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Production of oil & gas depends upon the recoverable amount of hydrocarbon existing beneath the underlying reservoir. Reservoir recovery factor provides of the production potential of ‘proven reservoirs’ which helps the planning of field development and production. Estimation of reservoir recovery factor, with a good degree of accuracy, is still a challenging task for engineers due to the high level of uncertainty, large inexactness, noise, and high dimensionality associated with reservoir measurements. In this paper, we propose a big data-driven ‘ensemble estimator’ (E2) module, comprising of wavelet associated ensemble models for the estimation of reservoir recovery factor. All the ensemble models in E2 were trained on big reservoir data and tested with unknown reservoir data samples obtained from U.S.A. oil & gas fields. Bagging and Random forest ensembles have been utilized to correlate several …