Oil and gas recovery rates from unconventional reservoirs are very low (<10% of resources are extracted). The current industry practices for reservoir development and management are generally ad-hoc and are mainly based on field experience. In addition, the physics processes related to hydrocarbons storage and recovery from unconventional reservoirs are not well understood. Here, we present machine learning analyses based on synthetic and real-world datasets representing oil and gas production from unconventional reservoirs. The analyses are based on the recently developed unsupervised and physics-informed machine learning method called SmartTensors (https://github.com/orgs/TensorDecompositions). Unsupervised methods utilize novel matrix and tensor factorization techniques. In the more general case of tensors, the factorization of a given tensor (high-dimensional array) is typically performed by minimization of the discrepancies between the original tensor and its approximation. Nonnegativity enforces parts-based representation of the original data which also allows the results to be easily interrelated. SmartTensors are capable to reveal the temporal and spatial hidden (latent) features associated with the physical processes embedded in the analyzed datasets. The SmartTensors analyses of synthetic and real-world datasets related to oil and gas production demonstrated the applicability of the developed methodology to extract features characterizing differences in the obtained oil and gas extraction rates at different production wells. Physics-informed machine learning methods directly embed physics constraints, laws, and simulators in the deep neural networks. We have applied SmartTensors to train, validate, and verify machine learning models predicting oil and gas production from unconventional reservoirs in Texas.