Search Results (22)

The aim of the current study was to select and test the appropriate model and input parameters for remote sensing retrieval of surface soil moisture (SSM) in the case of bare Chernozems on flat and sloping terrains in northern Bulgaria under different tillage systems. Normalized synthetic aperture radar (SAR) measurements from Sentinel-1 C-band dual-pol products (Gamma-Nought in VV, ratio) were utilized in two ways to delineate SSM from environmental factors that bias determination. The accuracy of the obtained SSM prediction was evaluated against ground-based volumetric water content (VWC) measured in the 0–3.8 cm soil layer at multiple points using a TDR meter. The TDR VWC data were preliminarily calibrated against gravimetric measurements in the 0–5 cm soil layer. The obtained data for soil water retention curves in all studied variants were used to determine the range of soil moisture variation. The measured ground-based data for surface roughness generally correlate with the co-pol Gamma-Nought in VV. The data modeled with the surface soil moisture script in Sentinel Hub (SSM-SH) was calibrated using the ground-based data. Incidence angle normalization of Sentinel-1 products improved the relationship between SAR observables and SSM, when expressed as the ratio of soil moisture to total porosity (rVWC). The modeling indicated the highest importance of the optical indices, together with the temporal differences of radar descriptors sensitive to variations in soil moisture over time. Although the applied Random Forest Regression (RFR) model achieved higher accuracy during training (nRMSE of 7.27%, R² of 0.86), the Gaussian Process Regression (GPR) model provided better generalization performance on the independent validation dataset. The results proved the advantages of the joint utilization of temporal Sentinel-1 SAR measurements with Sentinel-2 optical acquisitions to determine SSM in different bare soil conditions for achieving high accuracy. Full article

Marine concrete is often exposed to multiple aggressive ions, so mechanical deterioration cannot be reliably interpreted using single-ion durability concepts. This study investigates ocean-oriented concretes incorporating high contents of mineral admixtures under coupled sulfate/chloride/carbonate/magnesium actions and develops a pore-structure-based D–P dual-parameter framework linking microstructural descriptors to macroscopic peak stress and peak strain. Three binder systems were designed: ordinary Portland cement concrete (OPC), cement–silica fume concrete (CSC, 20% silica fume), and cement–silica fume–fly ash concrete (CSFC, 20% silica fume + 50% fly ash). Specimens were immersed for 12 and 24 months in four representative binary-salt solutions. Porosity evolution and pore-size-class distributions were quantified by low-field NMR, while pore complexity was characterized using multi-scale fractal dimensions. The results show that mineral admixtures generally refine the pore system and improve the integrity of fine pores; CSFC exhibits the most robust microstructural stability across the tested environments, whereas CSC shows a pronounced degradation of fine-pore structure under CE4. A second-order response surface model built on Z-score normalized fractal dimension (D) and porosity (P) achieves reliable predictability for peak strain (R² = 0.85) and peak stress (R² = 0.79). Global Sobol sensitivity analysis reveals distinct controlling mechanisms: peak strain is predominantly governed by porosity (S_P = 85.9%), whereas peak stress is controlled by the combined effects of porosity, pore complexity, and their interaction (S_P = 42.4%, S_D = 19.8%, S_{D × P} = 37.8%). Local sensitivity mapping further identifies high-sensitivity regimes at extreme pore states, providing mechanistic guidance for mixture optimization. Overall, the proposed D–P framework quantitatively bridges pore volume/geometry evolution and mechanical degradation, offering a practical predictive tool for durability-oriented design of marine concretes under multi-ionic attack. Full article

