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Volume 31 Issue 1
Jan.  2020
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Remote Detection of Hydrocarbon Microseepage in a Loess Covered Area

  • Hydrocarbon microseepage can result in related near-surface mineral alterations. In this study, we evaluated the potential of detecting these alterations with field measured and satellite acquired hyperspectral data. Fourteen soil samples and reflectance spectra were collected in the Xifeng Oilfield, a loess covered area. Soil samples were analyzed in the laboratory for calcite, dolomite, kaolinite, illite, and mixed-layer illite/smectite content, and we processed reflectance spectra for continuum removal to derive clay and carbonate mineral absorption depth (H). High correlation between absorption depth and mineral content was shown for clay and mineral carbonate with field measured spectra. Based on the result for the field spectra, we proposed and tested a fast index based on the absorption depth of clay and carbonate minerals with a hyperspectral image of the area. The detected hydrocarbon microseepage anomalies matched well with those shown in the geological map.
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  • Almeida-Filho, R., Miranda, F. P., Yamakawa, T., 1999. Remote Detection of a Tonal Anomaly in an Area of Hydrocarbon Microseepage, Tucano Basin, North-Eastern Brazil. International Journal of Remote Sensing, 20(13): 2683-2688. https://doi.org/10.1080/014311699212029
    Chen, S. B., Zhao, Y., Zhao, L., et al., 2017. Hydrocarbon Micro-Seepage Detection by Altered Minerals Mapping from Airborne Hyper-Spectral Data in Xifeng Oilfield, China. Journal of Earth Science, 28(4): 656-665. https://doi.org/10.1007/s12583-015-0604-1
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    Gemail, K., Abd-El Rahman, N. M., Ghiath, B. M., et al., 2016. Integration of ASTER and Airborne Geophysical Data for Mineral Exploration and Environmental Mapping: A Case Study, Gabal Dara, North Eastern Desert, Egypt. Environmental Earth Sciences, 75(7): 1-12. https://doi.org/10.1007/s12665-016-5368-0
    He, Z. L., Deng, X. L., Ai, S. M., 2014. Hydrocarbon Micro-Seepage Anomalies Detection Algorithm Based on FPCS for Hyperspectral Remote Sensing Data. Applied Mechanics and Materials, 596: 457-462. https://doi.org/10.4028/www.scientific.net/amm.596.457
    Hörig, B., Kühn, F., Oschütz, F., et al., 2001. HyMap Hyperspectral Remote Sensing to Detect Hydrocarbons. International Journal of Remote Sensing, 22(8): 1413-1422. https://doi.org/10.1080/01431160010013450
    Hou, F., Wang, D., Cai, Y., et al., 2011. The Abnormal Information Extraction of the Hydrocarbon Micro-Seepage Based on the Hyperspectral Image. Proceedings of 2011 19th International Conference on Geoinformatics, Shanghai. 1-4
    Huang, Z. Q., Yao, Z. X., Cheng, M. H., 2014. Lithologic Anomaly Identification of Hydrocarbon Microseepages in Kelasu Fold-and-Thrust Belt, West China Using ASTER Imagery. Geoscience and Remote Sensing Symposium IEEE, Québec. 863-866 https://doi.org/0.1109/igarss.2014.6946561
    Kruse, F. A., Boardman, J. W., Huntington, J. F., 2003. Comparison of Airborne Hyperspectral Data and Eo-1 Hyperion for Mineral Mapping. IEEE Transactions on Geoscience and Remote Sensing, 41(6): 1388-1400. https://doi.org/10.1109/tgrs.2003.812908
    Kühn, F., Oppermann, K., Hörig, B., 2004. Hydrocarbon Index-An Algorithm for Hyperspectral Detection of Hydrocarbons. International Journal of Remote Sensing, 25(12): 2467-2473. https://doi.org/10.1080/01431160310001642287
    Lammoglia, T., de Souza Filho, C. R., 2011. Spectroscopic Characterization of Oils Yielded from Brazilian Offshore Basins: Potential Applications of Remote Sensing. Remote Sensing of Environment, 115(10): 2525-2535. https://doi.org/10.1016/j.rse.2011.04.038
    Lammoglia, T., de Souza Filho, C. R., 2013. Unraveling Hydrocarbon Microseepages in Onshore Basins Using Spectral-Spatial Processing of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Data. Surveys in Geophysics, 34(3): 349-373. https://doi.org/10.1007/s10712-013-9225-3
