Cheng An

How to Improve our Understanding of Gas and Oil Production Mechanisms in Liquid-rich Shale

Three phase oil, gas, and water flow in liquid-rich shale plays is investigated in this paper, using a state-of-the-art technique of dividing shale matrix into different sub-media. Shale reservoirs always present numerous challenges to modeling and understanding, from unintuitive, heterogeneous, and difficult to characterize rock properties, to limited understanding of the governing flow equations, lack of fundamental knowledge on related desorption mechanisms, and nearly impermeable formations with pores on the order of magnitude as the mean free path of gas molecules. This work proposes a partitioning scheme to divide porous media in shale into three different sub-media (porosity systems) with distinctive characteristics: inorganic matter and kerogen (in the shale matrix), along with fracture network (natural or hydraulic). The current model gives us the capability of better analyzing the complex nature of mass transfer in shale. Relative permeabilities in our model are accounted for by employing the functions specifically presented for shale reservoirs. Our model can also handle various flow and storage mechanisms corresponding with shales such as molecule/wall interactions and slippage of the gas phase, multicomponent desorption, and capillarities. Simulation results show that hydrocarbon production from shale reservoirs exhibits complicated dynamics that are controlled by a number of different factors. Because of very high capillary pressure in shale, water is observed to imbibe into the water-wet inorganic matter during the late production period. On the contrary, mass flow in the oil-wet kerogen is mostly limited to two-phase oil and gas flow. Although kerogen is considered to be a rich source of hydrocarbon, relatively high capillary pressure and very low rock permeability hinder oil production in organic-rich shale. We might be able to address such problems by employing an appropriate production enhancement technique compatible with the ultra-tight nature of such reservoirs.

Modeling of Magnetic Nanoparticle Transport in Shale Reservoirs

Currently, the application of nanoparticles has attracted much attention due to the potential of nanotechnology to lead to revolutionary changes in the petroleum industry. The literature contains numerous references to the possible use of this technology for enhanced oil recovery, nano-scale sensors and subsurface mapping. Little work has been conducted to establish numerical models to investigate nanoparticle transport in reservoirs, and even less for shale reservoirs. Unlike conventional reservoirs, shale formations usually contain four pore systems: inorganic matter, organic matter dominated by hydrocarbon wettability, natural fractures and hydraulic fractures. Concurrently, hydraulic fractures and the associated stimulated reservoir volume (SRV) from induced fractures play a critical role in significantly increasing well productivity. In this paper, a mathematical model for simulating nanoparticle transport in shale reservoirs was developed. The simulator includes contributions from Darcy flow, Brownian diffusion, gas diffusion and desorption, slippage flow, and capillary effects based on the extremely low permeability and microto nano-scale of the pores. Moreover, these diverse mechanisms are separately applied to different portions of the reservoir due to the variation in media properties. Applications of the model include numerical examples from two-dimensional micro models to macro models, both with organic matter randomly distributed within the inorganic matrix. The effects of varying water saturation, grid pressure, and mass concentration of nanoparticles are shown graphically in these numerical examples. The main conclusion from these models is that, as expected, nanoparticles can only easily flow along with the aqueous phase into the fractures, but their transport into the shale matrix is quite limited, with little transport shown into the organic matter. In addition, based on the measured properties of synthesized magnetic carbon-coated iron-oxide nanoparticles, the distribution of the volumetric magnetic susceptibility and the magnetization of reservoir including SRV are simulated and displayed in the numerical cases with and without magnetic nanoparticles. The results demonstrate that magnetic nanoparticles can effectively enlarge the magnetic susceptibility and the magnetization of reservoir thus producing enhanced signals from well logging devices such as Nuclear magnetic resonance (NMR) and leading to improved reservoir and fracture characterization. This simulator can provide the benefits of both numerically simulating the transport and distribution of nanoparticles in hydraulically fractured shale formations and supplying helpful guidelines for nanoparticles injection plans to enhance well logging signals. Furthermore, this model can also allow us to mimic the tracer transport flow in unconventional reservoirs.