The dual imperative of mitigating carbon emissions and maximizing hydrocarbon recovery has amplified global interest in carbon capture, utilization, and storage (CCUS) technologies. These integrated processes hold significant promise for achieving net-zero targets while extending the productive life of mature oil reservoirs. However, their effectiveness hinges on a nuanced understanding of the complex interactions between geological formations, reservoir characteristics, and injection strategies. In this study, a comprehensive machine learning-based framework is presented for estimating CO₂ storage capacity and enhanced oil recovery (EOR) performance simultaneously in subsurface reservoirs. The methodology combines simulation-driven uncertainty quantification with supervised machine learning to develop predictive surrogate models. Simulation results were used to generate a diverse dataset of reservoir and operational parameters, which served as inputs for training and testing three machine learning models: Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN). The models were trained to predict three key performance indicators (KPIs): cumulative oil production (bbl), oil recovery factor (%), and CO₂ sequestration volume (SCF). All three models exhibited exceptional predictive accuracy, achieving coefficients of determination (R²) greater than 0.999 across both training and testing datasets for all KPIs. Specifically, the Random Forest and XGBoost models consistently outperformed the ANN model in terms of generalization, particularly for CO₂ sequestration volume predictions. These results underscore the robustness and reliability of machine learning models for evaluating and forecasting the performance of CO₂-EOR and sequestration strategies. To enhance model interpretability and support decision-making, SHapley Additive exPlanations (SHAP) analysis was applied. SHAP, grounded in cooperative game theory, offers a model-agnostic approach to feature attribution by assigning an importance value to each input parameter for a given prediction. The SHAP results provided transparent and quantifiable insights into how geological and operational features such as porosity, injection rate, water production rate, pressure, etc., affect key output metrics. Overall, this study demonstrates that integrating machine learning with domain-specific simulation data offers a scalable approach for optimizing CCUS operations. The insights derived from the predictive models and SHAP analysis can inform strategic planning, reduce operational uncertainty, and support more sustainable oilfield development practices. Full article

Conventional coalbed methane (CBM) reservoir models for injection fall-off testing often disregard the quadratic pressure gradient’s impact. This omission leads to discrepancies in simulating the transient behavior of formation fluids and extracting critical reservoir properties. Accurate determination of permeability, storability, and other properties is crucial for effective reservoir characterization and production forecasting. Inaccurate estimations can lead to suboptimal well placement, ineffective production strategies, and ultimately, missed economic opportunities. To address this shortcoming, we present a novel analytical model that explicitly incorporates the complexities of the quadratic pressure gradient and dual-permeability flow mechanisms, prevalent in many CBM formations where nanopores are rich, presenting a kind of natural nanomaterial. This model offers significant advantages over traditional approaches. By leveraging variable substitution, it facilitates the derivation of analytical solutions in the Laplace domain, subsequently converted to real-space solutions for practical application. These solutions empower reservoir engineers to generate novel type curves, a valuable tool for analyzing wellbore pressure responses during injection fall-off tests. By identifying distinct flow regimes within the reservoir based on these type curves, engineers gain valuable insights into the dynamic behavior of formation fluids. This model goes beyond traditional approaches by investigating the influence of the quadratic pressure gradient coefficient, inter-porosity flow coefficient, and storability ratio on the pressure response. A quantitative comparison with traditional models further elucidates the key discrepancies caused by neglecting the quadratic pressure gradient. The results demonstrate the proposed model’s ability to accurately depict the non-linear flow behavior observed in CBM wells. This translates to more reliable pressure and pressure derivative curves that account for the impact of the quadratic pressure gradient. Full article

Croatia has both significant CO₂ emissions from the point sources and a history of oil and gas exploration, and this is why the CCS technology surfaced as a viable solution for curbing CO₂ emissions on a national level. Since approximately half of emissions from the stationary industrial sources occur along the Adriatic coastline, the entire offshore area became an exploration target. Regional studies revealed the potential storage plays, one of which is in the aquifer of the Mesozoic carbonate complex with dual porosity extending all along the Croatian offshore area. Three structures were chosen in its central part–Klara, Kate and Perina. For the first two, the models were constructed based on the data from old exploration wells and a regional structural map, while for the Perina structure, a new seismic interpretation was added to better characterise its properties. It came out that the Kate structure appears to be the most prospective in general (45 Mt), with neighbouring Klara as the second (39 Mt), while the initially promising Perina (7 Mt) turned out to be of far lesser importance. The Perina structure case is an example that new seismic interpretation can reduce the capacity estimate if it reveals certain limiting factors, in this case, the limitation of structural closure. Full article

