Managing and Modeling Time-series Geoscience Data in GIS

Transcription

1 Managing and Modeling Time-series Geoscience Data in GIS LARRY ZHANG emap Division, Saudi Aramco West Park 1, Dhahran 31311, Saudi Arabia Abstract Many oil and mining companies are increasingly trying to leverage the power of GIS to more coherently manage spatial data and to make cross-discipline spatial data readily available to their users, because up to 70 percent of G&G data is spatially-enabled and time-associated. In order for geoscientists to able to use the powerful and extensible GIS environment for making use of massive GIS data widely available for G&G projects, G&G geodata (including the time-series data) are highly required to spatially enable them in GIS through using spatial engines with open standards and data models like ArcSDE, PPDM, or OpenSpirit, which is critical for successfully modeling dynamic time-series geodata in GIS. The paper briefly reviews some popular techniques how to integrate and map geodata in GIS, and then mainly focus on how to accurately manage and model time-series geodata such as time and dynamic groundwater levels in order to reduce risk of land management and exploration projects, when dealing with spatially-associated time-series geodata. Key Words: time-series spatial geodata, OpenSpirit, PPDM

2 Reviews of Geoscience Applications in GIS Many oil companies are increasingly trying to leverage the power of GIS to more coherently manage spatial data and to make cross-discipline spatial data readily available to their E&P users. It is common for geosciences professionals to internally apply for digital GIS 2D surface geologic mapping through using ESRI geodatabase and extending ESRI geoscience model 1 with high quality DEM, satellite and aircraft images to identify subtle relationships often overlooked by previous geological exploration, for example, shaded relief maps, or 3D regular grids, which are draped with satellite imagery or thematic maps (Figure 1a, 1b). GIS solutions to geoscience problems were mainly restricted to representation techniques of static surface mapping and simple 3D geometrical features for mapping surface geology without considering dynamic change over time (Figure 2). Figure 1a Draped Satellite Image Figure 1b Draped Surface Geology Map 2

3 Figure 2 Geological Mapping Representing Complicated Surface Units in GIS Because geological, geophysical and hydrological (G&G) data have traditionally been managed and modeled in E&P 2, 3 databases (Finder, GeoFrame, Petrel, OpenWorks, Discovery) or other geosciences database (EarthWorks, acquire, Surpac), most of oil companies that have deployed GIS to their E&P users face several common challenges: How to manage time in spatial projects? How to internally manage and model complicated G&G data (wellbore, well locations, 2D and 3D seismic locations, profile, cross-section, and geologic fault and horizon data) in GIS? How to get the G&G spatial data into the GIS and keep it current with the ever-changing contents of their G&G project data stores? How to motivate geoscientists and engineers to leverage the GIS in their day-to-day work when most of their time is spent using dedicated geologic, geophysical, or engineering technical applications? So, the first problem in GIS for modeling spatial change over time consists in the attribution of time to each time node. 3

4 The second problem for managing G&G data consists in the attribution of the elevation value to each vertex and node of linear elements. In addition, 3D geological solid bodies (geological horizon, altitude of bedding, thrust, strike-slip, normal fault, etc) was too complex to be managed in GIS. Obviously, more complex 3D subsurface geological bodies and structures could not be easily edited (moved, cut, glued) in GIS. The combination of surfaces also could not lead to the construction of discrete regions (faults divided), to which properties can be assigned. Furthermore, topographic and geological surfaces can not be used for the creation of irregular grids where discrete properties can be introduced. However, with rapid development of GIS with open standards and powerfully extensible capabilities (supporting raster catalogs), managing spatial change in large coverage over time becomes straightforward in GIS. Also, with more and more GIS systems supporting OpenSpirit 4 and G&G standards like PPDM 5 and POSC 6, GIS implementation of managing subsurface 3D data (well, well logs, seismic survey) becomes a breakthrough for internally managing, geoprocessing, and modeling spatially-associated dynamic geosciences data, including time-series data, in GIS. In fact, GIS can easily be interoperated and connected with external G&G or other geosciences databases (fully managing subsurface 3D bodies and 3D models) through using the customized extension, shapefile, or OpenSpirit modules in order either to assure the quality of G&G geodata through using powerful GIS spatial-query capabilities, accurate mapping, and real-time well positioning with GPS, or to do solid 3D geomodeling and interpretation with seismic horizons and well picks (Figure 3). 4

