Gui-Xian Liu
Open Access
Original paper

Abstract

Concerns about China’s energy security have escalated because of the country’s high dependency on oil and gas imports, so it is necessary to calculate the availability of domestic oil and gas resources and China’s ability to obtain foreign energy through trade. In this work, the calculation was done by using the energy return on investment (EROI) method. The results showed that the EROIstnd (i.e., standard EROI) of China’s oil and gas extraction decreased from approximately 17.3:1 in 1986 to 8.4:1 in 2003, but it increased to 12.2:1 in 2013. From a company-level perspective, the EROIstnd differed for different companies and was in the range of (8–12):1. The EROI2,d (EROI considering energy outputs after processed and direct energy inputs) for different companies was in the range of (3–7):1. The EROI of imported oil (EROIIO) declined from 14.8:1 in 1998 to approximately 4.8:1 in 2014, and the EROI of imported natural gas (EROIING) declined from 16.7:1 in 2009 to 8.6:1 in 2014. In 2015, the EROIIO and EROIING showed a slight increase due to decreasing import prices. In general, this paper suggests that from a net energy perspective, it has become more difficult for China to obtain oil and gas from both domestic production and imports. China is experiencing an EROI decline, which demonstrates the risk in the use of unsustainable fossil resources.

Keywords

EROI Oil and gas extraction Imported oil Imported natural gas China 

1 Introduction

Few issues, if any, are as fundamentally important to industrial societies and their economies as the future oil and gas supplies (Cleveland 2005; Gagnon et al. 2009). Oil and gas provide nearly 60 % of the world’s energy (BP 2014). Global food production and most economies rely heavily on oil and gas, and historical restrictions on the availability of oil have had major economic impacts (Munasinghe 2002). China has become the world’s largest energy consumer, with consumption increasing from 16.7 × 1012 in 1978 to 99.8 × 1012 MJ in 2012 (National Bureau of Statistics of China, 2014; Fan et al. 2015). Together, oil and gas comprise approximately 25.5 % of the consumption of primary energy resources (Safronov and Sokolov 2014). Due to China’s limited domestic production capacity, however, increasing amounts of oil and gas have been imported from counties such as Qatar, Indonesia, Malaysia, Russia, and Australia (Kong et al. 2015). Over the last 7 years, China’s dependency on imported oil (IO) has increased by 21.5 % annually, and in 2013, it reached 59 percent of total use (Fig. 1). Moreover, China’s dependence on imported natural gas (ING) is also rising (Fig. 1) and, according to the BP Energy Outlook 2030, will reach over 40 percent of total use by 2030 (Kong et al. 2015). Thus, oil and gas security has become an issue that cannot be ignored. If an interruption in energy imports occurs or the import price increases, China’s economy will be seriously affected. For example, as a result of the 1973 oil shock, the world economy endured the hitherto worst recession in postwar history (Kesicki 2010). Therefore, to ensure the country’s oil and gas security, it is necessary to calculate the availability of domestic oil and gas resources and China’s ability to obtain foreign oil and gas through trade.
Fig. 1

China’s dependence on foreign oil and gas

The energy return on investment (EROI) is a useful approach for assessing the productive availability of an energy source (Heun and Wit 2012). It is the ratio of energy that is produced by a process to the energy that is consumed in carrying out that process (Gagnon et al. 2009). If the EROI of a fuel is high, then only a small fraction of the energy produced is required to maintain production, and the majority of that produced energy can be used to run the general economy. In contrast, if the EROI is very low, the majority of the energy produced must be used to ensure continued energy production, and very little net energy is available for useful economic work. Thus, high EROI fuels are vital to economic growth and productivity (Gagnon et al. 2009).

Unfortunately, the peer-reviewed literature in this field has paid only minimal attention to the EROI of oil and gas extraction (OGE) in China. To the best of our knowledge, only three papers have examined the EROI of OGE in China. In 2011, Hu et al. (2011) derived an EROI of the Daqing oil field, the largest oil field in China. They estimated that its EROI was 10:1 in 2001 but declined to 6.5:1 in 2009. In 2013, Hu et al. (2013) found that the EROI for China’s OGE fluctuated from 12:1 to 14:1 in the mid-1990s and declined to 10:1 during the period of 2007–2010. In 2014, Xu et al. (2014) forecasted that the Daqing oil field’s EROI would continuously decline from 7.3:1 to 4.7:1.

In this paper, we address the EROI of OGE in China not only from an industry-level perspective, but also from a company-level perspective. An analysis of the EROI of oil companies could provide some information about energy inputs and thus serve as a reference for policymakers and investors. Besides, we prefer to analyze EROI not only at the mine mouth (the conventional approach) but also at the refinery, because crude oil cannot be directly used by cars and needs be processed. Of course, oil processing consumes energy, which should also be considered in energy inputs.

Lambert et al. (2014) studied the EROI of imported oil (EROIIO) for 12 developing countries, including China. They have found that most developing nations have EROI values below 8–10. As a large gas importer, it is necessary to estimate the EROI of imported natural gas (EROIING) of China. Through this study, we aimed to answer four questions:
  1. (1)

    What is the EROI of China’ oil and gas extraction in 1985–2012?

     
  2. (2)

    What are the EROI values of China’s four oil companies: CNPC (China National Petroleum Corporation), Sinopec (Sinopec Group), CNOOC (China National Offshore Oil Corporation), and Yanchang (Shanxi Yanchang Petroleum (Group) Co., Ltd.)?

     
  3. (3)

    Are the EROI values of these four oil companies lower or higher than the national average value?

     
  4. (4)

    What are the EROIIO and EROIING values of China?

