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Original Paper | Open Access

Research and application of thin sandstone identification technique based on post-stack seismic data: A case study of Ecuador’s M reservoir

Qi-Ming ZhengaHui Chenb( )Qiu-Xiang ZhubXue-Xiang GuaHai Xuc,dFa-You LicBen-He ChengdYu-Liang FengdChuan Wangd
School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing, 100083, China
Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, 610059, Sichuan, China
Research Institute of Petroleum Exploration and Development, Sinopec (Beijing), Beijing, 102206, China
Sinopec International Petroleum Exploration and Production Corporation (Beijing), Beijing, 100029, China

Edited by Meng-Jiao Zhou

Peer review under the responsibility of China University of Petroleum (Beijing).

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Abstract

The M sandstone member within the Cretaceous Napo Formation is a critical hydrocarbon reservoir in the Oriente Basin's foredeep belt. However, its characterization is severely challenged by its thin-bed nature (2–8 m) and rapid lateral facies variations. The available 3D seismic data has a dominant frequency of about 45 Hz, yielding an apparent resolution of approximately 26 m. Conventional seismic techniques, such as deconvolution, spectral whitening, and inverse Q filtering, rely on stationary wavelet assumptions and are sensitive to noise and parameter inaccuracies. This limitation leads to the misinterpretation of the “isolated reservoir” in the M01 well, which has long hindered progress in exploration and development in the area. To address these challenges, we develop a novel high-resolution seismic processing workflow centered on precise reflectivity inversion. Our approach systematically integrates three key innovations: First, targeted denoising is applied to enhance the signal-to-noise ratio, ensuring the stability of subsequent inversion. Second, we overcome the stationary wavelet assumption of conventional methods by employing the generalized S-transform to construct a discrete, time-varying wavelet library, which accurately captures the seismic wavelet's frequency attenuation with depth. Third, we integrate this time-varying wavelet model with a reflectivity inversion algorithm based on odd-even reflection theory and a least-squares optimization, enabling high-fidelity recovery of thin-layer reflection coefficients. The application of this workflow in the M Oilfield yields a transformative improvement in seismic resolution. The processed data achieves a significant broadening of the effective frequency bandwidth, elevating the dominant frequency from 45 Hz to 85 Hz. This enhancement consequently improves the theoretically detectable thickness from approximately 14 m to approximately 5 m. Most importantly, the processed seismic profiles clearly delineate ultra-thin sand bodies, ranging from 3 to 5 m, with drilling results exhibiting high consistency with seismic interpretation (e.g., successful wells M01 and M05-2 versus dry wells M05 and M05-1). The resulting seismic attributes also reveal clearer geological features, such as delta outlines and underwater distributary channels. This study successfully resolves the exploration dilemma that persisted in the area for over two decades, overturning the “isolated reservoir” model and revealing the continuous distribution characteristics of the M sandstone. The demonstrated workflow provides a robust and systematic solution for characterizing thin-bed reservoirs, directly leading to increased exploration success. The established technical framework offers a valuable and practical reference for the detailed exploration and development of similar thin sandstone reservoirs in foredeep and slope settings worldwide.

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Petroleum Science
Pages 2560-2573

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Cite this article:
Zheng Q-M, Chen H, Zhu Q-X, et al. Research and application of thin sandstone identification technique based on post-stack seismic data: A case study of Ecuador’s M reservoir. Petroleum Science, 2026, 23(5): 2560-2573. https://doi.org/10.1016/j.petsci.2026.01.013

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Received: 21 April 2025
Revised: 09 November 2025
Accepted: 13 January 2026
Published: 18 January 2026
© 2026 The Authors.

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).