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Subsurface multiphase reactive flow in geologic CO2 storage: Key impact factors and characterization approaches
Get PDFMultiple measurements and data sets show unequivocally that levels of carbon dioxide (CO2) have been increasing in the Earth's atmosphere for the past several centuries, with the rate becoming steeper in recent decades (Soeder, 2021). Carbon capture, utilization and storage (CCUS) has been regarded as an effective approach to swiftly cut CO2 emissions. Among the existing CCUS technologies, CO2 geological utilization and storage has the highest technological maturity, and is the most vital “sink” to consume the captured CO2. For CO2 geological utilization and storage, large amounts of CO2 need to be injected into the deep subsurface, and the CO2 flow in the subsurface is a very complicated process. The flow system is a two-phase or even a three-phase system, and flow in pores needs to be clearly distinguished from flow in fractures and wellbores. Most importantly, wettability, pore structure, geochemical reactions play very important roles in governing subsurface CO2 flow. Without a clear understanding of how the impact factors affect CO2 flow, it is difficult to predict the CO2 impairs the confidence of policy makers and investors to support large-scale geologic CO2 storage. To study CO2 flow, there is a need to develop effective approaches to characterize CO2 flow in subsurface system. This work discusses several key factors that have strong impact on subsurface CO2 flow, and an effective approach for CO2 flow characterization. Impact of wettability and pore structure on multiphase flow. The injection of CO2 into geological formations displaces brine from pore spaces, resulting in various CO2-brine displacement patterns, such as capillary fingering, viscous fingering, crossover, and compact displacement. These patterns also occur as the brine later flows back to displace supercritical CO2 when the injection stops. The CO2-brine displacement results in CO2 becoming trapped as droplets and ganglia in pore spaces, referred to as residual trapping or capillary trapping. Wettability and pore structure have significant effects on CO2-brine displacement patterns and capillary trapping. The wettability represents the affinity of fluid to the solid surface. By changing the capillary force governed by the Young-Laplace law, the wettability modifies the local porefilling events and thus impacts the displacement patterns. Increasing the wettability of the invading fluid from drainage to imbibition stabilizes the displacement front due to the cooperative pore-filling events at the pore scale (Holtzman and Segre, 2015). However, the displacement pattern will change extensively as a result of corner flow when the invading fluid is strongly wetting to the solid surface (Hu et al., 2018). On the other hand, the role of pore structure in displacement patterns may depend on the type of permeable media. The pore-scale disorder, which represents the randomness of pore size, changes the threshold capillary pressure and affects the local pore-filling paths. Increasing disorder promotes unstable displacement patterns for both drainage and imbibition conditions (Toussaint et al., 2005), but under certain wettability conditions, higher disorder may enhance cooperative porefilling events and thus smooth the displacement front. The roughness variations in the aperture between the two rough surfaces determines the flow path and controls the displacement patterns for a fractured medium. Therefore, the transition of CO2-brine displacement patterns under various wetting and pore structure conditions is an open challenge and a very active area of research.Impact of geochemical reactions on multiphase flow. Geochemical reactions play a key role in determining CO2 flow patterns. Though geochemical reaction-induced mineral trapping can only become vital after hundreds to thousands years of CO2 injection in reservoir scale, fast mineral dissolution and precipitation in micro-scale flow channels of host rocks and caprocks can change permeability of the rocks and thus influence the migration behaviour of injected CO2 (Zhang et al., 2019). For carbonate rocks, CO2 injection usually causes opening of flow channels due to dissolution of carbonates, which enhances CO2 injectivity and is beneficial for largescale CO2 storage (Yang et al., 2020). A sandstone reservoir that contains large amounts of feldspars and glauconite may have a strong CO2-sandstone interaction, which usually causes sealing of flow channels due to precipitation of secondary minerals (Xu et al., 2004). However, given different types of flow channels and varying reaction environments, it is very difficult to precisely predict if a given flow channel in