No abstract

Abstract. Hydraulic fracturing for natural gas extraction from unconventional reservoirs has not only impacted the global energy landscape but has also raised concerns over its potential environmental impacts. The concept of "features, events and processes" (FEP) refers to identifying and selecting the most relevant factors for safety assessment studies. In the context of hydraulic fracturing we constructed a comprehensive FEP database and applied it to six key focused scenarios defined under the scope of FracRisk project (http://www.fracrisk.eu, last access: 17 August 2018). The FEP database is ranked to show the relevance of each item in the FEP list per scenario. The main goal of the work is to illustrate the FEP database applicability to develop a conceptual model for regional-scale stray gas migration.

Tracer testing is a mature technology used for characterizing aquatic flow systems. To gain more insights from tracer tests a combination of conservative (non-reactive) tracers together with at least one reactive tracer is commonly applied. The reactive tracers can provide unique information about physical, chemical, and/or biological properties of aquatic systems. Although, previous review papers provide a wide coverage on conservative tracer compounds there is no systematic review on reactive tracers yet, despite their extensive development during the past decades. This review paper summarizes the recent development in compounds and compound classes that are exploitable and/or have been used as reactive tracers, including their systematization based on the underlying process types to be investigated. Reactive tracers can generally be categorized into three groups: (1) partitioning tracers, (2) kinetic tracers, and (3) reactive tracers for partitioning. The work also highlights the potential for future research directions. The recent advances from the development of new tailor-made tracers might overcome existing limitations.

No abstract

Hydraulic fracturing of horizontal wells is an essential technology for the exploitation of unconventional resources, but led to environmental concerns. Fracturing fluid upward migration from deep gas reservoirs along abandoned wells may pose contamination threats to shallow groundwater. This study describes the novel application of a nonlinear autoregressive (NAR) neural network to estimate fracturing fluid flow rate to shallow aquifers in the presence of an abandoned well. The NAR network is trained using the Levenberg–Marquardt (LM) and Bayesian Regularization (BR) algorithms and the results were compared to identify the optimal network architecture. For NAR-LM model, the coefficient of determination (R2) between measured and predicted values is 0.923 and the mean squared error (MSE) is 4.2 × 10−4, and the values of R2 = 0.944 and MSE = 2.4 × 10−4 were obtained for the NAR-BR model. The results indicate the robustness and compatibility of NAR-LM and NAR-BR models in predicting fracturing fluid flow rate to shallow aquifers. This study shows that NAR neural networks can be useful and hold considerable potential for assessing the groundwater impacts of unconventional gas development.