BUSCADOR RECOLECTA

This file contains data about wormhole and viscous finger growth. The contents of the zip file are: DissolutionCapacity.dat: Dissolution capacity versus Péclet and Damköhler numbers. Competition-ViscFing-Da0.dat: Comparison of competition model with viscous fingering data. Competition-ViscFing-Da32e-2.dat: Comparison of competition model plus chemistry with viscous fingering data. Competition-Wh-Da06.dat: Comparison of competition model with wormhole data. Competition-Wh-Da0.dat: Comparison of competition model without chemistry with wormhole data ViscFing-versus-depth.dat: Number of viscous fingers versus depth for different number of initial fingers. Wormholes-versus-depth.dat: Number of wormholes depth for different Damköhler x Tau numbers., Peer reviewed

The corresponding 2.5D electrical forward and inverse problem is solved on an unstructured mesh with tetrahedral elements using BERT (Boundless Electrical Resistivity Tomography) (Rucker et al. 2006, Gunther et al. 2006) and pyGIMLi (Generalized Inversion and Modeling Library) (Rucker et al. 2017)., Surface electrical resistivity tomography (ERT) is a widely used tool to study seawater intrusion (SWI). It is noninvasive and offers a high spatial coverage at a low cost, but it is strongly affected by decreasing resolution with depth. We conjecture that the use of CHERT (cross-hole ERT) can partly overcome these resolution limitations since the electrodes are placed at depth, which implies that the model resolution does not decrease in the zone of interest. The objective of this study is to evaluate the CHERT for imaging the SWI and monitoring its dynamics at the Argentona site, a well-instrumented field site of a coastal alluvial aquifer located 40 km NE of Barcelona. To do so, we installed permanent electrodes around boreholes attached to the PVC pipes to perform time-lapse monitoring of the SWI on a transect perpendicular to the coastline. After two years of monitoring, we observe variability of SWI at different time scales: (1) natural seasonal variations and aquifer salinization that we attribute to long-term drought and (2) short-term fluctuations due to sea storms or flooding in the nearby stream during heavy rain events. The spatial imaging of bulk electrical conductivity allows us to explain non-trivial salinity profiles in open boreholes (step-wise profiles really reflect the presence of fresh water at depth). By comparing CHERT results with traditional in situ measurements such as electrical conductivity of water samples and bulk electrical conductivity from induction logs, we conclude that CHERT is a reliable and cost-effective imaging tool for monitoring SWI dynamics., This work was funded by the project CGL2016-77122-C2-1-R/2-R of the Spanish Government.We would like to thank SIMMAR (Serveis Integrals de Manteniment del Maresme) and the Consell Comarcal del Maresme in the construction of the research site. This project also received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 480 Agreement No 722028. Author Albert Folch is a “Serra-Húnter Fellow”., Name of raw data files: chert_argentona_1.txt chert_argentona_2.txt chert_argentona_3.txt chert_argentona_4.txt chert_argentona_5.txt chert_argentona_6.txt chert_argentona_7.txt chert_argentona_8.txt chert_argentona_9.txt chert_argentona_10.txt chert_argentona_11.txt chert_argentona_12.txt chert_argentona_13.txt chert_argentona_14.txt chert_argentona_15.txt chert_argentona_16.txt chert_plus_surfaceert_argentona.txt Content of raw data files: List of electrodes X and Z positions (with topography), and list of quadripoles (a, b, m, n) with measured resistance (r), error (err), geometric factor (k), and apparent resistivity (rhoa). This files are formatted to go through the BERT program for 2.5D ERT modeling and inversion. The “chert_plus_surfaceert_argentona.txt” raw file combines cross-hole and surface ERT data from Sep 8th, 2015. Name of inverse data files: chert_argentona_sigma_model_1.vector chert_argentona_ sigma_model_2. vector chert_argentona_ sigma_model_3. vector chert_argentona_ sigma_model_4. vector chert_argentona_ sigma_model_5. vector chert_argentona_ sigma_model_6. vector chert_argentona_ sigma_model_7. vector chert_argentona_ sigma_model_8. vector chert_argentona_ sigma_model_9. vector chert_argentona_ sigma_model_10. vector chert_argentona_ sigma_model_11. vector chert_argentona_ sigma_model_12. vector chert_argentona_ sigma_model_13. vector chert_argentona_ sigma_model_14. vector chert_argentona_ sigma_model_15. vector chert_argentona_ sigma_model_16. Vector chert_plus_surfaceert_argentona.vector (Reference Model for Time-lapse Inversion), Peer reviewed

