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Steam reforming (SR) of bio-oil has been considered as one of the promising methods for hydrogen production. In this paper, cerium modified Ni/Attapulgite (ATP) catalysts were successfully synthesized by traditional chemical precipitation method and utilized for SR of various bio-oil derived oxygenated model compounds. The effect of Ce content on the structural properties of Ni/ATP was investigated by ICP-OES, N2 adsorption–desorption, XRD, H2-TPR and HRTEM characterizations. The results exhibited that 10Ce–Ni/ATP catalyst had the strongest metal-support interaction and highly dispersion of Ni species with smallest particle size of 6.21 nm. Therefore, it presented the maximum ethanol conversion of 97.21% and H2 yield of 83.15% at relative lower reaction temperature 500 °C during 4 h of SR of ethanol (SRE). In addition, it unfolded promising performance for SR of acetic acid (SRAA) and acetone (SRA). Furthermore, the influence of different oxygenates on the amount and nature of carbon deposits in used 10Ce–Ni/ATP catalyst was revealed by TG and Raman analyses. It found that the types of feedstock significantly affected the carbon species and amount and the formation of graphic layers around the Ni metal sites was the mainly factor for rapid deactivation of 10Ce–Ni/ATP during 4 h of SRA reaction.

The predictions of porosity and permeability from well logging data are important in oil and gas field development. Currently, many scholars use machine learning algorithms to predict reservoir properties. However, few scholars have researched the prediction of reservoir porosity and permeability while drilling. This approach requires not only a high prediction accuracy but also short model processing and calculation times as new logging data are incorporated while drilling. In this paper, four machine learning algorithms were evaluated: the one-versus-rest support vector machine (OVR SVM), one-versus-one support vector machine (OVO SVM), random forest (RF) and gradient boosting decision tree (GBDT) algorithms. First, samples of wireline logging data from the Yan969 wellblock of the Yan’an gas field were chosen for model training. To improve the accuracy and reduce the input parameter dimensions and model training time as much as possible, data correlation analysis was performed. Second, we used the grid search method to approximate ranges of reasonable parameter values and then used k-fold cross-validation to optimize the final parameters and avoid overfitting. Third, we used the four classification models to predict porosity and permeability while drilling with data from logging while drilling (LWD) logs. Finally, we indicate the best porosity and permeability prediction models to use while drilling. To ensure that the prediction accuracy is as high as possible and that the model training time is as short as possible, the OVO SVM algorithm was suggested for porosity and permeability prediction. Therefore, appropriate machine learning algorithms can be used to predict porosity and permeability while drilling.

Graphene oxide (GO) membranes have received considerable attention owing to their outstanding water-permeation properties; however, the effect of the membrane’s microstructures (such as the distribution of oxidized and pristine regions) on the transport mechanism remains unclear. In this study, we performed molecular simulations to explore the permeation of a water-ethanol mixture using a new type of Janus GO membranes with different orientations of oxidized and pristine surfaces. The results indicate that the oxidized upper surface endows the GO membrane with considerable water-capture capability and the in-built oxidized interlayer promotes the effective vertical diffusion of water molecules. Consequently, using the optimized Janus GO membrane, infinite water selectivity and outstanding water flux (∼40.9 kg·m−2·h−1) were achieved. This study contributes to explaining the role of oxidized regions in water permeation via GO membranes and suggests that Janus GO membranes could be used as potential candidates for water-ethanol separation.

In this paper, 3-isocyanatepropyl triethoxysilane (IPTS) was synthesized using one-pot reaction using 3-aminopropyl triethoxysilane (APTES) and triphosgeneas as the raw materials. IPTS was then reacted with three kinds of perfluoroalkyl alcohol to obtain 3-(1H,1H,2H,2H-perfluoroalkoxyamidepropyl)-triethoxysilanes with different fluoroalkyl chain lengths. The silicon wafer was etched to obtain an active and coarse surface followed by coating with self-assembled monolayer (SAMs) using fluoroalkylsilane as materials. AFM were used to reflect the variation of the surface morphologies and roughness of self-assembled films before and after coating. ATR-IR test indicated that a combination of possible bonding forms the SAMs. EDS was used to characterize the distribution of the elements on the SAMs. The result of contact angle testing and surface energy counting showed that the SAMS have good water- and oil repellency, which improved with the increment of fluoroalkyl chain length. XPS were used to characterize the change of surface element content of SAMs before and after heat treatment, which resulted in the microphase separation on the film surfaces and made the fluoroalkyl migrate from the internal of the monolayer to the outermost surface. After heating, the fluoroalkyl accumulated on the surface, which lowered the surface energy further and obtained the better water- and oil repellency.