    Liu, N., Chen, X., Li, Q. Q., 2014. Study on the Alteration Minerals Caused by Oil and Gas Microseepage by Extracting Endmembers from Hyperion. IEEE International Geoscience and Remote Sensing Symposium, Quebec City. https://doi.org/10.1109/igarss.2014.6946563
    Petrovic, A., Khan, S. D., Thurmond, A. K., 2012. Integrated Hyperspectral Remote Sensing, Geochemical and Isotopic Studies for Understanding Hydrocarbon-Induced Rock Alterations. Marine and Petroleum Geology, 35(1): 292-308. https://doi.org/10.1016/j.marpetgeo.2012.01.004
    Post, J. L., Crawford, S. M., 2014. Uses of Near-Infared Spectra for the Identification of Clay Minerals. Applied Clay Science, 95: 383-387. https://doi.org/10.1016/j.clay.2014.02.010
    Pour, A. B., Hashim, M., Park, Y., et al., 2017. Mapping Alteration Mineral Zones and Lithological Units in Antarctic Regions Using Spectral Bands of ASTER Remote Sensing Data. Geocarto International, 33(12): 1281-1306. https://doi.org/10.1080/10106049.2017.1347207
    Schaepman, M. E., Ustin, S. L., Plaza, A. J., et al., 2009. Earth System Science Related Imaging Spectroscopy—An Assessment. Remote Sensing of Environment, 113: S123-S137. https://doi.org/10.1016/j.rse.2009.03.001
    Shi, P. L., Fu, B. H., Ninomiya, Y., et al., 2012. Multispectral Remote Sensing Mapping for Hydrocarbon Seepage-Induced Lithologic Anomalies in the Kuqa Foreland Basin, South Tian Shan. Journal of Asian Earth Sciences, 46: 70-77. https://doi.org/10.1016/j.jseaes.2011.10.019
    Tian, Q., 2012. Study on Oil-Gas Reservoir Detecting Methods Using Hyperspectral Remote Sensing. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B7: 157-162. https://doi.org/10.5194/isprsarchives-xxxix-b7-157-2012
    van der Meer, F. D., van der Werff, H. M. A., van Ruitenbeek, F. J. A., et al., 2012. Multi- and Hyperspectral Geologic Remote Sensing: A Review. International Journal of Applied Earth Observation and Geoinformation, 14(1): 112-128. https://doi.org/10.1016/j.jag.2011.08.002
    van der Meer, F. D., 2004. Analysis of Spectral Absorption Features in Hyperspectral Imagery. International Journal of Applied Earth Observation and Geoinformation, 5(1): 55-68. https://doi.org/10.1016/j.jag.2003.09.001
    van der Meer, F. D., van Dijk, P., van der Werff, H. M. A., et al., 2002. Remote Sensing and Petroleum Seepage: A Review and Case Study. Terra Nova, 14(1): 1-17. https://doi.org/10.1046/j.1365-3121.2002.00390.x
    van der Meijde, M., Knox, N. M., Cundill, S. L., et al., 2013. Detection of Hydrocarbons in Clay Soils: A Laboratory Experiment Using Spectroscopy in the Mid- and Thermal Infrared. International Journal of Applied Earth Observation and Geoinformation, 23: 384-388. https://doi.org/10.1016/j.jag.2012.11.001
    Wu, X. Y., Xu, X. M., Wu, C. F., et al., 2014. Responses of Microbial Communities to Light-Hydrocarbon Microseepage and Novel Indicators for Microbial Prospecting of Oil/Gas in the Beihanzhuang Oilfield, Northern Jiangsu, China. Geomicrobiology Journal, 31(8): 697-707. https://doi.org/10.1080/01490451.2013.843619
    Xu, D. Q., Ni, G. Q., Jiang, L. L., et al., 2008. Exploring for Natural Gas Using Reflectance Spectra of Surface Soils. Advances in Space Research, 41(11): 1800-1817. https://doi.org/10.1016/j.asr.2007.05.073
    Xu, N., Hu, Y. X., Lei, B., et al., 2011. Mineral Information Extraction for Hyperspectral Image Based on Modified Spectral Feature Fitting Algorithm. Spectroscopy and Spectral Analysis, 31: 1639-1643 (in Chinese with English Abstract)
    Yang, H., Zhang, J., van der Meer, F., et al., 1998. Geochemistry and Field Spectrometry for Detecting Hydrocarbon Microseepage. Terra Nova, 10(5): 231-235. https://doi.org/10.1046/j.1365-3121.1998.00196.x
    Zhang, C. Y., Qin, Q. M., Chen, L., et al., 2015. Rapid Determination of Coalbed Methane Exploration Target Region Utilizing Hyperspectral Remote Sensing. International Journal of Coal Geology, 150-151: 19-34. https://doi.org/10.1016/j.coal.2015.07.010
    Zhao, H., Zhang, L., Zhang, X., et al., 2015. Hyperspectral Feature Extraction Based on the Reference Spectral Background Removal Method. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6): 1-15. https://doi.org/10.1109/jstars.2015.2401052
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Remote Detection of Hydrocarbon Microseepage in a Loess Covered Area