Fault water inflow is one of the most severe disasters that can occur during the construction of hard and brittle rock tunnels. These tunnels traverse brittle fault breccia zones comprising two key components: a damage zone dominated by low-strain fractures and an internally nested high-strain zone known as the fault core. Structural heterogeneity influences the mechanical and hydraulic properties within fault breccia zones, thereby affecting the evolving characteristics of water inflow in hard rock faulting. Based on the hydraulic characteristics within hard rock fault zones, this paper presents a generalized dual-porosity fluid-solid coupling water inflow model. The model is utilized to investigate the spatiotemporal evolution patterns of water pressure, inflow velocity, and water volume during tunneling through heterogeneous fault zones in hard rock. Research findings indicate that when tunnels pass through the damage zones, water inrush velocity is high, yet the water volume is low, and both decrease rapidly over time. Conversely, within the core regions of faults, water inflow velocity is low, yet the water volume is high, and both remain relatively stable over time. Simulation results closely align with the water inflow data from China’s largest cross-section tunnel, the Tiantai Mountain Tunnel, thus validating the accuracy of the evolutionary model proposed in this paper. These findings offer a new perspective for devising effective prevention strategies for water inflow from heterogeneous faults. Full article

Quantitative evaluation of stress sensitivity of ultra-deep carbonate reservoirs has been one of the challenges in exploration and development, and the problem of permeability loss law in ultra-deep carbonates under variable stress conditions has not been solved so far and further research is urgently needed. Through experimental and numerical simulation methods, the stress-sensitive evaluation equations were established based on matrix-type carbonate and fractured carbonate reservoirs, the stress-sensitive changes under different Young’s modulus were discussed, and the degree of permeability loss under different stresses was evaluated. Finally, the dual-media model of ultra-deep carbonate was established, and the practical application was carried out in the Shunbei area of the Tarim Basin. Studies have shown that (1) under the same effective stress, the stress sensitivity of matrix-type and fracture-type carbonate reservoirs is related to the Young’s modulus of the rock skeleton. In matrix-type carbonate reservoirs, rocks with a larger Young’s modulus have smaller rigidity and stronger stress sensitivity. In fracture-type carbonate reservoirs, the stress sensitivity is relatively weak under a smaller Young’s modulus, and relatively strong under a larger Young’s modulus. (2) Measured under the conditions of 87 MPa of peripheral pressure, 50 MPa of flow pressure, and 120 °C, the effective stress of matrix-type carbonate reservoirs has an exponential relationship with the permeability of reservoirs. The degree of stress sensitivity for fracture-type is generally higher than that of matrix-type reservoirs, and the smaller the Young’s modulus, the larger the difference in stress sensitivity. (3) The stress sensitivity of typical ultra-deep carbonates in the Shunbei area of the Tarim Basin is higher by establishing a dual-porosity model based on the initiating pressure gradient, which supports new evidence for the characteristics of ultra-deep carbonates with high-stress sensitivity. In actual production, the impact of stress sensitivity on the reservoir volume calculation and efficient development of ultra-deep carbonate reservoirs requires critical attention. Full article

The current study was performed for the experimental modeling of cyclic steam-air injection in a heavy oil reservoir model of dual porosity in the presence of a nickel-based catalyst for in situ oil upgrading enhanced by simultaneous hydrogen generation. The research was realized in the combustion tube setup with a sandpack core model under reservoir conditions due to the consistent injection of air followed by oil in situ combustion (ISC) and steam (water) injection. As a result, the original oil was upgraded regarding fractional composition and oil properties. In addition, simulated reservoir heterogeneity and cyclic stimulation intensified the hydrogen synthesis, which, in turn, could also contribute to oil upgrading. Full article

