4 results
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2. Pore structure characterization and deliverability prediction of fractured tight glutenite reservoir based on geophysical well logging.
- Author
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Hu, Tingting, Pan, Tuo, Chen, Liang, Li, Jing, and Liu, Yu
- Subjects
GEOPHYSICAL well logging ,POROSITY ,BRITTLENESS ,DATA logging ,DRILL stem ,NUCLEAR magnetic resonance ,FRACTURE toughness - Abstract
Tight glutenite reservoirs characterization and effective hydrocarbon-bearing formation identification faced great challenge due to ultra-low porosity, ultra-low permeability and complicated pore structure. Fracturing fracture-building technique always needed to obtain deliverability because of poor natural productive capacity. Pore structure characterization and friability prediction were essential in improving such type of reservoir evaluation. In this study, fractured tight glutenite reservoirs in Permian Jiamuhe Formation that located in northwest margin of Junggar Basin, northwest China, were chosen as an example, and 25 typical core samples were drilled and simultaneously applied for mercury injection capillary pressure (MICP), nuclear magnetic resonance (NMR) and whole-rock mineral X-ray diffraction experiments. A novel method of synthetizing pseudo-pore-throat radius (R
c ) distribution from porosity frequency spectra was established to characterize fractured formation pore structure. Quartz and calcite were considered as the fragile mineral, and rock mineral component ratio method was used to predict brittleness index. Meanwhile, the statistical model raised by Jin et al. (SPE J 20:518–526, 2015) was used to predict two types of fracture toughness. And then, brittleness index and fracture toughness were combined to characterize tight glutenite reservoirs friability. Combining with maximal pore-throat radius (Rmax , reflected rock pore structure) and friability, our target formations were classified into four clusters. In addition, relationships among pore structure, friability and daily hydrocarbon production per meter (DI) were analyzed, and a model to predict DI from well-logging data was established. Comparison of predicted DI with the extracted results from drill stem test (DST) data illustrated the reliability of our raised models. This would be valuable in determining optimal hydrocarbon production intervals and formulating reasonable developed plans. [ABSTRACT FROM AUTHOR]- Published
- 2024
- Full Text
- View/download PDF
3. An empirical method for predicting waterflooding performance in low-permeability porous reservoirs combining static and dynamic data: a case study in Chang 6 formation, Jingan Oilfield, Ordos Basin, China.
- Author
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Jiang, Zhihao, Li, Gaoren, Zhang, Lili, Mao, Zhiqiang, Liu, Zhidi, Hao, Xiaolong, and Xia, Pei
- Subjects
OIL field flooding ,EMPIRICAL research ,PETROLEUM distribution ,OIL wells ,DATA logging ,FORECASTING - Abstract
After waterflooding, the distribution of the remaining oil in low-permeability porous reservoirs is quite complicated. Strong heterogeneity of formations makes the waterflooding performance more complex. Therefore, accurate prediction and evaluation of the spatial distribution of the remaining oil and the waterflooding performance of low-permeability reservoirs are essential for understanding the waterflooding process and improving oil recovery. In the study, an empirical method is proposed to predict waterflooding performance combined with static and dynamic data for porous reservoirs. Static data, including logging curves, core porosity and permeability data, are adopted to classify the formation into three hydraulic flow units (HFUs). The proportions of the thicknesses of different HFUs (HFUp) are proposed to characterize the remaining oil distribution. In addition, a waterflooding performance prediction method based on the Koval method was built using dynamic production data. The results show that the HFUp plays the key role in predicting the distribution of the remaining oil in the research well group. The K-factor-based waterflooding prediction method is highly correlated with the history matching in low-permeability waterflooded layers. The study also found Type 3 HFUp shows a great effect in predicting the duration of the low water-cut oil production. Therefore, the empirical method can provide a quick and intuitive evaluation of waterflooding performance in space and time of low-permeability waterflooded reservoirs with the local average K-factor and the HFUp results. The empirical method is of great significance to evaluate the remaining oil, infilling of well pattern, and improving oil recovery. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
4. P-Impedance and Vp/Vs prediction based on AVO inversion scheme with deep feedforward neural network: a case study from tight sandstone reservoir.
- Author
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Mao, Xinjun, Han, Xuehui, Wu, Baohai, Wang, Zhenlin, Zhang, Hao, and Wang, Hongliang
- Subjects
FEEDFORWARD neural networks ,DATA logging ,SANDSTONE ,BAYESIAN field theory - Abstract
The low-frequency component of seismic data is an inevitable part to obtain absolute P-impedance ( I p ) and V p / V s ratio of the subsurface, especially for the reservoir sweet spot. In this work, we train the deep feedforward neural network (DFNN) with band-pass seismic data and well log data to obtain favorable low-frequency components. Specifically, the Bayesian inference strategy is first applied to the pre-stack constrained sparse spike inversion process, obtaining an "initial" inverted band-pass parameters, which are subsequently used as input when applying the DFNN algorithm to predict low- and band-pass parameters. Moreover, the high linear correlation coefficient between the DFNN-based inversion results and the realistic well logging curves of the blind wells demonstrates that the DFNN-based inversion scheme exhibits strong robustness and good generalization ability. Ultimately, we apply the proposed DFNN-based inversion strategy to a tight sandstone reservoir located at the Sichuan basin field from onshore China. Both low- and band-pass I p and V p / V s inverted for the clastic formation of the Sichuan basin show a strong correlation with the corresponding I p and V p / V s logs. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
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