Energy Efficiency of Thermal Power Generation in Ecuador's Sacha Oil Field

Authors

DOI:

https://doi.org/10.67294/s074wn40

Keywords:

Data Envelopment Analysis, Energy Efficiency, Thermal Generation, Oil Field, Benchmarking

Abstract

This study reassesses the relative energy efficiency of the thermal generation system serving Ecuador's Sacha Oil Field during 2020-2023. A quantitative, retrospective, nonexperimental longitudinal case-study design examined a census of 49,185 daily unit records from 11 plants; standardized operational spreadsheets and a structured extraction matrix were the data-collection instruments. The objective was to establish the system baseline and identify defensible operational conclusions. An input-oriented constant-returns-to-scale data envelopment analysis model used daily diesel, natural gas, and bunker consumption as inputs and daily electricity generation as output. The validated unbalanced panel contained 49,185 day-by-unit observations from generating units grouped within 11 plants. Conventional annual mean efficiency was 0.880, 0.910, 0.891, and 0.869, while bias-corrected means were 0.869, 0.903, 0.883, and 0.855. Medians exceeded means in every year, indicating a lower-performing tail rather than uniform underperformance. However, the observed decline in frontier membership during 2023 cannot be attributed entirely to technical deterioration because the panel expanded from 43 to 56 units and the empirical frontier changed. The study therefore interprets the index as an internal, sample-dependent benchmark, not absolute thermodynamic efficiency or proof of attainable savings. The corrected article identifies plant-level priorities, explains the limits of interannual comparison, and proposes engineering validation before operational targets are assigned.

References

Aigner, D., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21-37. https://doi.org/10.1016/0304-4076(77)90052-5

Amirteimoori, A., Allahviranloo, T., & Cezar, A. (2024). A bootstrap data envelopment analysis model with stochastic reducible outputs and expandable inputs: An application to power plants. RAIRO - Operations Research, 58(4), 3189-3202. https://doi.org/10.1051/ro/2024119

Campana-Díaz, A., Moya, M., Urresta, E., & Harnisth, R. (2024). Energy and environmental performance indicators at upstream oil facilities in Ecuador. Ciencia América, 13(2). https://doi.org/10.33210/ca.v13i2.473

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. https://doi.org/10.1016/0377-2217(78)90138-8

Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA) – Thirty years on. European Journal of Operational Research, 192(1), 1-17. https://doi.org/10.1016/j.ejor.2008.01.032

Cook, W. D., Tone, K., & Zhu, J. (2014). Data envelopment analysis: Prior to choosing a model. Omega, 44, 1-4. https://doi.org/10.1016/j.omega.2013.09.004

Daraio, C., & Simar, L. (2005). Introducing environmental variables in nonparametric frontier models: A probabilistic approach. Journal of Productivity Analysis, 24(1), 93-121. https://doi.org/10.1007/s11123-005-3042-8

Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C. S., & Shale, E. A. (2001). Pitfalls and protocols in DEA. European Journal of Operational Research, 132(2), 245-259. https://doi.org/10.1016/s0377-2217(00)00149-1

Eguchi, S., Takayabu, H., & Lin, C. (2021). Sources of inefficient power generation by coal-fired thermal power plants in China: A metafrontier DEA decomposition approach. Renewable and Sustainable Energy Reviews, 138, 110562. https://doi.org/10.1016/j.rser.2020.110562

Emrouznejad, A., & Yang, G. L. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4-8. https://doi.org/10.1016/j.seps.2017.01.008

Fallahi, A., Ebrahimi, R., & Ghaderi, S. F. (2011). Measuring efficiency and productivity change in power electric generation management companies by using data envelopment analysis: A case study. Energy, 36(11), 6398-6405. https://doi.org/10.1016/j.energy.2011.09.034

Fan, J. L., Zhang, H., & Zhang, X. (2020). Unified efficiency measurement of coal-fired power plants in China considering group heterogeneity and technological gaps. Energy Economics, 88, 104751. https://doi.org/10.1016/j.eneco.2020.104751

Fang, T., Fang, D., & Yu, B. (2022). Carbon emission efficiency of thermal power generation in China: Empirical evidence from the micro-perspective of power plants. Energy Policy, 165, 112955. https://doi.org/10.1016/j.enpol.2022.112955

Färe, R., Grosskopf, S., Lovell, C. A. K., & Pasurka, C. (1989). Multilateral productivity comparisons when some outputs are undesirable: A nonparametric approach. The Review of Economics and Statistics, 71(1), 90-98. https://doi.org/10.2307/1928055

Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253-290. https://doi.org/10.2307/2343100

Honma, S., & Hu, J. L. (2008). Total-factor energy efficiency of regions in Japan. Energy Policy, 36(2), 821-833. https://doi.org/10.1016/j.enpol.2007.10.026

Hu, J. L., & Wang, S. C. (2006). Total-factor energy efficiency of regions in China. Energy Policy, 34(17), 3206-3217. https://doi.org/10.1016/j.enpol.2005.06.015

Hussain, S., Shaikh, F., Kumar, L., & Ali, Z. (2023). Operational performance evaluation and efficiency assessment of thermal power sectors of Pakistan using data envelopment analysis. International Journal of Operational Research, 48(4), 429-444. https://ideas.repec.org/a/ids/ijores/v48y2023i4p429-444.html

Korhonen, P. J., & Luptacik, M. (2004). Eco-efficiency analysis of power plants: An extension of data envelopment analysis. European Journal of Operational Research, 154(2), 437-446. https://doi.org/10.1016/s0377-2217(03)00180-2

Lam, P. L., & Shiu, A. (2001). A data envelopment analysis of the efficiency of China’s thermal power generation. Utilities Policy, 10(2), 75-83. https://doi.org/10.1016/s0957-1787(02)00036-x

Martín-Gamboa, M., Iribarren, D., & Dufour, J. (2018). Environmental impact efficiency of natural gas combined cycle power plants: A combined life cycle assessment and dynamic data envelopment analysis approach. Science of The Total Environment, 615, 29-37. https://doi.org/10.1016/j.scitotenv.2017.09.243

Nakaishi, T., Takayabu, H., & Eguchi, S. (2021). Environmental efficiency analysis of China's coal-fired power plants considering heterogeneity in power generation company groups. Energy Economics, 102, 105511. https://doi.org/10.1016/j.eneco.2021.105511

Sarıca, K., & Or, I. (2007). Efficiency assessment of Turkish power plants using data envelopment analysis. Energy, 32(8), 1484-1499. https://doi.org/10.1016/j.energy.2006.10.016

Shrivastava, N., Sharma, S., & Chauhan, K. (2012). Efficiency assessment and benchmarking of thermal power plants in India. Energy Policy, 40, 159-176. https://doi.org/10.1016/j.enpol.2011.09.020

Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49-61. https://doi.org/10.1287/mnsc.44.1.49

Simar, L., & Wilson, P. W. (2000). A general methodology for bootstrapping in non-parametric frontier models. Journal of Applied Statistics, 27(6), 779-802. https://doi.org/10.1080/02664760050081951

Sözen, A., Alp, H., & Kilinc, C. (2012). Efficiency assessment of the hydro-power plants in Turkey by using data envelopment analysis. Renewable Energy, 46, 192-202. https://doi.org/10.1016/j.renene.2012.03.021

Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498-509. https://doi.org/10.1016/s0377-2217(99)00407-5

United Nations. (n.d.). Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all. Department of Economic and Social Affairs. https://sdgs.un.org/goals/goal7

Vaninsky, A. (2006). Efficiency of electric power generation in the United States: Analysis and forecast based on data envelopment analysis. Energy Economics, 28(3), 326-338. https://doi.org/10.1016/j.eneco.2006.02.007

Vikas, V., & Bansal, R. (2019). Efficiency evaluation of Indian oil and gas sector: Data envelopment analysis. International Journal of Emerging Markets, 14(2), 362-378. https://doi.org/10.1108/IJoEM-01-2018-0016

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J. W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., ... Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), 160018. https://doi.org/10.1038/sdata.2016.18

Wu, Y., Ke, Y., Xu, C., Xiao, X., & Hu, Y. (2018). Eco-efficiency measurement of coal-fired power plants in China using super efficiency data envelopment analysis. Sustainable Cities and Society, 36, 157-168. https://doi.org/10.1016/j.scs.2017.10.011

Zhou, P., Ang, B. W., & Poh, K. L. (2008). A survey of data envelopment analysis in energy and environmental studies. European Journal of Operational Research, 189(1), 1-18. https://doi.org/10.1016/j.ejor.2007.04.042

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Published

2026-10-31

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How to Cite

Taco Arias, M. S. (2026). Energy Efficiency of Thermal Power Generation in Ecuador’s Sacha Oil Field. International Multidisciplinary Journal of Emerging Technologies and Applications, 1(7), 28-43. https://doi.org/10.67294/s074wn40