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Experimental study on the influence of pore structure on spontaneous imbibition in marine black shale
Get PDFRecently, significant progress has been made in the exploration of marine shale gas in the Qiongzhusi Formation of the Sichuan Basin, China. Micro/nanopores within this formation play a crucial role in spontaneous imbibition and subsequent shale gas production. In this paper, to investigate the influence of pore structure in the Qiongzhusi shale on the spontaneous imbibition characteristics, two sets of samples with varying mineral contents were subjected to horizontal and vertical spontaneous imbibition experiments. Onedimensional transverse relaxation time, two-dimensional longitudinal-transverse relaxation time, and layer division transverse relaxation time spectra from low-field nuclear magnetic resonance were analyzed to elucidate fluid migration during spontaneous imbibition as well as contributions from different pore sizes toward the overall imbibition capacity. The results indicated that, among different pore sizes, mesopores have the greatest impact on the imbibition rate of marine carbonaceous shale, followed by micropores and macropores. The organic matter and clay minerals in carbonaceous shale were found to play a significant role in enhancing the permeability and absorption rate by the presence of abundant mesopores. Besides, the bedding development of marine carbonaceous shale in the Qiongzhusi Formation influences the imbibition process. The horizontal samples exhibited lower levels of imbibition efficiency than their vertical counterparts.Document Type: Original articleCited as: Fu, C., Xu, X., Du, Y., Kou, X. Experimental study on the influence of pore structure on spontaneous imbibition in marine black shale. Capillarity, 2024, 10(3): 57-72. https://doi.org/10.46690/capi.2024.03.0
Artificial intelligence methods for oil and gas reservoir development: Current progresses and perspectives
Get PDFArtificial neural networks have been widely applied in reservoir engineering. As a powerful tool, it changes the way to find solutions in reservoir simulation profoundly. Deep learning networks exhibit robust learning capabilities, enabling them not only to detect patterns in data, but also uncover underlying physical principles, incorporate prior knowledge of physics, and solve complex partial differential equations. This work presents the latest research advancements in the field of petroleum reservoir engineering, covering three key research directions based on artificial neural networks: data-driven methods, physics driven artificial neural network partial differential equation solver, and data and physics jointly driven methods. In addition, a wide range of neural network architectures are reviewed, including fully connected neural networks, convolutional neural networks, recurrent neural networks, and so on. The basic principles of these methods and their limitations in practical applications are also outlined. The future trends of artificial intelligence methods for oil and gas reservoir development are further discussed. The large language models are the most advanced neural networks so far, it is expected to be applied in reservoir simulation to predict the development performance.Document Type: PerspectiveCited as: Xue, L., Li, D., Dou, H. Artificial intelligence methods for oil and gas reservoir development: Current progresses and perspectives. Advances in Geo-Energy Research, 2023, 10(1): 65-70. https://doi.org/10.46690/ager.2023.10.0
Recent advances on fluid flow in porous media using digital core analysis technology
Get PDFThe scientific and engineering challenges of research on porous media have gained substantial attention in recent decades. These intricate issues span different disciplines and fields, manifesting in natural and industrial systems like soils, oil and gas reservoirs, tissues, plants, etc. Meanwhile, digital core analysis technology has rapidly developed, proving invaluable not just in oil and gas reservoirs development, but also in geothermal energy, carbon and hydrogen storage. The China InterPore Chapter and the Research Center of Multiphase Flow in Porous Media at China University of Petroleum (East China) have established a conference platform for global scholars to exchange ideas and research in porous media utilizing digital core analysis technology. The 6th International Conference on Digital Core Analysis & the 2023 China Interpore Conference on Porous Media was successfully held in Qingdao from July 5 to 7, 2023. The conference facilitated discussions among 150 participants, including over 20 invited experts from academia and industry, and the recent advances in research of fluid flow in porous media using digital core analysis technology were thoroughly presented.Document Type: EditorialCited as: Yang, Y., Horne, R. N., Cai, J., Yao, J. Recent advances on fluid flow in porous media using digital core analysis technology. Advances in Geo-Energy Research, 2023, 9(2): 71-75. https://doi.org/10.46690/ager.2023.08.0
Basic properties and exploitation strategies of source rock strata
Get PDFSource rock strata are filled and aggregated with large-scale continuous hydrocarbon resources, including significant volumes of in-place retained, short-distance migrated and potentially generated hydrocarbons. Source rock strata simultaneously possess the properties of reservoirs and hydrocarbon source rocks, known as source-reservoir coexisting systems. Reservoir properties refer to the physical properties concerning the storage and transmission of oil and gas, while hydrocarbon source rock properties refer to the physicochemical properties related to governing the generation, retention and expulsion of oil and gas in the source rock strata. These properties fundamentally determine the technical path for the successful exploitation of petroleum and natural gas in the source rock strata. With regard to reservoir properties, in-depth research and development of the advanced energy-storing fracturing technology can aid the construction of complex fracture networks to overcome the limitations in the connectivity properties of source rock strata. Focusing on the hydrocarbon source rock properties, an underground in-situ conversion technology should be created and developed to alleviate the shortcomings of organic matter quantity and maturity properties of the source rock strata. Furthermore, selecting the appropriate exploitation path based on the property characteristics can promote the achievement of commercial and sustainable development of oil and gas in the source rock strata.Document Type: PerspectiveCited as: Yang, Z., Zou, C., Fan, Y., Wu, S., Liu, H., Wei, Q. Basic properties and exploitation strategies of source rock strata. Advances in Geo-Energy Research, 2023, 10(2): 77-83. https://doi.org/10.46690/ager.2023.11.0