The updating of reservoir geological models has become a research hotspot. Nevertheless, two difficulties continue to hinder the development of reservoir geological model updating techniques. First, logging while drilling (LWD) is used mainly to guide geosteering operations and effectively identify the lithology; few scholars have researched the interpretation of reservoir physical characteristics while drilling, which is the basis of updating geological models. Second, interpretation results are difficult to transmit to geological models in real time. Based on the LWD technique, this paper uses logging interpretation, machine learning, computer science, and reservoir geological modeling theories and methods to conduct the research of real-time geological model updating around the well. First, based on effective logging data, two machine learning algorithms which are random forest (RF) and extreme gradient boosting tree (XGBoost) are used to establish interpretation models of the reservoir lithology, porosity, and permeability. The parameters of each model are optimized through cross-validation method, and LWD data are interpreted in real time by interpretation models. Second, based on the convenience of the Ocean secondary development platform and the functionality of Petrel software, a real-time transmission plug-in for the current well trajectory and reservoir interpretation results is compiled, and an automatic updating module for the geological model is established. A case study is performed with data from the Sulige gas field in the Ordos Basin, China. For the real-time interpretation of reservoir characteristics while drilling, after 228 trials, the XGBoost algorithm is chosen to establish reservoir lithology, porosity, and permeability interpretation models. For the real-time updating of the geological model around the well, given the consistent probability distributions and the agreement between adjacent wells, we obtain relative errors between the simulated and real values of the lithofacies, porosity, and permeability of 3.90%, 4.50%, and 7.60%, respectively. Therefore, this paper provides a new method for the real-time modification and updating of reservoir geological models, which preliminarily resolved the contradiction between accuracy and real time of geological model real-time updating.

The pyrolysis characteristics and kinetics of lignocellulosic biomass (cotton stalk) and seaweed (Gracilaria lemaneiformis) were studied comparatively. Results of the thermal degradation processes showed that the pyrolysis occurence of G. lemaneiformis is easier than that of cotton stalk. However, G. lemaneiformis released less volatile components and produced more solid residues. As the heating rate increased, the maximum mass loss rates for cotton stalk were decreased, while those for G. lemaneiformis were increased. Results of the kinetic analysis by Popescu method indicated that the pyrolysis mechanism of cotton stalk is three-dimensional diffusion, which can be described by Zhuralev, Lesokin, and Tempelmen (Z–L–T) equation $$(G(\alpha ) = \{ [1/(1 - \alpha )]^{1/3} - 1\}^{2} )$$ , whereas that of G. lemaneiformis is random nucleation and nuclei growth, which can be described by Avrami–Erofeev equation $$(G(\alpha ) = [ - \ln (1 - \alpha )]^{1/4} )$$ . The average activation energy values (192.17 and 146.11 kJ mol−1, respectively) of cotton stalk and G. lemaneiformis obtained by Popescu method are similar with those (189.88 and 153.79 kJ mol−1, respectively) calculated by Flynn–Wall–Ozawa (FWO) method. Moreover, the average activation energy of G. lemaneiformis is lower than that of cotton stalk.

As an important technology in improving oil recovery, CO2 miscible flooding has achieved great success in many oil fields. However, studies on CO2 transport in porous media are mostly based on equilibrium adsorption in one-dimensional displacement where rate-controlled adsorption is not taken into consideration. In this work, we develop a radial convection–dispersion model, which simultaneously combines rate-controlled adsorption, convection, and dispersion. Based on this model, four dimensionless groups representing CO2 dispersion, adsorption capacity, flow rate, and kinetic rate groups are proposed for the first time. Subsequently, the Barakat–Clark forward and backward difference methods are combined to solve the mathematical model. CO2 concentration and adsorption at different positions in the porous media (including the effluent concentration and adsorption histories) are then calculated. Furthermore, the effects of the parameters on CO2 transport behavior are studied in detail. The results reveal that CO2 gradually moves forward with the increase in the CO2 injection volume. Once CO2 reaches a certain position, its concentration there increases and an S-shaped curve is formed. Moreover, the adsorption capacity at this position also increases significantly, and the changing rate is much higher than CO2 concentration. CO2 effluent concentration grows more uniform, and the breakthrough occurs earlier with increased dispersion velocity and CO2 injection rate and decreased adsorption capacity and adsorption rate. The mathematical model developed in this study is of great importance in predicting CO2 transport behavior in radial miscible flooding.

This paper points out a deficiency of constant volumetric displacement (CVD) model used by Reis (JCPT 31, 1992), Zeinab Zargar and S. M. Farouq Ali (SPE J, 2016) and modifies the CVD model by considering the variation of the amount of oil in the area ahead the steam front to obtain a new oil production rate formula for steady-stage steam-assisted gravity drainage (SAGD) process. The new oil production rate formula is in a good agreement with experimental data as reported by Butler et al. (Can J Chem Eng 59:456–460, 1981) and indicates that the actual oil production rate in steady-stage SAGD process is not a constant no matter that the extent of the reservoir is infinite or finite, which is different from previous results. It is the sum of a constant oil rate and another oil rate which is a linear function of time. However, the value of the latter oil rate is so small compared with the constant oil rate that the experimental data of the whole oil production rate seems to be a constant. In addition, the distribution of the oil saturation in the area ahead the steam front is solved with detailed deduction in the solution process.