This dataset is composed of the input files of the numerical models for simulating Amposta fault stability during different phases of the project and poromechanical pressure changes induced by shear slip. The input files of each numerical simulation are included in a folder. The names of the folders and the description of the model are: - “1_Oil_production”: simulation of oil production and 7 years of post-production to quantify the changes in the Amposta fault stability - “2_Overpressure_plus_buoyancy_cushion”: simulation of the cushion gas injection considering both the effect of pore pressure increase and buoyancy to quantify the changes in the Amposta fault stability - “3_Overpressure_cushion”: simulation of the cushion gas injection considering only the effect of pore pressure increase (without gravity) to quantify the changes in the Amposta fault stability - “4_Buoyancy_cushion”: simulation of the cushion gas injection considering only the buoyancy of the gas to quantify the changes in the Amposta fault stability - “5_Buoyancy_full_capacity”: simulation of the gas storage at its maximum capacity considering only the buoyancy of the gas to quantify the changes in the Amposta fault stability - “6_Poromechanical_pressure_changes”: simulation of the pore pressure changes induced by shear slip and its effects in the short and long term., Each folder includes a file with the name of the folder and ended as “_gen.dat” that contains the input data of the model, including material properties, initial and boundary conditions and the time intervals. There is also a file ended as “_gri.dat” that includes the information on the mesh. The “root.dat” includes the name of the model. To run simulations, just execute the Code_Bright executable “Cb_vX_Y.exe” in a folder that contains the three input files and the executable, where X and Y denote the used version of the executable., Peer reviewed

A Heat Dissipation Test implies heating a conducting element within the saturated soil until its temperature increase reaches steady state while monitoring the temperature development of the heating element during heating and cooling phases. In this case, we used a single Fiber Optic (FO) cable to perform a Heat Dissipation Test, aiming to quantify groundwater flow. The FO cable is installed along the outer casing of a piezometer located in an unconsolidated shallow aquifer. The data presented are the maximum temperature reached each depth, the filtered temperature increment for the most representative depths, and the resulting values of thermal conductivity and groundwater flow based on the interpretation of the recorded data., A Heat Dissipation Test implies heating a conducting element within the saturated soil until its temperature increase reaches steady state while monitoring the temperature development of the heating element during heating and cooling phases. In this case, we used a single Fiber Optic (FO) cable to perform a Heat Dissipation Test, aiming to quantify groundwater flow. The FO cable is installed along the outer casing of a piezometer located in an unconsolidated shallow aquifer. The data presented are the maximum temperature reached each depth, the filtered temperature increment for the most representative depths, and the resulting values of thermal conductivity and groundwater flow based on the interpretation of the recorded data., Peer reviewed

(1) Five Fiels with Data obtained by computing numerical simulations. DSA results were performed in a linked code of TRACONF (Carrera et al. 1993) and CHEPROO (Bea et al. 2009). WMA results were performed by a self-made code based on TRACONF_CHEPROO original code. - Data in "FIG_4_half_domain_inj": SHEET 1 "u_data (fig 4b)": cross section of component at diferent node columns SHEET 2 "r_data (fig 4a)": cross section of reaction rates at diferent node columns - Data in "FIG_5a_CAL_equil_accur": Accuracy performance for WMA and DSA for CAL equilibrium case. Check the Numerical dispersion effect between models of just different time steps A3: Data case information A19: DSA data I19: WMA data - Data in "FIG_5b_CAL_kin_accur": Accuracy performance for WMA and DSA for CAL kinetic case. Check the Numerical dispersion effect between models of just different time steps Idem than previous - Data in "TABLE_3a_CAL_equil_cpu": Comparison between CPU performance between DSA and WMA algorithms on CAL EQUILIBRIUM case A4: CPU computed from numerical simulations A15: Obtain the constants of each step calculation A23: CPU calculated analiticaly (NOT shown in this versiona as a Figure) - Data in "TABLE_3b_CAL_kin_cpu": Comparison between CPU performance between DSA and WMA algorithms on CAL KINETIC case Idem than previous (2) Two files with computed models: - Data in "EXAMPLE_of_M": Easy 1D example of 5 nodes to understand the difference between classic (sheet "classic ADE solution") solutions and WMA solution (sheet "WMA solution") One can set the value (between 0 and 1) of B5 cell at first sheet "Data" to see how the matrix M looks (cells M3:Q7) - Data in "APPENDIX_C_RT_Pulse_2d_WMA": Comparison between analytical solution (De Simoni et al., 2005) and WMA results of 2D model after pulse injection of mass M, Five files with data obtained by computing numerical simulations and two files with computed numerical models, Peer reviewed

