THE HIDDEN TREASURES BENEATH

Related publications

• ARIMA modeling of simulated and of real-time pollutant data (2022) — Paul Johnson, Caroline Johnson, Ling Huang — American Chemical Society SciMeetings 3 (1), 2022
• Detecting and Characterizing Fluid Leakage Through Wellbore Flaws Using Fiber-Optic Distributed Acoustic Sensing (2022) — Ishtiaque Anwar et al. — 56th U.S. Rock Mechanics/Geomechanics Symposium
  Abstract

  ABSTRACT: A primary concern associated with the utilization of subsurface systems is that there may be pathways for fluid leakage from the resource storage, or disposal reservoir. Leakage of any fluid can contaminate groundwater, cause geo-environmental pollution, generate hazardous surface conditions, and potentially compromise the functionality of the subsurface system. A low-cost, innovative technique to detect and characterize fracture leakage that is functional over many years is needed for applications such as geothermal reservoirs, CO2 sequestration wells, deep borehole storage of nuclear waste, and strategic petroleum reserve caverns. In this experimental study, we investigate the use of fiber-optic distributed acoustic sensing (DAS) to measure dynamic strain changes caused by acoustic signals induced by fluid flow with an eventual goal of developing instrumentation and analytical techniques to detect and characterize the movement of fluids through leaky wellbores. In the first phase of the experiments reported here, we conducted fluid flow tests in a porous medium as an analog to a fracture filled with comminuted material. The measured effective permeability is then compared with the signals generated by the fiber-optic cable. The study indicated that acoustic signals generated from fluid flow through porous media could be effectively captured by the fiber-optic cable DAS technology. 1. INTRODUCTION Wellbores are used for gaining access to various subsurface systems such as underground fluid reserve (Miyazaki, 2009), CO2 sequestration (Watson and Bachu, 2008; Zhang and Bachu, 2011), geothermal energy development (Shadravan, Ghasemi, & Alfi, 2015), waste disposal, oil and gas exploration (Davies et al., 2014), etc. A primary concern associated with the utilization of subsurface systems is that there may be pathways for fluid leakage from the resource storage, or disposal reservoir through the wellbore flaws. Leakage of any fluid from a leaky wellbore can contaminate groundwater, cause geo-environmental pollution (Davies et al., 2014; Ingraffea, Wells, Santoro, & Shonkoff, 2014; Jackson, 2014), generate hazardous surface conditions, and potentially compromise the functionality of the subsurface system (Gasda, Celia, Wang, & Duguid, 2013). Researchers has identified different potential leakage pathways, including fractures in the cement or micro annuli from de-bonding at the cement-casing or cement-formation interface (Celia, Bachu, Nordbotten, Gasda, & Dahle, 2005; Theresa L Watson & Bachu, 2009) or the casing corrosion product (Anwar, Chojnicki, Bettin, Taha, & Stormont, 2019; Beltrán-Jiménez et al., 2021).
• SmartTensors: Unsupervised and physics-informed machine learning framework for the geoscience applications (2022) — Bulbul Ahmmed, Velimir V. Vesselinov, Maruti K. Mudunuru — Second International Meeting for Applied Geoscience & Energy
  Abstract

