查看更多>>摘要：The physical interactions between two immiscible fluids in porous media may lead to the formation of a shock front at the interface between the two fluid phases. Numerical methods for modeling fluid flow with shocks can be classified into two categories: the shock-capturing and the shock-fitting methods. The shock-fitting method can in general provide more accurate solution of the shock front than the shock-capturing method, but suffers from the extra complexity in accommodating the moving shock front inside the background mesh. In this study, we explore the possibility of integrating the mesh-free methods for solving partial-differential equations with the shock-fitting method. In this integrated method, which we call 'mesh-free shock-fitting (MFSF)', the nodes needed for mesh-free calculations of spatial derivatives are generated on the fly to adapt to the moving shock front. We demonstrate the implementation of MFSF using numerical experiments in one and two spatial dimensions. Preliminary results show that MFSF has the potential to provide more accurate solutions with lower computational cost than conventional shock-capturing methods and can simplify many of the operations in shock-fitting methods with unstructured meshes.
查看更多>>摘要：Heavy oil is considered an important alternative for traditional fossil fuels. Exploring and developing heavy oil reservoirs require time-lapse seismic data for dynamic monitoring of the reservoir elastic parameters and determining of the temperature field to evaluate the fluidity of the heavy oil. The key is establishing a relationship between the temperature and seismic elastic parameters to convert the elastic field into a temperature field. Therefore, a temperature-dependent rock physics model for heavy oil sand must be established. In this study, we investigated the temperature-dependent elastic wave velocities of natural and artificial heavy oil sands. The modified contact cement theory and the solid Gassmann equation were combined to construct a temperature-dependent rock physics model. The proposed model can predict variations in elastic wave velodties along with temperature changes in both natural and artificial heavy oil sands. Based on the model results, we propose a possible mechanism for the temperature-dependent elastic wave velocity of heavy oil sands, namely, the weakening of heavy oil cementation is the main controlling factor in the elastic wave velocity sensitivity of heavy oil sands. A temperature-dependent rock physics template (TDRPT) is proposed to diagnose the temperature, and the diagnostic results were verified using natural heavy oil sands. Our proposed model and TDRPT are essential tools to guide the thermal production of heavy oil reservoirs.
查看更多>>摘要：The oil industry faces with crude oil emulsions formation issue. It is a common problem for the most of oil-producing countries around the world, including the Republic of Kazakhstan. Mainly, water-in-oil type of emulsions (or reverse emulsions) are formed during oil extraction and transportation. The presence of water in crude oil causes an increase in the cost of oil processing and transportation and consequently increases the cost of oil refinery products. It also induces corrosion of equipment when oil is extracted and transported. In this paper, several major methods of demulsification based on chemical, membrane, electric, magnetic, microwave separation used for the breaking of water-in-oil emulsions in recent years are reviewed and their advantages and disadvantages are highlighted. It is shown that it is important to create cost-efficient and smart demulsifiers of stable oil emulsions including their ecological friendly characteristics. Therefore, the future perspective research areas include the development of sustainable and alternative demulsification methods which are in demands today. In addition, the development of the effective combination of destabilization methods is still relevant today to achieve the synergetic effect in dewatering of highly stable oil emulsions.
查看更多>>摘要：Temporary plugging and diverting fracturing (TDPF) is an effective stimulation method to achieve multiple uniform fracture propagation in unconventional reservoirs. The plugging mechanism of diverters and the feasibility of diverted fractures have been investigated well in previous studies. However, few studies aim at the relationship between the multi-fracture morphology and injection pressure curve during TDPF because multiple fractures can not be directly observed during the fracturing process. This study established a visual true tri-axial fracturing physical experiment based on transparent polymethyl methacrylate (PMMA) to investigate the multi-fracture initiation sequence and breakdown pressure in the horizontal wellbore during TDPF. The critical fracture parameters, including completion method, injection flow rate, injection fluid type, fracture density, perforation depth and stress difference coefficient, were analyzed in detail based on twelve PMMA samples. The results showed that all experiments just produced one hydraulic fracture in the initial fracturing stage, but sequentially created multiple fractures after multiple diversion stages. Meanwhile, due to the effect of stress shadow, the break-down pressure of the first and second diverted fracture was 1.36 times and 1.67 times of the initial fracture respectively. In addition to the transverse fractures produced in the diversion stages, longitudinal fractures were also created along the horizontal wellbore, which decreased the multiple fracture initiation effectiveness. Optimized parameters, i.e. perforation completion method, high injection flowrate, moderate fracture density, uniform perforation depth and high-viscosity carrier fluid with diverters, were recommended to obtain more transverse fractures during TPDF. In addition, our results also revealed three fracture initiation sequences of multiple fractures in the horizontal well during TPDF. This study described the relationship between the visual multi-fracture geometries and breakdown pressure in horizontal wells during TPDF, which provides new insights into the propagation mechanism of multiple fractures and guidance for fracturing design in the oilfield.
