Thermal Experiments for Fractured Rock Characterization: Theoretical Analysis and Inverse Modeling

Zhou, Zitong; Roubinet, Delphine; Tartakovsky, Daniel M.

doi:10.1029/2021wr030608

Cited by 21 publications

(2 citation statements)

References 62 publications

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“…Future work will also focus on improving the reliability of the simplified transport models by considering additional processes, such as dispersion. For this matter, various recent particle-based methods (Noetinger et al, 2016;Gouze et al, 2020;Roubinet et al, 2022) and machine learning techniques (Kang et al, 2021;Zhou et al, 2021) could be considered in order to consider more realistic configurations while keeping the low computational cost of the forward models.…”

Section: Discussionmentioning

confidence: 99%

Particle tracking as a vulnerability assessment tool for drinking water production

et al. 2022

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The simulation of concentration values and use of such data for history-matching is often impeded by the computation time of groundwater transport models based on the resolution of the advection-dispersion equation. This is unfortunate because such data are often rich in information and the prediction of concentration values is of great interest for decision making. Particle tracking can be used as an efficient alternative under a series of simplifying assumptions, which are often reasonable at groundwater sinks (wells and drains). Our approach consists of seeding particles around a sink and tracking particles backward, up to the source boundary condition, such as a contaminated stream. This particle tracking approach allows the use of parameter estimation and optimization methods requiring numerous model calls. We present a Python module facilitating the pre- and post-processing operations of a modeling workflow based on the widely used USGS MODFLOW6 and MODPATH7 programs. The module handles particle seeding around the sink and estimation of the mixing ratio of water withdrawn from the sink. This ratio is computed with a mixing law from the particle endpoints, accounting for particle velocities and mixing in the source model cells. We investigate the best practice to obtain robust derivatives with this approach, which is a benefit for the screening methods based on linear analysis. We illustrate the interest of the approach with a real world case study, considering a drinking water well field vulnerable to a contaminated stream. The configuration is typical of many other drinking water production sites. The modeling workflow is fully script-based to make the approach easily reproducible in similar cases.

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Section: Discussionmentioning

confidence: 99%

Particle tracking as a vulnerability assessment tool for drinking water production

et al. 2022

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“…Characterizing subsurface and groundwater flow is performed by collecting data from several techniques, including hydraulic tests, thermal experiments and electrical measurements (e.g., [1][2][3][4]), and inverting this data with the most appropriate inversion strategies (e.g., [5][6][7][8]). Among all these methods, the most-used method is the pumping test, which gives global estimates of the hydraulic properties of the system and provide information on its transient behavior when interpreting the data with transient-flow solutions (e.g., [9][10][11][12]).…”

Section: Introductionmentioning

confidence: 99%

Models and Interpretation Methods for Single-Hole Flowmeter Experiments

Lods

Roubinet

2023

Water

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Subsurface and groundwater flow characterization is of great importance for various environmental applications, such as the dispersion of contaminants and their remediation. For single-hole flowmeter measurements, key characteristics, such as wellbore storage, skin factor heterogeneities, and variable pumping and aquifer flow rates, have a strong impact on the system characterization, whereas they are not fully considered in existing models and interpretation methods. In this study, we develop a new semi-analytical solution that considers all these characteristics in a physics-based consistent manner. We also present two new interpretation methods, the Double Flowmeter Test with Transient Flow rate (DFTTF) and the Transient Flow rate Flowmeter Test (TFFT), for interpreting data collected during single and multiple pumping tests, respectively. These solution and methods are used as follows. (i) The impact of wellbore storage, transient pumping rate, and property heterogeneities on the interpretation of data collected during single pumping tests are studied over 49 two-aquifer cases. (ii) The effect of the skin factor heterogeneity on transmissivity and storativity estimates, as well as the variability range of the (non-unique) corresponding solutions, are analyzed for the interpretation of multiple-pumping experiments. The results presented in this work show the importance of the various properties and processes that are considered, and the need for the new models and methods that are provided.

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Application of machine learning and deep learning in geothermal resource development: Trends and perspectives

Al‐Fakih,

Abdulraheem,

Kaka

2024

Deep Underground Science and Engineering

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This study delves into the latest advancements in machine learning and deep learning applications in geothermal resource development, extending the analysis up to 2024. It focuses on artificial intelligence's transformative role in the geothermal industry, analyzing recent literature from Scopus and Google Scholar to identify emerging trends, challenges, and future opportunities. The results reveal a marked increase in artificial intelligence (AI) applications, particularly in reservoir engineering, with significant advancements observed post‐2019. This study highlights AI's potential in enhancing drilling and exploration, emphasizing the integration of detailed case studies and practical applications. It also underscores the importance of ongoing research and tailored AI applications, in light of the rapid technological advancements and future trends in the field.

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Thermal Experiments for Fractured Rock Characterization: Theoretical Analysis and Inverse Modeling

Cited by 21 publications

References 62 publications

Particle tracking as a vulnerability assessment tool for drinking water production

Particle tracking as a vulnerability assessment tool for drinking water production

Models and Interpretation Methods for Single-Hole Flowmeter Experiments

Application of machine learning and deep learning in geothermal resource development: Trends and perspectives

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