Oil production forecasting is one of the essential processes for organizations and governments to make necessary economic plans. This paper proposes a novel hybrid intelligence time series model to forecast oil production from two different oil fields in China and Yemen. This model is a modified ANFIS (Adaptive Neuro-Fuzzy Inference System), which is developed by applying a new optimization algorithm called the Aquila Optimizer (AO). The AO is a recently proposed optimization algorithm that was inspired by the behavior of Aquila in nature. The developed model, called AO-ANFIS, was evaluated using real-world datasets provided by local partners. In addition, extensive comparisons to the traditional ANFIS model and several modified ANFIS models using different optimization algorithms. Numeric results and statistics have confirmed the superiority of the AO-ANFIS over traditional ANFIS and several modified models. Additionally, the results reveal that AO is significantly improved ANFIS prediction accuracy. Thus, AO-ANFIS can be considered as an efficient time series tool.

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The knowledge on CO2 sequestration and CO2 enhanced oil recovery (CO2-EOR) in mature mixed and interbedded hydrocarbon reservoirs are limited. In this vein, the feasibility of CO2-water alternating gas (CO2-WAG) for coupling CO2 sequestration and CO2-EOR in a mature mixed sandstone-carbonate reservoir was investigated using the S1A reservoir. First, core sample analysis and scanning electron microscopy (SEM) were conducted to evaluate the reservoir characterization. Next, a geological model with dual porosity and permeability was developed and transferred to a reservoir simulation model, and 21-year field production data were utilized for history matching and constraining of the reservoir model. Then, continuous CO2 and CO2-WAG injection methods were simulated using newly developed and history-matched geo-models and compared to assess their CO2-EOR and CO2 storage mechanisms and determine their potential in a mixed sandstone-carbonate reservoir. The effect of the anisotropic permeability ratio and hysteresis on CO2 storage mechanisms was addressed in this work. The results indicate that the CO2-WAG scheme can yield a +3% oil recovery factor than the continuous CO2 injection method, and CO2-WAG injection can utilize up to 14 and 12% of the total CO2 injected for residual and solubility trappings, respectively, while a minimal 2.9 and 0.03% was utilized from continuous CO2 injection for residual and solubility trappings, respectively. The WAG ratio of 2:1 could yield a higher recovery factor and greater CO2 utilization for solubility and residual trappings in a mixed reservoir. In mixed and interbedded reservoirs, geological anisotropy can also strongly influence reservoir performance during the CO2-EOR process, in which higher values of the anisotropy ratio (K v/K h) in CO2-WAG can yield greater oil recovery and more CO2 storage; also, hysteresis has great impact on residual trapping. This study provides valuable insights into the potential of CO2-WAG for CO2 sequestration and CO2-EOR in mature mixed and interbedded reservoirs.

Underground CO2 storage is a promising technology for mitigating climate change. In this vein, the subsurface condition was inherited a lot of uncertainties that prevent the success of the CO2 storage project. Therefore, this study aims to build the 3D model under geological uncertainties for enhancing CO2 storage capacity in the Shahejie Formation (Es1), Nv32 block, China. The well logs, seismic data, and geological data were used for the construction of 3-D petrophysical models. The target study area model focused on four units (Es1 × 1, Es1 × 2, Es1 × 3, and Es1 × 4) in the Shahejie Formation. Well logs were utilized to predict petrophysical properties; the lithofacies indicated that the Shahejie Formation units are sandstone, shale, and limestone. Also, the petrophysical interpretation demonstrated that the $$Es1$$ E s 1 reservoir exhibited high percentage porosity, permeability, and medium to high net-to-gross ratios. The static model showed that there are lateral heterogeneities in the reservoir properties and lithofacies; optimal reservoir rocks exist in Es1 × 4, Es1 × 3, and Es1 × 2 units. Moreover, the pore volume of the Es1 unit was estimated from petrophysical property models, ranging between 0.554369 and 10.03771 × 106 sm3, with a total volumetric value of 20.0819 × 106 sm3 for the four reservoir units. Then, the 100–400 realizations were generated for the pore volume uncertainties assessment. In consequence, 200 realizations were determined as an optimal solution for capturing geological uncertainties. The estimation of CO2 storage capacity in the Es1 formation ranged from 15.6 to 207.9 × 109 t. This result suggests the potential of CO2 geological storage in the Shahejie Formation, China.