    Corresponding author: Liang Zhao, zhaoj12@mails.jlu.edu.cn
  • 1. College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
  • 2. Division of Petroleum Geology, China Geological Survey, Beijing 100037, China
  • 3. Department of Earth Sciences, Indiana University-Purdue University Indianapolis, Indianapolis 46202, USA
  • 4. College of Earth Science, Jilin University, Changchun 130061, China

Abstract: Hydrocarbon microseepage can result in related near-surface mineral alterations. In this study, we evaluated the potential of detecting these alterations with field measured and satellite acquired hyperspectral data. Fourteen soil samples and reflectance spectra were collected in the Xifeng Oilfield, a loess covered area. Soil samples were analyzed in the laboratory for calcite, dolomite, kaolinite, illite, and mixed-layer illite/smectite content, and we processed reflectance spectra for continuum removal to derive clay and carbonate mineral absorption depth (H). High correlation between absorption depth and mineral content was shown for clay and mineral carbonate with field measured spectra. Based on the result for the field spectra, we proposed and tested a fast index based on the absorption depth of clay and carbonate minerals with a hyperspectral image of the area. The detected hydrocarbon microseepage anomalies matched well with those shown in the geological map.

0.   INTRODUCTION
  • Hydrocarbons present at the land surface are a critical indicator for oil-gas microseepage (van der Meijde et al., 2013; Tian, 2012; Schaepman et al., 2009; van der Meer et al., 2002). Accumulated hydrocarbons can leak from the trap to the surface due to partial failure or a temporary breach of the top seal, resulting in numerous changes in the rocks and soils through which they pass. At the surface, hydrocarbon seepage could be responsible for subtle differences in mineral or the vegetation (Hörig et al., 2001). The clay minerals alteration, carbonate anomalies and red-bed bleaching as results of hydrocarbon microseepage was found in an oil and gas area (Yang et al., 1998). Mineral alteration near the surface caused by hydrocarbon leaks can be detected using diagnostic spectral features (Lammoglia and de Souza Filho, 2011).

    Multispectral remote sensing can be applied to detect mineral alteration, and has contributed significantly to oil-gas exploration (Pour et al., 2017). Mineral alteration is linked to its chemical composition and fundamental molecular vibrations which can be detected by multispectral remote sensing technology in visible and near infrared (VNIR) and shortwave infrared (SWIR) band (Gemail et al., 2016). The mineralization altered by hydrocarbon microseepage of a fold-and-thrust belt was mapped from advanced spaceborne thermal emission and reflection radiometer (ASTER) image data (Huang et al., 2014; Shi et al., 2012). Many methods such as band ratio, principle component analysis (PCA) and band fusion are used to explore hydrocarbon micro-seepages using multispectral data. The landsat thematic mapper (TM) band ratio (TM5/7) method has been applied to explore hydrocarbon microseepage alteration information in Jianghan Basin, China (Ding et al., 1993). The PCA and band ratios are used to map minerals associated with hydrocarbon microseepage in the Tucano Basin, northeastern Brazil (Almeida-Filho et al., 1999). Because of low spatial resolution, multispectral remote sensing has a number of limitations for exploring of hydrocarbon microseepage. With the development of remote sensing, hyperspectral remote sensing provides higher spatial resolution and better spectral resolution to detect mineral alteration. Many studies have used hyperspectral remote sensing to extract mineral alteration successfully. Comparison of Hyperion data to airborne hyperspectral data (AVIRIS) has shown that hyperspectral satellite Hyperion provides the ability to remotely map basic surface mineralogy in the Nevada and northern Death Valley sites (Kruse et al., 2003). Hyperion hyperspectral remote sensing image was used to extract abnormal alteration minerals caused by hydrocarbon microseepage in the Ordos Basin in western China (Liu et al., 2014).