a rock will open or close under the influence of geochemical reactions. Therefore, an important research direction in the future is to find out a criterion that can determine if a flow channel will open or close under the influence of geochemical reactions, with the consideration of complicated reaction environments.Pore-scale modeling of multiphase reactive flow. Compared with continuum-scale models, pore-scale modeling, which directly reflects the realistic porous structures, provides a powerful tool for studying the multiphase flow, species transport, chemical reaction and mineral dissolution/precipitation processes (Chen et al., 2022). Effects of pressure, temperature, fluid properties, wettability, pore size and porous morphology on the supercritical CO2-water two-phase flow and distributions have been extensively studied by pore-scale modeling. Pore-scale modeling that reveals the mechanisms of nonequilibrium supercritical CO2 dissolution into the surrounding brine will be beneficial for enhancing CO2 solubility trapping. Recently, supercritical CO2 storage in the depleted oil reservoir has also drawn increasing attention, and the resulting supercritical CO2-oil-water three-phase flow are extremely complicated (Zhu et al., 2021). Pore-scale modeling is an ideal tool to study the effects of structure heterogeneity, mineral composition and reaction kinetics on the rock dissolution processes. Further pore-scale modeling work to investigate the effects of twophase or three-phase flow on mineral dissolution/precipitation processes are helpful for better understanding the CO2 storage processes in saline formations or depleted oil reservoirs. AcknowledgementThis work was performed by the support of Key R&D Program of Inner Mongolia Province of China (No. 2021ZD0034-3) and the National Natural Science Foundation of China (Nos. 42172315, 42141011 and 52122905).Cited as: Zhang, L., Chen, L., Hu, R., Cai, J. Subsurface multiphase reactive flow in geologic CO2 storage: Key impact factors and characterization approaches. Advances in Geo-Energy Research, 2022, 6(3): 179-180. https://doi.org/10.46690/ager.2022.03.0
Characteristics, current exploration practices, and prospects of continental shale oil in China
Get PDFOil generation in the continental shale has laid the resource foundation for the originality and development of China’s petroleum industry; continental shale oil production is blazing a new trail in this field. In this paper, based on the geological conditions of continental shale oil in China, it is found that the main types of shale oil generally have four basic geological characteristics, which are large-scale continuous distribution, the domination of inorganic pores, the enrichment of “sweet areas”, and initial production that is controlled by relatively high organic maturity and high yield that is governed by relatively high formation pressure. Then, as examples for the geological characteristics and development practice of continental shale oil, four key areas of Longdong, Gulong, Jimsar, and Jiyang are systematically summarized. Finally, the future prospects of continental shale oil in China are put forward. Middle-high maturity shale oil is currently the main force of development, and middle-low maturity shale oil also has a considerable development prospect after technological improvement. Meanwhile, “sweet area/spot sections” assessment and technological innovation are still research areas to be improved.Cited as: Wang, X., Li, J., Jiang, W., Zhang, H., Feng Y., Yang Z. Characteristics, current exploration practices, and prospects of continental shale oil in China. Advances in Geo-Energy Research, 2022, 6(6): 454-459. https://doi.org/10.46690/ager.2022.06.0
Image feature recognition and gas permeability prediction of Gaomiaozi bentonite based on digital images and machine learning
Get PDFGas permeability, which is measured mainly through gas permeability experiments, is a critical technical index in many engineering fields. In this study, permeability is firstly calculated based on information from a digital image and an improved permeability prediction model. The calculated results are experimentally verified. Subsequently, a self-developed image-processing program is used to extract feature parameters from a scanning electron microscopy image. Meanwhile, an extreme learning machine algorithm is used to input the image feature parameters obtained using the image-processing program into the extreme learning machine algorithm for machine learning. Additionally, we compare several typically used machine learning algorithms, which confirmed the reliability and accuracy of our algorithm. The best activation function can be obtained by comparing the predicted permeability using an appropriate number of neuron nodes. Experimental results show that the program can accurately identify the features of the microscopy image. Combining the program with an extreme learning machine neural network algorithmgas permeability results to be obtained with high accuracy. This method yields good predictions of permeability in certain cases and has been adapted to other geomaterials.Cited as: Liu, J., Ma, S., Shen, W., Zhou, J., Hong, Y. Image feature recognition and gas permeability prediction of Gaomiaozi bentonite based on digital images and machine learning. Advances in Geo-Energy Research, 2022, 6(4): 314-323. https://doi.org/10.46690/ager.2022.04.0