Data obtained by computing numerical simulations. RW results were performed by a self-made Python code. WMA results were performed by a self-made code modifying the original TRACONF (Carrera et al. 1993) linked with CHEPROO (Bea et al. 2009). MAWMA results were performed by a self-made application of framework platform KRATOS (Dadvand et al. 2010) - Data in "Fig 3": Comparison of acccuracy performances between RW, WMA and MAWMA methods. A1: Information data about the problem A8: Dissipation rate results A23: Dispersion results - Data in "Analytical_solution (Dentz and Carrera 2007)": Calculation of the analytic solution of aparent dispersion, Data obtained by computing numerical simulations. RW results were performed by a self-made Python code. WMA results were performed by a self-made code modifying the original TRACONF (Carrera et al. 1993) linked with CHEPROO (Bea et al. 2009). MAWMA results were performed by a self-made application of framework platform KRATOS (Dadvand et al. 2010), Peer reviewed

Description: Data obtained by computing numerical simulations. RW results were performed by a self-made Python code. MAWMA results were performed by a self-made application of framework platform KRATOS (Dadvand et al. 2010) - Data in "LNs1_graph": Comparison of acccuracy performances between RW, MAWMA methods with 3 different Diffusion coefficients (0, 0.01 and 0.2). We compare 5 different statistical parameters: 1st moment, deviation , variance (Fig 4b), Global mixing (Fig 4c) and mean (Fig 4a) - Data in "MARKOV_MODELS": Comparing the Markov models using M_p matrices calculated at 3 different times of the RW model. Also the Markov models transforming M_p into M_c and M_w., Peer reviewed

[Date of data collection] Feb. 2020 for field data and Jan. 2022- Jan. 2023 for numerical models., This dataset includes the input files of the numerical models used in the manuscript for simulating the hydraulic stimulation at the Bedretto lab. Each folder is named after the corresponding model in the manuscript, i.e., EP (Elastic Prescribed), EE (Elastic Embedded), and VE (Viscoplastic Embedded). In each folder, there is a file with the name of the folder ended as “_gen.dat” which contains the input data of the model, including material properties, initial and boundary conditions and the time intervals. There is also a file ended as “_gri.dat” that includes the information on the mesh. The file “root.dat” includes the name of the model. The file ended as “_bcf.dat” contains injection rate input., European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Program through the Starting Grant GEoREST (www.georest.eu), under grant agreement No. 801809. Swiss Federal Office of Energy within the framework of the ZoDrEx GEOTHERMICA project under contract SI/501720-01. European Union’s Horizon 2020 Research and Innovation Program through the Marie Sklowdowska-Curie Individual Fellowship ARMISTICE, under Grant Agreement No. 882733. The “ZoDrEx” project, which has been subsidized through the ERANET Cofund GEOTHERMICA (Project No. 731117), from the European Commission and the Spanish Ministry of Economy, Industry and Competitiveness (MINECO). IMEDEA is an accredited "Maria de Maeztu Excellence Unit" (Grant CEX2021-001198, funded by MCIN/AEI/10.13039/501100011033)., With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2021-001198)., 1-EP_bcf.dat 1-EP_gen.dat 1-EP_gri.dat root.dat 2-EE_bcf.dat 2-EE_gen.dat 2-EE_gri.dat root.dat 3-VE_bcf.dat 3-VE_gen.dat 3-VE_gri.dat root.dat 3-VE-Extended_bcf.dat 3-VE-Extended_gen.dat 3-VE-Extended_gri.dat root.dat, Peer reviewed

Any usage of field data should aquire permission from Geo-Energie Suisse., This dataset includes the input files of the numerical models used in the manuscript for simulating the hydraulic stimulation in the conceptual models as well as the Bedretto model with the executable file of the numerical code (CODE_BRIGHT). Each folder is named after the corresponding model in the manuscript described in the sections 3.1.1 and 3.2.1. In each folder, there is a file with the name of the folder ended as “_gen.dat” which contains the input data of the model, including material properties, initial and boundary conditions and the time intervals. There is also a file ended as “_gri.dat” that includes the information on the mesh. The file “root.dat” includes the name of the model. The file ended as “_bcf.dat” contains injection rate input for Bedretto model. To run the simulation, copy and paste Code_Bright executable file, i.e., “Cb_2021.exe”, in a folder that contains the input files and execute it., European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Program through the Starting Grant GEoREST (www.georest.eu), under grant agreement No. 801809. Swiss Federal Office of Energy within the framework of the ZoDrEx GEOTHERMICA project under contract SI/501720-01. European Union’s Horizon 2020 Research and Innovation Program through the Marie Sklowdowska-Curie Individual Fellowship ARMISTICE, under Grant Agreement No. 882733. The “ZoDrEx” project, which has been subsidized through the ERANET Cofund GEOTHERMICA (Project No. 731117), from the European Commission and the Spanish Ministry of Economy, Industry and Competitiveness (MINECO). EURAD, the European Joint Programme on Radioactive Waste Management through the project DONUT (Grant No 847593), Peer reviewed