Estimated ultimate recovery prediction of fractured horizontal wells in tight oil reservoirs based on deep neural networks

Accurate estimated ultimate recovery prediction of fractured horizontal wells in tight reservoirs is crucial to economic evaluation and oil field development plan formulation. Advances in artificial intelligence and big data have provided a new tool for rapid production prediction of unconventional reservoirs. In this study, the estimated ultimate recovery prediction model based on deep neural networks was established using the data of 58 horizontal wells in Mahu tight oil reservoirs. First, the estimated ultimate recovery of oil wells was calculated based on the stretched exponential production decline model and a five-region flow model. Then, the calculated estimated ultimate recovery, geological attributes, engineering parameters, and production data of each well were used to build a machine learning database. Before the model training, the number of input parameters was reduced from 14 to 9 by feature selection. The prediction accuracy of the model was improved by data normalization, the early stopping technique, and 10-fold cross validation. The optimal activation function, hidden layers, number of neurons in each layer, and learning rate of the deep neural network model were obtained through hyperparameter optimization. The average determination coefficient on the testing set was 0.73. The results indicate that compared with the traditional estimated ultimate recovery prediction methods, the established deep neural network model has the strengths of a simple procedure and low time consumption, and the deep neural network model can be easily updated to improve prediction accuracy when new well information is obtained.Cited as: Luo, S., Ding, C., Cheng, H., Zhang, B., Zhao, Y., Liu, L. Estimated ultimate recovery prediction of fractured horizontal wells in tight oil reservoirs based on deep neural networks. Advances in Geo-Energy Research, 2022, 6(2): 111-122. https://doi.org/10.46690/ager.2022.02.0

Effect of Maize (Zea mays L.) Plant-Type on Yield and Photosynthetic Characters of Sweet Potato (Ipomoea balatas L.) in Intercropping System

Sweet potato/maize relay-cropping mode is considered as the main farming practices of dry land in Southwest China. Although relay-cropping would cause the reduction of fresh tuber yield, it still remained unclear that the reason was shade resulted from maize or genotype of sweet potato. The present work aims at exploring the effects of maize (Zea mays L.) plant-type on photosynthetic physiology and yield of sweet potato (Ipomoea balatas L.) in relay-cropping system. Besides, three plant-types maize cultivars including compact, semi-compact and expanded type were used for relay-cropping with different sweet potato cultivars (‘Yushu-2’, ‘Yushu-6’ and ‘Nanshu-88’) in field. The results showed that the photosynthetically active radiation (PAR) was declined with the increase of expansion of maize plant-type, which decreased by 77.5%, 80.1% and 82.1% respectively. When relay-cropped with extended maize, the yield reduction rate of sweet potato was the highest (67%). The shade-resistance of different genotype of sweet potatoes was different, and the yield reduction rate of ‘Yushu-2’ was the lowest (37.01%). Through conducting correlations analysis, it showed that fresh tuber yield had significant positive correlation with Effective Quantum Yield (Y(II)) and significant negative correlation with Non Photochemical Quenching Coefficient (NPQ). In terms of ‘Yushu-2’, the proportion of heat dissipation was the lowest, and its light quantum efficiency was higher than others. As a result, its reduction rate of yield was lower than the other two. We suggested that compact maize cultivar relay-cropping with strong shade-resistance sweet potato cultivar should be mainly applied in practice of sweet potato

Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning

Deep learning has started to revolutionize several different industries, and the applications of these methods in medicine are now becoming more commonplace. This study focuses on investigating the feasibility of tracking patients and clinical staff wearing Bluetooth Low Energy (BLE) tags in a radiation oncology clinic using artificial neural networks (ANNs) and convolutional neural networks (CNNs). The performance of these networks was compared to relative received signal strength indicator (RSSI) thresholding and triangulation. By utilizing temporal information, a combined CNN+ANN network was capable of correctly identifying the location of the BLE tag with an accuracy of 99.9%. It outperformed a CNN model (accuracy = 94%), a thresholding model employing majority voting (accuracy = 95%), and a triangulation classifier utilizing majority voting (accuracy = 95%). Future studies will seek to deploy this affordable real time location system in hospitals to improve clinical workflow, efficiency, and patient safety

GABAergic Projection Neurons Route Selective Olfactory Inputs to Specific Higher-Order Neurons