5 Figure 3 Visualizing Wellbore, Well Logs, Profile and Cross-section in GIS Identifying and Querying Land Cover Change over Time In practice, customers first need spatially query time-series images for their Area of Interest (AOI) through a reliable and well-designed system such as Change Detection system, which fully make use of temporal raster catalog(s) in spatial engine. And then, the spatial feature change over time can be virtually detected via using change enhancement and automatic change feature extraction approaches. Among these methods, the temporal composite image is usually used as spectral bands for change feature extraction. This composite image can be created from multi-date images (any two-date images preferred), such as relatively earlier T1-image and relatively later T2-image (Figure 6a, 6b). 5

6 Figure 6a Imagery of 2004 (T1) Figure 6b Imagery of 2005 (T2) With either pixel-based or object-oriented classification in remote sensing, its cost increases with the number of spectral bands in multispectral space. For classifiers like the parallelepiped and minimum distance procedures, this is linear increase with bands; however, for maximum likehood classification (most preferred in the procedures), the cost increases with bands is quadratic. Therefore it is sensible economically to ensure that no more bands than necessary are utilized, that is, band selection, before performing a classification. In addition, it is worth to realize that random band selection can not be performed indiscriminately. The method must be devised that allow the relative worth of bands accessed in a rigorous way. In our Change Detection system, temporal spatial change is efficiently enhanced by integrating two temporal images into a color composite, which consists of band 1 (Red) from band 1 in T2-image and band 2 (Green) & band 3 (Blue) from band 2 & 3 in T1-image. From the composite image, most real emerging objects can be easily identifying in red color, and disappearing objects are in cyan color. With this temporal composite image, both emerging objects and disappeared objects can be segmented and extracted through using either automatic object-oriented classification in remote sensing or manually digitizing in GIS. 6

7 It is worth to note that some grass lands and deciduous forests are also in red color. In fact, they change seasonally, and not real feature change annually. In practice, customers want to discriminate them from real change (Figure 7). Figure 7 Emerging Objects in Red Color and Disappeared Objects in Cyan Finally, for customers to conveniently query spatial change, geocoding spatial change over time is a very important process for this kind of change detecting system to monitor large areas across whole nation. So, temporal feature classes of spatial change can be used as a reference for geocoding process. Geocoding spatial change fully uses well-defined temporal change table schema in geodatabase so that it can be updated at any time without affecting client uses. Modeling 3D Geological Structures over Time In order to thoroughly understand the framework of the subsurface structures and the geological evolution over time in the prospect lease, geoscientists explore many approaches in GIS to model and visualize the geological structures and horizons over time with drillholes and geophysical data. 7

8 The simpler geological 3D model can be easily developed in GIS (X. Devleeschouwer & F. Pouriel, 2005). The drillhole database is imported into GIS. In 3D module, each drillhole is represented as a stick (letter A on the right). The interpolation method (Kriging, IDW, Spline, and NN) allows modeling of the roof for each geological layer, identified by specific colors, such as blue for the Quaternary (letter B on the right). The picture in the lower right corner shows the topographic map (1:10,000) draped on the digital terrain model (Figure 4). Figure 4 Display 3D Geological Structures in GIS More complex geological evolution over time and geological strata models also can be managed in GIS, which can be combined with wellbore, seismic, or gravitational surveying data (Figure 5a, 5b). In the figure 5a, the seismic layer, which is rendered from red color to blue, can be interpreted as time-based horizon changes. It is worth to realize that the profile or triangular survey (wellbore) and profiles (seismic survey) data can be interpolated with Kriging or other methods for display and verifying well picks. 8

9 Figure 5a Modeling Surface-Subsurface (Wellbore, Seismic Horizon) Data in GIS Figure 5b Visualizing Terrain-Geological Strata 3D Model in GIS 9

10 Managing and Modeling Groundwater Level Change over Time In many environmental and engineering contexts, hydrological staffs need to study regional groundwater level change over time from monitor wells. The main difficulty with managing thousands of hourly or daily raw groundwater level measurements from a monitor well, which is downloaded from dataloggers (comma-separated text files, Figure 8), is the tedious process of quality control for screening out bad data because this monitor well might be interfered from either its own pumpage or a nearby well. Traditionally, graphing data in MS Excel can be visualized, but can not be physically manipulated from the graph. When a bad data value occurred in the graph, the hydrological technicians were required to visually match errant water level values from the graph with the corresponding value in the table. The users have to potentially scroll through the entire data table to select the appreciate record to flag. This lengthy and tedious nature of the QA/QC procedures in such a case often results in less than timely data management. Figure 8Raw Datalogger Data, Depth Measurements as Bold A Groundwater Level Record Manager extension for ArcGIS can be developed to easily analyze and spatially manage continuous time-series groundwater level records, which were measured from monitor wells or their own pumpages, in order to screen out bad data records for quality assurance (QA). The procedure can be divided into three processes. First, the datalogger data (comma-delimited text files) were imported into a temporary Access database 10