Successful matrix acidizing for extremely thick carbonate reservoirs with long horizontal well sections and strong heterogeneity requires efficient temporary plugging and diverting of acid fluid, ensuring acid fluid distribution to each production layer. Foamed-viscoelastic-surfactant (Foamed-VES) acid combines the benefits of both foam acid and viscoelastic surfactant (VES) acid, integrating foam plugging and viscous plugging. It can achieve uniform acid distribution in highly heterogeneous reservoirs. However, little research has been conducted on the wormhole propagation law of foamed-VES acid. To address this gap, this study established a mathematical model of foamed-VES acid wormhole propagation based on the dual-scale model. The model was coupled with a random porosity distribution generated with geological statistical software. The effects of different factors on foamed-VES acid etching were simulated. Numerical results show that foamed-VES acid can stimulate low-permeability reservoirs with a permeability differential of 20. Its inherent mechanism lies in the synergy of foam plugging and VES viscous plugging. This study enhances our understanding of the acid diversion mechanism of foamed-VES acid, providing a theoretical foundation for on-site acidizing treatment. Full article

The magneto-hydrodynamic dual convection stagnation flow pattern behavior of a Tangent Hyperbolic (TH) fluid has been reported in this study. The radiation, Joule heating, and heat generation/absorption impacts have also been analyzed. The flow-narrating differential equations, which are constrained by a thermal and solutal stratified porous medium, are transmuted into a system of nonlinear differential equations. To provide a numerical solution to the flow problem, a computational model is created. Numerical solutions are obtained using the fifth-order exactness program (Bvp5c), and for validation of the results, a comparison is also made with the methodology of the Runge–Kutta fourth order. The physical implications are appraised and depicted using diagrams or tables against flow-controlling parameters, such as Hartmann number, porosity parameter, solutal stratification, the parameter of curvature, temperature stratification, local Weissenberg number, Schmidt number, etc. It has been observed that in the appearance of Joule heating phenomena, the fluid temperature is a lowering function of thermal stratification. The findings are compared to the existing literature and found to be consistent with earlier research. Full article

Multi-fractured horizontal well (MFHW) technology is a key technology for developing unconventional reservoirs, which can generate a complex fracture network called a stimulated reservoir volume (SRV). Currently, there are many relative analytical models to describe the fluid seepage law, which are not suitable for volcanic reservoirs as of yet. The reasons are as follows: (1) due to the development of natural fractures, multi-scaled flow (matrix, natural fractures, SRV) should be considered to characterize MFHW flow in volcanic reservoirs; (2) non-Darcy flow and stress sensitivity should be considered simultaneously for seepage in volcanic reservoirs. Thus, this paper presents a novel MFHW analysis model of volcanic reservoirs that uses a multi-scale dual-porosity medium model and complex flow mechanisms. Laplace transformation, the Duhamel principle, the perturbation method and Stehfest numerical inversion are employed to solve the model to obtain dynamic pressure response curves. The results show that the pressure response curve can be divided into eight stages. Sensitivity analysis shows that the parameters of hydraulic fractures mainly affect the early flow stage. The parameters of the SRV region mainly affect the middle flow stage. The parameters of unreconstructed regions, non-Darcy flow and stress sensitivity mainly affect the late flow stage. Full article

The estimation of the formation permeability is considered a vital process in assessing reservoir deliverability. The prediction of such a rock property with the use of the minimum number of inputs is mandatory. In general, porosity and permeability are independent rock petrophysical properties. Despite these observations, theoretical relationships have been proposed, such as that by the Kozeny–Carmen theory. This theory, however, treats a highly complex porous medium in a very simple manner. Hence, this study proposes a comprehensive ANN model based on the back propagation learning algorithm using the FORTRAN language to predict the formation permeability from available well logs. The proposed ANN model uses a weight visualization curve technique to optimize the number of hidden neurons and layers. Approximately 500 core data points were collected to generate the model. These data, including gamma ray, sonic travel time, and bulk density, were collected from numerous wells drilled in the Western Desert and Gulf areas of Egypt. The results show that in order to predict the permeability accurately, the data set must be divided into 60% for training, 20% for testing, and 20% for validation with 25 neurons. The results yielded a correlation coefficient (R²) of 98% for the training and 96.5% for the testing, with an average absolute percent relative error (AAPRE) of 2.4%. To validate the ANN model, two published correlations (i.e., the dual water and Timur’s models) for calculating permeability were used to achieve the target. In addition, the results show that the ANN model had the lowest mean square error (MSE) of 0.035 and AAPRE of 0.024, while the dual water model yielded the highest MSE of 0.84 and APPRE of 0.645 compared to the core data. These results indicate that the proposed ANN model is robust and has strong capability of predicting the rock permeability using the minimum number of wireline log data. Full article

