Estimating Stable Measured Values and Detecting Anomalies in Groundwater Geochemistry Time Series Data Across the Athabasca Oil Sands Area, Canada

Regional groundwater monitoring in the Athabasca region of Alberta, Canada, provides information on groundwater quality and geochemical changes over time, including data useful for evaluating potential impacts of industrial activity such as oil sands mining and in situ operations. Data collected from over 5000 wells from the 1950s to 2014, including 161 wells from government's monitoring network, were used to develop and apply bootstrap techniques for the detection of changes in groundwater geochemistry over time and at specific points in time. Increasing temporal anomalies were identified in Cl, TDS, B, and naphthenic acids in the McMurray formation across 2003 and 2008, while decreasing anomalies were found for SO4. Temporal variance for 15 indicators was quantified for a smooth bootstrap approach to arrive at stable values representative of the most recent samples taken from wells in the study area. Stable values revealed sampling bias in the Devonian, Grand Rapids, Empress, Channel Beverly, and Muriel Lake formations suggesting expansion of sampling may be necessary. Although temporal anomalies were found in the McMurray formation, sampling bias was not identified. The entropy and relative magnitude of time series were evaluated to identify candidate wells for continued observations, which consist of wells with low measurements and low entropy that are near active industry lease boundaries. Temporal anomalies, stable values, and entropy were combined into type-well information to provide plots for visual inspection and interpretation. Stable values are useful for regional mapping, for detecting future changes and trends, and for identifying areas of interest warranting further investigation.

Automated summary

Groundwater monitoring in the Athabasca region of Alberta, Canada, uses data from over 5000 wells to develop stable values for detecting changes in geochemistry over time and identifying candidate wells for continued observations. The study evaluates temporal anomalies, quantifies temporal variance, and assesses entropy of time series to determine wells near active industry lease boundaries that require further monitoring. Visual plots based on stable values provide valuable insights for regional mapping and detecting future changes.