    The methods used widely for hyperspectral data to detect hydrocarbon microseepage are spectral angle mapping (SAM), spectrum feature fitting (SFF) and the pixel purity index (PPI) method. The SAM method is a spectrum matching technique which calculate the angle between the spectra in an n-dimensional space (van der Meer et al., 2012). The SAM method was used to build an integrated, practical system for oil and gas exploration of mineral alteration, which can improve the accuracy of exploration targets determined through field surveys (Xu et al., 2008). The SFF method matches the library-endmember spectra and pixel spectra based on absorption feature characteristics. The SAM and SFF methods were applied on HyMap hyperspectral data to map changes in mineral content within outcrops and used to identify lithological changes and chemical changes in areas affected by hydrocarbon microseepage in Lisbon Valley, Utah (Petrovic et al., 2012). A modified spectral feature fitting method with user-defined constraints in spectral absorption feature was introduced to extract more accurate target information from hyperspectral image (Xu et al., 2011). The SAM was used to detect alteration minerals in a thin oil slick of microseepage in Liaodong Bay, China using Hyperion data (Tian, 2012). The PPI technique unmix the mixed pixel spectra as (linear or non-linear) combinations of Hyperspectral endmember spectra (van der Meer et al., 2012). The principle of spectral matching is to compare the unknown spectrum with the standard spectrum that come from the United States Geological Survey (USGS) spectral library. If the spectrum reconstruction of hyperspectral data is not good enough, then spectral matching method is no longer applicable.

    In recent years, the diagnostic spectral absorption features of mineral are applied to detect hydrocarbon microseepage. Mineralogical information and chemical composition can be obtained rapidly from soil spectral absorption in the visible and near-infrared bands (Galvão et al., 2008). Spectral absorption characterized by the absorption position (P), depth (H), width (W), symmetry (S), and area (A) have been used to quantitatively estimate sample composition from field and laboratory hyperspectral reflectance data, and develop mapping methods for hyperspectral image data analysis (van der Meer, 2004). Using absorption depth, Kühn et al. (2004) developed the hydrocarbon index (HI) for hyperspectral detection of hydrocarbons and demonstrated how to apply HI with remotely sensed images for oil and hydrocarbon detection. The HI method and the SAM method were applied to Hyperion data in the Ordos Basin, China to detect hydrocarbon microseepage (Hou et al., 2011). The feature-oriented principal component selection (FPCS) and band ratio method are used to detect hydrocarbon microseepage in the Xifeng Oilfield, China, using shortwave infrared airborne spectrographic imager (SASI) hyperspectral data (He et al., 2014). A new spectral feature extraction method named reference spectral background removal (RSBR) was developed to extract accurate absorption centers and absorption widths from mixing spectra that correlated strongly and linearly to mineral composition (Zhao et al., 2015). Chen et al. (2017) used airborne hyper-spectral data to detect altered minerals hydrocarbon micro-seepage from in Xifeng Oilfield, China. These studies are based on spectral absorption features and can effectively extract hydrocarbon microseepage with redundancy information in hyperspectral images.

    In this study, we analyzed 14 soil samples located in the Xifeng Oilfield for calcite, dolomite, kaolinite, illite, and mixed-layer illite/smectite content. We calculated the spectral position (P), depth (H), width (W), symmetry (S), and area (A) from reflectance spectra. We then used regression analysis to measure the correlation between spectral absorption features and mineral content and the quantity of cations Fe3+ and Fe2+. From this, we proposed a fast index for hydrocarbon microseepage detection using hyperspectral satellite images. Our development of this NDVI-like index was based on results from multiple regression analysis of spectral absorption parameters (independent variables) against mineral contents (dependent variables) indicating hydrocarbon related compositional alteration in loess soils. We evaluated its effectiveness in the Xifeng Oilfield, a loess covered area located in Gansu Province, China.

1.   THEORY AND DATA
  • Hydrocarbon microseepage theory establishes a series of cause-effect relationships describing the formation of special surface anomalies that result from hydrocarbon microseepage and related mineral alteration (Wu et al., 2014). The theory suggests that CO2, H2O, inert gases, methane and other hydrocarbon materials enclosed in hydrocarbon traps can penetrate through the overlying, compact rock, reach the earth's surface, and become responsible for the presence of "red-fading", "clay-mineralization", and "carbonatization" enrichment phenomenon.