Well-log attributes assist in the determination of reservoir formation tops in wells with sparse well-log data
Get PDFThe manual picking of reservoir formation boundaries using limited available well-log data in multiple wells across gas and oil reservoirs tends to be subjective and unreliable. The reasons for this are typically caused by the combined effects of spatial boundary complexity and limited well-log data availability. Formation boundary characterization and classification can be improved when treated as a binary classification task based on two or three recorded well logs assisted by their calculated derivative and volatility attributes assessed by machine learning. Two example wellbores penetrating a complex reservoir boundary, one with gamma-ray, compressional-sonic, and bulk-density logs recorded, the other with just gamma-ray and bulk-density logs recorded, are used to illustrate a more rigorous proposed methodology. By combining attribute calculation, optimized feature selection, multi-k-fold cross validation, confusion matrices, feature-influence analysis, and machine learning models it is possible to improve the classification of the formation boundary. With just gamma-ray and bulk-density recorded well logs plus selected attributes. K-nearest neighbour, support vector classification, and extreme gradient boosting machine learning models are able to achieve high binary classification accuracy: greater than 0.97 for training/validation in one well; and greater than 0.94 for testing in another well. extreme gradient boosting feature-influence analysis reveals the attributes that are the most important in the formation boundary predictions but these are likely to vary from reservoir to reservoir. The results of the study suggest that well-log attribute analysis, combined with machine learning has the potential to provide a more systematic formation boundary definition than relying only on a few recorded well-log curves.Document Type: Original articleCited as: Wood., D. A. Well-log attributes assist in the determination of reservoir formation tops in wells with sparse well-log data. Advances in Geo-Energy Research, 2023, 8(1): 45-60. https://doi.org/10.46690/ager.2023.04.0
Physics-informed machine learning for solving partial differential equations in porous media
Get PDFPhysical phenomenon in nature is generally simulated by partial differential equations. Among different sorts of partial differential equations, the problem of two-phase flow in porous media has been paid intense attention. As a promising direction, physics-informed neural networks shed new light on the solution of partial differential equations. However, current physics-informed neural networks’ ability to learn partial differential equations relies on adding artificial diffusion or using prior knowledge to increase the number of training points along the shock trajectory, or adaptive activation functions. To address these issues, this study proposes a physics-informed neural network with long short-term memory and attention mechanism, an ingenious method to solve the Buckley-Leverett partial differential equations representing two-phase flow in porous media. The designed network structure overcomes the dependency on artificial diffusion terms and enhances the importance of shallow features. The experimental results show that the proposed method is in good agreement with analytical solutions. Accurate approximations are shown even when encountering shock points in saturated fields of porous media. Furthermore, experiments show our innovative method outperforms existing traditional physics-informed machine learning approaches.Document Type: Original articleCited as: Shan, L., Liu, C., Liu, Y., Tu, Y., Dong, L., Hei, X. Physics-informed machine learning for solving partial differential equations in porous media. Advances in Geo-Energy Research, 2023, 8(1): 37-44. https://doi.org/10.46690/ager.2023.04.0
Pore-scale simulation of gas displacement after water flooding using three-phase lattice Boltzmann method
Get PDFWater flooding is a commonly used technique to improve oil recovery, although the amount of oil left in reservoirs after the procedure is still significant. Gas displacement after water flooding is an effective way to recover residual oil, but the occurrence state and flow principles of multiphase fluid after gas injection are still ambiguous. Therefore, the gas displacement process after water flooding should be studied on the pore scale to provide a basis for formulating a reasonable gas injection program. Most of the current pore-scale studies focus on two-phase flow, while simulations that account for the influence of oil-gas miscibility and injected water are seldom reported. In this work, the multi-component multi-phase Shan-Chen lattice Boltzmann model is used to simulate the gas displacement after water flooding in a porous medium, and the effects of injected water, viscosity ratio, pore structure, and miscibility are analyzed. It is established that the injected water will cause gas flow path variations and lead to premature gas channeling. Under the impact of capillary pressure, the water retained in the porous medium during the water flooding stage further imbibes into the tiny pores during gas injection and displaces the remaining oil. When miscibility is considered, the oil-gas interface disappears, eliminating the influence of the capillary effect on the fluid flow and enabling the recovery of remaining oil at the corner. This study sheds light on the gas displacement mechanisms after water flooding from the pore-scale perspective and provides a potential avenue for improving oil recovery.Document Type: Original articleCited as: Wang, S., Chen, L., Feng, Q., Chen, L., Fang, C., Cui, R. Pore-scale simulation of gas displacement after water flooding using three-phase lattice Boltzmann method. Capillarity, 2023, 6(2): 19-30. https://doi.org/10.46690/capi.2023.02.0
