Reservoir and lithofacies shale classification based on NMR logging

© 2020 Chinese Petroleum Society Shale gas reservoirs have fine-grained textures and high organic contents, leading to complex pore structures. Therefore, accurate well-log derived pore size distributions are difficult to acquire for this unconventional reservoir type, despite their importance. However, nuclear magnetic resonance (NMR) logging can in principle provide such information via hydrogen relaxation time measurements. Thus, in this paper, NMR response curves (of shale samples) were rigorously mathematically analyzed (with an Expectation Maximization algorithm) and categorized based on the NMR data and their geology, respectively. Thus the number of the NMR peaks, their relaxation times and amplitudes were analyzed to characterize pore size distributions and lithofacies. Seven pore size distribution classes were distinguished; these were verified independently with Pulsed-Neutron Spectrometry (PNS) well-log data. This study thus improves the interpretation of well log data in terms of pore structure and mineralogy of shale reservoirs, and consequently aids in the optimization of shale gas extraction from the subsurface

CRISPR/Cas9‐mediated somatic correction of a novel coagulator factor IX gene mutation ameliorates hemophilia in mouse

The X‐linked genetic bleeding disorder caused by deficiency of coagulator factor IX, hemophilia B, is a disease ideally suited for gene therapy with genome editing technology. Here, we identify a family with hemophilia B carrying a novel mutation, Y371D, in the human F9 gene. The CRISPR/Cas9 system was used to generate distinct genetically modified mouse models and confirmed that the novel Y371D mutation resulted in a more severe hemophilia B phenotype than the previously identified Y371S mutation. To develop therapeutic strategies targeting this mutation, we subsequently compared naked DNA constructs versus adenoviral vectors to deliver Cas9 components targeting the F9 Y371D mutation in adult mice. After treatment, hemophilia B mice receiving naked DNA constructs exhibited correction of over 0.56% of F9 alleles in hepatocytes, which was sufficient to restore hemostasis. In contrast, the adenoviral delivery system resulted in a higher corrective efficiency but no therapeutic effects due to severe hepatic toxicity. Our studies suggest that CRISPR/Cas‐mediated in situ genome editing could be a feasible therapeutic strategy for human hereditary diseases, although an efficient and clinically relevant delivery system is required for further clinical studies

CO2 saturated brine injected into fractured shale: An X-ray micro-tomography in-situ analysis at reservoir conditions

Fracture morphology and permeability are key factors in enhanced gas recovery (EOR) and Carbon Geo-storage (CCS) in shale gas reservoirs as they determine production and injection rates. However, the exact effect of CO2-saturated (live) brine on shale fracture morphology, and how the permeability changes during live brine injection and exposure is only poorly understood. We thus imaged fractured shale samples before and after live brine injection in-situ at high resolution in 3D via X-ray micro-computed tomography. Clearly, the fractures’ aperture and connectivity increased after live brine injection

Research of the single‐rotor UAV gimbal vibration test

Abstract An experimental study was conducted to investigate the phenomenon of UAV attitude instability caused by large vibrations affecting single‐rotor UAV airborne equipment. Appropriate measurement points were selected to collect vibration signals from the unmanned aerial platform during takeoff and flight of the drone. The time‐domain response and power spectral density of the unmanned aerial platform were then obtained. Establish a dynamic model of the vibration reduction system for an unmanned aerial platform and design a two‐stage vibration reduction structure for the unmanned aerial platform. Through field flight tests of unmanned aerial vehicles, it has been demonstrated that the maximum time domain response of the platform after vibration reduction is 8.75 g (less than 50 g), and the maximum root mean square value of the power spectral density (PSD) is 1.82 g (less than 3 g). The designed secondary vibration reduction structure can serve as a reference for the design of vibration reduction in unmanned aerial vehicles

Research on Tool Remaining Life Prediction Method Based on CNN-LSTM-PSO

Efficient and accurate prediction of tool Remaining Useful Life (RUL) is the key to improve product accuracy, improve work efficiency and reduce machining costs. Aiming at the problems of weak tool wear state features, difficult extraction, and low prediction precision and accuracy, this research proposes a CNN-LSTM-PSO tool remaining life prediction method based on multi-channel feature fusion. Firstly, based on computer vision, feature extraction, information fusion technology, the multi-source sensor signals collected during the tool life cycle are effectively processed and analyzed, and a sample data set of spatio-temporal correlation of traffic flow is constructed. Secondly, the sample data set was input into the CNN-LSTM-PSO model, the CNN network obtained the sequence feature vector by extracting the spatial characteristics of traffic flow data, and the feature vector was input into the multi-layer LSTM network to extract the time-dependent features, and the PSO algorithm optimized the hyperparameters in the CNN-LSTM model. The accuracy of tool RUL prediction model and the efficiency of model fitting are further improved. The results show that the CNN-LSTM-PSO model can effectively predict tool wear, with the mean absolute error (MAE) value of 1.0892, the root mean square error (RMSE) value of 1.3520, and the determination coefficient $R^{2}$ value of 0.9961; Through the comparative analysis of ablation experiments, it is found that the method proposed in the research has the highest efficiency in fitting the tool RUL prediction model, the lowest values of MAE value and root mean square error RMSE, and the value of determination coefficient $R^{2}$ is closest to 1, which has certain advantages.The proposed method has reference value and engineering practical significance for the related research of tool wear residual life prediction

The Coupled Thermal-Structural Resonance Reliability Sensitivity Analysis of Gear-Rotor System with Random Parameters

