5 citations · 6 across the 4 of their papers we have counts for
6 papers
A One class Classifier based Framework using SVDD : Application to an Imbalanced Geological Dataset
Soumi Chaki, Akhilesh Kumar Verma, Aurobinda Routray +2
Evaluation of hydrocarbon reservoir requires classification of petrophysical properties from available dataset. However, characterization of reservoir attributes is difficult due t…
A Novel Framework based on SVDD to Classify Water Saturation from Seismic Attributes
Soumi Chaki, Akhilesh Kumar Verma, Aurobinda Routray +2
Water saturation is an important property in reservoir engineering domain. Thus, satisfactory classification of water saturation from seismic attributes is beneficial for reservoir…
A novel multiclassSVM based framework to classify lithology from well logs: a real-world application
Soumi Chaki, Aurobinda Routray, William K. Mohanty +1
Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classif…
Development of a hybrid learning system based on SVM, ANFIS and domain knowledge: DKFIS
Soumi Chaki, Aurobinda Routray, William K. Mohanty +1
This paper presents the development of a hybrid learning system based on Support Vector Machines (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS) and domain knowledge to solve…
Well Tops Guided Prediction of Reservoir Properties using Modular Neural Network Concept A Case Study from Western Onshore, India
Soumi Chaki, Akhilesh K Verma, Aurobinda Routray +2
This paper proposes a complete framework consisting pre-processing, modeling, and post-processing stages to carry out well tops guided prediction of a reservoir property (sand frac…
Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach
Akhilesh K Verma, Soumi Chaki, Aurobinda Routray +2
In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency u…