Experimental Comparative Investigation of Hot Solvent/Steam-Assisted Gravity Drainage in Oil Sand Reservoirs.

Hot solvent-assisted gravity drainage (HS-AGD) is an effective way to exploit oil sands and heavy oil both economically and environmentally. The visualized microscopic seepage experiments and two-dimensional (2-D) macroscopic simulation experiments of HS-AGD are carried out, and the results are compared with that of steam-assisted gravity drainage (SAGD) in detail for the first time in order to compare their development effects of the oil sand reservoir. MacKay River oil sand bitumen is taken as an oil sample in the experiments, with n-hexane as the solvent. Micro seepage characteristics of the hot solvent and steam and the remaining oil distribution of the solvent and steam drive are investigated through microseepage experiments. The expanding process of the solvent/steam chamber and production performance of HS-SAGD and SAGD are investigated through macrosimulation experiments. The study found that the sweep efficiency of hot solvent is higher than that of steam at the same temperature due to the small interfacial tension between the condensed solvent and heated bitumen. Due to the severe gravity segregation, the steam accumulated at the top of the model during the 2-D physical simulation experiment, which results in the huge heat loss at the top of the model. The temperature of the steam chamber is significantly lower than that of the solvent chamber. The oil recovery of 200 °C hot solvent vapor is twice as much as that of 300 °C steam owing to the different drainage mechanisms of the HS-AGD and SAGD. In SAGD, only heat transfer reduces the viscosity of oil sand bitumen. The components of oil produced in SAGD have little difference compared with that of the original bitumen. In HS-AGD, both mass transfer and the sensible heat transfer reduce the viscosity of oil sand bitumen. The in situ asphaltene precipitation induced by heated-solvent extraction also upgrades the bitumen. The results of component analysis show that in HS-AGD, the content of heavy components in the oil sand bitumen is obviously reduced. This paper aims to reveal the oil drainage mechanism of HS-AGD and SAGD from the macroscopic and microscopic view and to provide theoretical guidance for the field application of this technology.

Data-Driven Proxy Model for Forecasting of Cumulative Oil Production during the Steam-Assisted Gravity Drainage Process.

The purpose of this study is to develop a data-driven proxy model for forecasting of cumulative oil (Cum-oil) production during the steam-assisted gravity drainage process. During the model building process, an artificial neural network (ANN) is used to offer a complementary and computationally efficient tool for the physics-driven model, and the von Bertalanffy performance indicator is used to bridge the physics-driven model with the ANN. After that, the accuracy of the model is validated by blind-testing cases. Average absolute percentage error of related parameters of the performance indicator in the testing data set is 0.77%, and the error of Cum-oil production after 20 years is 0.52%. The results illustrate that the integration of performance indicator and ANN makes it possible to solve time series problems in an efficient way. Besides, the data-driven proxy model could be applied to fast parametric studies, quick uncertainty analysis with the Monte Carlo method, and average daily oil production prediction. The findings of this study could help for better understanding of combination of physics-driven model and data-driven model and illustrate the potential for application of the data-driven proxy model to help reservoir engineers, making better use of this significant thermal recovery technology for oil sands or heavy oil reservoirs.

Gut Microbiota Composition and Structure of the Ob/Ob and Db/Db Mice.

INTRODUCTION: Gut microbiota is involved in the progression of metabolic diseases such as obesity and type 2 diabetes. The ob/ob and db/db mice are extensively used as models in studies on the pathogenesis of these diseases. The goal of this study is to characterize the composition and structure of gut microbiota in these model mice at different ages. MATERIALS AND METHODS: High-throughput sequencing was used to obtain the sequences of the highly variable 16S rRNA V3-V4 region from fecal samples. The taxa with high abundance in both model mice were identified by bioinformatics analysis. Moreover, the taxa with divergent abundance in one model mice at different ages or in both model mice at the same age were also recognized. DISCUSSION AND CONCLUSION: The high abundance of Bacteroidetes and Firmicutes in microbiota composition and their imbalanced ratio in both model mice reflect the state of metabolic disorders of these mice. Differences in microbiota composition between the two model mice of the same age or in one model mice with different ages were assumed to be closely linked to the fluctuation of their blood glucose levels with age. The data on gut microbiota in ob/ob and db/db mice investigated herein has broad implications for the pathogenesis study and drug discovery on obesity and related complications.