The major problems associated with shale formation when it interacted with water based mud are borehole instability. This Wellbore instability may be due to swelling and dispersion of clay present in shale and also leads to other well problems like pipe sticking, hole enlargement, improper rheological & fluid loss control properties, and additional reaming etc. In this research paper an attempt has been made to evaluate the feasibility of synthesized graft copolymer in the formulation of water based mud system for challenging formations. The microwave irradiation technique has been adopted for synthesizing PAA/AMPS-g-Sesbania gum copolymer. Further, it was processed in the formulation of mud system. The remarkable rheological and filtration properties of the mud system have been seen with synthesized additive. The developed mud has possessed strong pseudoplastic behavior which is a desired property of any mud which has been observed from shear rate vs. shear rate curve. In addition, shale stabilization properties were investigated with shale rotability test on the synthesized core sample prepared in the laboratory. Moreover, percentage reduction in permeability (i.e., formation damage effect) has been found lesser in developed copolymer system co paring to conventionally used PHPA system. Hence, the formulated mud system can be used as a potential drilling mud system for drilling any oil wells.

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The problem of growing carbon footprint calls for the exploitation of cleaner and sustainable energy resources. Geothermal energy is clean, renewable, and in abundant supply underneath the surface of the earth, which makes it one of the most optimum solutions to this problem. With the depletion of hydrocarbon resources, geothermal energy also helps to close the gap between demand and supply of cleaner energy resources reliably, although several problems need to be solved before producing geothermal energy globally. In this study, an effort is made to understand and improve the reservoir heat extraction through a geothermal well. There are approximately 3 million abandoned wells within the U.S. and this number will only increase in the future. Producing electricity from these abandoned hydrocarbon wells, as the source of geothermal energy, have intrinsic importance in the context of extending the life of the well in the context of energy production and as well as generation of future options for new wells. Whether the costs are sunken or not (for the existing wells), incremental costs for the new potential wells can be minimum to redesign them to fit for future geothermal energy production. Not only the design/retrofitting the wells, but also the selection of right power fluid is crucial to effectively produce the geothermal energy. Using CO2 as the power fluid to generate electricity from low temperature abandoned hydrocarbon wells while sequestering it will help in reducing the well costs to a minimum as well as optimizing the energy production to lower temperature thresholds. In this paper, a previously developed coupled well-reservoir model (Livescu and Dindoruk, 2022a, for fixed reservoir delivery as successions of steady state) is extended to study the effects of the fluid properties on the thermal output. Specifically, the previous model considered fluids with constant properties. Several correlations and look-up tables are used in this study for pressure- and temperature-dependent fluid properties (i.e., density and viscosity) to explicitly quantify their effect on the thermal balance of the geothermal system. These results are important for understanding the effects of the fluid PVT properties on the physics and economics of the entire geothermal project. This study is important for the design of closed-loop systems and can be extended to enhanced geothermal systems. For a given reservoir intake conditions, it can also be used to perform economic evaluation for abandoned oil and gas wells to assess their feasibility for geothermal energy production while reducing the overall CO2 footprint. In particular, the novelty and importance lie in Impact of choice of fluid/fluid PVT properties on the physics and economics of the entire geothermal project. Parametric study of using CO2 as the power fluid to generate electricity from relatively lower temperature abandoned hydrocarbon wells and variants in terms of P&T ranges.

Thermal Enhanced Oil Recovery (TEOR) such as SAGD, CSS and other steam injection processes have been employed in heavy oil reservoirs of North-American and Middle-East countries for oil recovery. Elevation of temperature during this process leads to wettability alteration, IFT variation, viscosity reduction, asphaltene and resin precipitation. These variations during TEOR impact relative permeability to each fluid phase in the reservoirs. Therefore, available models like the Corey model and Stone's model for estimating the relative permeability cannot be directly used for reservoir simulation/modelling study of such reservoir where TEOR is implemented. Hence, an attempt has been made to develop a reliable, accurate, and robust data-driven model for two-phase oil/water relative permeability using the XG-Boost machine learning algorithm which accounts for the temperature's effect. For this study, numerous sets of oil and relative permeability data have been sourced, compiled and validated using our proposed model via the supervised XG-Boost approach. For model construction, 1270 oil relative permeability and 1230 water relative permeability data points were obtained from literature covering different rock/fluid and reservoir conditions. This study presents a new data-driven model developed using the XG-Boost algorithm to predict two-phase oil/water relative permeability over a wide range of temperatures in unconsolidated sand and sandstone formations. Moreover, the proposed model gave us better results based on the statistical error analysis.

Characterizing an oil reservoir requires one to understand the Pressure- Volume-Temperature (PVT) properties of reservoir fluids, especially bubble point pressure, solution gas oil ratio and oil formation volume factor because of its more often utilization in reservoir engineering studies. The current correlations are restricted by the use of sample from a particular field. As the physical properties and the composition of the crude oil varies the results becomes erroneous after a specific range. This correlation will give results only over a specific range of properties like specific gravity, viscosity, composition etc. The challenge is to develop a new approach which overcomes the current shortcomings. In this paper a new machine learning based model has been developed using Interactive Multivariate Linear Regression (I-MLR) method by integrating a large number of datasets to predict above mentioned properties. It overcomes the restriction of the previous correlations as it does not use data from any particular field. As such it is applicable over wide range of physical properties and composition. This model does not require any laboratory studies which makes it more economical. The validation of the model is done after detailed comparative study done with various commercially used empirical correlations.