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- North America > United States > California (1.00)
- Europe (1.00)
- Asia (1.00)
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- Geology > Structural Geology > Tectonics > Plate Tectonics (1.00)
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- Geophysics > Gravity Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Passive Seismic Surveying (0.92)
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- Government > Regional Government > North America Government > United States Government (1.00)
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- North America > United States > Nevada > Dixie Valley Field (0.99)
- North America > United States > California > Mayacamas Mountains > Geysers Field (0.99)
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- Information Technology > Modeling & Simulation (0.92)
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- Information Technology > Knowledge Management (0.83)
- Information Technology > Communications > Collaboration (0.83)
Detecting fractures and monitoring hydraulic fracturing processes at the first EGS Collab testbed using borehole DAS ambient noise
Li, David (Los Alamos National Laboratory) | Huang, Lianjie (Los Alamos National Laboratory) | Zheng, Yingcai (University of Houston) | Li, Yingping (University of Houston, BlueSkyDAS LLC) | Schoenball, Martin (Lawrence Berkeley National Lab) | Rodriguez-Tribaldos, Verรณnica (GFZ German Research Center for Geosciences) | Ajo-Franklin, Jonathan (Rice University) | Hopp, Chet (Lawrence Berkeley National Lab) | Johnson, Tim (Pacific Northwest National Laboratory) | Knox, Hunter (Pacific Northwest National Laboratory) | Blankenship, Doug (Sandia National Laboratories) | Dobson, Patrick (Lawrence Berkeley National Lab) | Kneafsey, Tim (Lawrence Berkeley National Lab) | Robertson, Michelle (Lawrence Berkeley National Lab)
ABSTRACT Enhanced geothermal systems (EGS) require cost-effective monitoring of fracture networks. We validate the capability of using borehole distributed acoustic sensing (DAS) ambient noise for fracture monitoring using core photos and core logs. The EGS Collab project has conducted 10ย m scale field experiments of hydraulic fracture stimulation using 50โ60ย m deep experimental wells at the Sanford Underground Research Facility (SURF) in Lead, South Dakota. The first EGS Collab testbed is located at 1616.67ย m (4850ย ft) depth at SURF and consists of one injection well, one production well, and six monitoring wells. All wells are drilled subhorizontally from an access tunnel called a drift. The project uses a single continuous fiber-optic cable installed sequentially in the six monitoring wells to record DAS data for monitoring hydraulic fracturing during stimulation. We analyze 60ย s time records of the borehole DAS ambient noise data and compute the noise root-mean-square (rms) amplitude on each channel (points along the fiber cable) to obtain DAS ambient noise rms amplitude depth profiles along the monitoring wellbore. Our noise rms amplitude profiles indicate amplitude peaks at distinct depths. We compare the DAS noise rms amplitude profiles with borehole core photos and core logs and find that the DAS noise rms amplitude peaks correspond to the locations of fractures or lithologic changes indicated in the core photos or core logs. We then compute the hourly DAS noise rms amplitude profiles in two monitoring wells during three stimulation cycles in 72ย h and find that the DAS noise rms amplitude profiles vary with time, indicating the fracture opening/growth or closing during the hydraulic stimulation. Our results demonstrate that borehole DAS passive ambient noise can be used to detect fractures and monitor fracturing processes in EGS reservoirs.
