Exploiting the Thick Tight Gas Sandstone Formation of Periyakudi Field of India through Fracturing Stimulation - A Challenging Experience

2017 ◽  
Author(s):  
Dheeraj Mahawar ◽  
Rajesh Kumar ◽  
Santanu Das ◽  
K Rajendra ◽  
S P Nainwal
Keyword(s):  
Tight Gas ◽  
Sandstone Formation ◽  
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.

Leveraging Acoustic Logging for Identification & Quantification of Induced Fractures

2021 ◽  
Author(s):  
Adnan Bin Asif ◽  
Mustafa Alaliwat ◽  
Jon Hansen ◽  
Mohamed Sheshtawy
Keyword(s):  
Fracture Initiation ◽  
Gas Production ◽  
Tight Gas ◽  
Acoustic Logging ◽  
Hydrocarbon Production ◽  
Open Hole ◽  
Cumulative Production ◽  
Sandstone Formation ◽  
Induced Fractures ◽  
Tight Gas Sandstone

Abstract The main objective of the acoustic logging in 15K openhole multistage fracturing completions (OH MSFs) is to identify the fracture initiation points behind pipe and contributing fractures to gas production. The technique will also help to understand the integrity of the OH packers. A well was identified to be a candidate for assessment through such technique. The selected well was one of the early 15K OH MSF completions in the region that was successfully implemented with the goal of hydrocarbon production at sustained commercial rates from a gas formation. The candidate well was drilled horizontally to achieve maximum contact in a tight gas sandstone formation. Similar wells in the region have seen many challenges of formation breakdown due to high formation stresses. The objective of this work is to use the acoustic data to better characterize fracture properties. The deployment of acoustic log technology can provide information of fractures initiation, contribution for the production and the reliability of the isolation packers between the stages. The candidate well was completed with five stages open-hole fracturing completion. As the well is in an open hole environment, a typical PLT survey provides the contribution of individual port in the cumulative production but provides limited or no information of contributing fractures behind the pipe. The technique of acoustic logging helped to determine the fracture initiation points in different stages. If fractures can be characterized more accurately, then flow paths and flow behaviors in the reservoir can be better delineated. The use of acoustic logging has helped to better understand the factors influencing fracture initiation in tight gas sandstone reservoirs; resulting in a better understanding of fractures density and decisions on future openhole length, number of fracturing stages, packers and frac ports placement.

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
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