Using predictive uncertainty analysis to optimise tracer test design and data acquisition

- We investigate a field-scale injection trial of coal seam gas co-produced water.
- We use numerical simulations to establish impact of injection.
- We analyse the worth of field data to maximise reliability of impact assessment.
- We use “data worth analysis” to optimise monitoring for the site.
- We establish monitoring strategy which provides greatest return on investment.

SummaryTracer injection tests are regularly-used tools to identify and characterise flow and transport mechanisms in aquifers. Examples of practical applications are manifold and include, among others, managed aquifer recharge schemes, aquifer thermal energy storage systems and, increasingly important, the disposal of produced water from oil and shale gas wells. The hydrogeological and geochemical data collected during the injection tests are often employed to assess the potential impacts of injection on receptors such as drinking water wells and regularly serve as a basis for the development of conceptual and numerical models that underpin the prediction of potential impacts.As all field tracer injection tests impose substantial logistical and financial efforts, it is crucial to develop a solid a-priori understanding of the value of the various monitoring data to select monitoring strategies which provide the greatest return on investment.In this study, we demonstrate the ability of linear predictive uncertainty analysis (i.e. “data worth analysis”) to quantify the usefulness of different tracer types (bromide, temperature, methane and chloride as examples) and head measurements in the context of a field-scale aquifer injection trial of coal seam gas (CSG) co-produced water. Data worth was evaluated in terms of tracer type, in terms of tracer test design (e.g., injection rate, duration of test and the applied measurement frequency) and monitoring disposition to increase the reliability of injection impact assessments. This was followed by an uncertainty targeted Pareto analysis, which allowed the interdependencies of cost and predictive reliability for alternative monitoring campaigns to be compared directly.For the evaluated injection test, the data worth analysis assessed bromide as superior to head data and all other tracers during early sampling times. However, with time, chloride became a more suitable tracer to constrain simulations of physical transport processes, followed by methane. Temperature data was assessed as the least informative of the solute tracers. However, taking costs of data acquisition into account, it could be shown that temperature data when used in conjunction with other tracers was a valuable and cost-effective marker species due to temperatures low cost to worth ratio. In contrast, the high costs of acquisition of methane data compared to its muted worth, highlighted methanes unfavourable return on investment. Areas of optimal monitoring bore position as well as optimal numbers of bores for the investigated injection site were also established.The proposed tracer test optimisation is done through the application of common use groundwater flow and transport models in conjunction with publicly available tools for predictive uncertainty analysis to provide modelers and practitioners with a powerful yet efficient and cost effective tool which is generally applicable and easily transferrable from the present study to many applications beyond the case study of injection of treated CSG produced water.