Optimal Design and Prediction-Independent Verification of Groundwater Monitoring Network

Sreekanth Janardhanan,Trevor Pickett,Dan Gladish,Dennis Gonzalez,Dan Pagendam,Tao Cui

doi:10.3390/w12010123

Abstract

In this study, we developed a workflow that applies a complex groundwater model for purpose-driven groundwater monitoring network design and uses linear uncertainty analysis to explore the predictive dependencies and provide insights into the veracity of the monitoring design. A numerical groundwater flow model was used in a probabilistic modelling framework for obtaining the spatial distribution of predicted drawdown for a wide range of plausible combination of uncertain parameters pertaining to the deep sedimentary basin and groundwater flow processes. Reduced rank spatial prediction was used to characterize dominant trends in these spatial drawdown patterns using empirical orthogonal functions (EOF). A differential evolution algorithm was used to identify optimal locations for multi-level piezometers for collecting groundwater pressure data to minimize predictive uncertainty in groundwater drawdown. Data-worth analysis helps to explore the veracity of the design by using only the sensitivities of the observations to predictions independent of the absolute values of predictions. A 10-bore monitoring network that collects drawdown data from multiple depths at each location was designed. The data-worth analysis revealed that the design honours sensitivities of the predictions of interest to parameters. The designed network provided relatively high data-worth for minimizing uncertainty in the drawdown prediction at locations of interest.

Highlights

Onshore gas industry dominated by coal seam gas has been growing in Australia over the last decade
The main aim of this study is to address the practical challenges of design and verification of groundwater monitoring network using an integrated methodology and apply it to a complex groundwater flow model for groundwater monitoring network design and explore prediction-independent verification of the design
The workflow presented in this study for optimal design of groundwater monitoring network has three essential steps: (1) Probabilistic predictive analysis of the variables of interest and their spatial variability using a numerical groundwater model; (2) monitoring network design using the predictive analysis in conjunction with spatial basis function and global optimization algorithm; and (3) independent verification of the relative data-worth of observations collected at the designed optimal locations using linear analysis

Summary

Introduction

Onshore gas industry dominated by coal seam gas has been growing in Australia over the last decade. Production of coal seam gas often involves extraction of large volumes of water from the coal beds. Where freshwater aquifers are connected to the coal beds, it is important to assess and monitor whether depressurization of coal seams cause pressure drawdown in the connected freshwater sources over the life and beyond the gas development project. Prediction and monitoring of groundwater impacts caused by the large scale onshore gas resource development activities is challenged by the fact that such activities focus on coal seams or reservoirs in deeper parts of sedimentary basins where conventional groundwater monitoring datasets are often sparse. In data-poor areas, an iterative procedure of assessment of groundwater impacts from resource development should be adopted to inform adaptive

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