     

2 EROI methodology and data for China’s domestic production of oil and gas

2.1 EROI methodology for domestic production of oil and gas

EROI can broadly be described as the ratio of the energy made available to society through a certain process and the energy cost to implement this process (Cleveland and O’Connor 2011; Lundin 2013). The general equation for EROI is given in Eq. (1):
$${\text{EROI}} = \frac{{{\text{Energy}}\;{\text{produced}}\; ( {\text{outputs)}}}}{{{\text{Energy}}\;{\text{consumed}}\; ( {\text{inputs)}}}}$$
(1)

The numerator is the sum of all energy produced in a given timeframe, and the denominator is the sum of the energy costs. EROI is typically calculated without discounting for time. Because the numerator and denominator are usually assessed in the same units, the ratio derived is dimensionless and often expressed as EROI: 1 in text (Lundin 2013), e.g., 10:1. This implies that a particular process yields 10 joules on an investment of 1 J.

Some previous EROI analyses have generated a wide variety of results, including apparently conflicting results, when applied to the same energy resource. The reasons for these differences are not limited to intrinsic variations in energy resource quality, extraction technology, and varying geology but also include methodological issues including different boundaries of analysis, different methods used to estimate indirect energy inputs, and issues related to energy quality (Hu et al. 2013).

In order to formalize the analysis of EROI, Mulder and Hagens (2008) established a consistent theoretical framework for EROI analysis that encompasses the various methodologies. Murphy et al. (2011) propose a more explicit two-dimensional framework for EROI analysis that describes three boundaries for energy analysis and five levels of energy inputs, as shown in Table 1.
Table 1

Two-dimensional framework for EROI analysis

Levels for energy inputs

Boundary for energy outputs

1. Extraction

2. Processing

3. End-use

Direct energy and material inputs

EROI1,d

EROI2,d

EROI3,d

Indirect energy and material inputs

EROIstnd

EROI2,i

EROI3,i

Indirect labor consumption

EROI1,lab

EROI2,lab

EROI3,lab

Auxiliary services consumption

EROI1,aux

EROI2,aux

EROI3,aux

Environmental

EROI1,env

EROI2,env

EROI3,env

In Table 1, the numbers “1,” “2,” and “3” describe the boundary for energy outputs, i.e., where the analysis is terminated (mine mouths, refinery or point of use), while the “d,” “i,” “aux,” “lab,” or “env” in subscript refer to the abbreviations for different types of inputs considered: They are direct energy (d) used on site, indirect energy (i) used to purchase material inputs constructed offsite such as steel for sand pipes, embodied energy in the wages of labor (lab), energy afforded by governmental services in the public sector (aux), and energy embodied in environmental costs for assessment (env), respectively.

Because most EROI analyses account for both direct and indirect energy and material inputs, but not for labor or environmental costs, Murphy et al. (2011) deem this boundary to be the standard EROI and assign it the name EROIstnd. Using the standard calculation, we have the following equation:
$${\text{EROI}}_{\text{stnd}} = \frac{{E_{O} }}{{E_{d} + E_{i} }}$$
(2)
where E o is all energy outputs, J; E d , and E i represent the total input and direct input, J, respectively, of different types of energy. The challenge is that the indirect energy inputs are rarely available as physical energy units. Rather, the data are available in monetary units as, e.g., investments in industrial equipment. Thus, Eq. 3 is used to complete the EROI analysis:
$${\text{EROI}}_{\text{stnd}} = \frac{{E_{O} }}{{E_{d} + M_{i} \times E_{\text{ins}} }}$$
(3)
where M i represents the indirect inputs in monetary terms and E ins expresses the energy intensity of a dollar input for indirect components.

Other approaches (e.g., including environmental) can be conducted as sensitivity analyses, which will examine how changing variables affect the outcome. If both environmental and indirect costs are considered, the EROI can be expressed as EROI1,i+env. The critical point is to clarify what is included in the analysis (Murphy et al. 2011).

2.2 China’s oil and gas extraction data

2.2.1 Energy outputs

The National Bureau of Statistics of China provides data on energy outputs and energy inputs for OGE (Table 2) (National Bureau of Statistics of China 2014). Open access data are available from 1985 onward, so we calculated the EROIOGC from 1985 to 2012. This output was converted to heat units using the values in Table 3. Thus, we can obtain energy output as heat equivalents (Fig. 2).
Table 2

Energy output and energy inputs of OGE in China, in physical units (tce is tonnes of coal equivalent)

Year

Energy output

Energy inputs

Oil

Gas

Raw coal

Oil

Gasoline

Diesel

Fuel oil

Gas

Electricity

Others

104, metric tons

108, m3

104, metric tons

104, metric tons

104, metric tons

104, metric tons

104, metric tons

108, m3

108, kWh

104, tce

1985

12,490

129

92

141

31

28

47

34

85

95

1986

13,037

134

96

132

27

41

50

34

98

71

1987

13,392

135

88

120

28

46

79

39

109

62

1988

13,685

139

105

138

35

50

80

40

123

78

1989

13,748

145

121

138

36

57

71

39

131

86

1990

13,831

153

132

141

39

64

130

36

145

92

1991

13,968

154

117

133

38

64

98

32

158

37

1992

14,196

157

140

109

42

68

109

40

171

34

1993

14,400

163

215

141

58

137

157

39

232

75

1994

14,607

167

229

212

52

142

160

45

240

66

1995

15,005

180

220

175

59

146

161

42

259

132

1996

15,729

201

262

174

53

191

105

30

259

61

1997

16,044

223

310

317

36

157

108

38

315

211

1998

16,052

223

205

313

33

103

127

35

298

251

1999

16,000

252

176

331

42

145

144

46

308

325

2000

16,300

272

186

409

45

162

146

50

322

353

2001

16,396

303

162

423

44

177

150

58

356

356

2002

16,700

327

163

448

44

198

142

59

365

369

2003

16,960

350

186

552

39

168

121

62

357

355

2004

17,587

415

188

499

37

185

33

49

363

273

2005

18,135

493

184

504

26

186

26

49

385

269

2006

18,477

586

187

565

29

187

28

55

316

228

2007

18,632

692

179

569

31

198

26

64

311

186

2008

19,044

803

153

696

28

272

37

86

318

180

2009

18,949

853

155

487

25

230

26

89

333

164

2010

20,301

949

157

482

24

186

31

102

348

176

2011

20,288

1027

151

368

22

192

26

96

375

193

2012

20,748

1072

129

463

14

63

12

96

397

153

Table 3

Conversion factors from physical units to MJ

 

