The oil and gas industry’s view of water production, once regarded primarily as a waste stream, has shifted in recent years due to the growing environmental and economic challenges. Industries now recognize the substantial volumes of water produced during production operations and are actively exploring alternative water management strategies. Among these, water treatment stands out as a leading approach, aimed at purifying the water to achieve specific element concentrations suited for targeted applications. The produced water from oil and gas reservoirs is a complex mixture of various organic and inorganic compounds, as well as dissolved and suspended solids. It is considered a highly contaminated waste stream, making effective treatment essential to meet future critical water demand. The physical and chemical properties of the produced water vary depending on the extraction location, geological formations, and type of hydrocarbon produced. This review examines multiple treatment methods used for the beneficial reuse of produced water, covering physical, chemical, and biological techniques, along with examples demonstrating their effectiveness in field case studies.

Tight reservoirs around the world contain a significant volume of hydrocarbons; however, the heterogeneity of these reservoirs limits the recovery of the original oil in place to less than 20%. Accurate characterization is therefore needed to understand variations in reservoir properties and their effects on production. Water saturation (Sw) has always been challenging to estimate in ultra-tight reservoirs such as the Bakken Formation due to the inaccuracy of resistivity-based methods. While machine learning (ML) has proven to be a powerful tool for predicting rock properties in many tight formations, few studies have been conducted in reservoirs of similar complexity to the Bakken Formation, which is an ultra-tight, multimineral, low-resistivity reservoir. This study presents a workflow for Sw prediction using well logs, core data, and ML algorithms. Logs and core data were gathered from 29 wells drilled in the Bakken Formation. Due to the inaccuracy and lack of robustness of the tried and tested regression models (e.g., linear regression, random forest regression) in predicting Sw as a continuous variable, the problem was reformulated as a classification task. Instead of exact values, the Sw predictions were made in intervals of 10% increments representing 10 classes from 0% to 100%. Gradient boosting and random forest classifiers scored the best classification accuracy, and these two models were used to construct a voting classifier that achieved the best accuracy of 85.53%. The ML model achieved much better accuracy than conventional resistivity-based methods. By conducting this study, we aim to develop a new workflow to improve the prediction of Sw in reservoirs where conventional methods have poor performance.

This study evaluates the feasibility of repurposing produced water—an abundant byproduct of hydrocarbon extraction—for green hydrogen production in Wyoming, USA. Analysis of geospatial distribution and production volumes reveals that there are over 1 billion barrels of produced water annually from key basins, with a general total of dissolved solids (TDS) ranging from 35,000 to 150,000 ppm, though Wyoming’s sources are often at the lower end of this spectrum. Optimal locations for hydrogen production hubs have been identified, particularly in high-yield areas like the Powder River Basin, where the top 2% of fields contribute over 80% of the state’s produced water. Detailed water-quality analysis indicates that virtually all of the examined sources exceed direct electrolyzer feed requirements (e.g., <2000 ppm TDS, 10% LCOH) are notable, electricity pricing (50–70% LCOH) and electrolyzer CAPEX (20–40% LCOH) are dominant cost factors. While leveraging produced water could reduce freshwater consumption and enhance hydrogen production sustainability, further research is required to optimize treatment processes and assess economic viability under real-world conditions. This study emphasizes the need for integrated approaches combining water treatment, renewable energy, and policy incentives to advance a circular economy model for hydrogen production.

The solubility of CO2 in formation brines plays a critical role in the efficiency of carbon capture and storage (CCS) operations. It is strongly influenced by pressure, temperature, and brine composition. Various experimental studies and modeling approaches have been developed to estimate CO2 solubility under wide ranges of pressure, temperature, and salinities. This work makes three key contributions. First, we present an extensive literature review of experimental, theoretical, and simulation-based approaches for measuring and predicting CO2 solubility across a wide range of conditions and also a discussion of how the different parameters affect solubility. Second, we introduce an improved set of temperature-dependent Pitzer interaction parameters, yielding up to a 76% reduction in average absolute deviation compared to conventional values in the geochemical simulation software PHREEQC. Third, we develop a physics-informed machine learning model that integrates thermodynamic intuition with data-driven learning, achieving a 14% reduction in prediction error over the state-of-the-art and up to 40% improvement at high salinities. Together, these advances provide a robust and accurate framework for predicting CO2 solubility, supporting more reliable CCS design and deployment.

Drill-stem test (DST) data are used to evaluate formation pressures and other reservoir properties, providing critical insights into subsurface conditions. We present a digital dataset of legacy DST reports comprising 336 individual tests from 192 wells in Sweetwater County within the Greater Green River Basin of Wyoming, USA. For each DST, we provide both the digitized raw DST time-series data and in-situ reservoir pressures calculated using Horner’s analysis. Other key information includes well API numbers, locations, tested formations, depth intervals, and detailed test results including flow and shut-in periods with corresponding time and pressure data. This dataset was primarily collected to help with the Class VI filing process for new carbon storage wells but has significant reuse potential for applications such as developing regional pressure profiles, validating reservoir simulation models, and analyzing legacy well performance. By providing access to this dataset, the Class VI Database project aims to support subsurface characterization and resource evaluation efforts for carbon storage projects, conventional oil and gas activities, and geothermal energy.