Constraining Subsurface Model Resolution at Newberry Volcano Using a Weighted Spatial Analysis

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1 GRC Transactions, Vol. 40, 2016 Constraining Subsurface Model Resolution at Newberry Volcano Using a Weighted Spatial Analysis MacKenzie Mark-Moser 1,2, Emily Cameron 1,3, Kelly Rose 1, Lucy Romeo 1,3, Jeremy Schultz 1,2, and Adam Schultz 4 1 National Energy Technology Laboratory, USA 2 Oak Ridge Institute for Science and Education 3 AECOM, USA 4 Oregon State University, USA Keywords Newberry Volcano, enhanced geothermal system, ArcGIS, EarthVision, uncertainty, data resolution, multi-scale, Cumulative Spatial Impact Layer, spatial analysis, modeling ABSTRACT Geologic characterization, geothermal exploration, and assessment of resource potential have been ongoing for nearly a century in the region surrounding Newberry Volcano. As a result, a variety of surface and subsurface datasets have been acquired that are appropriate to constrain the subsurface geologic framework for Newberry Volcano and also to allow for the characterization of the high heat-capacity, magmatically-hosted geothermal system at multiple scales. To evaluate the impact of an engineered geothermal system (EGS) installation at Newberry, various datasets were incorporated into a 3-D geologic model utilizing EarthVision. A subset of the input data used in the 3-D model was then spatially analyzed with the Cumulative Spatial Impact Layer tool (CSIL), developed by the National Energy Technology Lab (NETL), to assess data resolution and characterize areas of higher model certainty. Results from the CSIL based analysis and implications for the Newberry 3-D model are examined. Introduction Newberry Volcano, a voluminous (500 km 3 ) volcano located in central Oregon in the western U.S., was first identified as an anomaly in the Cascade volcanic chain in 1935 due to its host of material types and its shield-like profile as compared to the typical stratovolcanic form of most other Cascade volcanoes (Williams, 1935). The volcano lies at the intersection of 3 major fault zones Walker Rim to the south, the Brothers fault zone to the east, and the Sisters fault zone to the north and more generally at the intersection between Cascade Arc subduction-related volcanism and the Basin and Range extensional volcanism (Figure 1). This relatively young volcano hosting a near-surface heat anomaly and high geothermal gradient at a fault zone intersection that could provide fluid flow conduits led researchers to investigate the geothermal potential of the area (MacLeod and Sherrod, 1988). Newberry has been the subject of geothermal exploration since the 1970s. Early exploration of the hot springs at Paulina Lake proved the magmatically-hosted, hydro-geothermal system within Newberry caldera was sufficiently hot for further development (Sammel et al., 1988). However, exploration shifted outside of the caldera due to the establishment of the Newberry National Volcanic Monument in 1990, which prohibited exploration inside the caldera. The most recent round of exploration, initiated by Davenport Energy in 2008 and continued by AltaRock Energy, Inc. in 2012 and 2014, is focused on the western flank of the volcano with the intent to install an enhanced or engineered geothermal system (EGS) to harness the geothermal potential (Waibel et al., 2012). The west flank of Newberry has been selected for Phase 1 of the Department of Energy (DOE) funded geothermal testing and monitoring effort Frontier Observatory for Research in Geothermal Energy (FORGE) (Bonneville et al., 2016). 567

2 Figure 1. Map of Newberry Volcano with geothermal wells in red. The area of interest for this paper is within the orange square, which represents the 3-D geologic model boundary. The Newberry National Volcanic Monument is outlined in green. The inset map of Oregon notes major geologic features and the geographic location of this map is shown as a black rectangle. To characterize both the geologic setting and geothermal potential at Newberry, many surface and subsurface datasets have been and continue to be gathered. A suite of measurements taken during the first stimulation in 2012 was repeated again during AltaRock s 2014 EGS stimulation. Measurements utilizing magnetotelluric, InSAR, ground-based interferometric radar, and microgravity acquisition techniques were made pre-, syn-, and post-stimulation within and surrounding the EGS stimulation zone. These datasets were integrated with borehole and microseismic stress field and location data from AltaRock and its collaborators, along with well log, petrologic and geochemical datasets, LiDAR mapping of fault traces and