no code implementations • 27 Feb 2023 • Soumi Chaki, David Weinberg, Ayca Özcelikkale
We investigate federated learning for training multiple SNNs at clients when two mechanisms reduce the uplink communication cost: i) random masking of the model updates sent from the clients to the server; and ii) client dropouts where some clients do not send their updates to the server.
no code implementations • 2 Dec 2016 • Soumi Chaki, Aurobinda Routray, William K. Mohanty, Mamata Jenamani
The classification results have been further fine-tuned applying expert knowledge based on the relationship among predictor variables i. e. well logs and target variable - oil saturation.
no code implementations • 2 Dec 2016 • Soumi Chaki, Akhilesh Kumar Verma, Aurobinda Routray, William K. Mohanty, Mamata Jenamani
Thus, satisfactory classification of water saturation from seismic attributes is beneficial for reservoir characterization.
no code implementations • 2 Dec 2016 • Soumi Chaki, Aurobinda Routray, William K. Mohanty, Mamata Jenamani
Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifiers, and finally the classification results are combined following either the one-against-one or one-against-all strategies.
no code implementations • 2 Dec 2016 • Soumi Chaki, Akhilesh Kumar Verma, Aurobinda Routray, William K. Mohanty, Mamata Jenamani
Evaluation of hydrocarbon reservoir requires classification of petrophysical properties from available dataset.
no code implementations • 23 Sep 2015 • Akhilesh K Verma, Soumi Chaki, Aurobinda Routray, William K. Mohanty, Mamata Jenamani
Though seismic data is helpful in extrapolation of reservoir properties away from boreholes; yet, it could be challenging to delineate thin sand and shale reservoirs using seismic data due to its limited resolvability.
no code implementations • 23 Sep 2015 • Soumi Chaki, Akhilesh K Verma, Aurobinda Routray, William K. Mohanty, Mamata Jenamani
The data set used in this study comprising three seismic attributes and well log data from eight wells, is acquired from a western onshore hydrocarbon field of India.
no code implementations • 23 Sep 2015 • Soumi Chaki, Aurobinda Routray, William K. Mohanty
The network yielding satisfactory performance in the validation stage is used to predict lithological properties from seismic attributes throughout a given volume.
no code implementations • 15 Jun 2015 • Soumi Chaki
Reservoir Characterization (RC) can be defined as the act of building a reservoir model that incorporates all the characteristics of the reservoir that are pertinent to its ability to store hydrocarbons and also to produce them. It is a difficult problem due to non-linear and heterogeneous subsurface properties and associated with a number of complex tasks such as data fusion, data mining, formulation of the knowledge base, and handling of the uncertainty. This present work describes the development of algorithms to obtain the functional relationships between predictor seismic attributes and target lithological properties.