查看更多>>摘要：Nanofluids, a new cost-effective chemical additive for enhanced oil recovery (EOR) applications, have attracted increasing attention in the development of tight oil reservoirs. However, the underlying role of nanofluids in the EOR processes has yet to be identified. In this work, the synergistic effects between various surfactant molecules and silica NPs at oil/water, solid/liquid, or oil/water/solid three-phase system were systematically studied, and their effects on EOR in the tight sandstone reservoir were explored. Several silica-based nanofluids were prepared by dispersing silica nanoparticles (NPs) and surface-modifying chemical agents (i.e., anionic, nonionic, anion-nonionic, and amphoteric surfactants) in deionized (DI) water, and the properties of these nanofluids were further evaluated via dispersion stability, interfacial tension (IFT), wettability, spontaneous imbibition and nuclear magnetic resonance (NMR) tests at 60 °C The experimental results showed that the imbibition efficiency potentials of stable nanofluids modified by different surfactants was: anionic-nonionic > anionic > nonionic > amphoteric. Micropores and mesopores contributed to majority of the imbibition recovery. The appropriate IFTs and oil contact angles for EOR potential were more than 1 mN/m and 140°, respectively. Anionic-nonionic and anionic surfactant nanofluids were suggested as better fracturing additives or EOR agents for sandstone reservoirs. In addition, it was proven that the IFT reduction and wettability alteration were mainly attributed to the synergistic effects of silica NPs and surfactant micelles. This research provides new insights into the underlying mechanism between surfactant nanofluids to interfacial interactions and EOR applications.
查看更多>>摘要：The characterization of 3D structures in porous media is crucial for predicting physical properties in many industries, such as CO2 capture and storage, hydrology, oil & gas. In contrast to the expensive and time-consuming acquisition of 3D images, 2D imaging can provide cheap and fast data. However, the reconstruction of a 3D image from a single 2D image is a complex non-deterministic inverse problem. Several statistical and deep learning-based algorithms have been introduced in the past, however, most of them fail to generalize structures and textures for different types of rocks, in addition to being time-consuming and only able to generate relatively small images (300~3 voxels cube). In this work, we propose a size-invariant multi-step 3D generation workflow from a single 2D image using a combination of Vector-Quantized Variational AutoEncoder(VQ-VAE), size-invariant Generative Adversarial Networks(GAN), and Image Transformer. The proposed workflow tackles several major challenges in the generation of 3D images since it is designed to not only satisfy the large size constraint (> 1000~3 voxels cube) but also to generate statistically representative pore structures. The combination of these different generative techniques allows us to overcome the scalability, stability, and complexity associated with GAN approaches. We trained the proposed workflow using several types of rocks with different physical properties, sizes, and resolutions. To validate our methodology, we have generated several large-size 3D rock images and compare them to real 3D images in terms of physical properties (porosity, permeability, and Euler characteristic).
查看更多>>摘要：This project considers a hybrid nanofluid flow of heat transmission towards an extending (lessening) surface with the effects of thermal radiation and non-uniform heat source/sink. The mixture nanofluid involves copper (Cu) and alumina (Al2O3) nano-molecules which are diluted into H2O to form Cu-Al2O3/H2O hybrid nanofluid. The governing partial differential equations derived from Navier-Stokes equations are converted into nonlinear ordinary differential equations via similarity transformations. The model of nonlinear coupled equations is then resolved numerically by applying the boundary-value problem solver (bvp4c) using MATLAB package. The numerical computations have been performed for different amounts of potential factors like nano-molecules size Al2O3 (φ1) and Cu (φ2), Prandtl number (Pr), radiative parameter (R), velocity ratio parameter e, and assisting/ opposing parameter (λ) on the fluid flow. The effect of the parameters on speed and energy outlines, drag force coefficient, and local Nusselt number at the wall of the nanofluid flowing and heat transmission features are presented graphically and evaluated. Radiation and heat generation parameters have a significant effect on flow profiles and their physical properties. We also observe that the heat boundary-layer thickener boosts with rising amounts of R and heat raises with big amounts of the heat source (sink). Cu-Al2O3/water has a higher heat transmission rate in comparison with Cu-water water nanofluid and water (base fluid). Comparison between the earlier available works and the current computational consequence for the restrictive status is in an adequate covenant.