Influence of micro-particles on gas hydrate formation kinetics: Potential application to methane storage and transportation
Get PDFMethane hydration is a safe, stable and environmentally friendly technology to bind and utilize excess coalbed methane gas. However, a limiting factor of the commercial application of coalbed methane hydration technology is the sluggish hydration reaction kinetics of methane hydrate formation, which needs to be improved. In this work, different micro-particle suspensions are prepared from an initial solution containing gellan gum and L-tryptophan, along with varying mass fractions of NiMnGa, Cu and carboxylated multi-walled carbon nanotubes, and their influence on the reaction kinetics in methane hydrate formation is examined. The results show that the formation of methane hydrate is enhanced by these micro-particles to varying degrees. Micro-particles show a synergistic solubilization effect with L-tryptophan and gellan gum at 6.2 MPa. The induction times of 1 wt.% NiMnGa system and 1 wt.% Cu system are the shortest. The 2 wt.% NiMnGa system has a pronounced impact on methane gas consumption, and the average gas consumption rates of the 0.1 wt.% Cu system and 1 wt.% NiMnGa system are faster. However, as the concentration of Cu micro-particles increases, both gas consumption and the average generation rate exhibits a linear decrease. This work offers valuable recommendations for choosing the experimental settings, micro-particle types and concentrations. We also lay the groundwork for the practical and sustainable application of coalbed methane storage and transportation technology employing the hydrate approach.Document Type: Original articleCited as: Wu, Q., Tang, T., Zhao, Z., Li, L., Elhefnawey, M., Zhang, B. Influence of micro-particles on gas hydrate formation kinetics: Potential application to methane storage and transportation. Advances in Geo-Energy Research, 2023, 10(3): 189-199. https://doi.org/10.46690/ager.2023.12.0
Spontaneous imbibition experiments for enhanced oil recovery with silica nanosols
Get PDFExperimental oil displacement as a result of spontaneous imbibition of silica nanosols has been carried out using two types of sandstone as the reservoir rock. The permeability of the cores ranged from 0.34 to 333 mD, while the porosity was 11% and 22%, respectively. During the research, the influence of the concentration and nanoparticle size, as well as the permeability of the rock, on the process of spontaneous imbibition, was studied. Silica nanosols were considered as an object of study. The nanoparticle size ranged from 10 to 35 nm. The mass concentration of nanoparticles varied from 0.01% to 0.25%. It was found that the use of silica nanosols significantly increases the rate of the spontaneous imbibition process. It was established that a silica nanosol with a nanoparticle size of 10 nm and a concentration of 0.25% allows to displace more than six times oil compared to the reservoir water model in the same time. As a result, it was shown that the oil displacement efficiency and the efficiency of spontaneous imbibition increase along with an increase in the nanoparticle concentration and a decrease in the nanoparticle size.Document Type: Original articleCited as: Pryazhnikov, M. I., Zhigarev, V. A., Minakov, A. V., Nemtsev, I. V. Spontaneous imbibition experiments for enhanced oil recovery with silica nanosols. Capillarity, 2024, 10(3): 73-86. https://doi.org/10.46690/capi.2024.03.0
Imbibition oil recovery from tight reservoir cores using microemulsion: Experiment and simulation
Get PDFDespite the promising results obtained from the utilization of interfacial-active additives in enhancing imbibition-based oil recovery from tight reservoirs, the predominant mechanisms governing this process remain inadequately understood. In this work, a meticulously designed workflow is implemented to conduct experiments and modeling focusing on imbibition tests performed on tight sandstone cores while utilizing surfactant and microemulsion. Our primary objective is to investigate the response of oil recovery to these additives and to develop a robust and reliable model that incorporates the intricate interactions, thereby elucidating the underlying mechanisms. Two imbibition fluids are designed, namely, surfactant and microemulsion. A comprehensive investigation is performed to analyze the physicochemical properties of these fluids, encompassing phase behavior, density, viscosity, and wettability alteration, with the aim of establishing fundamental knowledge in the field. Three imbibition tests are carried out to observe the response of oil production and optimize the experimental methodology. A numerical model is developed that fully couples the evolution of relative permeability and capillary pressure with the dynamic processes of emulsification, solubilization and molecular diffusion. The results demonstrate the crucial role of emulsification/solubilization in the imbibition process.Document Type: Original articleCited as: Li, Q., Wang, Y., Wei, B., Wang, L., Lu, J., Tang, J. Imbibition oil recovery from tight reservoir cores using microemulsion: Experiment and simulation. Capillarity, 2024, 10(2): 38-47. https://doi.org/10.46690/capi.2024.02.0