- North America > United States > Texas (0.47)
- North America > United States > New Mexico (0.28)
- North America > United States > California (0.28)
- Government > Regional Government > North America Government > United States Government (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Energy > Renewable > Geothermal > Geothermal Resource (0.87)
- Well Completion > Hydraulic Fracturing (1.00)
- Reservoir Description and Dynamics > Non-Traditional Resources > Geothermal resources (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
Detecting fractures and monitoring hydraulic fracturing processes at the first EGS Collab testbed using borehole DAS ambient noise
Li, David (Los Alamos National Laboratory) | Huang, Lianjie (Los Alamos National Laboratory) | Zheng, Yingcai (University of Houston) | Li, Yingping (University of Houston, BlueSkyDAS LLC) | Schoenball, Martin (Lawrence Berkeley National Lab) | Rodriguez-Tribaldos, Verรณnica (GFZ German Research Center for Geosciences) | Ajo-Franklin, Jonathan (Rice University) | Hopp, Chet (Lawrence Berkeley National Lab) | Johnson, Tim (Pacific Northwest National Laboratory) | Knox, Hunter (Pacific Northwest National Laboratory) | Blankenship, Doug (Sandia National Laboratories) | Dobson, Patrick (Lawrence Berkeley National Lab) | Kneafsey, Tim (Lawrence Berkeley National Lab) | Robertson, Michelle (Lawrence Berkeley National Lab)
ABSTRACT Enhanced geothermal systems (EGS) require cost-effective monitoring of fracture networks. We validate the capability of using borehole distributed acoustic sensing (DAS) ambient noise for fracture monitoring using core photos and core logs. The EGS Collab project has conducted 10ย m scale field experiments of hydraulic fracture stimulation using 50โ60ย m deep experimental wells at the Sanford Underground Research Facility (SURF) in Lead, South Dakota. The first EGS Collab testbed is located at 1616.67ย m (4850ย ft) depth at SURF and consists of one injection well, one production well, and six monitoring wells. All wells are drilled subhorizontally from an access tunnel called a drift. The project uses a single continuous fiber-optic cable installed sequentially in the six monitoring wells to record DAS data for monitoring hydraulic fracturing during stimulation. We analyze 60ย s time records of the borehole DAS ambient noise data and compute the noise root-mean-square (rms) amplitude on each channel (points along the fiber cable) to obtain DAS ambient noise rms amplitude depth profiles along the monitoring wellbore. Our noise rms amplitude profiles indicate amplitude peaks at distinct depths. We compare the DAS noise rms amplitude profiles with borehole core photos and core logs and find that the DAS noise rms amplitude peaks correspond to the locations of fractures or lithologic changes indicated in the core photos or core logs. We then compute the hourly DAS noise rms amplitude profiles in two monitoring wells during three stimulation cycles in 72ย h and find that the DAS noise rms amplitude profiles vary with time, indicating the fracture opening/growth or closing during the hydraulic stimulation. Our results demonstrate that borehole DAS passive ambient noise can be used to detect fractures and monitor fracturing processes in EGS reservoirs.
- North America > United States > Texas (0.47)
- North America > United States > New Mexico (0.28)
- North America > United States > California (0.28)
- Government > Regional Government > North America Government > United States Government (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Energy > Renewable > Geothermal > Geothermal Resource (0.87)
- Well Completion > Hydraulic Fracturing (1.00)
- Reservoir Description and Dynamics > Non-Traditional Resources > Geothermal resources (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
ABSTRACT Although trial-and-error modeling may give some level of interpretation about the subsurface while sacrificing certainty, it is a viable alternative for precise 3D interpretation of real ground-airborne frequency-domain electromagnetic (GAFEM) data. In this sense, a semiautomatic trial-and-error modeling approach is developed. Specifically, we first develop the 3D GAFEM forward-modeling code. Its accuracy is demonstrated using a 3D synthetic model with topography and a tilted anomalous body. Second, an initial model is established based on known geologic constraints. Then, the code is conducted repeatedly, and the parameters of the model are renewed semiautomatically based on a predefined geometry-resistivity combination list. Finally, the model that can achieve the minimum error between the computed response and the collected GAFEM data is selected as the final model. Furthermore, we apply the presented semiautomatic trial-and-error modeling approach to the geothermal resources survey at the Yishu Faulting Basin, China. The purpose of the survey is to interpret the resistivity structure of the subsurface and evaluate the potential development of the geothermal resources in the survey area. As a result, the final model obtained by the trial-and-error modeling, which is constrained by the known geologic information and subsurface geoelectric structures inferred from 2D models inverted by the magnetotelluric and controlled-source audio-frequency magnetotelluric data measured at the same location, indicates the existence of the geothermal resources. This indication is proven by the drilling result of a well site located on the survey line. To further verify the reliability, a comparative analysis is conducted between the model obtained by the trial-and-error modeling and the models obtained by 3D inversion of a GAFEM data set and apparent resistivity calculation using the same data. The results indicate that different approaches can achieve similar subsurface geometry and resistivity distribution of the faulting basin structure.
