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Collaborating Authors
fracture
During geophysical exploration, inpainting defective logging images caused by mismatches between logging tools and borehole sizes can affect fracture and hole identification, petrographic analysis and stratigraphic studies. However, existing methods do not describe stratigraphic continuity enough. Also, they ignore the completeness of characterization in terms of fractures, gravel structures, and fine-grained textures in the logging images. To address these issues, we propose a deep learning method for inpainting stratigraphic features. First, to enhance the continuity of image inpainting, we build a generative adversarial network (GAN) and train it on numerous natural images to extract relevant features that guide the recovery of continuity characteristics. Second, to ensure complete structural and textural features are found in geological formations, we introduce a feature-extraction-fusion module with a co-occurrence mechanism consisting of channel attention(CA) and self-attention(SA). CA improves texture effects by adaptively adjusting control parameters based on highly correlated prior features from electrical logging images. SA captures long-range contextual associations across pre-inpainted gaps to improve completeness in fractures and gravels structure representation. The proposed method has been tested on various borehole images demonstrating its reliability and robustness.
- Geology > Geological Subdiscipline > Stratigraphy (0.74)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.46)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (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 > Borehole imaging and wellbore seismic (1.00)
- (2 more...)
Heloise B. Lynn started working in reflection seismic in the oil/gas industry in 1975, processing seismic data at Texaco, in Houston, Texas. In 1978, she completed her MS in Exploration Geophysics, Stanford University, and in December, 1979, she completed her PhD in Geophysics, also at Stanford University, in (post-stack) depth migration and interpretation issues within migration algorithms. Lynn worked for Texaco, Amoco, BP, and then in 1984, she and her husband, Walt, formed Lynn Incorporated. Her consulting experience includes working in North America, Hungary, Qatar, Kuwait, Saudi Arabia, Pakistan, Australia, Thailand, China, and Japan. She specializes in the use of 3D multiazimuth and/or multicomponent data to obtain structure, lithology, porosity, pore fluids, in-situ stress, and aligned porosity (aka natural fractures).
- Asia (1.00)
- North America > United States > Texas > Harris County > Houston (0.55)
- Geology > Geological Subdiscipline > Geomechanics (0.91)
- Geology > Structural Geology > Tectonics > Plate Tectonics (0.47)
- Information Technology > Knowledge Management (0.86)
- Information Technology > Communications > Collaboration (0.76)
"Faults and fractures are not necessarily good or bad, but it's important to really understand them." Molly sheds light on the crucial role of imaging these hidden networks in understanding their impact on production, injection, and completions. With a clear message that faults and fractures are neither inherently good nor bad, this conversation challenges common misconceptions and emphasizes the importance of detailed imaging to gauge their significance.
Petroleum Engineering, University of Houston, 2. Metarock Laboratories, 3. Department of Earth and Atmospheric Sciences, University of Houston) 16:00-16:30 Break and Walk to Bizzell Museum 16:30-17:30 Tour: History of Science Collections, Bizzell Memorial Library, The University of Oklahoma 17:30-19:00 Networking Reception: Thurman J. White Forum Building
- Research Report > New Finding (0.93)
- Overview (0.68)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Mineral (0.72)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.68)
- (2 more...)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.93)
DAS microseismic reflection imaging for hydraulic fracture and fault lineament characterization
Ma, Yuanyuan (Rice University) | Ajo-Franklin, Jonathan (Rice University, Lawrence Berkeley National Laboratory) | Nayak, Avinash (Lawrence Berkeley National Laboratory) | Correa, Julia (Lawrence Berkeley National Laboratory) | Kerr, Erich (SM Energy)
This study presents a novel workflow designed for migrating reflected S-waves generated by microseismic events, as recorded by downhole Distributed Acoustic Sensing (DAS), to characterize hydraulic fractures in three dimensions. In contrast to existing fracture imaging techniques, which have encountered challenges in accurately representing fracture networks and often rely on simplified models, the proposed imaging technique does not assume that fractures are planar or in a pre-specified orientation. DAS seismic measurements benefit from the large aperture and dense spatial sampling enabled by the kilometers-long fiber and, therefore are able to capture a large number of strong reflections compared to traditional borehole geophones or accelerometers. We treat microseismic events as high-frequency sources and apply prestack Kirchhoff migration to each individual source after wavefield separation. Fracture imaging results for multiple selected events are then stacked to generate a 3D reflectivity volume, revealing subsurface fracture and fault networks in intricate detail. The high-resolution fracture images generated by the developed reflection migrating process illuminate the heart of the stimulated volume of the reservoir, a zone that is often challenging to access using conventional surface arrays or active sources. To validate the effectiveness of the proposed workflow, our study employs a dataset acquired during a multi-well project in the Eagle Ford Shale and Austin Chalk in South Texas. To assess the accuracy and reliability of the results, the reflection imaging output is integrated with both microseismicity distribution and strain measurements from low-frequency DAS for interpretation. The results of reflection imaging improve our understanding of fracture geometry including distal fractures that are away from the monitoring well, allow direct estimation of fracture height and length, and potentially signify the presence of pre-existing fluid-filled fault lineaments.
