Quantification and Prediction of Pore Structures in Tight Oil Reservoirs Based on Multifractal Dimensions from Integrated Pressure- and Rate-Controlled Porosimetry for the Upper Triassic Yanchang Formation, Ordos Basin, China
| Autor: | Kenneth A. Eriksson, Wei Li, Mei Lv, Xuefeng Qu, Xueting Zhang, Jiaqi Zhang, Wurong Wang, Dali Yue |
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| Rok vydání: | 2020 |
| Předmět: |
General Chemical Engineering
Petrophysics Tight oil Energy Engineering and Power Technology Mineralogy 02 engineering and technology Multifractal system Porosimetry 021001 nanoscience & nanotechnology Fractal dimension Permeability (earth sciences) Fuel Technology Fractal 020401 chemical engineering 0204 chemical engineering 0210 nano-technology Porosity Geology |
| Zdroj: | Energy & Fuels. 34:4366-4383 |
| ISSN: | 1520-5029 0887-0624 |
| Popis: | Understanding complex pore structures is important for evaluating tight oil reservoir performance and predicting favorable pore structure. However, quantitative characterization of pore structure in tight sandstones by combining different methods is still poorly understood. Using the Upper Triassic Yanchang Formation in Ordos Basin, China as a case study, we first introduce a new method to quantitatively characterize full-range pore-throat size distribution (PSD) through multifractal dimension analysis of integrated pressure-controlled porosimetry (PCP) and rate-controlled porosimetry (RCP). Second, we propose a technique using helium porosity and nitrogen permeability to obtain multifractal dimensions in an attempt to predict favorable pore structure in tight oil reservoirs. In the new method of obtaining full-range PSD, PCP and RCP data were merged at various positions instead of the same position for each sample. Multifractal dimension curves derived from full-range pores are divided into four segments as D₁, D₂, D₃, and D₄, corresponding to the fractal characteristics of large pores, large pore throats, small pores, and small pore throats, respectively. Among them, the fractal dimension D₂ of large pore throats and D₄ of small pore throats from the combination of PCP and RCP significantly control petrophysical properties (porosity and permeability). The multifractal dimensions obtained using porosity and permeability data input through a back-propagation (BP) neural network method show that the relatively large D₂ and the relatively small D₄ correspond to favorable pore structure and good reservoir quality. The results of this research significantly improve our understanding of complex pore characteristics and prediction of favorable pore structure in tight reservoirs, thus enhancing hydrocarbon exploration and production. |
| Databáze: | OpenAIRE |
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