- Asia > China (0.85)
- North America > Canada > Newfoundland and Labrador > Newfoundland (0.28)
- Energy > Oil & Gas > Upstream (1.00)
- Energy > Renewable > Geothermal > Geothermal Resource (0.65)
- North America > Canada > Saskatchewan > Athabasca Basin (0.99)
- North America > Canada > Alberta > Athabasca Basin (0.99)
- North America > Canada > Newfoundland and Labrador > Newfoundland > North Atlantic Ocean > Atlantic Margin Basin > Grand Banks Basin > Flemish Pass Basin (0.95)
- Asia > China > Shandong > Yishu Basin (0.95)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Non-Traditional Resources > Geothermal resources (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning (0.67)
- Information Technology > Modeling & Simulation (0.67)
Modeling and sparsity-promoting separation of wind turbine noise in common-shot gathers
Hu, Yanglijiang (Xiโan Jiaotong University) | Wang, Xiaokai (Xiโan Jiaotong University) | Hou, Qinlong (Xiโan Jiaotong University) | Liu, Dawei (Xiโan Jiaotong University, Purdue University) | Shang, Xinmin (Sinopec Shengli Oilfield) | Zhang, Meng (Sinopec Shengli Oilfield) | Chen, Wenchao (Xiโan Jiaotong University)
ABSTRACT In land seismic acquisition, the quality of common-shot gathers is severely degraded by wind turbine noise (WTN) when wind turbines are operating continuously in survey areas. The high-amplitude WTN overlaps or even completely submerges the body and surface waves (signals). Through time-space and frequency analysis, three main features of the WTN are observed: (1)ย it is periodic with nearly constant frequencies over time, (2)ย it is coherent but exhibits different apparent velocities in space, and (3)ย it has relatively narrow bands with varying central frequencies. The first feature enables WTN to distort signals from shallow to deep, whereas the latter two features make traditional methods that separate noise and signals based on velocity and frequency differences less effective. To suppress the WTN, we first analyze its formation and propagation mechanism and then develop a WTN simulation model to validate the presented mechanism. Based on our analysis of WTN and signals, we consider common-shot gathers as the linear superpositions of periodic WTN and relatively broadband signals (referred to as low-oscillatory signals). This additive mixture aligns with the feasibility premise of morphological component analysis (MCA). Finally, based on MCA theory, we develop a sparsity-promoting separation method to suppress WTN in common-shot gathers. To implement our separation method, we construct two dictionaries using the tunable Q-factor wavelet transform (TQWT) and the discrete cosine transform (DCT). TQWT and DCT can sparsely represent the oscillating waves (signals) and periodic waves (WTN), respectively. This work contributes to the existing knowledge of WTN separation by modeling the periodicity of WTN and the low-oscillatory behavior of a signal, rather than relying on velocity or frequency differences. Our method is tested on synthetic and field data, and both tests demonstrate its effectiveness in separating WTN and preserving signals.