  SmartTensors (https://github.com/SmartTensors) is a novel framework for unsupervised and physics-informed machine learning for geoscience applications. The methods in SmartTensors AI platform are developed using advanced matrix/tensor factorization constrained by penalties enforcing robustness and interpretability (e.g., nonnegativity, sparsity, physics, and mathematical constraints;etc.). This framework has been applied to analyze diverse datasets related to a wide range of problems: from COVID-19 to wildfires and climate. Here, we will focus on the analysis of geothermal prospectivity of the Great Basin, U.S. The basin covers a vast area that is yet to be thoroughly explored to discover new geothermal resources. The available regional geochemical data are expected to provide critical information about the geothermal reservoir properties in the basin, including temperature, fluid/heat flow, boundary conditions, and spatial extent. The geochemical data may also include hidden (latent) information that is a proxy for geothermal prospectivity. We processed the sparse geochemical dataset of 18 geochemical attributes observed at 14,341 locations. The data are analyzed using our GeoThermalCloud toolbox for geothermal exploration (https://github.com/SmartTensors/GeoThermalCloud.jl) whichis also a part of the SmartTensors framework. An unsupervised machine learning using non-negative matrix factorization with customized k-means clustering (NMFk) as implemented in SmartTensors identified three hidden geothermal signatures representing low-, medium-, and high-temperature reservoirs, respectively (Fig). NMFk also evaluated the probability of occurrence of these types of resources through the studied region. NMFk also reconstructed attributes from sparse into continuous over the study domain. Future work will add in the ML analyses other regional- and site-scale datasets including geological, geophysical, and geothermal attributes. © 2022 Society of Exploration Geophysicists and the American Association of Petroleum Geologists.
• Biogenic uranium isotope fractionation [Slides] (2020) — Ricardo Marti-Arbona et al. — Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2020
• Long-term stability of dithionite in alkaline anaerobic aqueous solution (2019) — Katherine Telfeyan et al. — Applied Geochemistry
• Nonnegative tensor factorization for contaminant source identification (2019) — Velimir V. Vesselinov, Boian S. Alexandrov, Daniel O'Malley — Journal of Contaminant Hydrology
• Contaminant source identification using semi-supervised machine learning (2018) — Velimir V. Vesselinov, Boian S. Alexandrov, Daniel O’Malley — Journal of Contaminant Hydrology
• A MULTICOMPONENT REACTIVE TRANSPORT MODEL OF IN SITU REDOX MANIPULATION FOR REMEDIATION OF CHROMIUM CONTAMINATED GROUNDWATER (2017) — Sachin Pandey, Satish Karra, Velimir Vesselinov — GSA Annual Meeting in Seattle, Washington, USA - 2017
• CHROTRAN 1.0: A mathematical and computational model for in situ heavy metal remediation in heterogeneous aquifers (2017) — Scott K. Hansen et al. — Geoscientific Model Development
  Abstract

  Abstract. Groundwater contamination by heavy metals is a critical environmental problem for which in situ remediation is frequently the only viable treatment option. For such interventions, a multi-dimensional reactive transport model of relevant biogeochemical processes is invaluable. To this end, we developed a model, chrotran, for in situ treatment, which includes full dynamics for five species: a heavy metal to be remediated, an electron donor, biomass, a nontoxic conservative bio-inhibitor, and a biocide. Direct abiotic reduction by donor–metal interaction as well as donor-driven biomass growth and bio-reduction are modeled, along with crucial processes such as donor sorption, bio-fouling, and biomass death. Our software implementation handles heterogeneous flow fields, as well as arbitrarily many chemical species and amendment injection points, and features full coupling between flow and reactive transport. We describe installation and usage and present two example simulations demonstrating its unique capabilities. One simulation suggests an unorthodox approach to remediation of Cr(VI) contamination.
• Active layer hydrology in an arctic tundra ecosystem: quantifying water sources and cycling using water stable isotopes (2016) — Heather M. Throckmorton et al. — Hydrological Processes
  Abstract

