The petroleum industry produces a large amount of wastewater, known as produced water (PW), during oil production and processing. This PW contains hazardous organic and inorganic components that can harm the environment. Conventional treatment methods have been used to purify PW, but they do not meet environmental regulations, especially when the goal is to reuse the water. Therefore, further research is needed to find an effective technology for managing PW. This review focuses on the characteristics and management of PW originating from oil and gas fields. Firstly, we provide a detailed overview of PW production scenarios worldwide and in the US with detailed quantities and chemical compositions of organic, inorganic, and physicochemical characteristics. Secondly, challenges and environmental concerns associated with treating PW are discussed. Thirdly, all relevant treatment technologies for PW are systematically explored. In addition, this review highlights the management of PW and suggests treatment options and best practices for the industry, and finally, future research needs and opportunities for sustainable water treatment and effective reuse technologies are addressed. Because PW contains a variety of severe contaminants, single methods have not been effective in converting it to a reusable form or fulfilling disposal criteria. As a result, integrated technologies may provide a potential approach that not only meets regulatory standards but also provides chances to employ PW as a non-conventional water supply. Advances in PW management are critical and demand a defined framework and risk-based approach to determine and build the most efficient plan.

Due to complexities in geologic structure, heterogeneity, and insufficient borehole information, shale formation faces challenges in accurately estimating the elastic properties of rock which triggers severe technical challenges in safe drilling and completion. These geomechanical properties could be computed from acoustic logs, however, accurate estimation is critical due to log deficit and a higher recovery expense of inadequate datasets. To fill the gap, this study focuses on predicting the sonic properties of rock using deep neural network (Bi-directional long short-time memory, Bi-LSTM) and random forest (RF) algorithms to estimate and evaluate the geomechanical properties of the potential unconventional formation, Permian Basin, situated in West Texas. A total of three wells were examined using both single-well and cross-well prediction algorithms. Log-derived single-well prediction models include a 75:25 ratio for training and testing the data whereas the cross-well includes two wells for training and the remaining well was used for testing. The selected well input logs include compressional wave slowness, resistivity, gamma-ray, porosity, and bulk density to predict shear wave slowness. The results using RF and Bi-LSTM show a promising prediction of geomechanical properties for Permian Basin wells. RF algorithm performed superior for both single and grouped well prediction. The single-well prediction method using the RF algorithm provided the highest accuracy of 99.90% whereas Bi-LSTM gave 93.60%. The best accuracy for a grouped well prediction was achieved employing Bi-LSTM and RF models, i.e., 96.01% and 93.80%. The average prediction including RF and Bi-LSTM algorithms demonstrated that accuracy for single well and cross well prediction is 96% and 94% respectively with an error below 7%. These outcomes show the astonishing capability of artificial intelligence (AI) models trained to create a realistic prediction to unlock unconventional potential when datasets are inadequate. Given adequate training data, operators could leverage these efficient tools by utilizing them to examine fracture interpretations with reduced cost and time when datasets are incomplete and thus increase the hydrocarbon recovery potential.