Remote Sensing and Spatial Analysis Applied to Prospectivity Mapping. Carlos R. Souza Filho S. Asadzade T. Lammoglia University of Campinas (UNICAMP)

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1 Remote Sensing and Spatial Analysis Applied to Prospectivity Mapping Carlos R. Souza Filho S. Asadzade T. Lammoglia University of Campinas (UNICAMP) Lisbon, December 2017

2 INTRODUCTION The detection of hydrocarbons (HCs) on continental basins by remote sensing systems in all acquisition levels (i.e. terrestrial, sub-orbital and orbital) can be guided by the spectral response of features produced by the interaction of HCs with the stratigraphic column or directly by the HC s spectral response. PROBLEM: relative shortage of models and tools to use RS in a integrated approach to produce HC prospectivity maps Objective: this study focus on the characterization and predictive mapping of HC prospectivity in onshore basins by means of: A theoretical detection model based on remote sensing and supporting data Digital processing and information extraction from ASTER and other exploration data Knowledge-driven and data-driven predictive modeling

3 GENERAL SCHEME FOR PREDICTIVE MAPPING OF Prospectivity Map HYDROCARBON PROSPECTIVITY percentage (%) of points classified as with low to high prospectivity. DEMs Geochemistry Remote Sensing Thematic Maps Quaternary Cover Geology Magnetics/Gamma-rays Gravimetry

4 METHODS FOR HYDROCARBON PROSPECTIVITY MAPPING DATA DRIVEN Neural Nets Weights of Evidence KNOWLEDGE DRIVEN Fuzzy Logic Boolean Logic Logistic Regression Weights for evidential themes are defined on the basis of training points Weights for evidential themes are defined by a exploration geologist

5 Arc-SDM Originally developed by Graeme Bonham-Carter (GSC) and Gary Raines (USGS) Works as a ArcGIS/ArcMap (ESRI) module Currently development: IG-UNICAMP (Prof. C R Souza Filho) Publicly available at: Future development: GTK (Geological Survey of Finland) Publicly available at:

6 Toolbox for ArcGIS - Fuzzy Logic - 3 Neural Nets (Fuzzy, RBF, Probabilistic) - Weights of Evidence - Logistic Regression

7 Case Study - 1

8 Northern Tucano Basin (Bahia, Brazil) Tona: Sandstones and Limestones Marizal: sandstones

9 Theoretical RS Detection Model Main microseepageinduced anomalies over HC accumulation. (after Duchscherer, 1982; Eventov, 2000; Saunders et al., 1993; Saunders et al., 1999; Schumacher, 1996; van der Werff et al., 2006).

10 Theoretical RS Detection Model An UPDATED MODEL for microseepage -induced mineralogic changes over HC accumulation.

11 FEATURE DETECTION & PROSPECTIVITY MODELS - SUMMARY The premise of the model used to detect microseepages in these onshore basins is that such phenomenon is associated with structural lineaments and headstreams, causes bleaching of rocks and soils (i.e., Fe3+ is reduced to Fe2+), increases the relative concentration of clays and carbonates and triggers geobotanical (stress) markers Soil gasometry (combined anomalies of ethane, propane, butane, pentane: C2-C5) used both as training points and as validation points, respectively, in the data-driven (Weights of Evidence) and knowledge-driven (Fuzzy) hydrocarbon prospective models

12 SOIL GEOCHEMISTRY Gasometry Geologic map merged with the drainage and C2-C5 concentrations (combined anomalies of ethane, propane, butane, pentane). Note the relationship between the principal anomalies with lithologic contacts and the drainage network.

13 INFORMATION EXTRACTION FLOWCHART

14 Gasometry vs Geology vs Mineral Maps a) geologic map of a sector of the study area b) Interpolated soil-gas anomaly map c) false color composite imagery d) abundance of clay alteration mapped using ASTER data f) abundance of ferrous iron minerals (e.g. Fe-carbonate) extracted from ASTER data.

15 DATA INTEGRATION FLOWCHART

16 RESULTS Fuzzy Logic (Gamma 0.8) Fuzzy predictive mapping of hydrocarbon prospectivity High HC concentration (gasometry) concur with sites mapped solely by the knowledge-driven model (Fuzzy models do not require gasometry or training points)

17 RESULTS Weights of Evidence Weights of Evidence predictive mapping of hydrocarbon prospectivity Besides gas anomalies, obviously good match with lineaments, lithologic contacts and stream network

18 CONCLUSIONS The HC prospectivity maps obtained here attest for the high potential of regions usually mapped by gasometry only, corroborating the usefulness of the methods tested here for this aim. Other sites with similar characteristics but for which no geochemical data were available were also revealed. These sites are taken as new potential targets for the presence of seeps and eventually HC plays. This work suggests a plausible consistency between the information extracted from surface and subsurface data, adding strength to the exploratory process in frontier areas. The research demonstrated the applicability of ASTER data and methods of extracting spectral-spatial information for HC exploration in onshore basins using publicly data and software available.

19 Thank you! Institute of Geosciences University of Campinas (UNICAMP)