Estimated ultimate recovery prediction of fractured horizontal wells in tight oil reservoirs based on deep neural networks
Get PDFAccurate estimated ultimate recovery prediction of fractured horizontal wells in tight reservoirs is crucial to economic evaluation and oil field development plan formulation. Advances in artificial intelligence and big data have provided a new tool for rapid production prediction of unconventional reservoirs. In this study, the estimated ultimate recovery prediction model based on deep neural networks was established using the data of 58 horizontal wells in Mahu tight oil reservoirs. First, the estimated ultimate recovery of oil wells was calculated based on the stretched exponential production decline model and a five-region flow model. Then, the calculated estimated ultimate recovery, geological attributes, engineering parameters, and production data of each well were used to build a machine learning database. Before the model training, the number of input parameters was reduced from 14 to 9 by feature selection. The prediction accuracy of the model was improved by data normalization, the early stopping technique, and 10-fold cross validation. The optimal activation function, hidden layers, number of neurons in each layer, and learning rate of the deep neural network model were obtained through hyperparameter optimization. The average determination coefficient on the testing set was 0.73. The results indicate that compared with the traditional estimated ultimate recovery prediction methods, the established deep neural network model has the strengths of a simple procedure and low time consumption, and the deep neural network model can be easily updated to improve prediction accuracy when new well information is obtained.Cited as: Luo, S., Ding, C., Cheng, H., Zhang, B., Zhao, Y., Liu, L. Estimated ultimate recovery prediction of fractured horizontal wells in tight oil reservoirs based on deep neural networks. Advances in Geo-Energy Research, 2022, 6(2): 111-122. https://doi.org/10.46690/ager.2022.02.0
Controllable electromechanical stability of a torsional micromirror actuator with piezoelectric composite structure under capillary force
Get PDFVarious types of micro/nano functional devices are being widely designed as optical switches, micro scanners, micromirrors and other core optical devices. The continuing miniaturization of the functional devices makes the size dependence of electromechanical property significant in micro/nano scale due to the sharp increase of surface interactions such as capillary force from liquid bridge, van der Waals and Casimir forces from quantum fluctuations. The surface interactions can cause the pull-in instability, adhesion between parts, and even failure of device. This work provides an active control method to avoid the pull-in instability of an electrostatically driven circular micromirror by applying voltage on a torsional piezoelectric composite structure. The influences of the three types are compared of dispersion forces on the electromechanical stability of the micromirror actuator. A comprehensive electromechanical model of a torsional piezoelectric beam was established to numerically investigate the electromechanical coupling of the micromirror. The results show that the influence of capillary force on the stability of the micromirror is as significant as van der Waals force and Casimir force. By introducing piezoelectric nanoplates into the laminated torsional structure, the micromirror stability can be controlled based on the piezoelectric effect of the torsional piezoelectric composite structure. This work can contribute to the structural optimization design and manufacture of micromirror systems.Cited as: Liu, M., Chen, Y., Cheng, W., Chen, S., Yu, T., Yang, W. Controllable electromechanical stability of a torsional micromirror actuator with piezoelectric composite structure under capillary force. Capillarity, 2022, 5(3): 51-64. https://doi.org/10.46690/capi.2022.03.0
Gas production from marine gas hydrate reservoirs using geothermal-assisted depressurization method