    In the hydrocarbon microseepage model, the main reactions initiated by hydrocarbons (CH) are

    The reaction in Eq. (1) causes "carbonatization" and "red-fading", and "clay-mineralization" and botanical anomalies attributed to generated H2S gas. Carbonate is promoted by the chemical oxidation and bacteriologic conversion in the hydrocarbon microseepage process. Carbonates can be detected by their spectral absorption feature at 2.34 μm. The "red-fading" phenomenon can be created by the reaction in equation (2) where ferric ion (Fe3+) is converted to ferrous ion (Fe2+) in rocks and soils caused by the action of acid/reducing solutions. This phenomenon can change spectral features of ferric Fe in the visible (VIS) and near infrared (NIR) region. Hematite, goethite, and limonite can be detected in these spectral ranges (Lammoglia and de Souza Filho, 2013). The clay minerals of kaolinite, illite, and chlorite generates by feldspars in acid environment caused by CO2 and H2S near surface. Spectral features of clay minerals can be detected in soils near 1.40 μm and near 2.2 μm.

    The reactions in Eqs. (3)-(6) reflect hydrocarbon related reductions in different forms. The products from these alterations exhibit detectable spectral absorption characteristics that are the foundations of hyperspectral detection of hydrocarbon microleakage.

  • Near infrared wavelengths from 1.3 to 2.5 μm could be a significant remote-sensing tool for clay mineral detection due to hydroxyl-group vibration (Post and Crawford, 2014). Hydroxyl is generally bound to Mg or Al. The first overtones of OH stretches occur at approximately 1.4 μm and the combinations of the H-O-H bend with OH stretches are found near 1.9 μm. Those near 2.2 μm are characteristic of combinations involving OH-stretching and A1OH-bending modes, but 1.4 and 1.9 μm are also water absorption positions. The location and relative intensity of groups in band at 2.2 μm is characteristic of specific clay minerals. Carbonates also appear in the diagnostic vibrational absorption band due to the CO32− ion at 2.30-2.35 μm (van der Meer, 2004). Most minerals contain Fe2+ and Fe3+. The charge-transfer of this forms weak absorption between 0.40-0.55 μm. Most Fe2+ has deeper and broader absorption between 0.8 and 1.2 μm relative to samples with the least Fe2+ (Lammoglia and de Souza Filho, 2013). The electron transition of Fe3+ forms a strong absorption band at 0.89 μm.

    The mineral and chemical composition of samples can be quantitatively estimated using spectral absorption features in the visible and near-infrared wavelengths from field and laboratory hyperspectral reflectance data (van der Meer, 2004). We used the spectral absorption parameters P, H, W, S and A to describe spectral absorption features after continuum removal (Fig. 1). In turn, we used continuum removal to normalize measured reflectance spectra and isolate individual absorption features of interest from a common baseline. This continuum is the overall albedo of the reflectance curve as shown in Fig. 1. The continuum removed spectrum is the ratio of the observed spectrum and continuum. The P is the spectral position of deepest absorption position. The H is the difference between unity and the reflectance of the spectral absorption position. The W is the full width at half maximum in the spectral absorption position. The A is full absorption area in the spectral absorption position. The spectral absorption symmetry (S) is ratio between absorption area on the left side (A1) and full absorption area (A), as S=A1/A.

    Figure 1.  Examples of a continuum, continuum removal, and isolated absorption features.

  • The Xifeng Oilfield, is located in the Loess Plateau of eastern Gansu Province, China. Geologically, this oilfield is developed in the Ordos Basin, the second largest sedimentary basin in China. In the Middle and Late Triassic periods, the warm and humid climate made the Ordos Lake gradually evolve into a typical freshwater lake. The Upper Triassic Yanchang Formation becomes an important oil bearing formation (Dou et al., 2017). The elevation of the plateau ranges from 1 050 to 1 460 m. The surface is entirely covered by loess and there are relatively abundant water and natural resources within the basin, such as coal, natural gas, petroleum and halite.

    Field work was conducted in August 2010. Reflectance spectra were measured using an ASD FieldSpec Pro field portable spectrometer with the spectral range of 350-2 500 nm and spectral resolution 3 nm for the range 350-1 000 and 10 nm for 1 000-2 500 nm. In total, 14 soil spectra were measured at 13 field locations. For the locations where spectral measurements were taken, fresh loess soil was sampled at a depth of 5-10 cm below the land surface. The line of sight was perpendicular to scale.