Conversion factor

Oil

41.8, MJ/kg

Natural gas

38.9, MJ/m3 or 57.18, MJ/kg

Raw coal

20.9, MJ/kg

Crude oil

41.8, MJ/kg

Gasoline

43.1, MJ/kg

Diesel

42.7, MJ/kg

Fuel oil

41.8, MJ/kg

Kerosene

43.1, MJ/kg

Electricity

3.6, MJ/kWh

Coal equivalent

29.3, MJ/kg

Fig. 2

Energy output and energy inputs for OGE in China

2.2.2 Energy inputs

Direct energy inputs to the OGE sector mainly include raw coal, crude oil, gasoline, diesel oil, fuel oil, natural gas, and electricity. All of the raw data for direct energy inputs in physical units (Table 2) are also converted into thermal units (Fig. 2) using the conversion factors in Table 3.

We derived indirect energy inputs through multiplying costs by industry energy intensity factors (Table 4), because there is no energy inputs accounting in routine economic data. Indirect monetary costs include the sum of “purchase of equipment and instruments” and “other expenses” of “investment” in the China Statistic Yearbook database (National Bureau of Statistics of China 2014). China Statistic Yearbook database did not provide the data about indirect monetary costs before 1995. The ratio of indirect energy inputs to total energy inputs between 1995 and 2001 fluctuated around 10 %, so it was assumed to be 10 % before 1995. Thus, the indirect energy inputs for OGE can be obtained, as shown in Fig. 2.
Table 4

Indirect energy inputs of oil and natural gas extraction in China

 

Raw data, 109, yuan

Energy intensity for industry, MJ/yuan

Indirect energy inputs, 109 MJ

Purchase of equipment and instruction

Other expenses

Total

1995

4.5

2.2

6.7

11.3

76

1996

5.2

2.3

7.5

9.6

72

1997

5.5

2.6

8.1

8.7

70

1998

6.1

3.2

9.3

8.4

78

1999

6.1

3

9.1

8.2

75

2000

6.9

3.6

10.5

7.6

80

2001

7.7

4.2

11.9

7.2

86

2002

8.6

5.6

14.2

7

99

2003

11

7.5

18.5

7

130

2004

13.6

10.5

24.1

6.8

164

2005

19.8

12.9

32.7

6.4

209

2006

25.1

19.7

44.8

5.9

264

2007

25.9

16.5

42.4

5.3

225

2008

44.4

20.2

64.6

4.7

304

2009

55.2

21.6

76.8

4.7

361

2010

58.4

24

82.4

4.2

346

2011

41.6

28.5

70.1

3.8

266

2012

33.3

28.8

62.1

3.7

230

2013

39.3

27

66.3

3.5

232

2.3 Oil and gas production data from companies

2.3.1 Energy outputs

The volumes of oil and gas extracted by each company (Table 5) can be easily found in their annual reports (CNPC Annual Reports; Sinopec Annual Reports; CNOOC Annual Reports; Yanchang Social Responsibility Reports). For EROIstnd, the energy outputs of each company in Table 6 are equal to the volumes (Table 5) multiplied by the conversion factors.
Table 5

Amount of oil and gas extracted by each company (oil: 104, tonnes; gas: 109, m3)

 

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Oil

             

 CNPC

11,484

11,757

11,695

12,097

12,598

13,471

13,762

13,875

13,745

14,144

14,927

15,188

15,981

 Sinopec

     

4017

4108

4180

4242

4256

4273

4318

4378

 CNOCC

    

3197

3154

3055

3244

3697

    

 Yanchang

          

1232

1264

1263

Gas

             

 CNPC

212

233

263

313

396

480

578

664

738

829

882

935

1039

 Sinopec

     

73

80

83

85

125

146

169

187

 CNOCC

    

70

88

99

105

107

    

 Yanchang

          

0.2

2.6

4.7

Table 6

Energy output of OGE for each company, in thermal units (109, MJ)

 

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Oil

             

 CNPC

4800

4914

4889

5057

5266

5631

5753

5800

5745

5912

6239

6349

6680

 Sinopec

     

1679

1717

1747

1773

1779

1786

1805

1830

 CNOCC

    

1336

1318

1277

1356

1545

    

 Yanchang

          

515

528

528

Gas

             

 CNPC

825

906

1023

1218

1540

1867

2248

2583

2871

3225

3431

3637

4042

 Sinopec

     

284

311

323

331

486

568

657

727

 CNOCC

    

272

342

385

408

416

    

 Yanchang

          

1

10

18

When calculating the EROI2,d of each company, it is found that the volumes of oil extracted are not equal to the volumes of oil processed, due to the existence of oil imports. To calculate the EROI2,d easily, it is assumed the volumes of oil extracted are 4.18 × 104 MJ per tonne. According to Brandt et al. (2013), the energy losses in the refining process are about 7 %. Then, the energy output is 3.89 × 104 MJ per tonne. We assume the energy output of CNPC, Sinopec, and Yanchang is 3.89 × 104 MJ, then that of CNOOC is 2.14 × 104 MJ, because the yield of light oil products for CNOOC is lower about 1.75 × 104 MJ per tonne than that of other companies.