In recent years, energy consumption has become an essential aspect in the manufacturing industry, and low heat transfer is one of the obstacles that affect the quality of the final product. This situation can be managed by suspending nanoparticles into ordinary heat transferring fluid (the base fluid). This newly prepared colloidal suspension has better heat transport capabilities. Keeping such usage of nanofluids in mind, this research was performed to better understand the heat transport characteristics during flow analysis saturated in porous media subject to Al2O3-SiO2/water hybrid nanofluids. This flow problem was generated by a stretching/shrinking surface. The surface of the sheet was under the influence of mass suction and second-order partial slip. The boundary layer flow was formulated in a system of partial differential equations by utilizing basic conservation laws in conjunction with the Tiwari and Das nanofluid model. Then, the appropriate form of the similarity transformation was adapted to transform the model into a system of ordinary differential equations. The built-in function, i.e., the bvp4c function in the MATLAB software, solved the reduced form of the boundary layer model. The novelty of this study lay in the predicting of two different exact and numerical solutions for both the flow and temperature fields. The computed results showed that the medium porosity as well as the nanoparticle volume fraction widened the existence range of the dual solutions. In addition, the investigational output exposed the fact that the temperature fields were significantly enhanced by the higher nanoparticle volume fraction. Moreover, the outcomes of this study showed a superb correlation with existing works. The present results can be utilized in various branches of science and engineering such as the polymer industry and in the treatment of different diseases. Full article

Mechanical and chemical processes experienced by carbonate rocks result in a complex network of natural fractures and dissolution features that have direct implications on porosity, permeability, and connectivity in reservoirs. Characterization of natural fractures is best done in core; however, it can be time-consuming due to the large amounts of individual features present and the long list of attributes typically collected for each feature. Additionally, karst features in core, such as vugs and small cavities, are seldom characterized in a quantitative way or are overlooked. We introduce a new methodology, called the non-matrix quick pass (NMQP), which allows for the collection of non-matrix features in a rapid yet quantitative fashion at a rate of 12 to 20 m of core per hour. The NMQP methodology offers enough vertical resolution so that observations can be integrated with other wellbore data types (e.g., wireline logs, well tests, and production logs). This method also yields estimates of density and porosity that are rigorous enough to provide the technical basis to build first-generation dual-porosity models describing the non-matrix component of a carbonate reservoir and its potential impact on field performance. Full article

Assessing the risk of using pesticides for the environment in general, and for groundwater in particular, necessitates prediction of pesticide migration. For this purpose, mathematical models of pesticide behavior are utilized, which must be parameterized and calibrated based on experimental data to make them perform properly. The behavior of the pesticide cyantraniliprole was examined in a long-term lysimetric experiment. The MACRO 5.2 dual porosity model was calibrated based on the percolate and the levels of pesticides in the soil profile and percolate. Despite employing experimentally verified soil parameters and pedotransfer functions (PTF), the model must be calibrated for percolation. This is due to the model’s properties as well as the complexity of the soil as an object of study, and its pore space, which is subject to daily and annual fluctuations. It is the parameters that describe the structure of the pore space that need to be calibrated. Calibrating for pesticide concentrations required a minor revision of the sorption and transformation rates, as well as an increase in the dispersivity and ASCALE values. Full article