The impact of geological heterogeneity on horizontal well-triplet performance in CO2-circulated geothermal reservoirs

CO2 circulated geothermal production can be integrated with CO2 geological sequestration as a utilization method to offset cost. Investigation of heterogeneity impact is limited to CO2 sequestration and its effect on CO2 circulation and associated heat recovery is unclear. This study is aimed to improve the understanding of this problem by numerical experiments. A set of spatially correlated heterogeneous porosity fields is generated using a variety of geostatistical parameters, i.e., variance, correlation lengths, anisotropy and azimuth. Heterogeneous fields of intrinsic permeability and initial/residual water saturation are derived from porosity using equations regressed from a field dataset. Twenty combinations of injection pressure and well space obtained by Latin-Hypercube sampling are deployed in each heterogeneous field, generating a suite of numerical geothermal reservoir models. Performance indicators, including lifespan, net stored CO2 , produced heat flux, and total recovered heat energy in lifespan, are calculated from each model simulation. The simulation results suggest that geologic heterogeneity could develop high-permeable CO2 flow paths, causing bypass of the hot low-permeable zones, shortened lifespan and reduced total recovered heat energy. Depending on the azimuth, anisotropy can create either flow barriers or preferential flow paths, increasing or decreasing heat sweeping efficiency. The relative angle between horizontal wells and the axis of maximum continuity of the heterogeneity can be optimized to maximize heat recovery efficiency. These finds provide useful insights of interplay between geological heterogeneity, well placement and operation of CO2 circulated geothermal production.Cited as: Chen, M., Al-Saidi, A., Al-Maktoumi, A., Izady, A. The impact of geological heterogeneity on horizontal well-triplet performance in CO2-circulated geothermal reservoirs. Advances in Geo-Energy Research, 2022, 6(3): 192-205. https://doi.org/10.46690/ager.2022.03.0

Conflict resolution in the multi-stakeholder stepped spillway design under uncertainty by machine learning techniques

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Numerical evaluation of hydrogen production by steam reforming of natural gas

Industry-scale hydrogen is mainly produced by steam methane reforming (SMR), which uses natural gas as the feedstock and fuel and co-produces CO2. This study aims to numerically evaluate hydrogen production by SMR under various reacting conditions. Unlike the previous studies with limited scenarios, the performance of SMR is continuously evaluated in a high-dimensional input-parameter space. The SMR plant including a combustor, a reformer, and a water-gas shifter is modeled in Aspen HYSYS software. The four key parameters, including methane fraction of the feedstock, reformer pressure and temperature, and shifter temperature, are treated uncertain and 50 samples are drawn from a four-dimensional parameter space defined by their ranges. Each sample is input to HYSYS model and mass ratio of each component in product streams is obtained as the output variables. Based on the 50 pairs of input-output data, response surfaces of the outputs are developed to surrogate HYSYS models. The fast response surface models are then used to calculate global sensitivity indices and evaluate SMR processes. Results show the reformer performance is controlled by temperature rather than pressure, and a temperature higher than 900 °C can maximize the reaction rate. The water-gas shifting reaction is inhibited in the reformer but significantly enhanced in the shifter. Hydrogen is mainly produced in the reformer while the major function of the shifter is to convert CO to nontoxic CO2.Cited as: Chen, M., Al-Subhi, K., Al-Rajhi, A., Al-Maktoumi, A., Izady, A. Al-Hinai, A. Numerical evaluation of hydrogen production by steam reforming of natural gas. Advances in Geo-Energy Research, 2023, 7(3): 141-151. https://doi.org/10.46690/ager.2023.03.0

Groundwater Modeling and Sustainability of a Transboundary Hardrock–Alluvium Aquifer in North Oman Mountains