surficial volcanic features such as vents or cinder cones, into an EarthVision 3-D geologic model that enables subsurface characterization of the EGS stimulation zone on the west flank and beyond (Mark-Moser et al., 2016) (Figure 2). Datasets incorporated into the 3-D model were then evaluated by the NETL s Cumulative Spatial Impact Layers (CSIL) tool (Bauer et al., 2015; Romeo et al., in prep.). Each dataset was ranked according to a priori knowledge and the quality of information it provides about the subsurface. As opposed to broad interpretations or inverse models, the CSIL approach allows researchers to assess a combination of factors, including data density and areas of less certainty within the 3-D model. Based on spatial overlap and numeric attributes represented by the input datasets, the CSIL tool summarizes the various datasets into a layer file, the values of which represent areas of greater data density and quality. The layer produced by the CSIL tool ultimately helps assess the integrity of the data coverage and its subsurface resolution, facilitating more robust geologic interpretation. Methods A spatial analysis of subsurface coverage based on these multiple geologic and geophysical datasets was performed using the CSIL tool. This tool digests multiple multivariate spatial datasets and analyzes their relationships based on areal overlap and, if applicable, common numeric attributes. The CSIL tool then summarizes the datasets based on spatial overlap using presence (1) and absence (0) binary values, or the user-specified common numeric attributes, both of which result in a density-based output raster layer. Each grid cell of the output raster layer contains the summarized binary or specified common numeric attributes, based on spatial overlap (Bauer et al., 2015; Romeo et al., in prep.). Figure 2. 3-D geologic model representing the subsurface of Newberry Volcano. Magmatic intrusions are indicated by the purple and red bodies and caldera fill by the cyan. Density inversion data from the gravity survey (restricted to a density of 2.8 g/cc) is indicated by the pixelated red and yellow point cloud. Wells with log data are represented by the multi-color rods. This model snapshot is representative thus not all datasets used in the study are visualized here. 568

3 For this study, the CSIL tool was applied to summarize the overlay of point, polyline, and polygon data representing eight different datasets within the 3-D geologic model s areal extent: core logs from wells, well log geophysical measurements, elevation measurements, seismic tomography stations, surface features indicative of subsurface activity (e.g. faults, vents, fissures or cinder cones), magnetotelluric (MT) station coverage, gravity station coverage, and subsurface locations derived from microseismic monitoring (Table 1). Each of the 8 datasets was weighted subjectively from a rank scale of 0.5 to 1 based on a combination of data resolution and how well the dataset represented certainty in the subsurface. Higher rank values were assigned to datasets where presence of the data is direct or represents in-situ conditions in combination with a high degree of certainty. Lower rank values indicate both less resolution and less certainty as the data are not direct. The intermediate ranking factors usually have either poor resolution and high certainty, or high resolution and poor certainty. To analyze each of the eight datasets using the CSIL tool, it was necessary to maintain spatial accuracy throughout the process. Each dataset was projected into UTM Zone 10N with the datum WGS 1984, a metric spatial reference system. An Observed Nearest Neighbor statistical analysis of the point layers within the extent of the 3-D geologic model indicated a best-fit CSIL grid resolution of square meters over an extent of 441 square kilometers. Datasets were analyzed based on spatial overlap and uncertainty was summarized using the previously applied numeric weightings. Datasets: Table 1. Datasets evaluated using CSIL. Weight is based on coverage and resolution of subsurface information. Coverage refers to how the data is spatially represented for input into the tool either as a point, polyline, or polygon. Data type Source Rank Coverage Physical well data (rock chips, core) DOGAMI, AltaRock Energy Inc. 1 Points Well logs DOGAMI, AltaRock Energy Inc. 0.7 Points Elevation data (LiDAR DEM) DOGAMI 0.5 Polygon Vents, faults, fissures, lineations Active seismic array Magnetotelluric stations Gravity survey Passive microseismic & earthquake data Grasso et al., Heath, et al., 2015, University of Oregon Geophysics Group Schultz et al., 2014, Zonge International Zonge