Numerical modeling of CO2-water-rock interactions in the Farnsworth, Texas Hydrocarbon Unit, USA

Numerical speciation, reaction path, and reactive transport modeling were used to evaluate potential for long-term CO2 sequestration in Farnsworth Hydrocarbon Unit in northern Texas. Speciation modeling showed the present Morrow B formation water to be supersaturated with respect to an assemblage of zeolite, clay, carbonate, mica, aluminum hydroxide minerals, and quartz. Feldspars and chlorite were predicted to dissolve. A reaction path model showed pH value decreased initially from 7 to 4.1 and 4.2 during titration of CO2. As the resultant CO2-charged fluid reacted with Morrow B mineral matrix, the pH rose to 5.1 to 5.2 with precipitation of diaspore, quartz, nontronite, siderite, witherite, dolomite, and calcite. CO2 sequestration by mineral trapping was predicted to maximum of 2% of the titrated CO2 with porosity increase of about 1.4 to 1.5%. Reactive transport modeling was executed injecting CO2 at nine wells at field rate for 10 years. During injection, fluid pressures near the wells rose from about 15 MPa to about 19.2 MPa, but quickly dissipated after injection ceased. A plume of immiscible CO2 gas built up around the wells, reaching pore saturations of about 50%. Over the 30 years time of the simulations thus far, ankerite was the only carbonate mineral predicted to precipitate, and only mineral sink for CO2. Injected CO2 after 30 years simulation was predicted to be sequestered by hydrodynamic trapping, followed by solubility and mineral trapping, respectively. The amounts of mineral precipitation and dissolution were too small to affect the porosity and permeability significantly

Scorpion Venom Analgesic Peptide, BmK AGAP Inhibits Stemness, and Epithelial-Mesenchymal Transition by Down-Regulating PTX3 in Breast Cancer

A scorpion peptide reported to exhibit both analgesic and antitumor activity in animal models may present as an alternative therapeutic agent for breast cancer. We aimed to investigate the effect of Buthus martensii Karsch antitumor-analgesic peptide (BmK AGAP) on breast cancer cell stemness and epithelial-mesenchymal transition (EMT). We treated MCF-7 and MDA-MB-231 cells with different concentrations of rBmK AGAP and observed that rBmK AGAP inhibited cancer cell stemness, epithelial-mesenchymal transition (EMT), migration, and invasion. Analysis by qPCR, ELISA, western blot, immunofluorescence staining, sphere formation, colony assay, transwell migration, and invasion assays demonstrated rBmK AGAP treatment decreased the expressions of Oct4, Sox2, N-cadherin, Snail, and increased the expression of E-cadherin. rBmK AGAP inhibited breast cancer cell stemness, EMT, migration, and invasion by down-regulating PTX3 through NF-κB and Wnt/β-catenin signaling Pathway in vitro and in vivo. Xenograft tumor model confirmed inhibition of tumor growth, stem-like features, and EMT by rBmK AGAP. Thus, rBmK AGAP is a potential therapeutic agent against breast cancer and related pain

Numerical modeling, uncertainty analyses, and machine learning for decision support in the geosciences.

My first paper shows the importance of numerical modeling and post-calibration uncertainty analyses for making decision to monitor waste transport at a naval waste repository site in Texas. For this, MODFLOW and MODPATH were used to simulate hydraulic head and particle/tracer travel times. Later, linear and nonlinear uncertainties were quantified for model parameters (hydraulic conductivities) and prediction of particle travel times along with identifiability and observation worth. Parameter uncertainties were reduced by up to 92%; a total of 19 parameters were at least moderately identifiable (>10%); travel-time uncertainties were reduced up to 92%. An observations-worth analysis found that 11 additional measurements at targeted locations could reduce travel-time uncertainties by factors from 1.04 to 4.3 over existing data. Finally, nonlinear uncertainty analyses predicted that conservative tracers exited the flow system within a year. My second paper explains a module for PFLOTRAN, PFLOTRAN–SIP, which was built to efficiently simulate waste remediation activities. PFLOTRAN–SIP coupled PFLOTRAN and E4D. PFLOTRAN solves coupled flow and solute transport process models to estimate solute concentrations, which were used with Archie’s Law to compute bulk electrical conductivities at near-zero frequency. These bulk electrical conductivities were modified using the Cole-Cole equation to account for frequency dependence. Using the estimated frequency-dependent bulk conductivities, E4D simulates the real and complex electrical potential signals for selected frequencies for spectral impedance polarization. The PFLOTRAN-SIP framework was demonstrated through a synthetic tracer-transport model simulating tracer concentration and electrical impedances for four frequencies. My third paper compares 20 machine learning (ML) models to predict reactive-mixing phenomena in subsurface porous media. The 20 ML emulators included linear methods, Bayesian methods, ensemble learning methods, and a multilayer perceptron (MLP). The ML emulators were trained to classify the state of mixing and predict three quantities of interest (QoIs) characterizing species production and decay. Linear classifiers and regressors failed; however, ensemble methods (classifiers and regressors) and the MLP accurately classified the state of reactive mixing and the QoIs. Computationally, trained ML emulators were ≈ 10^5 times faster than the high-fidelity numerical simulations. These three works either support or expedite decision making process in the geosciences