2.3.2 Energy inputs

2.3.2.1 Energy investment in OGE
  1. (1)

    Direct energy inputs

     
The CNPC’s and Yanchang’s total direct energy inputs data in Table 7 for the OGE sector are from the CNPC Statistical Yearbooks (CNPC 2014) and the Yanchang Social Responsibility Reports, respectively. The Sinopec Statistical Yearbook provides data on the units of direct energy inputs for OGE (Table 7) (Sinopec 2014). From the volume of oil and gas extracted by Sinopec (Table 5), the total direct energy inputs of Sinopec can be calculated and shown in Table 8.
Table 7

Total direct energy inputs for OGE for each company (104, tce)

 

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

CNPC

1840

1851

1905

1960

2106

2191

2410

2443

2467

2492

2517

2542

2568

Sinopeca

     

111.7

104.9

105.1

102.4

100.4

105.9

105.9

105.3

CNOCCb

    

391

413

480

581

746

    

Yanchang

          

147.8

146.6

145

aSinopec’s units of direct energy inputs for OGE are kgce/t

bThe 2009 data for CNOOC refer to the total direct inputs for OGE and oil processing

Table 8

Total direct energy inputs of OGE for each company, in thermal units (109, MJ)

 

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

CNPC

539.1

542.3

558.2

574.3

617.1

642.0

706.1

715.8

722.8

730.2

737.5

744.8

752.4

Sinopec

     

153.5

149.1

152.7

150.9

159.4

174.9

182.8

188.7

CNOOC

    

114.6

113.1

119.3

123.9

140.1

    

Yanchang

          

43.4

43.1

42.5

CNOOC’s annual reports published only the total direct energy inputs for the whole company without a division by sectors of other activities from 2005 to 2009 (Table 7) (CNOOC Annual Reports). From 2005 to 2008, the direct energy inputs were mainly consumed by OGE, while part of the direct energy inputs was used in the oil processing sector in 2009, when CNOOC entered the refining business (CNOOC Annual Reports). For CNOOC, we do not have any data on direct energy inputs separated by processing sector for 2009. However, knowing the volume of oil processed by company CNOOC (Table 9) and the specific direct energy inputs required for processing 1 tonne of oil, the amount of total direct energy inputs in the oil processing sector can be calculated. In this paper, it is assumed that the average energy requirement for oil processing by CNOOC equals that needed for the Huizhou Refinery, the largest refinery owned by CNOOC. In 2009, 0.28 × 104 MJ is needed per tonne of oil processed in the Huizhou Refinery (Gong and Wang 2013). Given this amount, the direct energy inputs for the OGE sector can be calculated by subtracting the direct energy inputs for the processing sector from the total direct energy inputs. After obtaining the total direct energy inputs of the four companies (CNPC, Sinopec, SNOOC, Yanchang), they were converted into thermal units (Table 8).
Table 9

Amount of oil processed (104, tonnes)

 

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

CNPC

8795

8947

9255

10,370

11,061

11,587

12,173

12,530

12,512

13,529

14,484

14,716

14,602

Sinopec

     

15,651

16,576

17,294

18,824

21,297

21,892

22,309

23,370

CNOCC

        

2081

    

Yanchang

          

1302

1400

1405

Table 10 shows the direct energy inputs per tonne of oil extraction. These data are required when calculating the EROI2,d of each company.
Table 10

Direct energy inputs per tonne of oil extraction, in thermal units (104, MJ)

 

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

CNPC

0.40

0.39

0.39

0.38

0.38

0.36

0.35

0.37

0.35

0.33

0.32

0.31

0.29

Sinopec

     

0.33

0.31

0.31

0.30

0.29

0.31

0.31

0.31

CNOOC

    

0.30

0.28

0.30

0.29

0.30

    

Yanchang

          

0.35

0.33

0.33

  1. (2)

    Indirect energy inputs

     
Currently, Chinese companies do not provide enough data on indirect monetary costs for oil and gas extraction. To calculate indirect energy inputs, in this work, it is assumed that the ratio of indirect energy inputs to total energy inputs for each company is equal to that for China’s oil and gas extraction. For example, the ratio of indirect energy inputs to total energy inputs is 9.4 % in 2001 for China’s oil and gas extraction; then, the ratio for CNPC in 2001 is also 9.4 %. The direct energy inputs for CNPC in 2001 is 539.1 × 109 MJ; then, the indirect energy inputs is 56 × 109 MJ. Indirect energy inputs of each company are in Table 11.
Table 11

Indirect energy inputs of each company (109, MJ)

 

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

CNPC

56

63

82

124

170

220

199

224

300

277

230

209

211

Sinopec

     

52

42

48

63

60

54

51

53

CNOOC

    

32

39

34

39

58

    

Yanchang

          

14

12

12

2.3.2.2 Energy investment in oil transportation
Oil companies do not provide an explicit accounting of energy consumption during the process of oil transportation. Oil transportation relies on pipelines in China, and the average distance from oilfield to oil processing plant is assumed to be 1000 km. The energy intensity by oil pipeline is approximately 0.3 MJ/tonne-km (Table 12) (Ou et al. 2011). Thus, the direct energy input of transporting one tonne of oil for 1000 km is 300 MJ.
Table 12

Energy intensity and fuel mix for each transportation mode

 

Energy intensity, MJ/tonne-km

Fuel mix and their percentage

Sea tanker

0.023

Residual oil (100 %)

Pipeline: oil

0.3

Residual oil (50 %) and electricity (50 %)

Pipeline: NG

0.372

NG (99 %) and electricity (1 %)

2.3.2.3 Energy investment in oil processing
The direct energy inputs of CNPC, Sinopec, and CNOOC for processing per tonne of oil in Table 13 are from literature (CNPC 2014; Sinopec 2014; Gong and Wang 2013). For Yanchang, the total direct energy inputs for oil processing from 2011 to 2013 are 60 × 109, 58 × 109, and 57 × 109 MJ, respectively (Yanchang Social Responsibility Reports). Then Yanchang’s direct energy input for processing per tonne of oil (Table 13) is equal to its total direct energy inputs for oil processing divided by the amount of oil processed (Table 9). Table 14 shows the direct energy inputs in MJ for processing per tonne of oil.
Table 13

