Microbial healing of nature-like rough sandstone fractures for rock weathering mitigation

Rock weathering fractures in nature are complex and fracture healing is an effective strategy for rock weathering mitigation. This study is a first attempt to apply microbially induced calcium carbonate precipitation (MICP) technology in the healing of nature-weathering-like rough fractures (NWLRF). Sandstone was studied as an example due to it is a wide-spread construction, sculpture and monuments material all over the world. In order to achieve a high healing efficiency, a repeated mixture injection strategy was proposed. Based on a series of laboratory MICP injection experiments on four types of NWLRF, we systematically explored the fundamental micro-healing mechanism and the influence of factors including fracture aperture, characteristics of branch fractures, and cementation solution concentration. Experimental results demonstrated that MICP healing with the repeated mixture injection strategy had the ability to efficiently heal the penetrated NWLRF well with length in centimeter-scale and aperture in millimeter-scale, but cannot heal the non-penetrated branch fractures under low injection pressure. The repeated mixture injection strategy furtherly achieved a high apparent fracture healing ratio and a significant reduction of transmissivity. The apparent fracture healing ratios of all main fractures were higher than 80% and the maximum was 99.1%. Fracture transmissivity was reduced by at least three orders of magnitude from about 1×10-4 m2/s to less than 1×10-7 m2/s, and the highest reduction reached to four orders. For the aspect of the effects, larger cementation solution concentration, finer aperture and the existing of penetrated branch fracture were beneficial to improve the healing effect. Moreover, the MICP healing mechanism with high fracture healing ratio and significant reduction of transmissivity on sandstone NWLRF was also analyzed. The research results have important theoretical significance and technical guidance value for the disaster prevention and mitigation of rock weathering.

Allocating Transmissivities from Constant Head Tests for the Development of DFN Models

Constant head tests are commonly used for field measurements of fracture transmissivity. As a bulk transmissivity is measured for each test section, it is frequently unclear how this transmissivity relates to the hydraulic properties of individual fractures. The goal of this study is to determine if constant head tests conducted at scales larger than the average fracture spacing can be used to generate discrete fracture network (DFN) models that describe transport. The methodology involved generating DFNs using measurements from constant head tests conducted at lengths both above and below the average fracture spacing at a site in Ontario, Canada. Transport predictions from DFNs produced from different scales of hydraulic tests were compared to determine if a method for proportioning larger-scale test results to obtain a DFN representative of the smaller-scale tests could be established. The results of this study indicate that the choice of method used to apportion bulk transmissivities has a significant impact on transport simulations, with a difference in mass arrival of over a factor of two at travel distances less than 50 m. While the most appropriate method is case specific, the error resulting from the choice needs to be considered when using DFN models.

Artificial Neural Network to Predict the Thermal Drawdown of Enhanced Geothermal System

This work presents the prediction of thermal drawdown of an enhanced geothermal system (EGS) using artificial neural network (ANN). A three-dimensional numerical model of EGS was developed to generate the training and testing data sets for ANN. We have performed a quantitative study of geothermal energy production for various injection operating conditions and reservoir fracture aperture. Input parameters for ANN include temperature, mass flux, pressure, and fracture transmissivity, while the production well temperature is the output parameter. The Levenberg–Marquardt back-propagation learning algorithm, the tan-sigmoid, and the linear transfer function were used for the ANN optimization. The best results were obtained with an ANN architecture composed of eight hidden layers and 20 neurons in the hidden layer, which made it possible to predict the production temperature with a satisfactory range (R2 > 0.99). An appropriate accuracy of the ANN model was obtained with a percentage error less than (± 4.5). The results from the numerical simulations suggest that fracture transmissivity has less effect on thermal drawdown than the injection mass flux and temperature. From our results, we confirm that ANN modeling may predict the thermal drawdown of an EGS system with high accuracy.

Predictions of the wetting of bentonite emplaced in a crystalline rock based on generic site characterization data

A geological disposal facility (GDF) is the widely accepted long-term solution for the management of higher-activity radioactive waste. It consists of an engineered facility constructed in a suitable host rock. The facility is designed to inhibit the release of radioactivity by using a system consisting of engineered and natural barriers. The engineered barriers include the wasteform, used to immobilize the waste, the waste disposal container and any buffer material used to protect the container. The natural barrier includes the rocks in which the facility is constructed. The careful design of this multi-barrier system enables the harmful effects of the radioactivity on humans and biota in the surface environment to be reduced to safe levels.Bentonite is an important buffer material used as a component of a multi-barrier disposal system. For example, compacted bentonite rings and blocks are used to protect the copper container, used for the disposal of spent fuel, in the KBS-3 disposal system. As the bentonite saturates, through contact with groundwater from the host rock, it swells and provides a low hydraulic conductivity barrier, enabling the container to be protected from deleterious processes, such as corrosion. The characteristic swelling behaviour of bentonite is due to the presence of significant quantities of sodium montmorillonite.Recently, there have been detailed in situ experiments designed to understand how bentonite performs under natural conditions. One such experiment is the Buffer–Rock Interaction Experiment (BRIE), performed at the Äspö Hard Rock Laboratory near Oskarshamn in the SE of Sweden. This experiment is designed to further understand the wetting of bentonite from the groundwater flow in a fractured granite host rock.In this paper, the observations from the BRIE are explained using an integrated model that is able to describe the saturation of bentonite emplaced in a heterogeneous fractured rock. It provides a framework to understand the key processes in both the rock and bentonite. The predictive capability of these models was investigated within the context of uncertainties in the data and the consequence for predictions of the wetting of emplaced bentonite. For example, to predict the wetting of emplaced bentonite requires an understanding of the distribution of fracture transmissivity intersecting the bentonite. A consequence of these findings is that the characterization of the fractured rock local to the bentonite is critical to understanding the subsequent wetting profiles. In particular, prediction of the time taken to achieve full saturation of bentonite using a simplified equivalent homogeneous description of the fractured host rock will tend to be too short.