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Application of MGI Inversion Technology in Coal Seam Thickness Prediction

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Proceedings of the International Field Exploration and Development Conference 2023 (IFEDC 2023)

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Abstract

The overlying strata of the No. 8 coal seam in Block XX of the Ordos Basin are Taiyuan Formation limestone, and the underlying strata are Benxi Formation sandstone and mudstone, with complex vertical lithology combinations. The impedance contrast between the target layer No. 8 coal seam and the roof wave is large and the impedance interface is easy to identify. However, the impedance contrast between the bottom plate wave is small and the impedance interface is difficult to identify. Moreover, due to strong reflection interference from the overlying limestone, seismic attributes cannot reflect the thickness distribution of the coal seam. Currently, the widely used sparse pulse inversion has limited longitudinal resolution and can only predict the approximate distribution of coal reservoirs, making it difficult to quantitatively predict the thickness of the No. 8 coal seam. MGI geological statistical inversion is based on geological information, applies random function theory and geological statistical methods, and can independently adjust low-frequency, medium-frequency, and high-frequency results during inversion, with high vertical resolution. Based on high-precision three-dimensional seismic, drilling, recording, and logging data, this paper first establishes a stratigraphic framework based on fine calibration of wells and shocks, and then interpolates and extrapolates logging data using the minimum curvature method to establish a P-wave impedance model. Then, based on spatial geological modeling, MGI geological statistical methods (rock physics feature analysis, variogram analysis, inversion parameter analysis, etc.) are applied to obtain a longitudinal high-resolution lithology inversion body, and the P-wave impedance profile is transformed into a lithology profile to analyze the distribution range of the coal seam. Finally, sensitive properties within the No. 8 coal are extracted from the high-resolution inversion body, and the spatial distribution characteristics of the No. 8 coal reservoir are quantitatively predicted. Compared with the coal thickness data drilled, the coal thickness map extracted by MGI geological statistical inversion reveals the developmental distribution range of the No. 8 coal seam more intuitively. This research result effectively improves the prediction accuracy of the No. 8 coal reservoir in Block XX and provides strong technical support for deploying high-yield wells.

Copyright 2023, IFEDC Organizing Committee

This paper was prepared for presentation at the 2023 International Field Exploration and Development Conference in Wuhan, China, 20–22 September 2023.

This paper was selected for presentation by the IFEDC Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the IFEDC Technical Team and are subject to correction by the author(s). The material does not necessarily reflect any position of the IFEDC Technical Committee its members. Papers presented at the Conference are subject to publication review by Professional Team of IFEDC Technical Committee. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of IFEDC Organizing Committee is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of IFEDC. Contact email: paper@ifedc.org.

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Correspondence to Pei Sun .

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Sun, P., Li, Xf., Zhang, F., Zhang, Bq., Jia, Xc., Jiang, Hy. (2024). Application of MGI Inversion Technology in Coal Seam Thickness Prediction. In: Lin, J. (eds) Proceedings of the International Field Exploration and Development Conference 2023. IFEDC 2023. Springer Series in Geomechanics and Geoengineering. Springer, Singapore. https://doi.org/10.1007/978-981-97-0268-8_11

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  • DOI: https://doi.org/10.1007/978-981-97-0268-8_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0267-1

  • Online ISBN: 978-981-97-0268-8

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