查看更多>>摘要：In this study, the (Cu, Ag) hybrid nanomaterials model is implemented in blood flow equations to investigate its effects on the flow patterns. An analysis for thermal transport is also performed through the melting heat transfer phenomenon. The effects of thermal radiation with Ohmic and viscous dissipation are also considered to explore in heat transport mechanism. Such formulations are going to produced PDEs by combining physical phenomenon through a permeable porous surface. Then, the governing equations are handled using the appropriate similarity transformations to obtain the coupled differential equations system, which is then numerically solved through the bvp4c scheme for dual solutions. The effects of various controlling parameters on fluid velocity and temperature distribution, as well as skin friction coefficient and heat transportation rate, are graphically depicted. According to the obtained findings, in comparison to the nanofluid, the velocity and heat transfer of the hybrid nanofluid model have significant impacts. This study reveals that the performance of the hybrid-nanofluid is more significant to that of the regular nanofluid. In limiting cases, the current results are also compared to those of the previous studies.
查看更多>>摘要：The pore structure of tight volcanic oil reservoirs is very complex and the permeability is extremely low. The oil recovery effect of traditional water-driven and gas-driven development is not ideal. In this paper, we first analyzed the pore structure characteristics and fluid movability characteristics of tight volcanic rocks. Then, the feasibility of water huff-n-puff in tight volcanic rocks was analyzed. Finally, we investigated the oil recovery characteristics, pore motility characteristics, and ORM of water huff-n-puff in tight volcanic rocks by using a high temperature and ultra-high pressure water huff-n-puff physical simulation experimental device under NMR monitoring. The study shows that the mineral composition of tight volcanic rocks is relatively simple, but the pore structure is very complex. The pores are mainly amygdule pores, and contain some vesicular pores and matrix pores. The fractures include structural fractures, dissolution fractures, and shrinkage fractures. Fractures are of great significance for communicating relatively independent pores and improving the seepage ability of fluids in tight volcanic rocks. The fluid movable saturation of tight volcanic rocks is 32.93%-83.75%, and the minimum movable pore radius is 0.0178 μm. The seepage capacity of large pores (r > 0.1 μm) is 2.19-7.75 times that of middle pores (0.001 μm < r < 0.1 μm). The oil recovery efficiency (the ratio of the volume of recovered oil to the volume of injected water) of the first two cycles of water huff-n-puff of tight volcanic rocks exceeds 20%, which is high-efficiency oil recovery period. However, with the increasing huff and puff cycles, the recovery factor and oil change rate both decrease rapidly. After five cycles of water huff-n-puff, the recovery factor of tight volcanic rocks increases by 29.13%. For the selected experimental core, middle pores are the main oil-producing pores, and their contribution rates in natural energy depletion and five cycles of water huff-n-puff are 59.665% and 78.173%. In five cycles of water huff-n-puff, the contribution rates of elastic displacement are 96.491%, 93.846%, 86.667%, 64.706%, and 55.556%, and the effect of imbibition displacement is relatively small. Increasing the water injection pressure is beneficial to water huff-n-puff oil recovery of tight volcanic rocks. The research can provide a good theoretical basis for the efficient development of tight volcanic oil reservoirs.
查看更多>>摘要：Seismic inverse modeling is a common method in reservoir architecture characterization associated with geology. The conventional seismic inversion method is difficult to combine with complicated and abstract knowledge on geological modes, and its uncertainty is difficult to be assessed. In this paper, an inversion modeling method based on a wasserstein generative adversarial networks with gradient penalty (WGAN-GP) is introduced to integrate geology, well data and seismic data. A WGAN-GP is a generation model algorithm based on generative adversarial networks (GANs) that extracts spatial structure and abstract features of a training image. A gradient penalty function is added to an original loss function of GANs to improve the robustness. After assessment of the loss function, variograms and connectivity functions, the trained network is applied to seismic inversion simulation. In inversion, an optimal model is selected by the Metropolis with Markov chain Monte Carlo algorithm. Results show that the trained network can reproduce a thousand models containing millions of grid cells with a specific mode similar to a training image in 1 s. The inversion models conform to well data with 100% accuracy and have an efficient correspondence with prior seismic data. A whole inversion process completes 360,000 iterations in 4 h. The optimal inversion model has a subequal Root Mean Square Error (RMSE) with the true model and visually resembles a channel. With the proposed method, geological knowledge has a stable characterization in model realizations.
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