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TitleQuantitative seismic interpretations to detect biogenic gas accumulations: a case study from Qaidam Basin, China
AuthorLiu, Y; Chen, Z; Wang, L; Zhang, Y; Liu, Z; Shuai, Y
SourceBiogenic gas fields in Canada and China: characterizations and new insights; by Chen, Z (ed.); Grasby, S (ed.); Bulletin of Canadian Petroleum Geology vol. 63, no. 1, 2015 p. 108-121, https://doi.org/10.2113/gscpgbull.63.1.108
Year2015
Alt SeriesEarth Sciences Sector, Contribution Series 20140095
PublisherCanadian Society of Petroleum Geologists
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf
AreaQaidam Basin; China
Lat/Long WENS 91.0000 100.0000 42.0000 34.0000
Subjectsgeophysics; fossil fuels; seismic interpretations; seismic data; seismic exploration; seismic methods; hydrocarbons; hydrocarbon potential; gas; hydrocarbon gases; petrophysics
Illustrationsprofiles
ProgramShale-hosted petroleum ressource assesment, Geoscience for New Energy Supply (GNES)
AbstractQuantitative seismic interpretations can be used to identify lithology and detect petroleum directly because rock properties and attributes resulting from advanced seismic inversion methods can be integrated effectively with existing petrophysical data and geological knowledge. We use quantitative seismic interpretations for the detection of shallow biogenic gas accumulations in Qaidam Basin, China employing an integrated workflow that incorporate petrophysical data, seismic attribute analysis, Constrained Simultaneous Inversion (C-SI) and Bayesian-based Support Vector Machine (B-SVM) inference. Previous petrophysical studies have shown that it is challenging to effectively identify gas-bearing intervals using parameters such as impedance, Poisson¿s ratio and porosity, because the reservoir sediments are unconsolidated and the depth is shallow. Resistivity well-log response is an effective tool for estimating gas saturation and identifying gas-bearing intervals. In this study, we propose the use of petroleum pore-volume, which is defined as the product of reservoir porosity and gas saturation to detect biogenic gas accumulations seismically. Rock properties inferred from seismic inversion, such as compressional velocity (Vp), shear velocity (Vs) and density, cannot be used effectively for petroleum pore-volume prediction. Therefore, we employ a Bayesian-based support vector machine approach to cross-link well-log properties, seismic AVO attributes and seismic rock properties to quantitatively predict petroleum pore-volume in 2D/3D seismic space. Because seismic information is crucial to the statistical inference, we propose C-SI to infer the Vp, Vs and density from seismic elastic impedance gathers, which can be generated from seismic gathers using a traditional recursive seismic inversion method. The C-SI procedures use the Interior-Point algorithm to optimize and solve elastic impedance equations. The Interior-Point method is a popular methods for handling constrained non-convex non-linear optimization problems that involves simultaneously inverting thousands of seismic properties. This case study indicates that the integrated study workflow is useful for quantitatively predicting petroleum pore-volume, especially in the depth-domain, and that it is an excellent potential indicator for biogenic gas accumulations in complicated geological settings.
Summary(Plain Language Summary, not published)
Biogenic gas occurs in shallow depth and is generated at low temperatures by decomposition of organic matter by anaerobic microorganisms. Biogenic gas accounts about 20% of the world's discovered gas reserves and the potential is huge. We developed a new method for detecting shallow biogenic gas accumulations and applied this method to Qaidam Basin, China. Previous studies shown that it is challenging to effectively identify gas-bearing intervals using parameters such as impedance, Poisson's ratio and porosity from conventional inversion techniques because the depth is shallow and sediments are unconsolidated. Our new method use Constrained Simultaneous Inversion and Bayesian-based Support Vector Machine to cross-link well-log properties, seismic AVO attributes and seismic rock properties to predict petroleum pore-volume. The application indicates that the integrated workflow is useful for predicting petroleum pore-volume in the depth-domain and that it is a potential tool for exploring biogenic gas accumulations in complicated geological settings.
GEOSCAN ID294802