11 table. And then, the records in this table can be expressed in Cartesian space as point event features. To produce a hydrograph in Cartesian map space, each measurement (date/hour) is the X coordinate, and the depth-to-water is the Y coordinate. The point event features, representing individual water level records, can be identified, queried, rendered, updated, edited, or selected dynamically between ArcMap and the temporary table for QA/QC (Figure 9). Figure 9 Queried Pumping Water Level Events Rendered as Red (Bad Data) in GIS Finally, the cleaned ground water level record table in the temporary Access database can be uploaded into enterprise underground water level database for hydrologists to do further visualize and model groundwater surface change over time with proper TIN (Delaunay triangulation) interpolation and ArcHydro 7 data model in GIS through using a number of monitor wells in 3D (Figure 10a, 10b). 11

12 Figure 10a Modeling of Underground Water with TIN Interpolation in GIS Figure 10b Mapping of Underground Levels over Time in GIS Mapping Seafloor with Time-series Data Seafloor surface mapping can be conducted with high-resolution swath bathymetry, side-scan sonar imagery, or seismic reflection profiles. Profiling seismic time data are firstly converted into depth seafloor (and other subsurface horizons), and interpreted in SeisWorks for digitizing and mapping seismic depth seafloor. The interpreted depth to bedrock (every 2-10 shots) can 12

13 be exported into LPS 8 for georeferencing, mosaicing, and enhancing. And then they can be interpolated into a proper resolution grid, for example, m per pixel. Similarly, bathymetric or sidescan sonar time data can be converted into water depth in SwathEd. And then they are processed in LPS for a mosaiced and enhanced image (Figure 11). The map shows seafloor topography in shaded relief view, colored by water depth. The shaded relief imagery was created by vertically exaggerating the seafloor topography five times, and then artificially illuminating the relief by a light source positioned 35 degrees above the horizon at an azimuth of 045 degrees. Grid cell resolution is 5 meters (USGS, 2006). Figure 11 Swath Bathymetry Map (USGS 9 ) In fact, using side-scan backscatter time data, which are combined with ground truth sampling data, substrate type can be also classified. Finally, the geological seafloor map can be in ArcGIS with ArcMarine 10 data model for further editing and analysis. Discussions As internal managing and presenting wellbore and spatially-associated timeseries geoscience data becomes feasible and common in GIS, geoscientists can accurately and easily model subsurface geoscience temporal and spatial properties in popular ArcGIS environment for unlimited earth applications, because of GIS providing the powerful spatial-query functions, unlimited extensible capabilities, accurate mapping, real-time positioning with GPS, most 13

14 surface data widely available in GIS formats, and delivering G&G /GIS analysis over Internet. However, it does not mean that the techniques in GIS will eventually take over G&G techniques in E&P or mining systems for managing geodata and modeling earth. Inversely, most subsurface geodata and models are still only available in E&P database or other geosciences database. Obviously, it is strongly necessary for geoscientists and GIS professionals to efficiently work together on the integration and interoperation solution for very complicated geomodeling applications and accurate surface-subsurface QA/QC processes. And also, through right integration and proper interoperation, geoscientists will be able more quickly to locate the geodata they need through, which will improve its efficiency and productivity at both national-wide and global-wide levels. References 1. Geosciences data model, 2005, ESRI 2. OpenWorks Geodata Management Manual, SeisWorks Training Manual, StratWorks Training Manual, and Integrated Workflows in SeisWorks and StratWorks, 1998, Landmark Graphics Corp. 3. Z-Map plus Workflows, Z-Map plus User Guide, and Z-Map plus Training Manual, 2004, Landmark Graphics Corp. 4. OpenSpirit 2.9 & 3.0 User s Guide, 5. PPDM 3.6 & 3.7, PPDM Lite 1.0 (the Public Petroleum Data Model), 6. POSC 2.2 (the Petrotechnical Open Standards Consortium), 7. ArcHydro data model, 8. Leica Photogrammetry Suite 9 AutoSync, Terrain Editor Tour Guide, and Automatic Terrain Extraction User s Guide, USGS, ArcMarine data model, 14