The growing energy demand and the need for climate mitigation strategies have spurred interest in the application of CO₂–enhanced oil recovery (CO₂–EOR) and carbon capture, utilization, and storage (CCUS). Furthermore, natural hydrogen (H₂) production and underground hydrogen storage (UHS) in geological media have emerged as promising technologies for cleaner energy and achieving net–zero emissions. However, selecting a suitable geological storage medium is complex, as it depends on the physicochemical and petrophysical characteristics of the host rock. Solubility is a key factor affecting the above–mentioned processes, and it is critical to understand phase distribution and estimating trapping capacities. This paper conducts a succinct review of predictive techniques and present novel simple and non–iterative predictive models for swift and reliable prediction of solubility behaviors in CO₂–brine and H₂–brine systems under varying conditions of pressure, temperature, and salinity (T–P–m salts), which are crucial for many geological and energy–related applications. The proposed models predict CO₂ solubility in CO₂ + H₂O and CO₂ + brine systems containing mixed salts and various single salt systems (Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻) under typical geological conditions (273.15–523.15 K, 0–71 MPa), as well as H₂ solubility in H₂ + H₂O and H₂ + brine systems containing NaCl (273.15–630 K, 0–101 MPa). The proposed models are validated against experimental data, with average absolute errors for CO₂ solubility in pure water and brine ranging between 8.19 and 8.80% and for H₂ solubility in pure water and brine between 4.03 and 9.91%, respectively. These results demonstrate that the models can accurately predict solubility over a wide range of conditions while remaining computationally efficient compared to traditional models. Importantly, the proposed models can reproduce abrupt variations in phase composition during phase transitions and account for the influence of different ions on CO₂ solubility. The solubility models accurately capture the salting–out (SO) characteristics of CO₂ and H₂ gas in various types of salt systems which are consistent with previous studies. The simplified solubility models for CO₂ and H₂ presented in this study offer significant advantages over conventional approaches, including computational efficiency and accuracy across a wide range of geological conditions. The explicit, derivative–continuous nature of these models eliminates the need for iterative algorithms, making them suitable for integration into large–scale multiphase flow simulations. This work contributes to the field by offering reliable tools for modeling solubility in various subsurface energy and environmental–related applications, facilitating their application in energy transition strategies aimed at reducing carbon emissions.

The accurate prediction of carbon dioxide (CO₂) adsorption in metal–organic frameworks (MOFs) is critical for accelerating the discovery of high-performance materials for post-combustion carbon capture. Experimental screening of MOFs is often costly and time-consuming, creating a strong incentive to develop reliable data-driven models. Despite extensive research, most studies rely on standalone models or generic ensemble strategies that fall short in handling the complex, nonlinear relationships inherent in adsorption data. In this study, a novel ensemble learning framework is developed by integrating five distinct regression algorithms: Random Forest, XGBoost, LightGBM, Support Vector Regression, and Multi-Layer Perceptron. These algorithms are combined into four custom ensemble strategies: equal-weighted voting, performance-weighted voting, stacking, and manual blending. A dataset comprising 1212 experimentally validated MOF entries with input descriptors including BET surface area, pore volume, pressure, temperature, and metal center is used to train and evaluate the models. The stacking ensemble yields the highest performance, with an R² of 0.9833, an RMSE of 1.0016, and an MAE of 0.6630 on the test set. Model reliability is further confirmed through residual diagnostics, prediction intervals, and permutation importance, revealing pressure and temperature to be the most influential features. Ablation analysis highlights the complementary role of all base models, particularly Random Forest and LightGBM, in boosting ensemble performance. This study demonstrates that custom ensemble learning strategies not only improve predictive accuracy but also enhance model interpretability, offering a scalable and cost-effective tool for guiding experimental MOF design.

The most primitive hole challenge is cleaning the hole, which is more severe in deviated wells. This problem was tackled in this research via experimental analysis and graphical evaluations. To hit this aim, rheological parameters were experimentally obtained, and Noah’s model was used to determine cutting bed erosion time at varying heights. A graphical evaluation was done using a case study of deviated wells X and Y from a Niger Delta field. The result shows that low-viscosity fluid, KCL polymer fluid and high-viscosity fluid take 124, 283 and 342 min, respectively, to erode equal height as graphical evaluation shows that hole cleaning will grow complex on deviation. Thus, the deduction from this work in reducing non-productive time (NPT) related to hole cleaning in drilling operation is first, prior to making a trip, pumping low-viscosity fluid at a high flow rate. Secondly, during drilling, increasing drill string rotation in deviated wells can effectively stir the cuttings into the annulus above the low session of the hole.

An accurate prediction of production rate for wellhead choke is highly vital in petroleum production engineering applications. It is deployed in the control of surface production, prevention of water and gas coning, and optimization of the entire production systems. Although there are several choke correlations in literature to estimate production rate; however, most of the published correlations were derived with datasets outside Niger Delta fields. Thus, this study presents a new empirical-based model, which is a derivative from Choubineh et al. model, to predict the liquid production rate of chokes for Niger Delta oil wells. The new model was developed and optimized using multivariate regression and the Generalized Reduced Gradient (GRG) optimization algorithm. Furthermore, a total of 283 production data points from 21 oil wells in 7 fields in the Niger Delta region, with a randomly generated ratio of 70: 30 of the datasets, was used to develop and validate the developed model. The developed Model 2 predicted the choke production rate with a fitting accuracy of average absolute percentage error (AAPE) of 23.73% and coefficient of determination (R2) of 0.973; in addition, the model predicted validating accuracy of AAPE of 9.33% while the coefficient of determination (R2) stands at 0.982. Consequently, this model can be relied on as a quick and robust tool for estimating the choke production rate of producing oil wells. Moreover, the sensitivity analysis results show that the choke size has the most significant impact on the predicted liquid rate. In contrast, gas gravity has the least impact.

Hydrogen (H2) is expected to become a key component of the global energy mix, both through engineered underground storage and the exploitation of naturally occurring subsurface deposits. A critical parameter governing the feasibility of these strategies is the solubility of H2 in water, which directly influences its reactivity, migration, and storage capacity in porous media. Experimental determination of H2 solubility under reservoir conditions is, however, costly, hazardous, and limited in scope. Here, we present two advanced machine learning models—gradient boosting (GB) and light gradient boosting machine (LightGBM)—trained on a dataset of 545 experimental measurements of H2 solubility in pure water across wide ranges of pressure and temperature. The GB model achieves superior performance (R2 = 0.9974; RMSE = 0.000165 mol mol−1), outperforming LightGBM. Trend analysis confirms that the models capture the underlying thermodynamic behavior of H2 solubility with respect to temperature and pressure, while leverage analysis using William’s plot indicates that fewer than 2% of the data points fall outside the model’s applicability domain. These results demonstrate that boosting-based algorithms can deliver highly accurate, rapid, and cost-effective predictions of H2 solubility, offering a robust alternative to conventional laboratory measurements. The validated GB model provides a practical tool for assessing hydrogen reactivity in geological systems and has immediate implications for the design and optimization of underground hydrogen storage and natural hydrogen production.