scholarly journals Study of Tunnel-Face to Borehole ERI (TBERI) Measurement Configurations and Its Optimization

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wei Zhou ◽  
Lichao Nie ◽  
Yongheng Zhang ◽  
Yonghao Pang ◽  
Zhao Dong ◽  
...  
Keyword(s):  
Electrical Resistivity ◽  
Electrical Resistivity Tomography ◽  
Effective Means ◽  
Target Area ◽  
Optimization Strategy ◽  
Tunnel Face ◽  
Resistivity Tomography ◽  
Redundant Data ◽  
Weighted Optimization ◽  
Pole Configuration

Most of the existing electrical-resistivity-based ahead prospecting methods in tunnel use only the tunnel cavity and tunnel face space to locate the water-bearing structures in front of the tunnel. However, due to the limitation of the narrow available space for arranging electrodes in tunnel, this kind of method is difficult to achieve more accurate image for water-bearing structures. The cross-hole electrical resistivity tomography (CHERT) and borehole-to-surface electrical resistivity tomography (BSERT) methods using borehole space have been proved effective means to achieve better images of deep anomalies on the surface. In this paper, the tunnel-face and borehole ERI (TBERI) method in tunnels was studied. To less affect the construction progress, the pole-pole configuration using a single borehole was studied in this paper. Moreover, the configuration is optimized based on the block weighted CR optimization strategy. After considering the data combination, an effective measurement configuration suitable for TBERI detection was formed. To accelerate calculation, some redundant data are removed from the obtained data after proposed block weighted optimization is conducted. By adopting the proposed configuration, the abnormal objects in the target area in the inversion are more accurate. The effectiveness of proposed configuration is verified by numerical simulation.

scholarly journals Comparison between electrical resistivity tomography and tunnel seismic prediction 303 methods for detecting the water zone ahead of the tunnel face: A case study

2020 ◽  
Vol 12 (1) ◽  
pp. 1094-1104
Author(s):  
Nima Dastanboo ◽  
Xiao-Qing Li ◽  
Hamed Gharibdoost
Keyword(s):  
Electrical Resistivity ◽  
Electrical Resistivity Tomography ◽  
Geological Structure ◽  
Rock Properties ◽  
Electrical Tomography ◽  
Tunnel Face ◽  
Resistivity Tomography ◽  
Geological Conditions ◽  
Seismic Prediction

AbstractIn deep tunnels with hydro-geological conditions, it is paramount to investigate the geological structure of the region before excavating a tunnel; otherwise, unanticipated accidents may cause serious damage and delay the project. The purpose of this study is to investigate the geological properties ahead of a tunnel face using electrical resistivity tomography (ERT) and tunnel seismic prediction (TSP) methods. During construction of the Nosoud Tunnel located in western Iran, ERT and TSP 303 methods were employed to predict geological conditions ahead of the tunnel face. In this article, the results of applying these methods are discussed. In this case, we have compared the results of the ERT method with those of the TSP 303 method. This work utilizes seismic methods and electrical tomography as two geophysical techniques are able to detect rock properties ahead of a tunnel face. This study shows that although the results of these two methods are in good agreement with each other, the results of TSP 303 are more accurate and higher quality. Also, we believe that using another geophysical method, in addition to TSP 303, could be helpful in making decisions in support of excavation, especially in complicated geological conditions.

scholarly journals Development of an Electrical Resistivity Tomography Monitoring Concept for the Svelvik CO<sub>2</sub> Field Lab, Norway

2020 ◽  
Vol 54 ◽  
pp. 41-53
Author(s):  
Tobias Raab ◽  
Wolfgang Weinzierl ◽  
Bernd Wiese ◽  
Dennis Rippe ◽  
Cornelia Schmidt-Hattenberger
Keyword(s):  
Electrical Resistivity ◽  
Electrical Resistivity Tomography ◽  
Co2 Storage ◽  
Pressure Effects ◽  
Co2 Injection ◽  
Target Area ◽  
Electrode Arrays ◽  
Resistivity Tomography ◽  
Monitoring Well ◽  
Co2 Plume

Abstract. Within the ERA-NET co-funded ACT project Pre-ACT (Pressure control and conformance management for safe and efficient CO2 storage – Accelerating CCS Technologies), a monitoring concept was established to distinguish between CO2 induced saturation and pore pressure effects. As part of this monitoring concept, geoelectrical cross-hole surveys have been designed and conducted at the Svelvik CO2 Field Lab, located on the Svelvik ridge at the outlet of the Drammensfjord in Norway. The Svelvik CO2 Field Lab has been established in summer 2019, and comprises four newly drilled, 100 m deep monitoring wells, surrounding an existing well used for water and CO2 injection. Each monitoring well was equipped with modern sensing systems including five types of fiber-optic cables, conventional- and capillary pressure monitoring systems, as well as electrode arrays for Electrical Resistivity Tomography (ERT) surveys. With a total of 64 electrodes (16 each per monitoring well), a large number of measurement configurations for the ERT imaging is possible, requiring the performance of the tomography to be investigated beforehand by numerical studies. We combine the free and open-source geophysical modeling library pyGIMLi with Eclipse reservoir modeling to simulate the expected behavior of all cross-well electrode configurations during the CO2 injection experiment. Simulated CO2 saturations are converted to changes in electrical resistivity using Archie's Law. Using a finely meshed resistivity model, we simulate the response of all possible measurement configurations, where always two electrodes are located in two corresponding wells. We select suitable sets of configurations based on different criteria, i.e. the ratio between the measured change in apparent resistivity in relation to the geometric factor and the maximum sensitivity in the target area. The individually selected measurement configurations are tested by inverting the synthetic ERT data on a second coarser mesh. The pre-experimental, numerical results show adequate resolution of the CO2 plume. Since less CO2 was injected during the field experiment than originally modeled, we perform post-experimental tests of the selected configurations for their potential to image the CO2 plume using revised reservoir models and injection volumes. These tests show that detecting the small amount of injected CO2 will likely not be feasible.

