Great Artesian Basin groundwater modelling

This paper describes the history of groundwater modelling in the Great Artesian Basin and the challenges the data pose to groundwater modellers. Whole of basin modelling commenced in the 1970s and was taken up again in the 1990s. Improvements in computing hardware and software, data quality and hydrodynamic understanding of the Basin enabled iterative improvements in the models. Some issues persist, such as data quality and a limited understanding of the hydrogeology of the Eulo-Nebine Ridge area in southern Queensland. A method of more accurately interpolating the hydraulic head where water level measurements are decades apart is presented

Survey and Evaluate Uncertainty Quantification Methodologies

The Carbon Capture Simulation Initiative (CCSI) is a partnership among national laboratories, industry and academic institutions that will develop and deploy state-of-the-art computational modeling and simulation tools to accelerate the commercialization of carbon capture technologies from discovery to development, demonstration, and ultimately the widespread deployment to hundreds of power plants. The CCSI Toolset will provide end users in industry with a comprehensive, integrated suite of scientifically validated models with uncertainty quantification, optimization, risk analysis and decision making capabilities. The CCSI Toolset will incorporate commercial and open-source software currently in use by industry and will also develop new software tools as necessary to fill technology gaps identified during execution of the project. The CCSI Toolset will (1) enable promising concepts to be more quickly identified through rapid computational screening of devices and processes; (2) reduce the time to design and troubleshoot new devices and processes; (3) quantify the technical risk in taking technology from laboratory-scale to commercial-scale; and (4) stabilize deployment costs more quickly by replacing some of the physical operational tests with virtual power plant simulations. The goal of CCSI is to deliver a toolset that can simulate the scale-up of a broad set of new carbon capture technologies from laboratory scale to full commercial scale. To provide a framework around which the toolset can be developed and demonstrated, we will focus on three Industrial Challenge Problems (ICPs) related to carbon capture technologies relevant to U.S. pulverized coal (PC) power plants. Post combustion capture by solid sorbents is the technology focus of the initial ICP (referred to as ICP A). The goal of the uncertainty quantification (UQ) task (Task 6) is to provide a set of capabilities to the user community for the quantification of uncertainties associated with the carbon capture processes. As such, we will develop, as needed and beyond existing capabilities, a suite of robust and efficient computational tools for UQ to be integrated into a CCSI UQ software framework

Understanding the DayCent model: Calibration, sensitivity, and identifiability through inverse modeling

AbstractThe ability of biogeochemical ecosystem models to represent agro-ecosystems depends on their correct integration with field observations. We report simultaneous calibration of 67 DayCent model parameters using multiple observation types through inverse modeling using the PEST parameter estimation software. Parameter estimation reduced the total sum of weighted squared residuals by 56% and improved model fit to crop productivity, soil carbon, volumetric soil water content, soil temperature, N2O, and soil NO3− compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil NO3− and NH4+. Post-processing analyses provided insights into parameter–observation relationships based on parameter correlations, sensitivity and identifiability. Inverse modeling tools are shown to be a powerful way to systematize and accelerate the process of biogeochemical model interrogation, improving our understanding of model function and the underlying ecosystem biogeochemical processes that they represent

Hydrology of Green Roofs: Inverse Modelling in MODFLOW 6 for Estimating the Saturated Hydraulic Conductivity of a LECA Storage Layer

