Data-driven high-resolution gas-bearing prediction in tight sandstones: A case study from block L, Eastern Ordos Basin
Year: 2025
Type: article
Abstract: The Upper Paleozoic Shihezi Formation in Block L of the eastern Ordos Basin harbors extensive tight sandstone gas reservoirs. However, these reservoirs exhibit strong heterogeneity, thin sand bodies, and overlapping elastic properties between gas- and water-bearing layers, which significantly limit the effectiveness of conventional pre-stack inversion methods in delineating thin sand bodies and predicting gas saturation. To address these challenges, we propose an integrated high-resolution gas prediction technique combining geostatistical inversion with deep learning. First, within a Bayesian sequential inversion framework, we jointly inverted well-log data, seismic data, and geological constraints to obtain high-resolution elastic parameters, substantially improving the identification of thin sand bodies (<5 m). Second, we employed a long short-term memory network to extract temporal features from inverted elastic parameter sequences and establish a non-linear mapping between gas/water-sensitive attributes and water saturation; this step incorporates horizon constraints and an attribute optimization strategy to enhance prediction accuracy. Field applications demonstrated that our method achieved superior performance compared to conventional approaches, with an 85% consistency rate between predicted gas saturation and drilling results. The integration of geostatistical inversion and deep learning provides a robust workflow for characterizing thin, heterogeneous tight gas reservoirs, offering significant potential for optimizing exploration and development strategies in the Ordos Basin.
Source: Journal of seismic exploration
Authors: Lixin Tian, Shuai Sun, Qixin Li, Jingxue Shi
Language: English
Subfield: Geophysics
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414970364