Varela Gonzalez, Patricia Ysolda (2013-05). Bayesian Networks and Geographical Information Systems for Environmental Risk Assessment for Oil and Gas Site Development. Master's Thesis. Thesis uri icon

abstract

  • The objective of this work is to develop a Bayesian Network (BN) model to produce environmental risk maps for oil and gas site developments and to demonstrate the model's scalability from a point to a collection of points. To reach this objective, a benchmark BN model was formulated as a "proof of concept" using Aquifers, Ecoregions and Land Use / Land Cover maps as local and independent input variables. This model was then used to evaluate the probabilistic geographical distribution of the Environmental Sensibility of Oil and Gas (O&G) developments for a given study area. A Risk index associated with the development of O&G operation activities based on the spatial environmental sensibility was also mapped. To facilitate the Risk assessment, these input variables (maps) were discretized into three hazard levels: high, moderate and low. A Geographical Information System (GIS) platform was used (ESRI ArcMap 10), to gather, modify and display the data for the analysis. Once the variables were defined and the hazard data was included on feature classes (layer shapefile format), Python 2.6 software was used as the computational platform to calculate the probabilistic state of all the Bayesian Network's variables. This allowed to define Risk scenarios both on prognostic and diagnostic analysis and to measure the impact of changes or interventions in terms of uncertainty. The resulting Python - ESRI ArcMap computational script was called "BN+GIS, which populated maps describing the spatial variability of the states of the Environmental Sensibility and of the corresponding Risk index. The latter in particular, represents a tool for decision makers to choose the most suitable location for placing a drilling rig, since it integrates three fundamental environmental variables. Also, results show that is possible to back propagate the information from the Environmental Sensibility to define the inherent triggering scenarios (hazard variables). A case of study is presented to illustrate the applicability of the proposed methodology on a specific geographical setting. The Barnett Shale was chosen as a benchmark study area because sufficient information on this region was available, and the importance that it holds on the latest developments of unconventional plays in the country. The main contribution of this work relies in combining Bayesian Networks and GIS to define environmental Risk scenarios that can facilitate decision-making for O&G stakeholders such as land owners, industry operators, regulators and Non-Governmental Organizations (NGOs), before and during the development of a given site.
  • The objective of this work is to develop a Bayesian Network (BN) model to produce environmental risk maps for oil and gas site developments and to demonstrate the model's scalability from a point to a collection of points. To reach this objective, a benchmark BN model was formulated as a "proof of concept" using Aquifers, Ecoregions and Land Use / Land Cover maps as local and independent input variables. This model was then used to evaluate the probabilistic geographical distribution of the Environmental Sensibility of Oil and Gas (O&G) developments for a given study area. A Risk index associated with the development of O&G operation activities based on the spatial environmental sensibility was also mapped. To facilitate the Risk assessment, these input variables (maps) were discretized into three hazard levels: high, moderate and low.

    A Geographical Information System (GIS) platform was used (ESRI ArcMap 10), to gather, modify and display the data for the analysis. Once the variables were defined and the hazard data was included on feature classes (layer shapefile format), Python 2.6 software was used as the computational platform to calculate the probabilistic state of all the Bayesian Network's variables. This allowed to define Risk scenarios both on prognostic and diagnostic analysis and to measure the impact of changes or interventions in terms of uncertainty.

    The resulting Python - ESRI ArcMap computational script was called "BN+GIS, which populated maps describing the spatial variability of the states of the Environmental Sensibility and of the corresponding Risk index. The latter in particular, represents a tool for decision makers to choose the most suitable location for placing a drilling rig, since it integrates three fundamental environmental variables. Also, results show that is possible to back propagate the information from the Environmental Sensibility to define the inherent triggering scenarios (hazard variables).

    A case of study is presented to illustrate the applicability of the proposed methodology on a specific geographical setting. The Barnett Shale was chosen as a benchmark study area because sufficient information on this region was available, and the importance that it holds on the latest developments of unconventional plays in the country. The main contribution of this work relies in combining Bayesian Networks and GIS to define environmental Risk scenarios that can facilitate decision-making for O&G stakeholders such as land owners, industry operators, regulators and Non-Governmental Organizations (NGOs), before and during the development of a given site.

publication date

  • May 2013