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Bgl Property Management – PetroPhysical Property Database (P3) – an international collection of laboratory-measured rock properties The PetroPhysical Property Database (P3) – an international collection of laboratory-measured rock properties Kristian Bär et al.
Kristian Bär1, Thomas Reinsch2, a, and Judith Bott2 Kristian Bär et al. Kristian Bär1, Thomas Reinsch2, a, and Judith Bott2
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Received: 27 January 2020 – Discussion started: 10 March 2020 – Revised: 03 Jun 2020 – Accepted: 02 Aug 2020 – Published: 13 Oct 2020
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Petrophysical properties are useful for filling regional and/or regional numerical models and interpreting results from geophysical survey methods. Finding rock property values measured on samples from a specific rock unit in a specific area can be a time-consuming challenge given that such data are distributed in various collections and that the number of publications on new measurements continues to grow and the data are of varying quality. Benefiting from existing laboratory data to populate numerical models or interpret geophysical observations at specific locations or for specific reservoir units is often hampered if information about sampling location, petrography, setting, measurement method, and conditions is limited or not documented.
Within the framework of the project funded by EC IMAGE (Integrated Methods for Advanced Geothermal Research, EU grant agreement no. 608553), an open access database of laboratory-measured degradation properties has been created (Bär et al., 2017 , 2019b: P3 – database, https://doi.org/10.5880/GFZ.4.8.2019.P3. The goal of this hierarchical database is to provide easily accessible information on rock properties relevant for geothermal exploration and reservoir properties. in one collection. The collected data include traditional petrophysical, climatic and mechanical properties as well as electrical conductivity and magnetic susceptibility. Each measured value is complemented by relevant meta information such as the corresponding locality, petrographic information , chronostratigraphic ages, if available, and original citations. Original stratigraphic and petrographic information is transferred to standardized catalogs following a hierarchical structure. Comparison between statistical analyzes (Bär and Mielke, 2019: P3 – petrography, https://doi.org/10.5880/GFZ.4.8.2019.P3.p; Bär et al., 2018, 2019a: P3 – stratigraphy, https://doi.org/10.5880/GFZ.4.8.2019.P3.s). In addition, information on experimental setup (method) and measurement conditions are listed for quality control. Therefore, rock properties can be directly related to in situ conditions to obtain specific parameters suitable for modeling subsurface processes or interpreting geophysical data.
We describe the structure, content and status of the database and discuss its limitations and benefits for the end user.
The characterization and exploitation of underground reservoirs generally relies on using geophysical survey methods and/or numerical simulation codes – both of which require, in turn, knowledge of the physical properties of the rocks at depth. The strategy for filling numerical models and petrophysical properties may vary. For local scale models, laboratory data from individual samples collected from the geological unit of interest may be available. In this case, this direct information should be used together with modern laws (physical and experimental) to fill in the entire geological unit. For regional and continental scale models, on the contrary, the parameters should be general with respect to the spatial and physical diversity of the studied lithological units.
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Specific types of rocks or petrographies usually show great variation in associated properties due to different mineral compositions, different textures and different porosity distributions (Schön, 2015). The existing collection of rock properties is an example of the great diversity and for the different purposes of such databases (e.g. Cermak and Rybach, 1982; Clark, 1966; Clauser and Huenges, 1995a, b; Landolt-Börnstein et al., 2020; Mortimer, 2005; Hantschel and Kauerauf, 2009; Liolios and Exadaktylos, 2011; Descamps et al., 2013; Aretz et al., 2015; PetroMod, 2020). Since such collections are often published with limited meta information, it is difficult to provide data in interesting formats. This is even exacerbated by additional constraints such as the careful coverage of certain rock types or geographical areas – e.g. Germany: FIS Geophysik is hosted by the Leibniz Institute for Applied Geophysics (LIAG) (http://www.fis-geophysik.de, last access: 14 August 2020); United Kingdom: BritGeothermal (http://www.britgeothermal.org, last access: 14 August 2020) hosted by the British Geological Survey (BGS); United States: National Geothermal Data System (NGDS) managed by a federal infrastructure including national and academic organizations (e.g. US Geological Survey, Southern Methodist University, American Association of State Geologists, National Geospatial Data Fund US Department of Geothermal, http://geothermaldata.org, last access: 14 August 2020); Ireland: IRETHERM Project (http://www.iretherm.ie/, last access: 14 August 2020); Australia: Rock Properties Explorer (http://www.ga.gov.au/explorer-web/rock-properties.html, last access: 14 August 2020); New Zealand: PETLAB: National Rock and Geoanalytical Database (http://pet.gns.cri.nz/#/, last access: 14 August 2020); and many more.
