Hydraulic fracturing has enabled production from unconventional reservoirs in the U.S., but production rates often decline sharply, limiting recovery factors to under 10%. This study proposes an optimization workflow for the CO₂ huff-n-puff process for multistage-fractured horizontal wells in the Wolfcamp A formation in the Delaware Basin. The potential for enhanced oil recovery and CO₂ sequestration simultaneously was addressed using a coupled geomechanics–reservoir simulation. Geomechanical properties were derived from a 1D mechanical earth model and integrated into reservoir simulation to replicate hydraulic fracture geometries. The fracture model was validated using a robust production history matching. A fluid phase behavior analysis refined the equation of state, and 1D slim tube simulations determined a minimum miscibility pressure of 4300 psi for CO₂ injection. After the primary production phase, various CO₂ injection rates were tested from 1 to 25 MMSCFD/well, resulting in incremental oil recovery ranging from 6.3% to 69.3%. Different injection, soaking and production cycles were analyzed to determine the ideal operating condition. The optimal scenario improved cumulative oil recovery by 68.8% while keeping the highest CO₂ storage efficiency. The simulation approach proposed by this study provides a comprehensive and systematic workflow for evaluating and optimizing CO₂ huff-n-puff in hydraulically fractured wells, enhancing the recovery factor of unconventional reservoirs.

College student retention is one of the most important metrics in higher education. With institutions across the US facing decreasing enrollment, developing a reliable retention prediction method is crucial. In recent years, the use of the Markov chain model in forecasting student enrollment and progression has become more common, but there is little work on its application in student retention. One key factor in determining this model's effectiveness is what parameters should be used in the student population’s segmentation or grouping. This study presents a rigorous algorithm, coupled with a prediction model, capable of selecting parameters that provide the most accurate results for term-to-term retention prediction using the Markov chain analysis. This is a pioneering attempt to optimize the Markov chain for retention prediction. Results are verified against an enrollment dataset from a public institution. With high accuracy, flexibility, and interpretability, the coupled algorithm model is an effective tool for institutional planning. Future iterations will focus on adding behavioral data and integrating the model with advanced deep learning methods to predict the detailed status of returning students, such as full-time equivalent and class level.