This study presents a comparative analysis of CO₂-EOR and water flooding scenarios to optimize oil recovery in a geologically heterogeneous reservoir with a dome structure and partial aquifer support. Using production data from twelve production and three monitoring wells, a dynamic reservoir model was built and successfully history-matched with a 1% deviation from actual field data. Three main recovery methods were evaluated: water flooding, continuous CO₂ injection, and water-alternating-gas (WAG) injection. Water flooding resulted in a four-fold increase from primary recovery, while continuous CO₂ injection provided up to 40% additional oil recovery compared to water flooding. WAG injection further increased recovery by 20% following water flooding. The minimum miscibility pressure (MMP) was determined using a 1D slim-tube simulation to ensure effective CO₂ performance. A sensitivity analysis on CO₂/WAG ratios (1:1, 2:1, 3:1) revealed that continuous CO₂ injection, particularly in high permeability zones, offered the most efficient recovery. An economic evaluation indicated that the optimal development strategy is 15 years of water flooding followed by 15 years of continuous CO₂ injection, resulting in a net present value (NPV) of USD 1 billion. This study highlights the benefits of CO₂-EOR for maximizing oil recovery and suggests further work on hybrid EOR techniques and carbon sequestration in depleted reservoirs.

Large-scale underground hydrogen storage in saline aquifers requires an understanding of hydrogen–brine two-phase flow properties, particularly relative permeability, which influences reservoir injectivity and hydrogen recovery. However, such hydrogen–brine relative permeability data remain scarce, hindering the predictive modeling of hydrogen injection and withdrawal. In this study, steady-state hydrogen–brine co-injection coreflood experiments were conducted on an Austin Chalk core sample to measure the relative permeabilities. Klinkenberg slip corrections were applied to the gas flow measurements to determine the intrinsic (slip-free) hydrogen permeability. The core’s brine permeability was 13.2 mD, and the Klinkenberg-corrected hydrogen gas permeability was 13.8 mD (approximately a 4.5% difference). Both raw and slip-corrected hydrogen relative permeability curves were obtained, showing that the gas-phase conductivity increased as the water saturation decreased. Gas slippage caused higher apparent gas permeability in the raw data, and slip correction significantly reduced hydrogen relative permeability at lower hydrogen saturations. The core’s irreducible water saturation was 39%, at which point the hydrogen relative permeability reached 0.8 (dropping to 0.69 after slip correction), which is indicative of strongly water-wet behavior. These results demonstrate a measurable impact of gas slippage on hydrogen flow behavior and highlight the importance of accounting for slip effects when evaluating hydrogen mobility in brine-saturated formations.

This paper aims to evaluate the efficiency of various machine learning algorithms integrating with numerical simulations in optimizing oil production for a highly heterogeneous reservoir. An approach leveraging a machine learning workflow for reservoir characterization, history matching, sensitivity analysis, field development and optimization was proposed to accomplish the above goal. A 3D subsurface model representing studied sand-shale sequences was constructed based on geophysical and petrophysical logs, core measurements, and advanced machine learning techniques. After that, a robust sensitivity analysis and history matching process were conducted using a machine learning workflow. The most sensitive control variables were the aquifer properties, permeability heterogeneity in different directions, and water–oil contacts. The history matching results from the constructed geological model showed that the oil rate, water rate, bottom hole pressure, and average reservoir pressure were matched within a 10% deviation from the observed data. Several field development scenarios were generated using the validated model to optimize cumulative oil recovery. Different injection well placement locations, well patterns, and the possibility of converting existing oil-producing wells to water injection wells were investigated. A machine learning-based proxy model was built for the prediction of cumulative oil production and then optimized with hybrid machine learning techniques. The Artificial Neural Network (ANN) algorithm was found to provide higher field cumulative oil production compared with the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) of 3.5% and 26.5%, respectively. Following the detailed proposed machine learning-based workflow, one can effectively decide on the development strategy and apply the findings from this research to their field.

