Well logging evaluation of clastic reservoir productivity in the Wulanhua depression, Hailaer basin, China
Year: 2026
Type: article
Abstract: Productivity evaluations are essential for reservoir characterization and development, particularly in low-porosity and low-permeability clastic systems. In the Wulanhua (WH) depression of the Hailaer basin in China, most reservoirs exhibit porosities below 15% and permeabilities lower than 100 × 10 −3 μm 2 , posing significant challenges for reliable productivity prediction. To address this, a multisource tiered evaluation approach is proposed herein that integrates mercury injection capillary pressure, nuclear magnetic resonance (NMR), and conventional well logging data. Quantitative analysis shows that reservoirs with mean pore-throat radii greater than 0.12 μm and displacement pressures below 5 MPa generally achieve natural productivities above 20 t/d, whereas those with radii below 0.05 μm require stimulation to reach industrial levels. NMR-based parameters, including an S 2 + S 3 pore fraction exceeding 65% and a T 2 geometric mean time greater than 20 ms, correspond to high-yield zones (>15 t/d). When only conventional logs are available, deep resistivity (>20 Ω·m), low natural gamma (<70 API), and a resistivity multiplication coefficient (AII) exceeding 1 × 10 6 can effectively be used to discriminate productive intervals. Field validation of this approach demonstrates that the integrated scheme predicts the well productivity with an average deviation of ±15%. The novelty of this study lies in the establishment of a quantitative and multitiered evaluation framework that is adaptable to varying data availability, providing a robust reference for efficient development of tight clastic reservoirs in the WH depression and similar basins.
Source: Frontiers in Earth Science
Authors: Xianhua Huang, Houjiang Fan, Jianru Tang, Jialin Zhao, Jin Li
Language: English
Subfield: Mechanics of Materials
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7122717121