    Due to instrument instability, variation in illumination conditions, and atmospheric water vapor interference, large reflectance fluctuations exist in spectral ranges 1 350-1 420, 1 800-1 970, and 2 308-2 500 nm. To accurately characterize soil spectral characteristics, we conducted noise-suppressed processing on the measured reflectance spectra. We applied high-order polynomial curve fitting to smooth the whole curve. The order is 50 (Fig. 2).

    Figure 2.  Soil spectral measurements. Noise has been removed.

  • We used X-Ray Diffraction (XRD) to analyze 14 field samples collected from the study area for calcite, dolomite, kaolinite, illite and mixed-layer illite/smectite content. With an atomic absorption spectrometer, the content of CaCO3, CO32-, Fe3+, and Fe2+ were also measured (Table 1). In sites G1971, G1035, G1345, G1765 and G1866, soil sample composition test analysis does not indicate clay-mineralization compared with other soil samples. In sites G0724, G1449, G1660, G1866, soil sample composition reveals carbonatization compared with other samples. In sites G0313, G0622, G0828 and G1971, soil sample composition test analysis indicates a "red-fading" phenomenon compared with other soil samples. This analysis suggests that "carbonatization", "red-fading", and "clay-mineralization" phenomena do not necessarily appear in single field soil samples.

    Table 1.  Content of minerals and iron ions in soil samples. Light gray shadow represents samples where the component content is higher than the sum of mean and deviation. Dark gray shadow represents samples where the component content is lower than an average (plus standard deviation) indicator

  • Image data used in this study are the Hyperion L1R product acquired on January 20, 2003. Hyperion is one of three sensors onboard the earth observation satellite EO-1 launched by NASA on January 21, 2000. The sensor obtains hyperspectral images in the VNIR (400-1 000 nm) and SWIR (900-2 500 nm) regions using the push-broom method. Atmospheric correction and band removal were performed during preprocessing. The Hyperion VNIR sensor has 70 bands and the SWIR has 172 bands, however we used only 198 bands and set the others to zero values.

    The Hyperion spectral bands in the ranges 121-127, 167-178 and 224 are affected by water vapor (Zhang et al., 2015). In this study, we removed the ranges 1-7, 58-78, 121-127, 167-178 and 224-240. The bad line is caused by sensor calibration and interpolated by immediate left and right neighbors. The fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) was applied on Hyperion L1R data for atmospheric correction.

2.   RESULTS
  • After continuum removal, the absorption features of carbonate minerals in the spectral region 2 250-2 380 nm and the absorption features of clay minerals at 2 150-2 280 nm were highlighted. In the case of Fe3+ and Fe2+, there is an obvious absorption position between 350 and 550 nm in the soil spectra (Fig. 3). The spectral absorption parameters P, H, W, S and A in 350-550 and 2 250-2 380 nm are calculated to make regression analysis to investigate the relationship between absorption parameters and mineral contents as well as the density of Fe3+ and Fe2+ in soil samples.

    Figure 3.  Continuum removed soil spectra.

    This relationship was evaluated with R2 and significance (Sig). R2 values close to 1 indicate a high correlation between the dependent and independent variables. Sig represents a test of significance with the Sig value less than 0.05 indicating that the regression equation passes the test and accurately represents a correlation between the dependent and independent variables. Compared with other regression models, cubic curve estimation gave rise to the highest R2 value (Table 2). We applied multiple stepwise regression analysis to select the statistically significant variables. The correlation of five spectral absorption features generally sort as follow: H, A, S, W, P. Total carbonate is the sum of calcite, dolomite and calcium carbonate. Calcite has the highest correlation (R2=0.8) with five spectral absorption features. The calcium carbonate has the lowest correlation with five spectral absorption features. Total clay and all clay mineral including illite/smectite, illite and kaolinite are not correlated with five spectral absorption features. The five spectral absorption features are highly positively correlated with Fe2+ and negatively correlated with Fe3+.