- Asia > China (0.68)
- North America > United States (0.46)
- Information Technology > Data Science > Data Quality > Data Transformation (1.00)
- Information Technology > Artificial Intelligence > Machine Learning (0.93)
Detecting fractures and monitoring hydraulic fracturing processes at the first enhanced geothermal system Collab testbed using borehole distributed acoustic sensing ambient noise
Li, David (Los Alamos National Laboratory) | Huang, Lianjie (Los Alamos National Laboratory) | Zheng, Yingcai (University of Houston) | Li, Yingping (University of Houston, BlueSkyDAS LLC) | Schoenball, Martin (GFZ German Research Center for Geosciences, Lawrence Berkeley National Lab) | Rodriguez-Tribaldos, Verรณnica (GFZ German Research Center for Geosciences, Lawrence Berkeley National Lab) | Ajo-Franklin, Jonathan (Rice University) | Hopp, Chet (GFZ German Research Center for Geosciences, Lawrence Berkeley National Lab) | Johnson, Tim (Pacific Northwest National Laboratory) | Knox, Hunter (Pacific Northwest National Laboratory) | Blankenship, Doug (Sandia National Laboratories) | Dobson, Patrick (GFZ German Research Center for Geosciences, Lawrence Berkeley National Lab) | Kneafsey, Tim (GFZ German Research Center for Geosciences, Lawrence Berkeley National Lab) | Robertson, Michelle (GFZ German Research Center for Geosciences, Lawrence Berkeley National Lab)
ABSTRACT Enhanced geothermal systems (EGS) require cost-effective monitoring of fracture networks. We validate the capability of using borehole distributed acoustic sensing (DAS) ambient noise for fracture monitoring using core photos and core logs. The EGS Collab project has conducted 10ย m scale field experiments of hydraulic fracture stimulation using 50โ60ย m deep experimental wells at the Sanford Underground Research Facility (SURF) in Lead, South Dakota. The first EGS Collab testbed is located at 1616.67ย m (4850ย ft) depth at SURF and consists of one injection well, one production well, and six monitoring wells. All wells are drilled subhorizontally from an access tunnel called a drift. The project uses a single continuous fiber-optic cable installed sequentially in the six monitoring wells to record DAS data for monitoring hydraulic fracturing during stimulation. We analyze 60ย s time records of the borehole DAS ambient noise data and compute the noise root-mean-square (rms) amplitude on each channel (points along the fiber cable) to obtain DAS ambient noise rms amplitude depth profiles along the monitoring wellbore. Our noise rms amplitude profiles indicate amplitude peaks at distinct depths. We compare the DAS noise rms amplitude profiles with borehole core photos and core logs and find that the DAS noise rms amplitude peaks correspond to the locations of fractures or lithologic changes indicated in the core photos or core logs. We then compute the hourly DAS noise rms amplitude profiles in two monitoring wells during three stimulation cycles in 72ย h and find that the DAS noise rms amplitude profiles vary with time, indicating the fracture opening/growth or closing during the hydraulic stimulation. Our results demonstrate that borehole DAS passive ambient noise can be used to detect fractures and monitor fracturing processes in EGS reservoirs.
- North America > United States > Texas (0.47)
- North America > United States > New Mexico (0.28)
- Government > Regional Government > North America Government > United States Government (1.00)
- Energy > Renewable > Geothermal > Geothermal Resource (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Energy > Renewable > Geothermal > Geothermal Resource for Power Generation > Enhanced Geothermal System (0.60)
- Well Completion > Hydraulic Fracturing (1.00)
- Reservoir Description and Dynamics > Non-Traditional Resources > Geothermal resources (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
- Information Technology > Sensing and Signal Processing (0.71)
- Information Technology > Communications > Networks > Sensor Networks (0.71)
ABSTRACT The impermeable caprock within a geothermal system serves the purpose of effectively sealing the reservoir, resulting in an elevation of both pressure and temperature. This sealing mechanism plays a crucial role in the long-term preservation of the system while also contributing to its overall sustainability. Caprock failure subsequent to seismic activity near a geothermal site can lead to the permeation of the caprock structure, resulting in diminished sealing capabilities and a decline in the reservoir temperature. In addition, this process alters the geochemical composition of the water by creating a hydrothermal mixture zone that disrupts the resistivity structure of the caprock, which is typically characterized by low resistivity values due to its substantial clay content and mineral alteration. This study focuses on investigating the integrity of the caprock at the รanakkale-Tuzla geothermal field in Turkey, where water temperature and conductivity were reported to have decreased after a moderate-magnitude earthquake and subsequent aftershocks. For this purpose, we have performed magnetotelluric (MT) measurements, a method known for its sensitivity to geochemical reactions. These measurements are conducted along two parallel profiles that encompassed a total of 32 stations. The particle swarm optimization (PSO) technique is used to overcome the subtle difficulties associated with conventional inversion methods in modeling the MT data of complex formations. This is the first study that overcomes the difficulties emanating from the caprock failure by modeling MT data using PSO. Our modeling approach produces resistivity images that we interpreted as the signature of the failed caprock following the earthquake at the study site. Our results appear to confirm the documented geochemical changes, or hydrothermal mixture zone around the caprock structure.