Direct energy inputs for processing per tonne of oil, in physical units

 

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

CNPC, kgoe

86.4

89.3

83.4

78.7

80.6

78.0

75.6

71.6

67.6

65.5

65.0

64.1

64.0

Sinopec, kgoe

     

66.9

65.9

63.8

61.3

58.2

57.0

56.2

57.5

CNOCC, kgoe

        

63.9

    

Yanchang, kgce

          

157.7

143.9

139.5

Table 14

Direct energy inputs for processing per tonne of oil, in thermal units (104, MJ)

 

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

CNPC

0.36

0.37

0.35

0.33

0.34

0.33

0.32

0.30

0.28

0.27

0.27

0.27

0.27

Sinopec

     

0.28

0.28

0.27

0.26

0.24

0.24

0.24

0.24

CNOCC

        

0.28

    

Yanchang

          

0.46

0.42

0.41

3 EROI methodology and data for imports of oil and gas

3.1 EROI methodology for imported oil (IO) and imported natural gas (ING)

An economy without enough domestic fossil fuel must import fuel and pay for it with some type of surplus economic activity. The ability to purchase critically required energy depends upon what else the economy can generate to sell it to the world, as well as the fuel required to grow or produce that material (Hall et al. 2009). In 1986, Kaufmann (1986) derived an explicit method to quantitatively assess the EROIIO (Eq. 4). Because such financial data are usually available, the EROIIO can be derived with a moderate degree of accuracy (Lambert et al. 2014). In 2010, King (2010) developed a metric called the energy intensity ratio (EIR), which is similar to Kaufmann’s EROIIO, and calculated it for various industrial fuels in the US over time. His study suggested that the EIR is an easily calculated and effective proxy for the EROI for individual fuels.
$${\text{EROI}}_{\text{IO}} = \frac{{E_{\text{IO}} }}{{E_{\text{p,OIL}} }} = \frac{{E_{\text{OIL}} \times M_{\text{IO}} }}{{EI_{\text{GDP}} \times P_{\text{OIL}} \times M_{\text{IO}} }}$$
(4)
where, E OIL is the unit energy content of oil, P OIL is the price of total oil imported, M IO is the amount of oil purchased, EI GDP is the economic intensity of the economy, E IO is the total energy content of the oil purchased, and E p,OIL is the total energy inputs in the purchasing phase. Usually, IO includes two phases: purchasing and international transportation (Fig. 3). Equation (4) only considers the purchasing phase, while both phases are considered in this paper (Eq. 5).
$${\text{EROI}}_{\text{IO}} = \frac{{E_{\text{IO}} }}{{E_{\text{p,OIL}} + E_{{{\text{t}},{\text{OIL}}}} }}$$
(5)
where \(E_{\text{t,OIL}}\) refers to the total energy inputs in the international transportation phase.
Fig. 3

System boundaries of IO and ING

Similar to the EROIIO equation, the EROIING can be calculated as follows:
$${\text{EROI}}_{\text{ING}} = \frac{{E_{\text{ING}} }}{{E_{\text{p,NG}} + E_{\text{t,NG}} }}$$
(6)
where E ING is the total energy content of the gas purchased, and E p,NG and E t,NG refer to the total energy inputs in the purchasing phase (equal to the energy required to make the goods exported to pay for the gas) and the international transportation phase, respectively. Unlike coal and oil, which remain almost unchanged after long distance transportation, gas may suffer some losses in transportation (Lin et al. 2010). Therefore, gas losses in transportation will be estimated and excluded from the total energy outputs. The EROIING can be calculated with Eq. (7).
$${\text{EROI}}_{\text{ING}} = \frac{{E_{\text{ING}} - L_{\text{t,NG}} }}{{E_{\text{p,NG}} + E_{\text{t,NG}} }}$$
(7)
where L t,NG refers to the gas losses in international transportation.
For gas transportation by pipeline, gas losses usually result from fugitive emissions and flaring. According to the Intergovernmental Panel on Climate Change, gas losses in pipeline transportation can be calculated as follows (Zhang et al. 2013):
$$L_{\text{t}} = M_{\text{t}} \times LR$$
(8)
where L t is the volume of gas losses, kg; M t is the volume of gas transported, m3; and LR is the loss rate caused by fugitive emissions and flaring, kg/m3.
For gas transportation by tanker, gas losses result from boil-off gas (BOG) (Zakaria et al. 2013). Due to heat transfer from the surroundings to cryogenic LNG (liquefied natural gas), LNG is unavoidably vaporized, thus generating BOG in LNG tankers. To reduce the losses caused by BOG, some technologies are applied to re-liquefy the BOG at the expense of power consumption for liquefaction and the initial cost of the liquefying facilities (Lin et al. 2010). Here, we assume that the recovery rate of BOG is r, so L t can be calculated using Eq. (9).
$$L_{\text{t}} = B(1 - r) = M_{\text{t}} \left[ {1 - (1 - BR)^{{\frac{{D_{\text{t}} }}{S \times 24}}} } \right](1 - r)$$
(9)
here B is the BOG in transportation by LNG tankers, m3; M t is the volume of gas transported, m3; BR is the boil-off rate, which refers to the percentage of LNG that needs to be boiled off to keep the LNG at the same temperature when heat is added to the LNG fuel (×%/day, e.g., 0.5 %/day); D t is the distance of international transportation, km; and S is the speed of the LNG tanker, km/h.