In addition, a heterogeneous collection does not provide a homogenised set of meta-information. Furthermore, access to surveillance data often depends on national laws. In some countries industrial resource survey data, including destructive properties measured in deep well samples, may become public after some time and then be incorporated into national information systems. In some cases, surveillance data remains confidential for a long time or even indefinitely, resulting in limited access to data for the respective countries.
Due to the current publication policy of international research institutions where a large number of peer-reviewed publications have become more and more important for an individual’s scientific career, the amount of petrophysical data recorded worldwide has increased rapidly. These publications, however, are spread among many different scientific geographical journals and are dispersed in hundreds of publications. Considering the amount of new property data published as well as the number of publishing journals, countries and authors, research and data collection can take a lot of time. Recent studies show that domain experts spend almost 80 % of their working hours collecting, cleaning and managing their domain-specific data (CrowdFlower, 2016). The efficient, comprehensive collection, collation and dissemination of these data is considered essential to promote rapid, innovative and accurate research (Gard et al., 2019).
To enable (i) effective search and research on physical rock properties, (ii) further evaluation of property data using complementary meta-information, and (iii) adequate aggregation of properties for individual units, a comprehensive database was created within that system. of a project funded by EC IMAGE (Integrated Methods for Advanced Geothermal Detection, grant agreement no. 608553). The purpose of this database is to collect, store and make publicly available damage property data from published laboratory test results on rock samples of any type including as much meta-information as possible. So far, literature data relevant to the IMAGE project and laboratory data collected during the IMAGE project were entered into the novel PetroPhysical Property Database (P3). Here, we present the current state of P3 and version 1.0 of the version in a better format (Bär et al., 2019b: P3 – database, https://doi.org/10.5880/GFZ.4.8.2019.P3).
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Figure 1 Concept for the classification of various types of geological reservoirs with (examples of) integrated petrological, petrophysical or geophysical methods that link external analog studies to numerical simulation of the reservoir.
P3 is publicly available and contains actual rock properties measured in laboratory tests. It is licensed under a creative commons license (CC-BY 4.0), and its design follows the FAIR guidelines for scientific data management and governance (Wilkinson et al. (2016). All data are selected to represent sample size characteristics. of rocks. of a few centimeters per decimeter, according to measurement methods (as defined by many regulatory institutions or committees such as the International Society for Rock Mechanics and Rock Engineering (ISRM), the European Regulatory Committee (C) EN , the International Organization for Standardization (ISO), the American Society for Testing and Materials (ASTM International) and many others) for different properties. Within P3 we aimed to standardize the description of the measurement method to increase the comparability between the reported values. large scale from logging of geophysical wells. , hydraulic well testing, integrating geophysical methods or other field measurements, which connect over large rocks. several quantities or types of rocks, are not yet included in the database (Fig. 1). This will reduce the bias introduced by heterogeneities within the larger geobody including obvious or partial discontinuities such as cracks, fractures, bedding or schistose. In addition, based on lithological information, we did not include data from very small samples, where the volume of interest may be less than the representative core volume (REV) (e.g. Ringrose and Bentely, 2015) for the investigated rock. type. The full extent of the size dependence of damage properties as described in previous studies (e.g. Enge et al., 2007; Jahn et al., 2008; Howell et al., 2014; Rühaak et al., 2015) has therefore not yet been demonstrated. and database but is planned to be included in future releases.
To ensure that source data are publicly available to researchers, only data from scientific publications (peer-reviewed books or journals) or proceedings (e.g. IGA Geothermal Papers/Conference Database) as well as published research reports (e.g. theses or theses) are available to the public, project reports) were included in P3. The database has dimensions with a minimum level of meta-information to allow appropriate interpretations, integration or modeling based on the collected data. The lowest associated meta information is a reference to the origin of the data (citation) and information about the petrography to allow classification according to a particular lithotype. If available, additional meta-data were included, such as sample location (can include type, e.g. open pit, abandoned or active quarry, vertical or deviated grain), registered sample set membership (e.g. International Geo Sample Number ( IGSN) , cf. Devaraju et al., 2016; Lehnert et al., 2006)), stratification, sample dimensions, measurement method, or equipment and measurement conditions (pressure, temperature, stress) including saturation level and type of fluid. Conversion of printed values to SI units
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