Horizontal drilling and hydraulic fracturing are technologies designed to increase natural gas flow and to improve productivity in low permeability formations. During this drilling operation, tons of flowback and produced water, which contain several organic compounds, return to the surface with a potential risk of influencing the surrounding environment and human health. In order to conduct predictive risk assessments a mathematical model is needed to evaluate organic compound behaviour along the water transportation process as well as concentration changes over time throughout the operational life cycle. A comprehensive model, which fits the experimental data, combining an Organic Matter Transport Dynamic Model with a Two-Compartment First-order Rate Constant (TFRC) Model has been established to quantify the organic compounds concentrations. This algorithm model incorporates two transportation rates, fast and slow. The results show that the higher the value of the organic carbon partition coefficient (k) in chemicals, the later the maximum concentration in water will be reached. The maximum concentration percentage would reach up to 90% of the available concentration of each compound in shale formation (whose origin may be associated to drilling fluid, connate water and/or rock matrix) over a sufficiently long period of time. This model could serve as a contribution to enhance monitoring strategy, increase benefits out of optimizing health risk assessment for local residents and provide initial baseline data to further operations.

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In the framework of CO 2 capture and geological storage, risk analysis plays an important role, because it is an essential requirement of knowledge to make up a local, national and supranational definition and planning of carbon injection strategies. This is because each project is at risk of failure. Even from the early stages, it should take into consideration the possible causes of this risk and propose corrective methods along the process, i.e., managing risk. Proper risk management reduces the negative consequences arising from the project. The main method of reduction or neutralizing of risk is mainly the identification, measurement and evaluation of it, together with the development of decision rules. This report presents a methodology developed for risk analysis and the results of its application. The risk assessment requires determination of the random variables that will influence the functioning of the system. It is very difficult to set-up a probability distribution of a random variable in the classical sense (objective probability) when a particular event rarely occurred or even it has an incomplete development. In this situation, we have to determine the subjective probability, especially at an early stage of projects, when we have not enough information about the system. This subjective probability is constructed from assessment of expert judgement to estimate the possibility of certain random events could happen depending on geological features of the area of application. The proposed methodology is based on the application of Bayesian probabilistic networks to estimate the probability of risk of leakage. These probabilistic networks can define graphically the relations of dependence between the variables and joint probability function through a local factorization of probability functions.

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