International, Davenport Newberry Points, polyline 0.7 Points 0.6 Polygon 0.8 Polygon Lawrence Berkeley National Lab 0.7 Polygon 1. Well Data Mud logs, or descriptions of the subsurface formations being drilled through, are a required component of exploration drilling, whereas the acquisition of well cores, rock chips or core plugs from specific formations is up to the operator. Publicly available subsurface well data from the Oregon Department of Geology and Mineral Industries (DOGAMI), as well as proprietary well data acquired by AltaRock Energy, were used to anchor the 3-D geologic model. They are evaluated by CSIL as point data. A weighting of 1 is given to this dataset as it provides direct information of subsurface parameters and high certainty at each point. 2. Well Log Geophysics Geophysical measurements (e.g. gamma ray, resistivity, etc.) are recorded during exploration drilling. The method and type of measurement vary from well to well. In this evaluation, both DOGAMI s publicly available data and AltaRock Energy s proprietary well data were combined into a single dataset. A weight of 0.7 is given to this dataset at each point as it provides subsurface information, however does not actually evaluate lithology directly, and well logs tend to be error-prone in igneous environments (Khatchikian, 1982). 3. Elevation Data DOGAMI s ft LiDAR digital elevation model (DEM) surveys for the Newberry area were overlain onto the 3-D geologic model, covering the entire extent of the model. This dataset is incorporated by the CSIL tool as a polygon. A weight of 0.5 is given to this dataset as it has excellent coverage of the area, but only provides surface elevation information that may be used to make inferences about the subsurface, so certainty is limited. 4. Surface Expressions of Subsurface Activity: Vents, Faults, Fissures, Cinder Cones The 2012 research Kyla Grasso et al. (2012) to map and characterize surface geologic features using geologic survey and LiDAR verification is evaluated as point and polyline data in the CSIL tool. This dataset is given a 0.7 weight based on the presence of surface geologic features that provide some subsurface certainty of fault or fracture networks where fluid flow may have occurred previously, however lack ability to convey deeper subsurface structure and composition. 569

4 Results 5. Active seismic array A seismic line deployed by the University of Oregon Geophysics Group (Heath et al., 2015) collected data to support imaging of Newberry s subsurface. As the seismic array was deployed in a line, the stations are evaluated as point data sources along that line. Seismic information collected may be interpreted using tomography and teleseismic analyses to process the velocity information of the subsurface. A CSIL weight of 0.7 is assigned to the point dataset based on the subsurface imaging potential that is limited regarding certainty. 6. Magnetotelluric (MT) survey stations This dataset includes stations from magnetotelluric surveys completed by Zonge International for Davenport Newberry, Zonge International for the DOE project Novel use of 4D Monitoring Techniques to Improve Reservoir Longevity and Productivity in Enhanced Geothermal Systems, and Dr. Adam Schultz s National Geoelectromagnetic Facility in support of the DOE EGS monitoring project at Newberry. As the stations are deployed to cover the west flank EGS stimulation area, the MT survey area is evaluated as a polygon. A CSIL weight of 0.6 is assigned to the points based on the subsurface imaging potential, however limitations in interpretation and lack of in-situ, direct detection data does not merit a full rank value. 7. Gravity survey stations Gravity stations deployed by Zonge International and Davenport Energy cover the entire Newberry area, thus the combined station data survey is evaluated as a polygon to the extent of the model. The gravity survey has relatively high resolution in the subsurface compared to other geophysical techniques and may be used to interpret density of the subsurface material. Because of limitations in interpretation, processing and lack of in-situ, direct detection data, this survey is assigned a CSIL rank of Passive microseismic survey stations Seismic stations deployed by Lawrence Berkeley National Lab to monitor AltaRock Energy s 2012 and 2014 EGS stimulations at well recorded microseismic earthquakes. The stations evaluate the west flank area surrounding well and provide earthquake event information for that area and are thus evaluated as a polygon in the CSIL tool. This dataset is ranked as 0.7 for its relative accuracy in providing microseismic event locations in the subsurface of Newberry s west