  Abstract Climate change and thawing permafrost in the Arctic will significantly alter landscape hydro‐geomorphology and the distribution of soil moisture, which will have cascading effects on climate feedbacks (CO 2 and CH 4 ) and plant and microbial communities. Fundamental processes critical to predicting active layer hydrology are not well understood. This study applied water stable isotope techniques ( δ 2 H and δ 18 O) to infer sources and mixing of active layer waters in a polygonal tundra landscape in Barrow, Alaska (USA), in August and September of 2012. Results suggested that winter precipitation did not contribute substantially to surface waters or subsurface active layer pore waters measured in August and September. Summer rain was the main source of water to the active layer, with seasonal ice melt contributing to deeper pore waters later in the season. Surface water evaporation was evident in August from a characteristic isotopic fractionation slope ( δ 2 H vs δ 18 O). Freeze‐out isotopic fractionation effects in frozen active layer samples and textural permafrost were indistinguishable from evaporation fractionation, emphasizing the importance of considering the most likely processes in water isotope studies, in systems where both evaporation and freeze‐out occur in close proximity. The fractionation observed in frozen active layer ice was not observed in liquid active layer pore waters. Such a discrepancy between frozen and liquid active layer samples suggests mixing of meltwater, likely due to slow melting of seasonal ice. This research provides insight into fundamental processes relating to sources and mixing of active layer waters, which should be considered in process‐based fine‐scale and intermediate‐scale hydrologic models. Copyright © 2016 John Wiley & Sons, Ltd.
• Contaminant point source localization error estimates as functions of data quantity and model quality (2016) — Scott K. Hansen, Velimir V. Vesselinov — Journal of Contaminant Hydrology
• W14_CONTAMINANTREMEDIATION: High Performance Computing (HPC) for Multi-scale Decision Analyses: From Pore-scale Processes to Field-scale Contaminant Remediation (2016) — Velimir Vesselinov, Daniel O'Malley, Boian Alexandrov — Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2016
• Parameter estimation and prediction for groundwater contamination based on measure theory (2015) — S. A. Mattis et al. — Water Resources Research
• A Combined Probabilistic/Nonprobabilistic Decision Analysis for Contaminant Remediation (2014) — D. O'Malley, V. V. Vesselinov — SIAM/ASA Journal on Uncertainty Quantification
• Groundwater remediation using the information gap decision theory (2014) — D. O'Malley, V. V. Vesselinov — Water Resources Research
• Isotopic evidence for reduction of anthropogenic hexavalent chromium in Los Alamos National Laboratory groundwater (2014) — Jeffrey M. Heikoop et al. — Chemical Geology
• Robust Decision Analysis for Environmental Management of Groundwater Contamination Sites (2014) — Velimir V. Vesselinov, Daniel O'Malley, Danny Katzman — Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty, Modeling, and Analysis (ISUMA)
  Abstract

  In contrast to many other engineering fields, the uncertainties in subsurface processes (e.g., fluid flow and contaminant transport in aquifers) and their parameters are notoriously difficult to observe, measure, and characterize. This causes severe uncertainties that need to be addressed in any decision analysis related to optimal management and remediation of groundwater contamination sites. Furthermore, decision analyses typically rely heavily on complex data analyses and/or model predictions, which are often poorly constrained as well. Recently, we have developed a model-driven decisionsupport framework (called MADS; http://mads.lanl.gov) for the management and remediation of subsurface contamination sites in which severe uncertainties and complex physics-based models are coupled to perform scientifically defensible decision analyses. The decision analyses are based on Information Gap Decision Theory (IGDT). We demonstrate the MADS capabilities by solving a decision problem related to optimal monitoring network design.
• Contaminant remediation decision analysis using information gap theory (2013) — Dylan R. Harp, Velimir V. Vesselinov — Stochastic Environmental Research and Risk Assessment
  Abstract

  Decision making under severe lack of information is a ubiquitous situation in nearly every applied field of engineering, policy, and science. A severe lack of information precludes our ability to determine a frequency of occurrence of events or conditions that impact the decision; therefore, decision uncertainties due to a severe lack of information cannot be characterized probabilistically. To circumvent this problem, information gap (info-gap) theory has been developed to explicitly recognize and quantify the implications of a severe lack of information in decision making. This paper presents a decision analysis based on info-gap theory developed for a contaminant remediation scenario. The analysis provides decision support in determining the fraction of contaminant mass to remove from the environment. An info-gap uncertainty model is developed to characterize uncertainty due to a lack of information concerning the contaminant flux. The info-gap uncertainty model groups nested, convex sets of functions defining contaminant flux over time based on their level of deviation from a nominal contaminant flux. The nominal contaminant flux defines a best estimate of contaminant flux over time based on existing, though incomplete, information. A robustness function is derived to quantify the maximum level of deviation from nominal that still ensures compliance for alternative decisions. An opportuneness function is derived to characterize the possibility of meeting a desired contaminant concentration level. The decision analysis evaluates how the robustness and opportuneness change as a function of time since remediation and as a function of the fraction of contaminant mass removed.
• Near-optimal placement of monitoring wells for the detection of potential contaminant arrival in a regional aquifer at Los Alamos National Laboratory (2012) — Charles Castello et al. — 2012 Southeastern Symposium on System Theory (SSST)
• High-accuracy acoustic detection of nonclassical component of material nonlinearity (2011) — Sylvain Haupert et al. — The Journal of the Acoustical Society of America
  Abstract

