Prediction of Compressional Wave Velocity Using Regression and Neural Network Modeling and Estimation of Stress Orientation in Bokaro Coalfield, India

Paul, Suman; Ali, Muhammad; Chatterjee, Rima

doi:10.1007/s00024-017-1672-1

Cited by 24 publications

(7 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SH direction is estimated from DIFs in resistivity image from depth interval 618.0-650.0 m is observed along NE-SW (N26°-35°E) in W-4 (Figure 13a). The DIF direction obtained in this work matches well with the previous study as N15°E to N35°E by Paul et al (2017). Figure 13b represents seam-V showing fractures, cleats, and coal bed in W-4 with its dip magnitude and direction.…”

Section: Resultssupporting

confidence: 89%

“…For instance, the trajectory of the horizontal/inclined well is stable and creates better connectivity as the trajectory bisects maximum numbers of fractures when it passes through the S h direction. The S H direction obtained supports the previous study along N15°E to N35°E by Paul et al (2017). Seamwise planning for oriented perforation is needed for production optimization.…”

Section: Discussion Ssupporting

confidence: 85%

“…The S H direction obtained supports the previous study along N15°E to N35°E by Paul et al . (2017). Seam‐wise planning for oriented perforation is needed for production optimization.…”

Section: Discussionmentioning

confidence: 99%

“…In a previous study on west Bokaro Coalfield (CF), the resistivity image log has identified the Breakouts (BOs) oriented along N60°W and drilling-induced fractures (DIFs) oriented along N15°E to N35°E, that are an important indicator of minimum (Sh) and maximum (SH) horizontal stress direction (Paul et al 2017). But, in this CF, anisotropy analysis and its characterization were not performed using sonic log, and the literature survey has not mentioned the presence of a low resistivity signature (LRS).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Anisotropy and fracture analysis for coalbed methane reservoir development in Bokaro coalfield, India

Banerjee

Chatterjee

Singha

2022

Geophysical Prospecting

Self Cite

View full text Add to dashboard Cite

The efficient production of Coalbed Methane (CBM) gas is facing challenges due to the larger dewatering period from fracture connectivity to the aquifer zone. Also, commingled production from well makes it more difficult to identify the coal seam-wise problem. Therefore, prior knowledge of sub-surface fractures in coal seams is necessary to execute an accurate simulation model for planning hydraulic fracturing treatment. This paper highlights the studies in Bokaro CBM reservoir to mitigate challenges in few wells by characterizing anisotropy, determining fast shear wave polarization angle, maximum horizontal stress direction, fracture orientation, and analysis of low resistivity signature. Both the fast shear wave polarization angle and fracture orientation in resistivity image are observed in the same direction (N26°-35°E) in coal. The fast and slow shear slowness versus frequency plot concludes stress-induced anisotropy resulting from fractures that are supported by resistivity image and drilling core.Processing of the resistivity image log shows the maximum horizontal stress is along NE-SW direction, as identified from drilling-induced fractures. The observation of low resistivity signature with resistivity ranging from 0.4 to 0.8 ohm-m in few wells confirms the presence of conducting minerals such as siderite and pyrite from the x-ray diffraction studies of sidewall core. The present work guides in making production, drilling, and hydraulic fracturing design strategies to better understand the fluid propagation for optimized CBM production and will also help in future geomechanical studies.

show abstract

Section: Resultssupporting

confidence: 89%

Section: Discussion Ssupporting

confidence: 85%

“…The S H direction obtained supports the previous study along N15°E to N35°E by Paul et al . (2017). Seam‐wise planning for oriented perforation is needed for production optimization.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Anisotropy and fracture analysis for coalbed methane reservoir development in Bokaro coalfield, India

Banerjee

Chatterjee

Singha

2022

Geophysical Prospecting

Self Cite

View full text Add to dashboard Cite

show abstract

“…By employing the DNN model, the authors in [22] predicted the Von Mises stress distribution and peak Von Misesstress in experiments, and they achieved excellent results with average errors of 0.492% and 0.891%, respectively. Meanwhile, neural network modeling performs better than the regression model at predicting Vp [46]. As a consequence, we use this three-layer neural network structure to predict the in situ stress at some specified point, as illustrated in Fig.…”

Section: ) Deep Neural Network(dnn)mentioning

confidence: 99%

A Deep Learning Approach Replacing the Finite Difference Method for In Situ Stress Prediction

Gao

Peng

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In the domain of geotechnical engineering analysis, fast Lagrangian analysis based on the finite difference method is the most commonly used numerical analysis method. It is a classical numerical algorithm applied to calculate the in situ stress. In this paper, we propose a deep learning (DL) architecture called the enhance-and-split feature capsule network embedded in fully convolutional neural networks (ES-Caps-FCN) to predict the in situ stress for a strain-softening model when using the finite difference method for the numerical computation. Experiments indicate that this novel approach is stable and convergent. Compared with some classical prediction methods including linear regression analysis and a deep neural network, the mean squared error of our proposed algorithm is as low as 0.059866%, which is lower than the 0.616676% of the deep neural network prediction algorithm and the 0.978495% of the conventional machine learning algorithm. Additionally, the calculation efficiency of fully trained deep learning models is superior to that of the conventional finite difference method. Therefore, DL is a feasible and promising fast and accurate surrogate for the finite difference method for solving the in situ stress.

show abstract

Lithology Identification Using Lithology Impedance in Mumbai Offshore

Roy

Chatterjee

2019

Petro-Physics and Rock Physics of Carbonate Reservoirs

View full text Add to dashboard Cite

Prediction of Compressional Wave Velocity Using Regression and Neural Network Modeling and Estimation of Stress Orientation in Bokaro Coalfield, India

Cited by 24 publications

References 36 publications

Anisotropy and fracture analysis for coalbed methane reservoir development in Bokaro coalfield, India

Anisotropy and fracture analysis for coalbed methane reservoir development in Bokaro coalfield, India

A Deep Learning Approach Replacing the Finite Difference Method for In Situ Stress Prediction

Lithology Identification Using Lithology Impedance in Mumbai Offshore

Contact Info

Product

Resources

About