Hydraulic fracturing technology requires securing sufficient water resources to access and unlock the pores of unconventional formations. Therefore, successful treatment depends on the fracture fluids, which mainly consist of water-based fluids with a low percentage (around 1%) of chemical additives. However, the oil and gas industry is among the largest freshwater consumers: three to six million gallons of water per well based on the number of fracturing stages. As a result, traditional water resources from subsurface and surface supplies are getting depleted, and freshwater is becoming more difficult to access with higher costs associated with continued demand. For example, operator companies in West Texas face many challenges, including a recent increase from USD 2 to 8 per barrel of freshwater. Also, the transportation of raw water to fracture sites, such as the Bakken shale play, has an environmental impact, with costs of up to USD 5 per barrel, while costs of water disposal range from USD 9 per barrel. This paper aims to investigate produced water as an alternative water-based fluid to several fracture fluids, such as crosslinked, linear gel, and high viscosity friction reducers (HVFRs) to reduce environmental footprints and economic costs. The workflow of this research started with a comprehensive review of extant publications, reports, and case studies to summarize the application of produced water with fracturing fluids in unconventional shale plays, such as the Bakken (North Dakota), Barnett (Texas), Eagle Ford (Texas), Wolfcamp (Texas), Marcellus (Pennsylvania), and Periman Bain (Texas). The critical review begins with explaining the features of produced water, its challenges, and water management options. Furthermore, the different fracturing fluids in a high TDS environment are described using recent lab fluid characterizations of produced water as 10% to 50% of produced water usage at a temperature range between 70 to 210 deg F. Moreover, 2D and 3D pseudo frac simulations are utilized using real field data from the Middle Bakken Formation to construct reliable models to evaluate the feasibility of reused water in shale plays development. The outcomes show that recycling water with high TDS in a high-temperature environment can create a fracture network and proppant transport when high viscosity friction reducers with surfactant (HVFR-PRS) was used. In addition, the result of this critical review is a powerful tool for predicting the future of hydraulic fracturing technology, which might help operator companies reduce costs and develop unconventional wells successfully for a return on their investment. The opportunities and challenges conclusions of water management are provided a survey of future hydraulic fracturing applications in North American shale plays by offering recommendations of environmental and economic impacts. The general guidelines obtained can promote the sustainability of using hydraulic fracturing treatment to produce more oil and gas from unconventional resources without compromising on environmental issues.

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One of the significant unconventional oil reserves in the USA is the Bakken Petroleum System located in the Williston Basin. It is known for its complex lithology, composed of three prominent members, Upper and Lower Bakken, with similar properties of organic-rich shale relatively uniform compared to the middle member with five distinct lithofacies, formed mainly from calcite, dolomite, or silica. The higher properties variability makes the reservoir characterization more challenging with low permeability and porosity. Understanding lithology by quantifying mineralogy is crucial for accurate geological modeling and reservoir simulation. Besides that, the reservoir’s capacity and the oil production are affected by the type and the mineral volume fractions, which impact the reservoir properties. Conventionally, to identify the mineralogy of the reservoir, the laboratory analysis (X-Ray Diffraction, XRD) using core samples combined with the well logs interpretation is widely used. The unavailability of the core data due to the high cost, as well as the discontinuities of the core section of the reservoir due to the coring failures and the destructive operations, are one of the challenges for an accurate mineralogy quantification. The XRD cores analysis is usually used to calibrate the petrophysical evaluation using well logs data because they are economically efficient. To remedy to these limitations, artificial intelligence and data-driven based models have been widely deployed in the oil and gas industry, particularly for petrophysical evaluation. This study aims to develop machine learning models to identify mineralogy by applying six different machine learning methods and using real field data from the upper, middle, and lower members of the Bakken Formation. Efficient pre-processing tools are applied before training the models to eliminate the XRD data outliers due to the formation complexity. The algorithms are based on well logs as inputs such as Gamma Ray, bulk density, neutron porosity, resistivity, and photoelectric factor for seven (07) wells. XRD mineral components for 117 samples are considered outputs (Clays, Dolomite, Calcite, Quartz, and other minerals). The results' validation is based on comparing the XRD Data prediction from the developed models and the petrophysical interpretation. The applied approach and the developed models have proved their effectiveness in predicting the XRD from the Bakken Petroleum system. The Random Forest Regressor delivered the best performance with a correlation coefficient of 78 percent. The rest of the algorithms had R-scores between 36 and 72 percent, with the linear regression having the lowest coefficient. The reason is the non-linearity between the inputs and outputs.