    Correlation R2 Sig Regression analysis
    Total carbonate H > A > S 0.530 0.048 Y=30.44-3.392A+336.561H-17.163S
    Calcite H > A > S 0.824 0.000 Y=10.914-1.706A+171.41H-6.307S
    H > A > S > W 0.836 0.001 Y=7.776-2.101A+185.222H-4.814S+0.44W
    H > A > S > W > P 0.846 0.004 Y=45.453-2.099A+182.186H-3.832S+0.045W-0.016P
    Dolomite H > A > S > P 0.591 0.065 Y= -7.661-0.055A+6.308H-1.878S+0.004P
    Calcium carbonate H > A > S > W > P 0.376 0.493 Y=771.176-10.808A+71.049H-368.836S-0.307W-0.313P
    Total clay H > A > S > W > P 0.214 0.812 Y= -125.334-0.046A-31.107H+2.423S+0.0025W+0.068P
    Illite/smectite H > A > S > W > P 0.288 0.672 Y= -183.693+0.364A-86.156H+1.623S-0.009W+0.089P
    Illite H > A > S > W > P 0.114 0.712 Y=62.124-0.332A-40.629H+1.709S-0.013W-0.025P
    Kaolinite H > A > S 0.101 0.774 Y=5.392-0.095A+14.432H-0.842S
    Fe2+ H > A > S > W (+) 0.816 0.008 Y=31.328+0.319A-55.304H+1.008S-1.67W-0.000 09P
    H > A > S > W (+) 0.816 0.002 Y=31.401+0.32A-55.49H+0.997S-0.167W
    Fe3+ H > A > W > S (-) 0.640 0.039 Y= -40.328-0.302A+51.144H-0.283S+0.23W
    H > A > W > S (-) 0.634 0.015 Y= -41.411-0.273A+45.219H+0.235W

    Table 2.  Correlation between minerals and spectral absorption features. The spectral absorption position (P), depth (H), width (W), symmetry (S), and absorption area (A) are used to multiple stepwise regression analysis

    Among the five absorption parameters, the H resulted in the highest correlation to the mineral content, Fe3+ and Fe2+ using cubic curve regression (Table 2). The H is highly negatively correlated with Fe3+ and positive correlated with Fe2+ (Table 2). In this case, the H is not significantly correlated with mineral clay.

  • To find an effective and fast way to investigate the hydrocarbon microseepage anomalies in a loess covered area using Hyperion data, we developed an indicator. The spectral absorption depth (H) provided this opportunity by automatically detecting the "red-fading", "clay-mineralization", and "carbonatization" enrichment phenomenon of Hyperion image data.

    Our absorption feature regression indicates that absorption depth and mineral content were highly correlated. Although hydrocarbon microseepage anomalies can be delineated only using absorption depth for individual minerals, we proposed an index for effectively detecting hydrocarbon microseepage anomalies in this study, where two kinds of alteration (clay-mineralization and carbonatization) are combined

    H1, H2 and FI represent the absorption depth of clay-mineralization, carbonatization and fast index, respectively. In the altered area, the sum of H1 and H2 will be larger than that where no alteration area exists and the absolute difference of H1 and H2 will be smaller than that of where alteration area exists. In this case, the value of FI will also be larger. In the no alteration area, H1+H2 will be relatively small and |H1-H2| will be relatively large. In comparison, the value of FI will be small. Therefore, the area of oil and gas can have been determined by the size of the FI. In such cases though, the Hyperion spectral bands at 350-420 nm have no data and cannot be used to calculate the absorption depth of iron ions.

  • After atmospheric calibration with FLAASH, we used the continuum removal algorithm to transform Hyperion image data. This enhanced the contrast of individual absorption features prior to calculating the spectral absorption depth (H) of clay mineralization and carbonatization. We used Eq. (7) to calculate FI for each of the 14 soil samples based on their spectral absorption depths H1 (2 213 nm, clay) and H2 (2 335 nm, carbonate) (Table 3). With the exception of G0313, all samples in the known oil and gas area have FI values higher than the sum of mean and standard deviation of all FI values collected. Except sample G0313 and G0828, all samples near the oil wells have FI values higher than the sum of mean and standard deviation of all FI values. There are 5 samples, namely G1449, G1453, G1660, G1971 and G2075, which are located within the known oil and gas region and close to oil wells. We used the mean and standard deviation of FI of these 5 samples as a threshold to detect anomalous clay-mineralization and carbonatization in the study area. We applied this threshold to detect clay-mineralization and carbonatization information in Hyperion image data in our study area (Table 3). The absorption depth (H) and FI of carbonate and clay minerals. All soil samples are displayed with geographic information that belongs to known oil and gas areas or near oil wells. Geological mapping in this area determined the oil and gas area.