This thesis presents a study on the modelling of green roofs as a stormwater management tool using MODFLOW 6 with the ModelMuse interface. The objective of the research is to evaluate the reliability and capability of MODFLOW 6 as a modelling tool for green roofs and to estimate the hydraulic conductivity of the drainage layer on green roofs. The research work is based on a steady-state experiment conducted on the green roof (4cm of sedum and 15cm of Lightweight Expanded Clay Aggregate; LECA 0-6mm) at the research facility for green roofs at the Norwegian University of Life Sciences (NMBU). The roof was constructed in 2018 and monitored since then, with live data logging from eight water head sensors, and recording of run-off, precipitation, temperature, and several other hydrological parameters. The use of MODFLOW 6 to simulate the flow in green roofs is not as common as its use as a simulating tool for groundwater flow. In this thesis, the reliability of MODFLOW to model the flow in green roofs under steady-state conditions was successfully evaluated by matching the modelling result with an analytical solution of the flow equations in the drainage layer of the green roof. In this research work, the model was calibrated for hydraulic conductivity ( ) against the observation data from 8 sensors representing the hydraulic head in the green roof. The calibration processes were carried out using both manual calibrations with the Mean Absolute Error (MAE) as the objective function, and inverse modelling with the Parameter ESTimation (PEST) modelling in MODFLOW 6. The results of this work showed that the model was able to accurately predict the hydraulic conductivity based on the observed hydraulic head in the green roof. Based on the modelling of the steady-state experiment, the hydraulic conductivity of LECA 0-6mm as the drainage layer of the green roof was estimated as . This modelled value closely approximates the laboratory-measured value . Furthermore, It has been found that the lower values of hydraulic conductivity ( ) made the hydraulic head rise higher than the thickness of the drainage layer, while the higher values ( ) made the model dry. Additionally, It was discovered that The calibrated K-value is relatively insensitive to the choice of boundary condition, i.e. the head value at the drain outlet. MODFLOW 6 proved to be a very robust and powerful physical-based modelling tool that was able to effectively simulate the steady flow in the drainage layer of the green roof in this research work. For the transient flow model which simulates the scenario of time-depending precipitation (unsteady-state conditions), MODFLOW 6 should be evaluated and more research should be conducted

Automatic calibration of CODESA-3D using PEST

We describe here our experience in using the Model Independent Pameter ESTimation (PEST) free software tool [Doherty, 2002] to perform the automatic calibration of the COupled DEnsity-dependent variably SAturated flow and miscible transport (CODESA-3D) groundwater model [Gambolati et al., 1999]. Generally speaking, calibration of a model requires that a suitable method of spatial parameter characterization be defined in order to adjust model parameters until model outputs correspond well to specific laboratory and/or field measurements of the system which is simulated. In particular, for groundwater models the adjustable parameters are usually given by main hydrogeological properties (e.g. hydraulic permeability) and/or system excitations (e.g. abstraction volumes) while control data are represented by piezometric heads and/or salt concentrations measured in the field. Model calibration is a complex task. To perform it for a 3D fully-distributed physically-based hydrological model we need to build up a chain of interdependent software tools and data through the interdisciplinary expertise of GIS experts, modelers and hydrogeologists (Figure 1). The newly generated optimization model is comprised by the two pieces of software CODESA-3D and PEST with the latter wrapping the former up. The optimization model is not restricted in its use solely to the calibration of the groundwater model, through this tool modeler can gain valuable insight into the strengths and weakness of the input dataset allowing future data gathering to be undertaken in an optimal manner. In addition, lessons learned will be applicable also to the estimation of the degree of uncertainty associated with a given calibrated model prediction and to make decisions regarding appropriate levels of model complexity. In the following we discuss in detail the optimization model development and test using synthetic observations generated by the groundwater model itself

Scientific basis for safely shutting in the Macondo Well after the April 20, 2010 Deepwater Horizon blowout

As part of the government response to the Deepwater Horizon blowout, a Well Integrity Team evaluated the geologic hazards of shutting in the Macondo Well at the seafloor and determined the conditions under which it could safely be undertaken. Of particular concern was the possibility that, under the anticipated high shut-in pressures, oil could leak out of the well casing below the seafloor. Such a leak could lead to new geologic pathways for hydrocarbon release to the Gulf of Mexico. Evaluating this hazard required analyses of 2D and 3D seismic surveys, seafloor bathymetry, sediment properties, geophysical well logs, and drilling data to assess the geological, hydrological, and geomechanical conditions around the Macondo Well. After the well was successfully capped and shut in on July 15, 2010, a variety of monitoring activities were used to assess subsurface well integrity. These activities included acquisition of wellhead pressure data, marine multichannel seismic pro- files, seafloor and water-column sonar surveys, and wellhead visual/acoustic monitoring. These data showed that the Macondo Well was not leaking after shut in, and therefore, it could remain safely shut until reservoir pressures were suppressed (killed) with heavy drilling mud and the well was sealed with cement

Ground-Water Availability in the Atlantic Coastal Plain of North and South Carolina

2012 S.C. Water Resources Conference - Exploring Opportunities for Collaborative Water Research, Policy and Managemen

Ground-Water Availability in the Atlantic Coastal Plain of North and South Carolina

2010 South Carolina Water Resource Conference. Informing strategic water planning to address natural resource, community and economic challenges