3.2 Data for IO and ING

3.2.1 Energy content of oil and gas purchased

The amounts of China’s imported oil and gas (Table 15) are available from Wind Information Co., Ltd (Wind Info). Wind Info Import data cover the period from 1996 to 2015 (2015 data are only the sum of the first seven months). E IO and E ING (Table 16) are equal to these annual import volumes (Table 15) multiplied by the energy content factors in Table 3.
Table 15

Amounts and costs of IO and ING

Year

The amounts (108, kg)

The costs (108, dollars)

IO

ING

IO

ING

1996

155

 

21

 

1997

340

 

53

 

1998

274

 

33

 

1999

328

 

53

 

2000

640

 

135

 

2001

558

 

108

 

2002

634

 

116

 

2003

865

 

188

 

2004

1141

 

315

 

2005

1232

 

464

 

2006

1385

 

634

 

2007

1534

 

750

 

2008

1733

 

1247

 

2009

1952

55

850

13

2010

2309

119

1302

40

2011

2464

226

1904

104

2012

2635

305

2142

168

2013

2732

378

2128

204

2014

2864

404

2122

225

2015

1759

226

754

101

Table 16

Energy outputs and inputs for IO and ING

Year

E IO

E p,OIL

E t,OIL

E ING

L t,NG

E p,NG

E t,NG

1996

6479

569

28

    

1997

14,212

1317

62

    

1998

11,453

761

49

    

1999

13,710

1172

59

    

2000

26,752

2851

114

    

2001

23,324

2176

99

    

2002

26,501

2272

110

    

2003

36,157

3857

148

    

2004

47,694

6818

189

    

2005

51,498

9877

202

    

2006

57,893

12,774

222

    

2007

64,121

13,695

247

    

2008

72,439

19,710

288

    

2009

81,594

12,730

321

3163

9

189

10

2010

96,516

18,541

379

6833

16

571

37

2011

102,995

25,366

398

12,908

24

1387

100

2012

110,143

26,876

422

17,432

31

2107

145

2013

114,198

25,710

437

21,631

38

2463

181

2014

119,715

25,381

458

23,113

41

2689

189

2015

73,526

8800

282

12,944

22

1180

112

3.2.2 Energy inputs

Energy investment in purchasing: The costs of purchasing oil and gas are from Wind Info (Table 15). The EI GDP (in 2010 constant prices) in Fig. 4 is from National Bureau of Statistics of China (2014) and The People’s Bank of China (2014). EI GDP is multiplied by the cost of purchasing oil (gas) to create a time series of E p,OIL (E p,NG) (Table 16).
Fig. 4

Energy intensity of GDP

Energy investment in international transportation: The oil imported by tanker and by pipeline and the gas imported by tanker and by pipeline are shown in Fig. 5. Table 12 presents all of the data on the energy intensity and the fuel mix for each transportation mode. We assume that the transport distances for pipelines and tankers are 2000 and 8000 km, respectively. Thus, the E p,OIL and E p,NG in Table 16 can be calculated; these values are equal to the transport distance multiplied by the traffic intensity and then multiplied by the transport volume.
Fig. 5

Amounts of IO and ING by tanker and by pipeline (NB. 2015 data are only for 7 months)

3.2.3 Gas losses in international transportation

The values for transport volume by pipeline and by LNG tanker are shown in Fig. 5. Zhang et al. (2013) found that the loss rate (LR) is 0.607 × 10−3 kg/m3. The volume of gas losses (L t) for pipeline transport can be calculated using Eq. (8). In this paper, we set the distance for LNG transport at 8000 km. According to Lin et al. (2010), for large LNG carriers, the boil-off rate (BR) is usually between 0.05 %/day and 0.1 %/day, where the middle value is 0.075 %/day. Chu (2000) collected data on approximately 108 LNG ships and found the operating speed to be concentrated within the range of 32.4–37 km/h. The average speed is 34.7 km/h, which is the speed used in this paper. Assuming that the recovery rate of BOG (r) is 0.7, the volume of gas losses (L t) for tanker transport can be calculated using Eq. (9). The L t,NG (Table 16) is the sum of the L t for pipeline transport and the L t for tanker transport.

4 Results

4.1 EROI for China’s oil and gas extraction

The EROIstnd for China varies from about 8.4:1 to 17.3:1, decreasing from 1985 to 2003 and then increasing again (Fig. 6). Obviously, with the depletion of oil reserves, production becomes more costly. In addition, the growth in energy consumption in turn leads to a decrease in the EROI, which is entirely consistent with the fact that until 2003, China’s EROI was continually declining. However, after 2003, the EROI increased from 8.4:1 to 12.2:1. This increase is mainly a result of the increasing EROI for gas extraction. Another factor is that energy savings programs in the industry have been implemented.
Fig. 6

EROI of China’s OGE

4.2 The EROI for oil and gas companies

As shown in Table 17, the EROIstnd for CNPC increased significantly from 9.5:1 in 2001 to 11.1:1 in 2013. This increase in EROIstnd is attributed to the fact that CNPC has made great effort to develop natural gas, which has a higher EROI than oil. From 2001 to 2013, natural gas production has increased from 825 × 109 to 4042 × 109 MJ, which is an average annual increase in 14.2 %. Meanwhile, in the development of natural gas, in 2012, the OGE of the CNPC Changqing Oilfield reached 1800 × 109 MJ (for the first time it produced more oil and gas than the CNPC Daqing Oilfield), making it the largest oil and gas field in China. Moreover, CNPC vigorously developed energy-saving technologies to improve its energy efficiency, which in turn helped to improve its EROI.
Table 17

EROIstnd of each company

 

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

CNPC

9.5

9.6

9.2

9.0

8.6

8.7

9.0

8.7

8.4

9.1

10.0

10.5

11.1

Sinopec

     

9.5

10.6

10.3

9.9

10.3

10.3

10.5

10.6

CNOOC

    