Post-Calibration Uncertainty Analysis for Travel Times at a Naval Weapons Industrial Reserve Plant

The Naval Weapons Industrial Reserve Plant (NWIRP) in McGregor, Texas began manufacturing explosives in 1980 and several hazardous chemicals were discovered in lakes and streams surrounding the site in 1998. Contaminants traveled to local lakes and streams much faster than initially predicted. This research estimated contaminant travel times and identified locations where monitoring wells should be installed to yield the greatest reductions in uncertainties in travel-time predictions. To this end, groundwater and particle-tracking models for NWIRP site were built to predict hydraulic heads and contaminant travel times. Next, parameter (hydraulic conductivities) uncertainties, parameter identifiabilities, observation (hydraulic heads) worth, and predictive (contaminant travel times) uncertainties were quantified. Parameter uncertainties were reduced by up to 92%; a total of 19 of 158 parameters were at least moderately identifiable; travel-time uncertainties were reduced up to 92%. Additionally, travel-time predictions and post-calibration parameter distributions were generated using the null-space Monte Carlo (NSMC) technique. NSMC predicted that conservative tracers exited the flow system within a year, which matches with field data. Finally, an observations-worth analysis found that additional 11 more measurements would reduce travel-time uncertainties by factors from 1.04 to 4.3 over existing data if monitoring wells were installed at the suggested locations

Post-Calibration Uncertainty Analysis for Travel Times at a Naval Weapons Industrial Reserve Plant

The Naval Weapons Industrial Reserve Plant (NWIRP) in McGregor, Texas began manufacturing explosives in 1980 and several hazardous chemicals were discovered in lakes and streams surrounding the site in 1998. Contaminants traveled to local lakes and streams much faster than initially predicted. This research estimated contaminant travel times and identified locations where monitoring wells should be installed to yield the greatest reductions in uncertainties in travel-time predictions. To this end, groundwater and particle-tracking models for NWIRP site were built to predict hydraulic heads and contaminant travel times. Next, parameter (hydraulic conductivities) uncertainties, parameter identifiabilities, observation (hydraulic heads) worth, and predictive (contaminant travel times) uncertainties were quantified. Parameter uncertainties were reduced by up to 92%; a total of 19 of 158 parameters were at least moderately identifiable; travel-time uncertainties were reduced up to 92%. Additionally, travel-time predictions and post-calibration parameter distributions were generated using the null-space Monte Carlo (NSMC) technique. NSMC predicted that conservative tracers exited the flow system within a year, which matches with field data. Finally, an observations-worth analysis found that additional 11 more measurements would reduce travel-time uncertainties by factors from 1.04 to 4.3 over existing data if monitoring wells were installed at the suggested locations

PFLOTRAN-SIP: A PFLOTRAN Module for Simulating Spectral-Induced Polarization of Electrical Impedance Data

Spectral induced polarization (SIP) is a non-intrusive geophysical method that collects chargeability information (the ability of a material to retain charge) in the time domain or its phase shift in the frequency domain. Although SIP is a temporal method, it cannot measure the dynamics of flow and solute/species transport in the subsurface over long times (i.e., 10–100 s of years). Data collected with the SIP technique need to be coupled with fluid flow and reactive-transport models in order to capture long-term dynamics. To address this challenge, PFLOTRAN-SIP was built to couple SIP data to fluid flow and solute transport processes. Specifically, this framework couples the subsurface flow and transport simulator PFLOTRAN and geoelectrical simulator E4D without sacrificing computational performance. PFLOTRAN solves the coupled flow and solute-transport process models in order to estimate solute concentrations, which were used in Archie’s model to compute bulk electrical conductivities at near-zero frequency. These bulk electrical conductivities were modified while using the Cole–Cole model to account for frequency dependence. Using the estimated frequency-dependent bulk conductivities, E4D simulated the real and complex electrical potential signals for selected frequencies for SIP. These frequency-dependent bulk conductivities contain information that is relevant to geochemical changes in the system. This study demonstrated that the PFLOTRAN-SIP framework is able to detect the presence of a tracer in the subsurface. SIP offers a significant benefit over ERT in the form of greater information content. It provided multiple datasets at different frequencies that better constrained the tracer distribution in the subsurface. Consequently, this framework allows for practitioners of environmental hydrogeophysics and biogeophysics to monitor the subsurface with improved resolution