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.89)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Austin Chalk Formation (0.89)
- North America > United States > Texas > Sabinas - Rio Grande Basin > Eagle Ford Shale Formation (0.89)
- (9 more...)
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)
Research on focal mechanism of microseismic events and the regional stress during hydraulic fracturing at a shale play site in southwest China
Chen, Xin-Xing (Chengdu University of Technology) | Meng, Xiao-Bo (Chengdu University of Technology) | Chen, Hai-Chao (China University of Petroleum) | Chen, Xin-Yu (Chengdu University of Technology) | Li, Qiu-Yu (Optical Science and Technology (Chengdu) Ltd.) | Guo, Ming-Yu (Chengdu University of Technology)
ABSTRACT We develop a waveform-matching inversion method to determine the focal mechanism of microseismic events recorded by a single-well observation system. Our method uses the crosscorrelation technique to mitigate the influence of anisotropy on the S wave. Then, by conducting a grid search for strike, dip, and rake, we match the observed waveforms of P and S wave with the corresponding theoretical waveforms. A synthetic test demonstrates the robustness and accuracy of our method in resolving the focal mechanism of microseismic events under a single-well observation system. By applying our method to the events that have been categorized into two clusters based on spatial and temporal evolution recorded during the hydraulic fracturing operation in the Weiyuan shale reservoir, we observe that the two clusters have distinct focal mechanism and stress characteristics. The events in the remote cluster (cluster A) exhibit consistent focal mechanisms, with a concentrated distribution of P-axis orientations. The inverted maximum principal stress direction of cluster A aligns with the local maximum principal stress direction (). This implies that events in cluster A occur in a uniform stress condition. In contrast, the other cluster (cluster B) near the injection well exhibits significant variation in focal mechanisms, with a scattered distribution of P-axis orientations. The inverted maximum principal stress direction deviates from local maximum principal stress direction (), indicating that events in cluster B occur in a more complicated stress condition.
- North America > Canada > Alberta (0.47)
- North America > United States > Texas (0.47)
- Asia > China > Sichuan Province (0.29)
- Geology > Geological Subdiscipline > Geomechanics (0.94)
- Geology > Structural Geology > Tectonics > Plate Tectonics > Earthquake (0.70)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.68)
- Geology > Petroleum Play Type > Unconventional Play > Shale Play (0.50)
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- North America > Canada > Alberta > Western Canada Sedimentary Basin > Alberta Basin > Deep Basin > West Pembina Field (0.99)
- North America > Canada > Alberta > Western Canada Sedimentary Basin > Alberta Basin > Deep Basin > Pembina Field > Viking Formation (0.99)
- (2 more...)
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)
ABSTRACT The lack of knowledge of lateral heterogeneity in unconventional reservoirs commonly has negative impacts on drilling, completion efficiency, and production. However, current methods, such as well logging and seismic surveying, are limited in their ability to characterize unconventional reservoirs. We develop an alternative geophysical approach that uses distributed acoustic sensing (DAS) and perforation shots to characterize unconventional reservoirs. In our field data set, DAS-recorded perforation shots show strong P-wave signals. The recorded P-wave waveforms from the study area exhibit dispersive behavior, which can be clearly identified after signal processing. The spatial variations in phase velocity along the horizontal wellbore can be reliably measured by averaging the measurements from multiple closely situated perforation shots. We observe a low phase-velocity zone along the study well, which is spatially consistent with the well logs and root mean square amplitude extracted from the 3D seismic volume. The observed dispersive behavior of P waves is validated through numerical modeling. By comparing the results from the proposed method with those from modeling results and other measurements, we conclude that the proposed method results in a reasonable radius of investigation for unconventional reservoir characterization. The method also has the potential to infer hydraulic fracturing effectiveness by comparing the phase-velocity difference before and after stimulation. The data acquisition of the proposed workflow can be combined with perforation shot operations, which provides a cost-effective and suitable approach to investigating lateral heterogeneity in unconventional reservoirs.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (1.00)
- Geophysics > Seismic Surveying > Passive Seismic Surveying > Microseismic Surveying (1.00)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying > Vertical Seismic Profile (VSP) (0.68)