Conceptual model development using a generic Features, Events, and Processes (FEP) database for assessing the potential impact of hydraulic fracturing on groundwater aquifers

<p>Hydraulic fracturing for natural gas extraction from unconventional reservoirs has not only impacted the global energy landscape but has also raised concerns over its potential environmental impacts. The concept of <q>features, events and processes</q> (FEP) refers to identifying and selecting the most relevant factors for safety assessment studies. In the context of hydraulic fracturing we constructed a comprehensive FEP database and applied it to six key focused scenarios defined under the scope of FracRisk project (<a href="http://www.fracrisk.eu" target="_blank">http://www.fracrisk.eu</a>, last access: 17 August 2018). The FEP database is ranked to show the relevance of each item in the FEP list per scenario. The main goal of the work is to illustrate the FEP database applicability to develop a conceptual model for regional-scale stray gas migration.</p

Modeling of methane migration from gas wellbores into shallow groundwater at basin scale

Abstract Methane contamination of drinking water resources is one of the major concerns associated with unconventional gas development. This study assesses the potential contamination of shallow groundwater via methane migration from a leaky natural gas well through overburden rocks, following hydraulic fracturing. A two-dimensional, two-phase, two-component numerical model is employed to simulate methane and brine upward migration toward shallow groundwater in a generic sedimentary basin. A sensitivity analysis is conducted to examine the influence of methane solubility, capillary pressure–saturation relationship parameters and residual water saturation of overburden rocks, gas leakage rate from the well, tilted formations, and low-permeability sediments (i.e., claystones) on the transport of fluids. Results show that the presence of lithological barriers is the most important factor controlling the temporal–spatial distribution of methane in the subsurface and the arrival time to shallow groundwater. A pulse of high leakage rate is required for early manifestation of methane in groundwater wells. Simulations reveal that the presence of tilted features could further explain fast-growing methane contamination and extensive lateral spreading reported in field studies.Horizon 2020 http://dx.doi.org/10.13039/501100007601Georg-August-Universität Göttingen (1018

Modeling of methane migration from gas wellbores into shallow groundwater at basin scale

Methane contamination of drinking water resources is one of the major concerns associated with unconventional gas development. This study assesses the potential contamination of shallow groundwater via methane migration from a leaky natural gas well through overburden rocks, following hydraulic fracturing. A two-dimensional, two-phase, two-component numerical model is employed to simulate methane and brine upward migration toward shallow groundwater in a generic sedimentary basin. A sensitivity analysis is conducted to examine the influence of methane solubility, capillary pressure–saturation relationship parameters and residual water saturation of overburden rocks, gas leakage rate from the well, tilted formations, and low-permeability sediments (i.e., claystones) on the transport of fluids. Results show that the presence of lithological barriers is the most important factor controlling the temporal–spatial distribution of methane in the subsurface and the arrival time to shallow groundwater. A pulse of high leakage rate is required for early manifestation of methane in groundwater wells. Simulations reveal that the presence of tilted features could further explain fast-growing methane contamination and extensive lateral spreading reported in field studies.Horizon 2020 http://dx.doi.org/10.13039/501100007601Georg-August-Universität Göttingen (1018

Nonlinear Autoregressive Neural Networks to Predict Hydraulic Fracturing Fluid Leakage into Shallow Groundwater

Hydraulic fracturing of horizontal wells is an essential technology for the exploitation of unconventional resources, but led to environmental concerns. Fracturing fluid upward migration from deep gas reservoirs along abandoned wells may pose contamination threats to shallow groundwater. This study describes the novel application of a nonlinear autoregressive (NAR) neural network to estimate fracturing fluid flow rate to shallow aquifers in the presence of an abandoned well. The NAR network is trained using the Levenberg-Marquardt (LM) and Bayesian Regularization (BR) algorithms and the results were compared to identify the optimal network architecture. For NAR-LM model, the coefficient of determination (R-2) between measured and predicted values is 0.923 and the mean squared error (MSE) is 4.2 x 10(-4), and the values of R-2 = 0.944 and MSE = 2.4 x 10(-4) were obtained for the NAR-BR model. The results indicate the robustness and compatibility of NAR-LM and NAR-BR models in predicting fracturing fluid flow rate to shallow aquifers. This study shows that NAR neural networks can be useful and hold considerable potential for assessing the groundwater impacts of unconventional gas development

Modeling of methane migration from gas wellbores into shallow groundwater at basin scale