11.0

10.9

10.9

10.8

9.9

    

Yanchang

          

9.1

9.8

10.0

For Sinopec’s OGE, since 2009, the EROI increased from 9.9:1 in 2009 to 10.6:1 in 2013 (Table 17). The EROI2,d increased from 6.1:1 in 2006 to 6.9:1 in 2010 and remained unchanged at approximately 6.7:1 between 2010 and 2013 (Table 18). Because the information is limited in the public domain, it is impossible to explain accurately all of the above phenomena in this paper. However, we can see that one reason is that the potential for energy savings was being exhausted, as shown in Fig. 7 (CNPC Annual Reports; Sinopec Annual Reports; CNOOC Annual Reports; Yanchang Social Responsibility Reports).
Table 18

EROI2,d of each company

 

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

CNPC

4.9

4.9

5.0

5.2

5.2

5.4

5.5

5.6

5.9

6.1

6.3

6.4

6.6

Sinopec

     

6.1

6.3

6.4

6.6

6.9

6.7

6.7

6.7

CNOOC

        

3.5

    

Yanchang

          

4.6

5.0

5.1

Fig. 7

Amount of energy savings for China’s oil companies

CNOOC’s EROIOGE is higher than that of CNPC, Sinopec, and Yanchang. The main reason may be that compared with onshore oil fields, the degree of development of offshore oil resources is relatively low, and the production cost is relatively cheap. There are two reasons for the low degree of exploration: On the one hand, offshore oil resource development takes longer time than onshore oil development; on the other hand, at present, only CNOOC is engaged in offshore oil exploration and development, whereas CNPC and Sinopec, the two largest oil companies in China, are engaged mainly in onshore oil exploration and development. Currently, because of the lack of data, we cannot provide an answer as to why the CNOOC’s EROIOGE between 2005 and 2008 has been slightly fluctuating near approximately 10.9:1. To provide an explanation, we need more detailed data on energy consumption. In terms of refining, in 2009, CNOOC’s 12 Mt/a Huizhou Refinery Project Phase I was completed and put into production, marking the CNOOC entrance into the refining business (CNOOC Annual Reports). The Huizhou Refinery Project is a large-scale refinery in China that is especially designed to process high acid heavy crude oil, which requires more energy consumption and has a lower light oil extraction rate, resulting in a lower EROI. As shown in Table 18, the EROI2,d for CNOOC is significantly lower than that of the other companies in 2009.

Yanchang’s EROIOGE is lower than that of CNPC, Sinopec, and CNOOC (Table 17), because the Yanchang has been producing oil from depleted fields. In 1905, the Yanchang Petroleum Factory was established, and in 1907, Yanchang drilled the first oil well in mainland China (Zuo 2009). As shown in Fig. 8, the growth rate of crude oil production of Yanchang declines with time; in 2013, it fell 0.1 % compared with that in 2012. However, the energy efficiency of OGE for Yanchang has a trend of slight increase, although the Yanchang’s EROI is lower than that of the other companies. This may be attributed to the increased natural gas production and energy efficiency measures. In 2010, Yanchang began to produce natural gas, and the production of natural gas increased from 0.4 × 109 in 2010 to 18 × 109 MJ in 2013 (Fig. 8). In terms of energy efficiency, Yanchang formulated the “Environmental Governance Programme 2011–2013,” in which energy conservation was considered an important environmental protection measure. Yanchang’s energy savings in 2013 was 2 × 109 MJ, which was slightly higher than in 2011 (Fig. 8) (Yanchang Social Responsibility Reports).
Fig. 8

OGE of Yanchang

4.3 The EROI for imported oil and imported natural gas

The EROIIO and EROIING are calculated based on Eqs. (5) and (7), respectively. The EROIIO values in China during “good times” (i.e., the late 1990s) and “bad times” (i.e., 2006–2008) are shown in Fig. 9. The EROIIO shows a peak of approximately 14.8 in 1998 and a value of approximately 8.4 in 2015; overall, the figure presents a fluctuating but declining trend over the entire study period (slope of −0.44, R 2 = 0.6) reflecting increasing relative prices of petroleum. The patterns in the EROI values for IO and ING have broadly similar trends during the period from 2009 to 2015. From 2009 to 2012, they show a sharp and sustained decline. From 2012 to 2014, they remained relatively stable. In 2015, they began to increase as oil prices again decreased relative to exported commodities.
Fig. 9

EROIIO and EROIING in China

5 Discussion

5.1 Comparison with previous estimates for China’s oil and gas

Hu et al. (2013) showed that the EROIstnd for China’s oil and gas extraction fluctuated from 12 to 14:1 in the mid-1990s and declined to 10:1 in the period from 2007 to 2010 (Fig. 10). The EROIstnd trends documented in this paper are similar to those of Hu et al. (2013) with the only difference being that the EROIs in this paper are somewhat lower than theirs, which results from that their study considering only 8 main fuels (natural gas, crude oil, electricity, diesel oil, raw coal, fuel oil, gasoline, and refinery gas), and ignoring some fuels that are used in small amounts such as liquefied petroleum gas, while this paper considers them. The discrepancy between EROI1,d and EROIstnd has been increasing, which suggests that indirect energy inputs are increasing.
Fig. 10