The resonance of the gear-rotor system will produce a large number of responses that do not exceed the threshold value, resulting in structural fatigue failure and transmission failure, affecting its life and reliability. It is particularly critical to consider the temperature rise under high-speed and heavy-load conditions. Therefore, the research will take the main drive gear-rotor system of a certain type of aeroengine accessory gearbox as the research object, consider the influence of the temperature field on the natural frequency of the gear-rotor system, and take the difference between the natural frequency of the gear-rotor system and the excitation frequency (gear meshing frequency) as the performance function. The PC-Kriging and adaptive design of experimental strategies are applied to the thermal-structural coupling parametric model to analyze the resonance reliability and sensitivity of the gear-rotor system. For complex mechanical mechanisms, the method has better accuracy than other surrogate models and greatly saves the time of finite element simulation in reliability analysis. The results show that the natural frequency of a gear rotor decreases with an increase in temperature, and the natural frequency of different orders varies with the change in temperature. The influence of the sensitivity of different random parameters on the resonance reliability of the gear-rotor system is obtained. Reliability research on resonance failure of high-speed and heavy-load aviation gear-rotor systems considering random parameters under a temperature rise field has important practical engineering application value and scientific research significance

Deep Learning-Based Predictive Framework for Groundwater Level Forecast in Arid Irrigated Areas

An accurate groundwater level (GWL) forecast at multi timescales is vital for agricultural management and water resource scheduling in arid irrigated areas such as the Hexi Corridor, China. However, the forecast of GWL in these areas remains a challenging task owing to the deficient hydrogeological data and the highly nonlinear, non-stationary and complex groundwater system. The development of reliable groundwater level simulation models is necessary and profound. In this study, a novel ensemble deep learning GWL predictive framework integrating data pro-processing, feature selection, deep learning and uncertainty analysis was constructed. Under this framework, a hybrid model equipped with currently the most effective algorithms, including the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for data decomposition, the genetic algorithm (GA) for feature selection, the deep belief network (DBN) model, and the quantile regression (QR) for uncertainty evaluation, denoted as CEEMDAN-GA-DBN, was proposed for the 1-, 2-, and 3-month ahead GWL forecast at three GWL observation wells in the Jiuquan basin, northwest China. The capability of the CEEMDAN-GA-DBN model was compared with the hybrid CEEMDAN-DBN and the standalone DBN model in terms of the performance metrics including R, MAE, RMSE, NSE, RSR, AIC and the Legates and McCabe’s Index as well as the uncertainty criterion including MPI and PICP. The results demonstrated the higher degree of accuracy and better performance of the objective CEEMDAN-GA-DBN model than the CEEMDAN-DBN and DBN models at all lead times and all the wells. Overall, the CEEMDAN-GA-DBN reduced the RMSE of the CEEMDAN-DBN and DBN models in the testing period by about 9.16 and 17.63%, while it improved their NSE by about 6.38 and 15.32%, respectively. The uncertainty analysis results also affirmed the slightly better reliability of the CEEMDAN-GA-DBN method than the CEEMDAN-DBN and DBN models at the 1-, 2- and 3-month forecast horizons. The derived results proved the ability of the proposed ensemble deep learning model in multi time steps ahead of GWL forecasting, and thus, can be used as an effective tool for GWL forecasting in arid irrigated areas

Different multi-scale structural features of oat resistant starch prepared by ultrasound combined enzymatic hydrolysis affect its digestive properties

In this research, oat resistant starch (ORS) was prepared by autoclaving-retrogradation cycle (ORS-A), enzymatic hydrolysis (ORS-B), and ultrasound combined enzymatic hydrolysis (ORS-C). Differences in their structural features, physicochemical properties and digestive properties were studied. Results of particle size distribution, XRD, DSC, FTIR, SEM and in vitro digestion showed that ORS-C was a B + C-crystal, and ORS-C had a larger particle size, the smallest span value, the highest relative crystallinity, the most ordered and stable double helix structure, the roughest surface shape and strongest digestion resistance compared to ORS-A and ORS-B. Correlation analysis revealed that the digestion resistance of ORS-C was strongly positively correlated with RS content, amylose content, relative crystallinity and absorption peak intensity ratio of 1047/1022 cm−1 (R1047/1022), and weakly positively correlated with average particle size. These results provided theoretical support for the application of ORS-C with strong digestion resistance prepared by ultrasound combined enzymatic hydrolysis in the low GI food application

Sbs modified bitumen with organic layered double hydroxides: Compatibility and aging effects on rheological properties

SBS-modified bitumen (SMB) is susceptible to aging, which seriously influences its service performance and life. In order to strengthen the anti-aging ability of SMB, triethoxyvinylsilane was designed to organically modify layered double hydroxides (LDHs) and was applied to modify SMB. The dispersibility and storage stability of LDHs in SMB were markedly enhanced after triethoxyvi-nylsilane organic modification, and the compatibility and storage stability of SBS in bitumen were simultaneously enhanced. Compared with SMB, the introduction of LDHs and organic LDHs (OLDHs) could ameliorate the high-temperature properties of SMB, and the thermostability of SBS in bitumen at a high temperature was also distinctly improved, especially OLDHs. After aging, due to the oxidation of molecular bitumen and the degradation of molecular SBS, SMB became hardened and brittle, and the rheological properties were significantly deteriorated, which had serious im-pacts on the performance of SMB. LDHs can mitigate the detriment of aging to bitumen and SBS, and the deterioration of the rheological properties of SMB is obviously alleviated. As a result of the better dispersibility and storage stability, OLDHs exerted superior reinforcement of the anti-aging ability of SMB.</p