flank. Output from the CSIL tool reveals areas within the Newberry 3-D geologic model that have the best data certainty for subsurface interpretation and three-dimensional evaluation (Fig. 3). Point and polyline data within the 3-D geologic model extent have the finest resolution of information for interpreting the subsurface, while the polygon datasets provide Figure 3. Input layers to the CSIL tool are shown on the left, while output from CSIL tool evaluating all data sources from Table 1 is shown on the right. Layers are evaluated by the CSIL tool only within the model extent. High rank -- density and quality of data-- is represented by red, while low rank is represented by yellow. Notice the high density and quality of data on the west flank of the volcano, where current investigation for EGS implementation is underway. 570

5 excellent coverage and extent of data to support interpretation. This 2-D visualization of data sources, types and resolutions communicates differences in the 3-D model resolution and quality. While the geophysical data have broad extents, their resolution is lower than that of well data, which includes directly detected core and wireline data. Thus, the value or resolution of those areas where only geophysical surveys exist displays a lower summarized rank, while areas where data sources coincide are integrated and receive a higher cumulative score. The output CSIL raster layer represents summarized ranked-values, ranging from 1.3 to Within the CSIL output, high resolution data from faults, wells, and vents coincide with the broad extent geophysical surveys result in a higher summarized rank value indicating greater confidence in the 3-D geologic model for those areas (Figure 3). In this CSIL result, data density and summarized ranking are highest on the west flank of Newberry Volcano, markedly within the boundary of microseismic data coverage and along geologic lineations. Conclusions Approximately four decades of research and development investment at Newberry have resulted in evolving interpretations of the Newberry volcanic system and its geothermal potential. To effectively target the best locations for a workable EGS solution at Newberry, a method to visualize and interpret the combined effect of these overlapping datasets was needed. The 3-D geologic model created in EarthVision provided the ideal solution, although this model still depends heavily on interpretations of lithology from well, surface, and indirect geophysical measurements as well as extrapolation of parameters away from surface observations and wellbores. An advantage of modern computing power is not only the ability to create an integrated model in three dimensions, but also to perform complex analytics on a variety of spatial datasets. The intent of the NETL CSIL tool is to provide an efficient way to summarize multivariate data. CSIL provides a novel approach for ranking the presence and quality of data coverage used to build the 3-D EarthVision model for Newberry Volcano. The layer clearly indicates areas of dense, quality data coverage over the entire region, and also a concentration of data on the western flank around the EGS stimulation area. The CSIL layer output for the extent of the 3-D model visually confirms the best data constraint in the area of the EGS stimulation zone and along geologic lineations. Therefore, we can infer that interpretations on the west flank are the most reliable with the least uncertainty. The 3-D model provides subsurface interpretation, and the analysis performed by the CSIL tool informs us to the certainty of the model. The CSIL output highlights areas where subsurface interpretations of the volcano and the geothermal system are most robust versus areas where further research and survey could improve data quality and resolution. For future work, this information layer can be coupled with the NETL tool Variable Grid Method (Bauer and Rose, 2015), which displays uncertainty along interpolated surfaces via a varying grid size. These surfaces can impact the interpretations that are inputted to and derived from the 3-D model by clearly showing areas where there is less data and perhaps more interpretation, or where interpretation is data-driven and well constrained. A combination of these approaches allow for a more comprehensive statistical interpretation of the subsurface and geothermal potential at Newberry Volcano. Acknowledgements This technical effort was performed in support of the National Energy Technology Laboratory s ongoing research under the RES contract DE FE The authors acknowledge and are grateful to AltaRock Energy for releasing the use of geophysical and well log datasets for our