Comparison with previous estimates of China’s oil and gas

Hu et al. (2011) estimated that the EROI of CNPC’s Daqing Oilfield, China’s largest, declined continuously from 10:1 in 2001 to 6:1 in 2009. From Fig. 10, we can find that the EROI of the Daqing Oilfield is lower than that of CNPC overall, and the discrepancy continues to increase. There are two reasons for this result. The principal reason is that as Daqing’s fields age, they require more energy-intensive techniques, such as high pressure water and polymer injections. The discovery of the Daqing oil field in 1959 made China an oil-rich country (Hu et al. 2011). After 40 years of development, its oil production began to decline in 1998. The production of the Daqing oil field has been decreasing from its peak of 2328 × 109 to 1672 × 109 MJ in 2013. During this period, oil production was maintained, and the water content was mainly controlled by increasing the water pressure beneath the oil and using polymer flooding technology, thus leading to an increase in energy inputs. Daqing’s natural gas production in 2013 was 134 × 109 MJ, and compared with 1998, it only increased by 43 × 109 MJ. The other reason is that other CNPC oil fields produced more oil and gas. Of these fields, the most noteworthy is the Changqing Oilfield. Over the period from 1998 to 2013, its oil and gas production increased by 12 per cent and 31 per cent annually, respectively, reaching 1017 × 109 and 1349 × 109 MJ (Fig. 11) (Wang 2004a, b; Zhao and Xiao 2009a, b; Xiao 2014a, b).
Fig. 11

Oil (left) and gas (right) production of CNPC’s oilfields

Our EROI estimates for IO fall within the range of previously published studies. Lambert et al. (2014) provided an EROI analysis for IO for 12 developing countries, including China. The authors found that most developing nations had EROIIO values below 8–10, which are very similar to our results. They also found that there were two EROIIO peaks for oil imported into China. One peak occurred in 1998, which is the same as our results. Until now, no studies have estimated the EROIING. Our study shows that the EROIING has also shown a declining trend, similar to the EROIIO.

5.2 Policy implications

From 1985 to 2003, the EROIstnd in China has shown a declining trend, while after 2003, the EROIstnd started to increase due the development of NG, which may have a better energy return. Therefore, the government should take measures to increase gas production. We believe that the most significant measure would be to rationalize the domestic NG pricing mechanisms, which would improve the enthusiasm of enterprises to develop NG. Since the 1990s, the Chinese government has implemented several NG price reforms. The pricing method for NG has evolved from government pricing to a two-track implementation to prices set with government guidance and finally to the current market net back value method (Kong et al. 2015). Although the pricing mechanisms have gradually improved, market-oriented pricing is still not used for China’s NG.

In Sect. 5.1, by comparison with EROIstnd and EROI1,d, we found that indirect energy inputs have a significant and negative impact on China’s EROI. Therefore, to improve the EROI, indirect energy inputs should be controlled. Using the “purchase of equipment and instruments” as an example, on the one hand, oil companies should reduce their amount of equipment and instruments by improving their utilization. On the other hand, equipment manufacturing enterprises should improve their energy efficiency, thereby reducing energy consumption in the equipment production process.

The EROIIO and EROIING are both continually decreasing. If import prices continue to increase, and hence, the EROIIO declines, the result will likely correspond to lower quality of life indices for China’s citizens (Lambert et al. 2014). Therefore, the question that must be asked is, “What opportunities does China have to mitigate the effects of these rising energy prices and the declining EROI of its imported fuel?” Improving the efficiency at which China’s economy converts energy and material into marketable goods, and services is one means of improving the country’s energy security (Lambert et al. 2014). The other method is to support moderate EROI renewable energy production, which might serve to improve China’s net energy balance based on a poor EROI for imported oil and gas through trade.

China is experiencing an EROI decline, which is a risk of unsustainable fossil resource use. Therefore, China must be able to foresee, understand, and plan for changes in its broad energy landscape, particularly during what researchers have characterized as post-peak oil production. Furthermore, sustainability could be effectively addressed by the emergence of a new field: Transition Engineering, which is a new approach to engineering to address the risks of unsustainability so that a vision of a desirable future can be identified and delivered (Transition Engineering). It addresses three key engineering challenges:

Climate: re-engineering systems so they do not cause and are resilient to climate change.

Peak oil: re-engineering systems so they do not depend on fuel; e.g., using 90 % less fuel.

EROI: re-engineering systems so they only use energy with high EROI; e.g., on the order of >10.

Meeting these three challenges could be the way that Chinese society reduces both fossil fuel use and the detrimental social and environmental impacts of industrialization (Krumdieck 2013). Although it is still in its infancy, Transition Engineering should be investigated as a direction for future research by governments, scholars, scientists, and even ordinary people.

6 Conclusion

In this paper, we calculated the EROI for domestic production and imports of oil and gas in China. Our estimates show that the EROIstnd in China decreased from approximately 17.3:1 in 1986 to 8.4:1 in 2003, and it increased to 12.2:1 in 2013. From a company-level perspective, the EROIstnd differs for different companies and was in the range of 8–12:1. Compared with the EROIstnd, the EROI2,d declined by 50 %–80 % and was in the range of 3–7:1. The EROIIO declined from 14.8:1 in 1998 to approximately 4.8:1 in 2014, and the EROIING declined from 16.7:1 to 8.6:1 from 2009 to 2014. In 2015, the EROIIO and EROIING have shown a slight increase due to decreasing oil and gas prices. In general, this paper suggests that from a net energy perspective, it will become more difficult for China to obtain oil and gas from both domestic production and imports.

Notes

Acknowledgments

We gratefully acknowledge that this work is supported by the National Natural Science Foundation of China (No. 71273277) and the Philosophy and Social Sciences Major Research Project of the Ministry of Education (No. 11JZD048).

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© The Author(s) 2016

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Zhao-Yang Kong
    • 1
  • Xiu-Cheng Dong
    • 1
    Email author
  • Qian Shao
    • 2
  • Xin Wan
    • 3
  • Da-Lin Tang
    • 4
  • Gui-Xian Liu
    • 1
  1. 1.School of Business AdministrationChina University of Petroleum (Beijing)BeijingChina
  2. 2.School of BusinessTianjin University of Finance and EconomicsTianjinChina
  3. 3.Tangshan Iron and Steel Group Co., LtdTangshanChina
  4. 4.China Petroleum Enterprise AssociationBeijingChina

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