- Geology > Structural Geology > Tectonics > Plate Tectonics > Earthquake (1.00)
- Geology > Petroleum Play Type (1.00)
- Geology > Geological Subdiscipline (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Energy > Renewable > Geothermal > Geothermal Resource (0.49)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Evolutionary Systems (1.00)
ABSTRACT Although trial-and-error modeling may give some level of interpretation about the subsurface while sacrificing certainty, it is a viable alternative for precise 3D interpretation of real ground-airborne frequency-domain electromagnetic (GAFEM) data. In this sense, a semiautomatic trial-and-error modeling approach is developed. Specifically, we first develop the 3D GAFEM forward-modeling code. Its accuracy is demonstrated using a 3D synthetic model with topography and a tilted anomalous body. Second, an initial model is established based on known geologic constraints. Then, the code is conducted repeatedly, and the parameters of the model are renewed semiautomatically based on a predefined geometry-resistivity combination list. Finally, the model that can achieve the minimum error between the computed response and the collected GAFEM data is selected as the final model. Furthermore, we apply the presented semiautomatic trial-and-error modeling approach to the geothermal resources survey at the Yishu Faulting Basin, China. The purpose of the survey is to interpret the resistivity structure of the subsurface and evaluate the potential development of the geothermal resources in the survey area. As a result, the final model obtained by the trial-and-error modeling, which is constrained by the known geologic information and subsurface geoelectric structures inferred from 2D models inverted by the magnetotelluric and controlled-source audio-frequency magnetotelluric data measured at the same location, indicates the existence of the geothermal resources. This indication is proven by the drilling result of a well site located on the survey line. To further verify the reliability, a comparative analysis is conducted between the model obtained by the trial-and-error modeling and the models obtained by 3D inversion of a GAFEM data set and apparent resistivity calculation using the same data. The results indicate that different approaches can achieve similar subsurface geometry and resistivity distribution of the faulting basin structure.
- Asia > China (0.85)
- North America > Canada > Newfoundland and Labrador > Newfoundland (0.28)
- Energy > Oil & Gas > Upstream (1.00)
- Energy > Renewable > Geothermal > Geothermal Resource (0.65)
- North America > Canada > Saskatchewan > Athabasca Basin (0.99)
- North America > Canada > Alberta > Athabasca Basin (0.99)
- North America > Canada > Newfoundland and Labrador > Newfoundland > North Atlantic Ocean > Atlantic Margin Basin > Grand Banks Basin > Flemish Pass Basin (0.95)
- Asia > China > Shandong > Yishu Basin (0.95)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Non-Traditional Resources > Geothermal resources (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning (0.67)
- Information Technology > Modeling & Simulation (0.67)
- North America > United States > Texas (1.00)
- Europe (0.93)
- Research Report > New Finding (0.93)
- Overview (0.88)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.68)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.47)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.93)