Ultrathin two-dimensional metal–organic frameworks (2D MOFs) have recently attracted extensive interest in various catalytic fields (e.g., electrocatalysis, photocatalysis, thermocatalysis) due to their ultrathin thickness, large surface area, abundant accessible unsaturated...
In this paper, we report on the simple, reliable synthesis of polypyrrole (PPy)/graphene oxide (GO) composite nanosheets by using sacrificial-template polymerization method. Herein, MnO2 nanoslices were chosen as a sacrificial-template to deposit PPy, which served as the oxidant as well. During the polymerization of pyrrole on surface of GO nanosheets, MnO2 component was consumed incessantly. As a result, the PPy growing on the surface of GO nanosheets has the morphology just like the MnO2 nanoslices. This method can provide the fabrication of PPy nanostructures more easily than conventional route due to its independence of removing template, which usually is a complex and tedious experimental process. The as-prepared PPy/GO composite nanosheets exhibited an enhanced properties for Cr(VI) ions removal in aqueous solution based on the synergy effect. The adsorption capacity of the PPy/GO composite nanosheets is about two times as large as that of conventional PPy nanoparticles. We believe that our findings can open a new and effective avenue to improve the adsorption performance in removing heavy metal ions from waste water.
The large uncertainty in fracture characterization for shale gas reservoirs seriously affects the confidence in making forecasts, fracturing design, and taking recovery enhancement measures. This paper presents a workflow to characterize the complex fracture networks (CFNs) and reduce the uncertainty by integrating stochastic CFNs modeling constrained by core and microseismic data, reservoir simulation using a novel edge-based Green element method (eGEM), and assisted history matching based on Ensemble Kalman Filter (EnKF).
In this paper, the geometry of CFNs is generated stochastically constrained by the measurements of hydraulic fracturing treatment, core, and microseismic data. A stochastic parameterization model is used to generate an ensemble of initial realizations of the stress-dependent fracture conductivities of CFNs. To make the eGEM practicable for reservoir simulation, a steady-state fundamental solution is applied to the integral equation, and the technique of local grid refinement (LGR) is applied to refine the domain grids near the fractures. Finally, assisted-history-matching based on EnKF is implemented to calibrate the DFN models and further quantify the uncertainties in the fracture characterization.
The proposed technique is tested using a multi-stage fractured horizontal well from a shale gas field. After analyzing the history matching results, the proposed integrated workflow is shown to be efficient in characterizing fracture networks and reducing the uncertainties. The advantages are exhibited in several aspects. First, the eGEM-based Discrete-Fracture Model (DFM) is shown to be quite efficient in assisted history matching of large field applications because of eGEM’s high precision with coarse grids. This enables simulations of CFNs without upscaling the fractures using continuum approaches. In addition, CFNs geometry can be generated with the constraints of core and microseismic data, and a primary conductivity of CFNs can be generated using the hydraulic fracturing treatment data. Moreover, the uncertainties for CFNs characterization and EUR predictions can be further reduced with the application of EnKF in assimilating the production data.
This paper provides an efficient integrated workflow to characterize the fracture networks in fractured unconventional reservoirs. This workflow, which incorporated several efficient techniques including fracture network modeling, simulation and calibration, can be readily used in field applications. In addition, various data sources could be assimilated in this workflow to reduce the uncertainty in fracture characterization, including hydraulic fracturing treatment, core, microseismic and production data.
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