scholarly journals Prediction Method of Favorable Area of Tight Sandstone Gas——Taking Hechuan Area as an Example

2020 ◽  
Author(s):  
Lin Jiang
2021 ◽  
pp. 1-45
Author(s):  
Zhaohui Song ◽  
Sanyi Yuan ◽  
Zimeng Li ◽  
Shangxu Wang

Gas-bearing prediction of tight sandstone reservoirs is significant but challenging due to the relationship between the gas-bearing property and its seismic response being nonlinear and complex. Although machine learning (ML) methods provide potential for solving the issue, the major challenge of ML applications to gas-bearing prediction is that of generating accurate and interpretable intelligent models with limited training sets. The k Nearest neighbor ( kNN) method is a supervised ML method classifying an unlabeled sample according to its k neighboring labeled samples. We have introduced a kNN-based gas-bearing prediction method. The method can automatically extract a gas-sensitive attribute called the gas-indication local waveform similarity attribute (GLWSA) combining prestack seismic gathers with interpreted gas-bearing curves. GLWSA uses the local waveform similarity among the predicting samples and the gas-bearing training samples to indicate the existence of an exploitable gas reservoir. GLWSA has simple principles and an explicit geophysical meaning. We use a numerical model and field data to test the effectiveness of our method. The result demonstrates that GLWSA is good at characterizing the reservoir morphology and location qualitatively. When the method applies to the field data, we evaluate the performance with a blind well. The prediction result is consistent with the geologic law of the work area and indicates more details compared to the root-mean-square attribute.


2020 ◽  
Vol 206 ◽  
pp. 01007
Author(s):  
Zhang junlin ◽  
Wan huan ◽  
Liu yumin ◽  
He yumei ◽  
Zhang hao ◽  
...  

The determination of the sweet spot of tight sandstone reservoir is the primary problem in the exploration and development of tight sandstone reservoir. Practice has proved that the prediction of reservoir spatial pore characteristics, thickness distribution and oil and gas potential (i.e. geological sweet spot) of tight sandstone reservoir can not meet the demand of exploration and development of tight sandstone reservoir. In view of the requirements of tight sandstone fracturing engineering, it is also necessary to analyze the engineering field of the reservoir. In this paper, based on seismic data preprocessing and petrophysical analysis, thin reservoir quantitative characterization and AVO fluid prediction are used to evaluate the tight reservoir. At the same time, combining with the fracture analysis method of FMI imaging logging, pre stack fracture description and brittleness prediction, the “engineering sweet spot” prediction is carried out. Finally, the multi-attribute RGB digital fusion visualization research method is used to comprehensively evaluate the tight reservoir. The practice shows that the research method has guiding significance for the exploration and development of tight reservoir.


2019 ◽  
Vol 175 ◽  
pp. 430-443
Author(s):  
Liu Ling ◽  
Tang Dazhen ◽  
Wo Yujin ◽  
Liu Lihui ◽  
Sun Wei ◽  
...  

2018 ◽  
pp. 214-223
Author(s):  
AM Faria ◽  
MM Pimenta ◽  
JY Saab Jr. ◽  
S Rodriguez

Wind energy expansion is worldwide followed by various limitations, i.e. land availability, the NIMBY (not in my backyard) attitude, interference on birds migration routes and so on. This undeniable expansion is pushing wind farms near populated areas throughout the years, where noise regulation is more stringent. That demands solutions for the wind turbine (WT) industry, in order to produce quieter WT units. Focusing in the subject of airfoil noise prediction, it can help the assessment and design of quieter wind turbine blades. Considering the airfoil noise as a composition of many sound sources, and in light of the fact that the main noise production mechanisms are the airfoil self-noise and the turbulent inflow (TI) noise, this work is concentrated on the latter. TI noise is classified as an interaction noise, produced by the turbulent inflow, incident on the airfoil leading edge (LE). Theoretical and semi-empirical methods for the TI noise prediction are already available, based on Amiet’s broadband noise theory. Analysis of many TI noise prediction methods is provided by this work in the literature review, as well as the turbulence energy spectrum modeling. This is then followed by comparison of the most reliable TI noise methodologies, qualitatively and quantitatively, with the error estimation, compared to the Ffowcs Williams-Hawkings solution for computational aeroacoustics. Basis for integration of airfoil inflow noise prediction into a wind turbine noise prediction code is the final goal of this work.


2018 ◽  
Vol 138 (9) ◽  
pp. 1075-1081
Author(s):  
Yasuhide Kobayashi ◽  
Mitsuyuki Saito ◽  
Yuki Amimoto ◽  
Wataru Wakita

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