This study aims at modeling groundwater flow using MODFLOW in a transboundary hardrock–alluvium aquifer, located in northwestern Oman. A three-dimensional stratigraphic model of the study area representing the vertical and spatial extent of four principal hydro-geologic units (specifically, the Hawasina, ophiolite, Tertiary and alluvium) was generated using data collected from hundreds drilled borehole logs. Layer elevations and materials for four layers grid cells were taken from the generated stratigraphic model in which the materials and elevations were inherited from the stratigraphic model that encompasses the cell. This process led to accurate grid so that the developed groundwater conceptual model was mapped to simulate the groundwater flow and to estimate groundwater balance components and sustainable groundwater extraction for the October 1996 to September 2013 period. Results show that the long-term lateral groundwater flux ranging from 4.23 to 11.69 Mm3/year, with an average of 5.67 Mm3/year, drains from the fractured eastern ophiolite mountains into the alluvial zone. Moreover, the long-term regional groundwater sustainable groundwater extraction is 18.09 Mm3/year for 17 years, while it is, respectively, estimated as 14.51, 16.31, and 36.00 Mm3/year for dry, normal, and wet climate periods based on standardized precipitation index (SPI) climate condition. Considering a total difference in groundwater levels between eastern and western points of the study area on the order of 228 m and a 12-year monthly calibration period (October 1996 to September 2008), a root mean squared error (RMSE) in predicted groundwater elevation of 2.71 m is considered reasonable for the study area characterized by remarkable geological and hydrogeological diversity. A quantitative assessment of the groundwater balance components and particularly sustainable groundwater extraction for the different hydrological period would help decision makers to better understand the water resources in the Al-Buraimi region. In addition, it would assist decision makers to improve existing strategies to enhance the decision making for future developments

A Novel Hybrid Entropy-Clustering Approach for Optimal Placement of Pressure Sensors for Leakage Detection in Water Distribution Systems Under Uncertainty

This study presents a novel hybrid entropy-clustering framework for placing pressure sensors in water distribution systems (WDS) to detect leakage. Leakages are simulated at all potential nodes of WDS, and then potential pressure sensors (PPS) in WDS are classified using a K-means clustering algorithm. Transinformation entropy for each potential pair of PPS was also computed, which in turn helped to reduce redundant information. PPS locations were subsequently optimized using a multi-objective optimization model. Furthermore, to capture the sensitivity of sensors\u27 layout in WDS to sensor error, a fuzzy-based analysis is integrated with a multi-objective optimization model. Finally, the best compromise solution of PPS placement in each category was selected using an ELECTRE multi-criteria decision making model. Reducing redundant information of pressure sensors based on information theory and choosing the best possible solution based on the ELECTRE model are the main novelties of this study. Results of C-Town WDS attest to the proposed framework\u27 efficiency

Estimation of air-flow parameters and turbulent intensity in hydraulic jump on rough bed using Bayesian model averaging

A hydraulic jump is an abrupt transition between subcritical and supercritical flows which is associated with energy dissipation, air entrainment, spray, splashing, and surface waves. Both physical and numerical modeling were largely applied to study hydrodynamics, turbulence and air-entrainment in the hydraulic jump, while the literature about the application of classifier models is quite limited. Determining air-flow parameters and turbulent intensity has been merely performed by costly and time-consuming experimental methods, while this study is the first attempt to estimate the mentioned parameters using a computer-based methodology with desired precision. In the present study, air-flow parameters including void fraction (C) and bubble count rate (F ), as well as turbulent intensity (Tu) on rough bed were estimated using Bayesian model averaging (BMA) and three multilayer perceptron (MLP), support vector regression (SVR) and generalized regression neural network (GRNN) as classifier models. To develop the stated models, the experimental data from Felder and Chanson (2016) were divided into four classes based on longitudinal distance from the jump toe. Results highlighted that the MLP and GRNN models have more accurate results compared to the SVR model. For F and Tu, the GRNN model and for C, the MLP model showed better performance than other models in four classes. The average acceptance rate between 15 and 30% of the BMA model performance for all classes proved the accuracy and efficiency of the proposed methodology. The average RMSE value of BMA results and the bests classifier models were 0.41 and 0.42, respectively, for the estimation of all three parameters. Results revealed that the BMA model by weighting individual classifier models could be able to estimate parameters with better accuracy than the best classifier model in each class. The significant outcome of this study is that the proposed model is able to render accurate results in a complex system such as hydraulic jump