Methane contamination of drinking water resources is one of the major concerns associated with unconventional gas development. This study assesses the potential contamination of shallow groundwater via methane migration from a leaky natural gas well through overburden rocks, following hydraulic fracturing. A two-dimensional, two-phase, two-component numerical model is employed to simulate methane and brine upward migration toward shallow groundwater in a generic sedimentary basin. A sensitivity analysis is conducted to examine the influence of methane solubility, capillary pressure-saturation relationship parameters and residual water saturation of overburden rocks, gas leakage rate from the well, tilted formations, and low-permeability sediments (i.e., claystones) on the transport of fluids. Results show that the presence of lithological barriers is the most important factor controlling the temporal-spatial distribution of methane in the subsurface and the arrival time to shallow groundwater. A pulse of high leakage rate is required for early manifestation of methane in groundwater wells. Simulations reveal that the presence of tilted features could further explain fast-growing methane contamination and extensive lateral spreading reported in field studies

A constrained machine learning surrogate model to predict the distribution of water-in-oil emulsions in electrostatic fields

Abstract Accurately describing the evolution of water droplet size distribution in crude oil is fundamental for evaluating the water separation efficiency in dehydration systems. Enhancing the separation of an aqueous phase dispersed in a dielectric oil phase, which has a significantly lower dielectric constant than the dispersed phase, can be achieved by increasing the water droplet size through the application of an electrostatic field in the pipeline. Mathematical models, while being accurate, are computationally expensive. Herein, we introduced a constrained machine learning (ML) surrogate model developed based on a population balance model. This model serves as a practical alternative, facilitating fast and accurate predictions. The constrained ML model, utilizing an extreme gradient boosting (XGBoost) algorithm tuned with a genetic algorithm (GA), incorporates the key parameters of the electrostatic dehydration process, including droplet diameter, voltage, crude oil properties, temperature, and residence time as input variables, with the output being the number of water droplets per unit volume. Furthermore, we modified the objective function of the XGBoost algorithm by incorporating two penalty terms to ensure the model’s predictions adhere to physical principles. The constrained model demonstrated accuracy on the test set, with a mean squared error of 0.005 and a coefficient of determination of 0.998. The efficiency of the model was validated through comparison with the experimental data and the results of the population balance mathematical model. The analysis shows that the initial droplet diameter and voltage have the highest influence on the model, which aligns with the observed behaviour in the real-world process

Geomechanics for Energy and the Environment

Geological energy is an abundant source of energy on Earth, encompassing both fossil and non-fossil forms such as oil, natural gas, coal, geothermal energy, shale gas, and coalbed methane [...

Experimental Data on Solubility of the Two Calcium Sulfates Gypsum and Anhydrite in Aqueous Solutions

Calcium sulfate exists in three forms, namely dihydrate or gypsum (CaSO4&middot;2H2O), anhydrite (CaSO4), and hemihydrate or bassanite (CaSO4&middot;0.5H2O) depending on temperature, pressure, pH, and formation conditions. The formation of calcium sulfates occurs widely in nature and in many engineering settings. Herein, a dataset containing the experimental solubility data of calcium sulfate minerals, i.e., gypsum and anhydrite, in aqueous solutions is presented. The compiled dataset contains calcium sulfates solubility values extracted from 42 papers published between 1906 and 2019. The dataset can be used for various scientific and engineering purposes such as environmental applications (e.g., gas treatment, wastewater treatment, and chemical disposal), geotechnical applications (e.g., clay-sulfate rock swelling), separation processes (e.g., crystallization, extractive distillation, and seawater desalination), and electrochemical processes (e.g., corrosion and electrolysis)

Enhancing Tank Leaching Efficiency through Electrokinetic Remediation: A Laboratory and Numerical Modeling Study

Electrokinetic remediation is a cost-effective and efficient method that utilizes electrical current to transport ions within the subsurface. This process aims to remediate soil contamination caused by industrial activities, which poses threats to wildlife, water quality, and air quality. To assess the impact of the electrokinetic process on tank leaching efficiency, two electrode configurations were tested: vertical and horizontal arrays. These tests considered variable electrode spacing and different voltages in the soil residue. Additionally, the movement of copper cations from the anode to the cathode under this process was investigated. Results show that the horizontal electrode array is more effective in transporting soil moisture because of its broader contact with the soil. After 20 days of using the electrokinetic method with vertical electrodes, the soil moisture content decreased by 12.28%; with horizontal electrodes, it dropped by 38.4%. Also, the concentration of copper in the soil near the cathode electrode increased from 0.54 to 0.77% after 20 days. The estimated copper ion content in the cathode area after 20 days was between 150 and 350 mol/m3, aligning closely with the measured value of 192.5 mol/m3. These results indicate that the electrokinetic process can significantly enhance copper recovery efficiency in tank leaching processes and curtail environmental side effects. Overall, this study provides valuable insights into the benefits of using the electrokinetic process to remediate leaching residue and improve the efficiency of industrial processes