Laboratory study of partial water saturation in tight gas sandstone at seismic and ultrasonic frequencies

2014 ◽  
Author(s):  
X. Wei ◽  
S. X. Wang ◽  
J. G. Zhao ◽  
G. Y. Tang ◽  
H. J. Yin ◽  
...  
Keyword(s):  
Laboratory Study ◽  
Water Saturation ◽  
Tight Gas ◽  
Partial Water ◽  
Tight Gas Sandstone

Prediction of water saturation in a tight gas sandstone reservoir by using four intelligent methods: a comparative study

2016 ◽  
Vol 30 (4) ◽  
pp. 1171-1185 ◽  
Author(s):  
Sadegh Baziar ◽  
Habibollah Bavarsad Shahripour ◽  
Mehdi Tadayoni ◽  
Majid Nabi-Bidhendi
Keyword(s):  
Comparative Study ◽  
Water Saturation ◽  
Tight Gas ◽  
Sandstone Reservoir ◽  
Intelligent Methods ◽  
Tight Gas Sandstone

Application of Various Machine Learning Techniques in Predicting Water Saturation in Tight Gas Sandstone Formation

2021 ◽  
pp. 1-14
Author(s):  
Ahmed Farid Ibrahim ◽  
Salaheldin Elkatatny ◽  
Yasmin Abdelraouf ◽  
Mustafa Al Ramadan
Keyword(s):  
Machine Learning ◽  
Water Saturation ◽  
Well Logs ◽  
Percentage Error ◽  
Support Vector ◽  
Tight Gas ◽  
Data Set ◽  
Unseen Data ◽  
Sandstone Formation ◽  
Tight Gas Sandstone

Abstract Water saturation (Sw) is a vital factor for the hydrocarbon in-place calculations. Sw is usually calculated using different equations; however, its values have been inconsistent with the experimental results due to often incorrectness of their underlying assumptions. Moreover, the main hindrance remains in these approaches due to their strong reliance on experimental analysis which are expensive and time-consuming. This study introduces the application of different machine learning (ML) methods to predict Sw from the conventional well logs. Function networks (FN), support vector machine (SVM), and random forests (RF) were implemented to calculate the Sw using gamma-ray (GR) log, Neutron porosity (NPHI) log, and resistivity (Rt) log. A dataset of 782 points from two wells (Well-1 and Well-2) in tight gas sandstone formation was used to build and then validate the different ML models. The data set from Well-1 was applied for the ML models training and testing, then the unseen data from well-2 was used to validate the developed models. The results from FN, SVM and RF models showed their capability of accurately predicting the Sw from the conventional well logging data. The correlation coefficient (R) values between actual and estimated Sw from the FN model were found to be 0.85 and 0.83 compared to 0.98, and 0.95 from the RF model in the case of training and testing sets, respectively. SVM model shows an R-value of 0.95 and 0.85 in the different datasets. The average absolute percentage error (AAPE) was less than 8% in the three ML models. The ML models outperform the empirical correlations that have AAPE greater than 19%. This study provides ML applications to accurately forecast the water saturation using the readily available conventional well logs without additional core analysis or well site interventions.

Quantifying tight-gas sandstone permeability via critical path analysis

2016 ◽  
Vol 92 ◽  
pp. 316-322 ◽  
Author(s):  
Behzad Ghanbarian ◽  
Carlos Torres-Verdín ◽  
Todd H. Skaggs
Keyword(s):  
Path Analysis ◽  
Critical Path ◽  
Tight Gas ◽  
Critical Path Analysis ◽  
Sandstone Permeability ◽  
Tight Gas Sandstone

scholarly journals Effect of Partial Water Saturation on Attenuation Characteristics of Low Porosity Rocks

2010 ◽  
Vol 44 (2) ◽  
pp. 245-251 ◽  
Author(s):  
Tae-Min Oh ◽  
Tae-Hyuk Kwon ◽  
Gye-Chun Cho
Keyword(s):  
Water Saturation ◽  
Attenuation Characteristics ◽  
Partial Water ◽  
Low Porosity

A new method of multi-scale fracture identification in tight gas sandstone reservoir

2018 ◽  
Vol 22 (2) ◽  
pp. 112-118
Author(s):  
Tao Wu ◽  
Yuezhi Wang ◽  
Bin Fu ◽  
Peng Wu
Keyword(s):  
New Method ◽  
Tight Gas ◽  
Multi Scale ◽  
Sandstone Reservoir ◽  
Fracture Identification ◽  
Tight Gas Sandstone

Trained BPNN Method and Application in Tight Gas Sandstone Formation Lithology classification

2021 ◽  
Author(s):  
Z. Wang ◽  
Y. Gao ◽  
J. Zhang ◽  
R. He ◽  
H. Dong
Keyword(s):  
Tight Gas ◽  
Sandstone Formation ◽  
Tight Gas Sandstone
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