  The aim is to assess the nonclassical component of material nonlinearity in several classes of materials with weak, intermediate, and high nonlinear properties. In this contribution, an optimized nonlinear resonant ultrasound spectroscopy (NRUS) measuring and data processing protocol applied to small samples is described. The protocol is used to overcome the effects of environmental condition changes that take place during an experiment, and that may mask the intrinsic nonlinearity. External temperature fluctuation is identified as a primary source of measurement contamination. For instance, a variation of 0.1 °C produced a frequency variation of 0.01%, which is similar to the expected nonlinear frequency shift for weakly nonlinear materials. In order to overcome environmental effects, the reference frequency measurements are repeated before each excitation level and then used to compute nonlinear parameters. Using this approach, relative resonant frequency shifts of 10−5 can be measured, which is below the limit of 10−4 often considered as the limit of NRUS sensitivity under common experimental conditions. Due to enhanced sensitivity resulting from the correction procedure applied in this work, nonclassical nonlinearity in materials that before have been assumed to only be classically nonlinear in past work (steel, brass, and aluminum) is reported.
• An Investigation of Numerical Grid Effects in Parameter Estimation (2006) — George A. Zyvoloski, Velimir V. Vesselinov — Groundwater
  Abstract

  Abstract Modern ground water characterization and remediation projects routinely require calibration and inverse analysis of large three‐dimensional numerical models of complex hydrogeological systems. Hydrogeologic complexity can be prompted by various aquifer characteristics including complicated spatial hydrostratigraphy and aquifer recharge from infiltration through an unsaturated zone. To keep the numerical models computationally efficient, compromises are frequently made in the model development, particularly, about resolution of the computational grid and numerical representation of the governing flow equation. The compromise is required so that the model can be used in calibration, parameter estimation, performance assessment, and analysis of sensitivity and uncertainty in model predictions. However, grid properties and resolution as well as applied computational schemes can have large effects on forward‐model predictions and on inverse parameter estimates. We investigate these effects for a series of one‐ and two‐dimensional synthetic cases representing saturated and variably saturated flow problems. We show that “conformable” grids, despite neglecting terms in the numerical formulation, can lead to accurate solutions of problems with complex hydrostratigraphy. Our analysis also demonstrates that, despite slower computer run times and higher memory requirements for a given problem size, the control volume finite‐element method showed an advantage over finite‐difference techniques in accuracy of parameter estimation for a given grid resolution for most of the test problems.
• Development and Application of Numerical Models to Estimate Fluxes through the Regional Aquifer beneath the Pajarito Plateau (2005) — Elizabeth H. Keating, Bruce A. Robinson, Velimir V. Vesselinov — Vadose Zone Journal
  Abstract

  Before recent drilling and characterization efforts in the vicinity of Los Alamos National Laboratory (LANL), conceptual models had been developed for recharge and discharge in the regional aquifer on the basis of sparse data. By integrating site‐wide data into a numerical model of the aquifer beneath the plateau we provide new insight into large‐scale aquifer properties and fluxes. This model is useful for understanding hydrologic mechanisms, assessing the magnitudes of different terms in the overall water budget, and, through sampling, for interpreting contaminant migration velocities in the overlying vadose zone. Modeling results suggest that the majority of water produced in well fields on the plateau, extracted at rates approaching 70% of total annual recharge, is derived from storage. This result is insensitive to assumptions about the percentage of total recharge that occurs in the near vicinity of water supply wells, because of strong anisotropy in the aquifer that prevents fast transport of local recharge to deeper units from which production occurs. Robust estimates of fluxes in the shallow portion of the aquifer immediately down gradient of LANL are important for contaminant transport simulations. Our model calculations show that these fluxes have decreased in the past 50 years by approximately 10% because of production in water supply wells. To explore the role of parameter uncertainty in flux prediction, a predictive analysis method was applied. Results showed that predicted flux through older basalts in the aquifer can vary by a factor of three because of uncertainty in aquifer properties and total recharge. We explored the impact of model parameter uncertainty on these results; however, the true uncertainty of our predictions, including the impact of possible conceptual model errors, is likely to be larger and is difficult to quantify.

