| Form of studies |
Master |
| Title of the study programm |
Logistics and Supply Chain Management |
| Title in original language |
Piegāžu ķēžu ietekme uz apkārtējo vidi naftas un gāzes ieguves industrijā |
| Title in English |
Environmental Footprint of Supply Chain in Oil and Gas Industry |
| Department |
Faculty Of Computer Science Information Tehnology And Energy |
| Scientific advisor |
Egils Ginters |
| Reviewer |
Olga Girvica |
| Abstract |
This master thesis explores the hazards and effects that happen within the oil and gas industry which is widely responsible for the environmental footprint and how to improve them by observing the risk factors in sustainability assessment through risk modeling. This thesis mainly focuses on the aspect of the oil and gas industry as a whole and explores its processes in segments thus providing the necessary information required for the analysis of the hazards. The thesis contains a theoretical analysis of the industry and hazard, data analytical component of the hazards, and finally, a risk assessment model using Bayesian Network to sum it all. The online data is used for doing the survey research quantitative analysis gives information on the risk factor that each of the hazards mentioned would pose in the industry and same is done for accidents but the only difference being that the occurrence of accidents is precedented by the hazard itself. Finally, the risk assessment model is designed to give the outcome i.e. the effects that these hazards would cause. Territory planners, regional policymakers, geological experts, and municipalities are all can be the potential audience of the results as the results from the model can be directly used to determine the state of the oil and gas company that needs and where they stand in terms of the environmental consideration.
The master thesis contains 131 pages, 45 images, 41 tables, 125 reference sources, and 1 appendix. |
| Keywords |
RISK ANALYSIS, OIL AND GAS INDUSTRY, BAYESIAN NETWORK |
| Keywords in English |
RISK ANALYSIS, OIL AND GAS INDUSTRY, BAYESIAN NETWORK |
| Language |
eng |
| Year |
2021 |
| Date and time of uploading |
28.05.2021 15:10:30 |
