The current simple nanofluid flooding method for tertiary or enhanced oil recovery is inefficient, especially when used with low nanoparticle concentration. We have designed and produced a nanofluid of graphene-based amphiphilic nanosheets that is very effective at low concentration. Our nanosheets spontaneously approached the oil–water interface and reduced the interfacial tension in a saline environment (4 wt % NaCl and 1 wt % CaCl₂), regardless of the solid surface wettability. A climbing film appeared and grew at moderate hydrodynamic condition to encapsulate the oil phase. With strong hydrodynamic power input, a solid-like interfacial film formed and was able to return to its original form even after being seriously disturbed. The film rapidly separated oil and water phases for slug-like oil displacement. The unique behavior of our nanosheet nanofluid tripled the best performance of conventional nanofluid flooding methods under similar conditions. Significance Improving crude oil recovery by 1% worldwide would result in a huge amount of crude oil resources becoming available. However, the economic and environmental concerns are too serious to ignore when chemical methods (surfactants or polymers flooding, etc.) are used for an average 10–20% enhancement for tertiary oil recovery. Simple nanofluid (containing only nanoparticles) flooding at low concentration (0.01 wt % or less) is a promising alternative, but the efficiency is below 5% in a saline environment (2 wt % or higher NaCl content). We report a simple nanofluid of graphene-based Janus amphiphilic nanosheets for enhanced oil recovery with efficiency of about 15.2%, comparable to chemical methods, which is both economically and environmentally beneficial to the petroleum industry.

The tight sandstone gas reservoir in southern Songliao Basin is naturally fractured and is characterized by its low porosity and permeability. Large‐scale hydraulic fracturing is the most effective way to develop this tight gas reservoir. Quantitative evaluation of fracability is essential for optimizing a fracturing reservoir. In this study, nine fracability‐related factors, particularly mechanical brittleness, unconfined compressive strength (UCS), mineral brittleness, cohesion, internal friction angle (IFA), natural fracture, fracture toughness, horizontal stress difference, and fracture barrier were obtained based on a series of petrophysical and geomechanical experiments. Taking above factors into consideration, a modified comprehensive evaluation model is proposed based on analytic hierarchy process (AHP) method. The UCS and IFA were removed from the AHP model based on the results of factor correlation analysis. The transfer matrix in the weighting procedure was applied to improve the consistency of judgment matrix, and the fuzzy matrix was employed to promote the objectiveness of final decision. The fracability evaluation of four reservoir intervals in Jinshan gas field was analyzed. Field fracturing tests were conducted to verify the feasibility of the proposed evaluation model. Results showed that the tubing pressure curve is more fluctuated in the reservoir interval with more developed natural fractures, and gas production is higher in the reservoir interval with greater fracability coefficient. The field test data coincide with the results of the proposed evaluation model.

Non–intrusive load monitoring based on power measurements is a promising topic of appliance identification in the research of smart grid; where the key is to avoid the power sub-item measurement in load monitoring. In this paper; a three–step non–intrusive load monitoring system (TNILM) is proposed. Firstly; a one dimension convolution neural network (CNN) is constructed based on the structure of GoogLeNet with 2D convolution; which can zoom in on the differences in features between the different appliances; and then effectively extract various transient features of appliances. Secondly; comparing with various classifiers; the Linear Programming boosting with adaptive weights and thresholds (ALPBoost) is proposed and applied to recognize single–appliance and multiple–appliance. Thirdly; an update process is adopted to adjust and balance the parameters between the one dimension CNN and ALPBoost on–line. The TNILM is tested on a real–world power consumption dataset; which comprises single or multiple appliances potentially operated simultaneously. The experiment result shows the effectiveness of the proposed method in both identification rates.