    Numbers H1
    (2 335 nm)
    H2
    (2 213 nm)
    FI Oil and gas point
    (Yes/No)
    Near oil wells
    (Yes/No)
    G0313 0.054 5 0.050 1 23 Y Y
    G0622 0.026 2 0.072 8 2.3 N N
    G0724 0.029 6 0.071 7 2.4 N N
    G0828 0.054 4 0.046 3 12 N Y
    G0931 0.033 3 0.063 0 25 N N
    G1035 0.029 4 0.047 5 4.2 N N
    G1345 0.051 1 0.451 0 16 N N
    G1449 0.161 9 0.165 3 81 Y Y
    G1453 0.098 8 0.096 3 78 Y Y
    G1660 0.047 1 0.046 5 147 Y Y
    G1765 0.040 7 0.045 4 18 N N
    G1866 0.064 5 0.067 2 48 N N
    G1971 0.036 9 0.038 1 63 Y Y
    G2075 0.066 4 0.068 7 58 Y Y

    Table 3.  The absorption depth (H) and FI of carbonate and clay minerals. All soil samples are displayed with geographic information that belongs to known oil and gas areas or near oil wells. Geological mapping in this area determined the oil and gas area

    A calculation suggests that FI values above 90 indicate possible hydrocarbon microseepage anomalies. Figure 4 shows the anomalies plotted on a Hyperion image of the study area. The blue line indicates the boundary of the oil and gas region adopted from a geological map.

    Figure 4.  Hydrocarbon microseepage anomalies information detected by applying the developed fast index to a Hyperion image of the study area.

    The comparison between the fast index map and the geological map of this area indicates that most of hydrocarbon microseepage abnormal locations match oil and gas areas. From Fig. 4 we see that the anomalous microseepage region well covers the known oil and gas region, and the anomaly intensity in the oil and gas region is in general greater than that outside it. Therefore, we conclude that our method can be used to extract hydrocarbon microseepage from the hyperspectral satellite image of the study area.

3.   DISCUSSION
  • The aim of the research is to detect hydrocarbon microseepage anomalies in the Xifeng Oilfield. Based on the high positive correlation between the spectral absorption depth (H) and altered mineral content, we developed a fast index using the absorption depth of clay and carbonate minerals to detect hydrocarbon microseepage anomalies. The detected hydrocarbon microseepage anomalies matched well with those shown in the geological map.

    In the previous studies, hydrocarbon microseepage anomalies were detected widely using spectrum matching technique which is based on comparing the unknown spectrum with the reference spectrum. If the spectrum reconstruction of hyperspectral data is not good enough, then spectral matching method is no longer applicable. In the present study, the fast index can effectively detect carbonate and clay alteration minerals, but cannot be implemented in ferric ion alteration minerals. This is due to the Hyperion spectral data missing at 350-420 nm. Only fourteen field samples collected from the study area were tested. The low number of measured field samples spectrum will affect the selection of fast index threshold value and limit the alteration mineral extraction in this study area. Hence, important direction for further work is to use more soil samples to measure the spectral absorption depth.

4.   CONCLUSIONS
  • Our study developed an approach for the detection of hydrocarbon microseepage in loess areas with field measured hyperspectral reflectance data. The approach is based on a correlation analysis between the contents of carbonate minerals, clay minerals, Fe2+ and Fe3+ to the corresponding absorption depth parameter. Moreover, our approach has been tested with satellite hyperspectral image of a loess geological area. Our results support the following conclusions: (1) A multivariate stepwise regression analysis revealed that the spectral absorption position (P), depth (H), width (W), symmetry (S), and absorption area (A) were correlated to the content of clay, carbonate minerals and iron ions to various degrees. The absorption depth (H) had the highest correlation and can be applied to detect hydrocarbon microseepage. (2) The fast index based on absorption depth is effective in extracting oil and gas microseepage anomalies present in the Xifeng Oilfield, a loess covered area. This fast index can provide information towards further exploration of oil and gas.

ACKNOWLEDGMENTS
  • This paper was supported by the National Natural Science Foundation of China (No. 41402293) and the GF-5 Satellite Hyperspectral Porphyry Deposit Alteration Information Intelligent Identification Technology Program (No. 04-Y20A35-9001-15/17-4). The final publication is available at Springer via https://doi.org/10.1007/s12583-019-1235-8.

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