Carbon sequestration

• Detecting and Characterizing Fluid Leakage Through Wellbore Flaws Using Fiber-Optic Distributed Acoustic Sensing (2022) — Ishtiaque Anwar et al. — 56th U.S. Rock Mechanics/Geomechanics Symposium
  Abstract

  ABSTRACT: A primary concern associated with the utilization of subsurface systems is that there may be pathways for fluid leakage from the resource storage, or disposal reservoir. Leakage of any fluid can contaminate groundwater, cause geo-environmental pollution, generate hazardous surface conditions, and potentially compromise the functionality of the subsurface system. A low-cost, innovative technique to detect and characterize fracture leakage that is functional over many years is needed for applications such as geothermal reservoirs, CO2 sequestration wells, deep borehole storage of nuclear waste, and strategic petroleum reserve caverns. In this experimental study, we investigate the use of fiber-optic distributed acoustic sensing (DAS) to measure dynamic strain changes caused by acoustic signals induced by fluid flow with an eventual goal of developing instrumentation and analytical techniques to detect and characterize the movement of fluids through leaky wellbores. In the first phase of the experiments reported here, we conducted fluid flow tests in a porous medium as an analog to a fracture filled with comminuted material. The measured effective permeability is then compared with the signals generated by the fiber-optic cable. The study indicated that acoustic signals generated from fluid flow through porous media could be effectively captured by the fiber-optic cable DAS technology. 1. INTRODUCTION Wellbores are used for gaining access to various subsurface systems such as underground fluid reserve (Miyazaki, 2009), CO2 sequestration (Watson and Bachu, 2008; Zhang and Bachu, 2011), geothermal energy development (Shadravan, Ghasemi, & Alfi, 2015), waste disposal, oil and gas exploration (Davies et al., 2014), etc. A primary concern associated with the utilization of subsurface systems is that there may be pathways for fluid leakage from the resource storage, or disposal reservoir through the wellbore flaws. Leakage of any fluid from a leaky wellbore can contaminate groundwater, cause geo-environmental pollution (Davies et al., 2014; Ingraffea, Wells, Santoro, & Shonkoff, 2014; Jackson, 2014), generate hazardous surface conditions, and potentially compromise the functionality of the subsurface system (Gasda, Celia, Wang, & Duguid, 2013). Researchers has identified different potential leakage pathways, including fractures in the cement or micro annuli from de-bonding at the cement-casing or cement-formation interface (Celia, Bachu, Nordbotten, Gasda, & Dahle, 2005; Theresa L Watson & Bachu, 2009) or the casing corrosion product (Anwar, Chojnicki, Bettin, Taha, & Stormont, 2019; Beltrán-Jiménez et al., 2021).
• Machine learning to discover mineral trapping signatures due to CO2 injection (2021) — Bulbul Ahmmed et al. — International Journal of Greenhouse Gas Control
• Nonlinear acoustics evaluation of CO2 exposed sandstone (2017) — James Bittner et al. — The Journal of the Acoustical Society of America
  Abstract

  In an effort to better understand the hygro-thermo-mechanical behavior of geologic CO2 reservoir material, we investigate the non-linear behavior of elastic wave propagation in Berea sandstone samples, which are used as a standard for reservoir rock formations. Nonlinear characterization methods, including resonant ultrasound spectroscopy (RUS), dynamic acousto-elasticity (DAET), and single-impact nonlinear resonance techniques, are applied to pristine, damaged (distributed microfractures), and CO2 injected Berea samples; conventional linear vibrational and wave propagation measurements are also applied to the samples. The results of these sensitive test methods are compared to reveal the characteristics of geologic reservoir materials that are most affected by varying microstructural and environmental conditions. An analysis of the work also leads to potential bases for test methods that could be deployed in the field in the future to monitor the condition of reservoir formations and lead to better understanding of CO2 injection-induced seismic events. This work is done within the framework of the GSCO2 center for geologic storage of CO2 from the U.S. department of Energy whose purpose is to better understand CO2 sequestration to make it safer and more efficient. As such the results obtained by elastic waves measurement will also be compared to other testing, providing an insight on the physical origin of the nonlinear behavior of geomaterials.
• Decision analysis for robust CO2 injection: Application of Bayesian-Information-Gap Decision Theory (2016) — Matthew Grasinger et al. — International Journal of Greenhouse Gas Control
• Interdisciplinary studies on the technical and economic feasibility of deep underground coal gasification with CO2 storage in bulgaria (2016) — Yong Sheng et al. — Mitigation and Adaptation Strategies for Global Change