Details

Autor(en) / Beteiligte

• Niu, Zhongkun
• Yang, Zhengming
• Luo, Yutian
• Zhang, Yapu
• Zhao, Xinli
• Chang, Yilin
• Chen, Xinliang

Titel

Study on fine characterization and reconstruction modeling of porous media based on spatially-resolved nuclear magnetic resonance technology

Ist Teil von

• Open Physics, 2022-11, Vol.20 (1), p.1048-1061

Ort / Verlag

De Gruyter

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• At present, image analysis and digital core are the main approaches for porous media reconstruction modeling, and they are both based on the real pore skeleton physical structure of porous media. However, it is difficult to reconstruct the reservoir and seepage characteristics of the real samples because of the limitations of accuracy in characterization techniques (imaging). In order to solve this problem and break through the barriers caused by the lack of accuracy, Spin-echo serial peripheral interface sequence of low field nuclear magnetic resonance is used to test the saturated water rock core with spatially resolved T distributions. Based on the experimental results of 1D T distributions, a novel method for fine reconstruction modeling of porous media is proposed, and the porous media model reconstructed by this new method better reproduces the reservoir and seepage characteristics of the original samples. Taking some of the tested porous media cores (P58 and Y75) as examples, representative elementary volume (REV)-lattice Boltzmann method (LBM) is used to simulate the flow field. Ensuring that the error of standard case is only 0.36% when multi-relaxation time REV-LBM is used, the distribution of porosity and permeability have been calculated and compared with the experimental data. The overall permeability error of the reconstructed porous media model is only 6.15 and 7.60%, respectively. Furthermore, the porosity and permeability error of almost all measuring points can be maintained within 3 and 8%. In addition, this method improves the efficiency of the existing reconstruction modeling methods, reduces the test cost, and makes the reconstruction modeling of porous media easier to operate, which has promising development prospects.