Electrical Resistivity Tomography Concept for CO2 Injection Monitoring at the Svelvik CO2 Field Lab

2020 ◽  
Author(s):  
Tobias Raab ◽  
Wolfgang Weinzierl ◽  
Dennis Rippe ◽  
Bernd Wiese ◽  
Cornelia Schmidt-Hattenberger
Keyword(s):  
Electrical Resistivity ◽  
Apparent Resistivity ◽  
Electrical Resistivity Tomography ◽  
Co2 Storage ◽  
Carbon Capture And Storage ◽  
Integrated Approach ◽  
Reservoir Modeling ◽  
Co2 Injection ◽  
Target Area ◽  
Resistivity Tomography

&lt;p&gt;Carbon Capture and Storage technology is considered to be able to contribute to a carbon neutral society and is again receiving increased attention in the efforts to reduce CO&lt;sub&gt;2 &lt;/sub&gt;emissions. To ensure safe operation of such CO&lt;sub&gt;2&lt;/sub&gt; storage projects, reliable monitoring technologies are required. Due to the generally high electrical resistivity contrast between CO&lt;sub&gt;2&lt;/sub&gt; and formation water, Electrical Resistivity Tomography (ERT) can be considered one of the most effective geophysical techniques in the monitoring of CO&lt;sub&gt;2&lt;/sub&gt; migration in the subsurface.&lt;/p&gt;&lt;p&gt;Within the ERA-NET co-funded ACT project Pre-ACT (Pressure control and conformance management for safe and efficient CO2 storage - Accelerating CCS Technologies) a CO&lt;sub&gt;2&lt;/sub&gt; injection and monitoring experiment was planned at the Svelvik CO&lt;sub&gt;2&lt;/sub&gt; Field Lab, located on the Svelvik ridge at the outlet of the Drammensfjord in Norway. The Svelvik field lab consists of four 100 m deep monitoring wells, drilled in July 2019, surrounding an existing well used for brine and CO&lt;sub&gt;2&lt;/sub&gt; injection. Each monitoring well is equipped with modern sensing systems including five types of fiber-optic cables, conventional and capillary pressure monitoring systems, as well as 16 ERT electrodes with a spacing of five meters.&lt;/p&gt;&lt;p&gt;With 64 installed electrodes, a large number of measurement configurations is possible. We combine the free and open-source geophysical modeling library pyGIMLI with ECLIPSE reservoir modeling to simulate the expected behavior of all cross-well electrode configurations during a CO&lt;sub&gt;2&lt;/sub&gt; injection experiment. Simulated CO&lt;sub&gt;2 &lt;/sub&gt;saturations are converted to changes in apparent resistivity using Archie's law. Different considerations have to be made to select a suitable set of electrode configurations, i.e. not too large geometric factors, maximum response to the predicted change, as well as sensitivity in the target area. We select sets of configurations based on different criteria, i.e. the ratio between the measured change in resistivity in relation to the geometric factor, the maximum change in apparent resistivity, and maximum sensitivity in the target area. The individually selected measurement schedules are tested by inverting them with different assumed data errors. The numerical results show adequate resolution of the CO&lt;sub&gt;2&lt;/sub&gt; plume.&lt;/p&gt;&lt;p&gt;The CO&lt;sub&gt;2&lt;/sub&gt; injection took place between 27th October 2019 and 5th November 2019. Approximately two metric tonnes of CO&lt;sub&gt;2&lt;/sub&gt; were injected in 65 m depth. Preliminary field results indicate a considerably lower response than predicted by our model. These discrepancies can potentially be explained by oversimplified simulations as well as operational uncertainties. Results from baseline and repeat surveys can therefore support an integrated approach towards a revised static and dynamic model for the test site.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Acknowledgements:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;This work was produced within the SINTEF-coordinated Pre-ACT project (Project No. 271497) funded by RCN (Norway), Gassnova (Norway), BEIS (UK), RVO (Netherlands), and BMWi (Germany) and co-funded by the European Commission under the Horizon 2020 programme, ACT Grant Agreement No 691712. We also acknowledge industry partners Total, Equinor, Shell, TAQA.&lt;/p&gt;&lt;p&gt;Finally, we thank the SINTEF-owned Svelvik CO&lt;sub&gt;2&lt;/sub&gt; Field Lab (funded by ECCSEL through RCN, with additional support from Pre-ACT and SINTEF) for assistance during installations and for financial support.&lt;/p&gt;

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