research. The authors are grateful to Benjamin Heath, Jennifer Bauer, Esteban Bowles-Martinez, and Kyla Grasso for their support and advice in this research. Data Sources AltaRock Energy well files, unpublished data. Accessed [Data file] Davenport Newberry well files, unpublished data. Accessed [Data file] DOGAMI-- Oregon Department of Geology and Mineral Industries, Newberry LiDAR data [Data files]. Available from DOGAMI s Lidar Data Viewer site: DOGAMI-- Oregon Department of Geology and Mineral Industries, Geothermal Information Layer for Oregon [Data files]. Available from DOGAMI s GTILO-2 site: Grasso, K., Meigs, A., Cladouhos, T. T., Origin of Faults, Fissures, and Volcanic Vent Alignments at a Structural Triple Junction, Newberry Volcano, Central Oregon. [GIS data layer] LBNL Lawrence Berkley National Laboratory, Induced Seismicity Enhanced Geothermal Systems (EGS) Newberry [Location data]. Available from LBNL s Induced Seismicity site: 571

6 PNSN Pacific Northwest Seismic Network, Historic Catalog of Earthquakes [Data file]. Available from PNSN s Historic Catalog site: pnsn.org/earthquakes/historic-catalog. Schultz, A., Long-period and wideband magnetotelluric data [Data file] University of Oregon Geophysics Group, Seismic array locations [Data file] Zonge International, Geosystems and magnetotelluric data collected for Davenport Newberry [Data file] Zonge International, Report on the gravity survey on Newberry Volcano, Oregon for Davenport Newberry [Unpublished report] Zonge International, Magntetotelluric data collected for Davenport Newberry [Data file] Zonge International, Modeling of the MT data on Newberry Volcano, Oregon, for the DOE project Novel use of 4D Monitoring Techniques to Improve Reservoir Longevity and Productivity in Enhanced Geothermal Systems [Unpublished report and results] Zonge International Gravity survey data [Data file] References Bauer, J. R.; Nelson, J.; Romeo, L.; Eynard, J.; Sim, L.; Halama, J.; Rose, K.; Graham, J, A Spatio-Temporal Approach to Analyze Broad Risks and Potential Impacts Associated with Uncontrolled Hydrocarbon Release Events in the Offshore Gulf of Mexico. NETL-TRS ; EPAct Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV; p. 60. Bauer, J. R., and Rose, K., Variable Grid Method: an Intuitive Approach for Simultaneously Quantifying and Visualizing Spatial Data and Uncertainty, Transactions in GIS, v. 19(3), p Bonneville, A., Cladouhos, T. T., Schultz, A., Establishing the Frontier Observatory for Research in Geothermal Energy (FORGE) on the Newberry Volcano, Oregon. Proceedings, 41 st Stanford Geothermal Workshop Fitterman, D. V., Stanley, W. D., Bisdorf, R. J., 1988 Electrical Structure of Newberry Volcano, Oregon. Journal of Geophysical Research, v. 93, p Grasso, K., Meigs, A., Cladouhos, T. T., Origin of Faults, Fissures, and Volcanic Vent Alignments at a Structural Triple Junction, Newberry Volcano, Central Oregon. American Geophysical Union Annual Meeting, December Heath, B. A., Hooft, E. E. E., Toomey, D. R., and Bezada, M. J, Imaging the magmatic system of Newberry Volcano using joint active source and teleseismic tomography. Geochem. Geophys. Geosyst., v. 16, p Khatchikian, A., Log Evaluation of Oil-Bearing Igneous Rocks. SPWLA Twenty-Third Annual Logging Symposium, Society of Petrophysicists and Well-Log Analysts. MacLeod, N. S., Sherrod, D. R., Geologic Evidence for a Magma Chamber Beneath Newberry, Volcano, Oregon. Journal of Geophysical Research, v. 93, p Mark-Moser, M., Schultz, J., Schultz, A., Heath, B., Rose, K., Urquhart, S., Bowles-Martinez, E., Vincent, P., A Conceptual Geologic Model for the Newberry Volcano EGS Site in Central Oregon: Constraining Heat Capacity and Permeability through Interpretation of Multicomponent Geosystems Data. Proceedings, 41 st Stanford Geothermal Workshop Romeo, L., Bauer, J., Nelson, J., Rose, K., and Dick, D., in prep. Cumulative Spatial Impact Layers: a robust multivariate decision-support toolbox. Journal TBD. Sammel, E. A., Ingebritsen, S. E., Mariner. R. H., The hydrothermal system at Newberry Volcano, Oregon. Journal of Geophysical Research, v. 93, p Waibel, A. F., Frone, Z., & Jaffe, T., Geothermal Exploration at Newberry Volcano, Central Oregon. GRC Transactions, v. 36. Williams, H., Newberry Volcano of Central Oregon. GSA Bulletin, v. 46, p Disclaimer: This project was funded by the Department of Energy, National Energy Technology Laboratory, an agency of the United States Government, through a support contract with AECOM. Neither the United States Government nor any agency thereof, nor any of their employees, nor AECOM, nor any of their employees, makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. 572