Get PDFNatural gas production from marine gas hydrate reservoirs has become attractive to the oil and gas industry in recent years. It is still a great challenge to recover natural gas from hydrate reservoirs efficiently mainly due to sand production and wellbore collapse problems associated with the production scheme of depressurization. The thermal recovery method has not been proven economical due to the high cost of energy consumption. This study focuses on using geothermal energy to assist the depressurization process so that well pressure drawdown can be reduced and thus sand production and wellbore collapse problems can be mitigated. The authors investigated the transfer of heat energy from a natural geothermal zone to a marine gas hydrate reservoir and its effect on gas well productivity using analytical models. The result of our investigation shows that the initial well productivity can be significantly improved using geothermal energy more than 10-fold. This work provides engineers with an analytical tool for the feasibility analysis of using geothermal energy to improve well performance in gas hydrate reservoirs.Document Type: Original articleCited as: Mahmood, M. N., Guo, B. Gas production from marine gas hydrate reservoirs using geothermal-assisted depressurization method. Advances in Geo-Energy Research, 2023, 7(2): 90-98. https://doi.org/10.46690/ager.2023.02.0
Prediction of permeability of tight sandstones from mercury injection capillary pressure tests assisted by a machine-learning approach
Get PDFMercury injection capillary pressure analysis is a methodology for determining different petrophysical properties, including bulk density, porosity, and pore throat distribution. In this work, distinct parameters derived from mercury injection capillary pressure tests was considered for the prediction of permeability by coupling machine learning and theoretical approaches in a dataset composed of 246 tight sandstone samples. After quality checking the dataset, the feature selection was carried out by correlation analysis of different theoretical permeability models and statistical parameters with the measured permeability. Finally, porosity, median capillary pressure, Winland model, and mean pore-throat radius (corresponding to the saturation range 0.4-0.8) were chosen as the input features of the machine learning model. As the machine learning approach, a support vector machine (SVM) model with a radial basis function kernel was proposed. Furthermore, the model and its metaparameters were trained with a particle swarm optimization (PSO) algorithm to avoid over-fitting or under-fitting. In contradiction to the theoretical models, the implemented SVM-PSO model could acceptably predict the experimentally measured permeability values with an R2 rate of over 0.88 for training and testing datasets. The introduced approach could reduce the mean relative errors from about 10 to values less than 0.45. The improvements were more significant for low permeability samples. This successful implementation shows the potential of coupled usage of theoretical and machine learning methodologies for improved prediction of permeability of tight sandstone rocks.Cited as: Abbasi, J., Zhao, J., Ahmed, S., Jiao, L., Andersen, P., Cai, J. Prediction of permeability of tight sandstones from mercury injection capillary pressure tests assisted by a machine-learning approach. Capillarity, 2022, 5(5): 91-104. https://doi.org/10.46690/capi.2022.05.0
Potential resources of conventional, tight, and shale oil and gas from Paleogene Wenchang Formation source rocks in the Huizhou Depression
Get PDFConventional and tight unconventional oil and gas resources in the Huizhou Depression have shown broad exploration prospects, which mainly originate from Wenchang Formation source rocks. Thus far, studies on Wenchang Formation source rocks mainly focused on the geochemical characteristics and conventional petroleum resource evaluation; however, the correlation of conventional, tight, and shale oil and gas, and their resources are still unknown. In fact, the formation of conventional, tight, and shale oil and gas are intrinsically related, which allows for a more objective evaluation to consider the three types of oil and gas resources simultaneously in the whole dynamic process of both hydrocarbon generation and expulsion, as well as reservoir tightness history. In this work, based on geological and geochemical analyses, the improved hydrocarbon generation potential method was utilized to establish a hydrocarbon generation and expulsion model of the Wenchang Formation source rocks. Then, combined with the reservoir tightness history, the conventional, tight, and shale oil and gas resources were evaluated. The results show that the Wenchang Formation source rocks are distributed in the whole depressions, with a thickness of 50-1850 m and an average total organic carbon content of 2.2%. The organic matter is mainly type II and is mature-high maturity. The Wenchang Formation source rocks reached hydrocarbon generation threshold and expulsion threshold at a vitrinite reflectivity of 0.43% and 0.65%, respectively, and the reservoir evolved completely tight at 2.3 Ma. Overall, the Lower and Upper Wenchang Formation contain a large amount of conventional, tight, and shale oil and gas resources.Cited as: Hu, T., Wu, G., Xu, Z., Pang, X., Liu, Y., Yu, S. Potential resources of conventional, tight, and shale oil and gas from Paleogene Wenchang Formation source rocks in the Huizhou Depression. Advances in Geo-Energy Research, 2022, 6(5): 402-414. https://doi.org/10.46690/ager.2022.05.0