Electrofacies are log-related signatures that reflect specific physical and compositional characteristics of rock units. The concept was developed to encapsulate a collection of recorded well-log responses, enabling the characterization and differentiation of one rock unit from another. The analysis of the lateral and vertical distribution of electrofacies is crucial for understanding reservoir properties; however, well-log analysis can be labor-intensive, time-consuming, and prone to inaccuracies due to the subjective nature of the process. In addition, there is no unique way of reliably classifying logs or deriving electrofacies due to the varying accuracy of different methods. In this study, we develop a workflow that mitigates the variability in results produced by different clustering algorithms using a committee machine. Using several unsupervised machine learning methods, including k-means, k-median, hierarchical clustering, spectral clustering, and the Gaussian mixture model, we predict electrofacies from wireline well log data and generate their 3D vertical and lateral distributions and inferred geological properties. The results from the different methods are used to constitute a committee machine, which is then used to implement electrofacies-guided well placement. 3D distributed petrophysical properties are also computed from core-calibrated porosity and permeability data for reservoir simulation. The results indicate that wells producing from a specific electrofacies, as predicted by the committee machine, have significantly better production than wells producing from other electrofacies. This proposed detailed machine learning workflow allows for strategic decision-making in development and the practical application of these findings for improved oil recovery.

This study evaluates the feasibility of CO₂ Water-Alternating-Gas (WAG) injection for enhanced oil recovery (EOR) in a highly heterogeneous reservoir using cost-effective and efficient tools. The Rule of Thumb method was initially used to screen the reservoir, confirming its suitability for CO₂-WAG injection. A fluid model was constructed by comparing several component lumping methods, selecting the approach with the least deviation from experimental data to ensure accuracy. The minimum miscibility pressure (MMP), a critical parameter for CO₂-EOR, was estimated using three methodologies: 1D simulation based on the slim tube test, semi-empirical analytical correlations, and fluid modeling. These techniques provided complementary insights into the reservoir’s miscibility conditions. The CO₂ Prophet software version 1 was employed to history-match production data and evaluate different development strategies. The Kinder Morgan CO₂ Scoping Model was used to perform production forecasting and assess the economic viability of implementing CO₂-WAG. Quantitative comparisons showed that the CO₂ Prophet version 1 model revealed minimal deviations from the history match results: oil production estimates differed by only 3.5%, and water production estimates differed by −4.11%. Cumulative oil recovery was projected to reach approximately 20.26 MMSTB over a 25-year production period. The results indicate that CO₂-WAG injection could enhance oil recovery significantly compared to water flooding while maintaining economic feasibility. This study demonstrates the practical integration of analytical tools and inexpensive models to evaluate and optimize CO₂-EOR strategies in complex reservoirs. The findings provide a systematic workflow for deploying CO₂-WAG in heterogeneous reservoirs, balancing technical and economic considerations.

Underground hydrogen storage (UHS) in saline aquifers offers a viable alternative to surface-based storage systems, which are limited by capacity constraints, high operational pressures, complex thermal regulation, low energy densities, and potential safety hazards. This study uses a fully compositional reservoir simulation model to evaluate hydrogen behavior in the Mt. Simon Sandstone in the Illinois Basin. The analysis focuses on the effects of hysteresis, solubility, diffusivity, and production well perforation location on recovery efficiency. Cyclic injection and withdrawal scenarios were simulated to assess storage performance and operational strategies. The results show that accounting for hydrogen diffusivity shows essentially unchanged withdrawal efficiency at 79%, the same as the base case. Solubility causes a slight decrease to 78%, while hysteresis leads to a more significant reduction to 63%. The location of injection well perforations also influences recovery: top-perforated wells increase efficiency from 60% after the first cycle to 74% after six cycles, whereas bottom-perforated injection wells increase efficiency from 56% to 79% over the same period. These findings emphasize the importance of accounting for multiphase flow dynamics and strategic well placement in optimizing UHS system performance. The insights contribute to advancing reliable, large-scale hydrogen storage solutions essential for supporting renewable energy integration and long-term energy security.