Impact of salinity and temperature variations on relative permeability and residual oil saturation in neutral-wet sandstone
Get PDFLow-salinity water flooding has become one of the major emerging enhanced oil recovery techniques where lower salinity water is flooded into a hydrocarbon reservoir in order to increase oil recovery. It’s been widely reported that reservoir wettability alteration from oil-wet to water-wet in a low-salinity water process improves oil recovery. Many factors control system wettability, however, role and intensity of each factor is not completely understood. Therefore, several reported affecting factors on wettability alteration were eliminated in the present work in order to determine the impact of different low-salinity water on oil and water relative permeability curves and residual oil saturation. A series of experiments were performed where three simulated brine solutions were injected into oil saturated thoroughly cleaned neutral-wet sandstone core plugs. The effect of injected brine temperature on oil and water relative permeability curves and residual oil saturation was also determined by injecting 115,000 ppm salinity brine at three different temperatures. Results indicate that decreasing flooded water salinity alters the wettability preference of the rock towards favorable water-wetting conditions. Water-wet conditions decrease water mobility and enhance oil mobilization leading to better oil microscopic displacement efficiency and reduced residual oil saturation. Elevated temperature reduces water relative permeability and residual oil saturation.Cited as:Mahmud, W. M. Impact of salinity and temperature variations on relative permeability and residual oil saturation in neutral-wet sandstone. Capillarity, 2022, 5(2): 23-31. https://doi.org/10.46690/capi.2022.02.0
Gamma-ray log derivative and volatility attributes assist facies characterization in clastic sedimentary sequences for formulaic and machine learning analysis
Get PDFRate of change, second derivative and volatility of gamma-ray (GR) well-log curves provide useful indicators with which to characterize lithofacies in clastic sedimentary sequences. Rolling averages of these variables, as they change with depth, are also able to distinguish certain lithofacies features. These attributes make it possible to accurately distinguish key facies by using only gamma-ray data, both with formulaic calculations and employing machine-learning (ML) algorithms. This is useful in the many wellbores for which only basic logging suites are available. As well as enhancing lithofacies classification more generally using well-log variables, these GR attributes can be used to forecast facies in real time based on logging-while-drilling data. The application is demonstrated with simple formula using synthetic GR logs featuring common clastic lithofacies and their transitions. Seven widely used ML methods are each trained and validated with a synthetic GR curve (1450 data points) displaying six distinct facies. The ability of the ML model to distinguish those facies using seven GR attributes is compared and further tested with an independent GR data set (800 data points). The random forest algorithm outperforms the other ML models in this facies prediction task, achieving a mean absolute error of 0.25 (on a facies class range of 1 to 6) for the independent testing dataset. The results highlight the benefit of this technique in providing reliable facies analysis based only on GR data. Random forest, support vector classification and eXtreme Gradient Boost are the ML models that provide the most reliable facies classification from the GR attributes defined. Annotated confusion matrices assist in revealing the details of facies class prediction accuracy and precision achieved by the ML and models and classification formulas.Cited as: Wood, D.A. Gamma-ray log derivative and volatility attributes assist facies characterization in clastic sedimentary sequences for formulaic and machine learning analysis. Advances in Geo-Energy Research, 2022, 6(1): 69-85. https://doi.org/10.46690/ager.2022.01.0