SummaryWe characterize an inhibitory circuit motif in the Drosophila olfactory system, parallel inhibition, which differs from feedforward or feedback inhibition. Excitatory and GABAergic inhibitory projection neurons (ePNs and iPNs) each receive input from antennal lobe glomeruli and send parallel output to the lateral horn, a higher center implicated in regulating innate olfactory behavior. Ca2+ imaging of specific lateral horn neurons as an olfactory readout revealed that iPNs selectively suppressed food-related odor responses, but spared signal transmission from pheromone channels. Coapplying food odorant did not affect pheromone signal transmission, suggesting that the differential effects likely result from connection specificity of iPNs, rather than a generalized inhibitory tone. Ca2+ responses in the ePN axon terminals show no detectable suppression by iPNs, arguing against presynaptic inhibition as a primary mechanism. The parallel inhibition motif may provide specificity in inhibition to funnel specific olfactory information, such as food and pheromone, into distinct downstream circuits

First realization of macroscopic Fourier ptychography for hundred-meter distance sub-diffraction imaging

Fourier ptychography (FP) imaging, drawing on the idea of synthetic aperture, has been demonstrated as a potential approach for remote sub-diffraction-limited imaging. Nevertheless, the farthest imaging distance is still limited around 10 m even though there has been a significant improvement in macroscopic FP. The most severely issue in increasing the imaging distance is FoV limitation caused by far-field condition for diffraction. Here, we propose to modify the Fourier far-field condition for rough reflective objects, aiming to overcome the small FoV limitation by using a divergent beam to illuminate objects. A joint optimization of pupil function and target image is utilized to attain the aberration-free image while estimating the pupil function simultaneously. Benefiting from the optimized reconstruction algorithm which effectively expands the camera's effective aperture, we experimentally implement several FP systems suited for imaging distance of 12 m, 90 m, and 170 m with the maximum synthetic aperture of 200 mm. The maximum imaging distance and synthetic aperture are thus improved by more than one order of magnitude of the state-of-the-art works with a fourfold improvement in the resolution. Our findings demonstrate significant potential for advancing the field of macroscopic FP, propelling it into a new stage of development

Molecular dynamics simulation of CO2 dissolution-diffusion in multi-component crude oil

In order to study the dissolution-diffusion process and mechanism of CO2 in multi-component crude oil, a model of multi-component crude oil system with octane as the main component and 16 other alkanes as a compound was constructed by using molecular dynamics simulation method. We estimated the CO2 density distribution in crude oil model and the shift in crude oil model volume change. We then investigated the microscopic influence mechanism of CO2 dissolution-diffusion on the volume expansion of crude oil by simulating the action of CO2 dissolution-diffusion in the multi-component crude oil model. Based on the variation law of mean square displacement between crude oil molecules, the dissolution and diffusion coefficients of CO2 were predicted, and the influence of CO2 dissolution-diffusion on crude oil mobility was analyzed. It is found that temperature intensifies the molecular thermal motion and increases the voids between alkane molecules, which promotes the dissolution of CO2 and encourages CO2 molecules to transmit, making the crude oil expand and viscosity decrease, and improving the flow ability of crude oil; with the enhancement of given pressure, the potential energy difference between the inside and outside of the crude oil model becomes larger, and the voids between alkane molecules become larger, which is favorable to the dissolution of CO2. Nevertheless, the action of CO2 molecules’ diffusing in the crude oil sample is significantly limited or even tends to zero, besides, the mobility of crude oil is affected due to the advance of external pressure. The mechanism of CO2 dissolution and diffusion in multi-component crude oil is revealed at the microscopic level, and provides theoretical guidance for the development of CO2 flooding

Mechanisms of the enhanced DDT removal from soils by earthworms: identification of DDT degraders in drilosphere and non-drilosphere matrices

The remediation of soil contaminated by 1,1,1-trichloro-2,2-bis(4-chlorophenyl)ethane (DDT) remains an important issue in environmental research. Although our previous studies demonstrated that earthworms could enhance the degradation of DDT in soils, the underlying mechanisms and microorganisms involved in these transformation processes are still not clear. Here we studied the transformation of DDT in sterilized/non-sterilized drilosphere and non-drilosphere matrices and identified DDT degraders using the technique of DNA-stable isotope probing. The results show that DDT degradation in non-sterilized drilosphere was quicker than that in their non-drilosphere counterparts. Earthworms enhance DDT removal mainly by improving soil properties, thus stimulating indigenous microorganisms rather than abiotic degradation or tissue accumulating. Ten new genera, including Streptomyces, Streptacidiphilus, Dermacoccus, Brevibacterium, Bacillus, Virgibacillus, were identified as DDT ring cleavage degrading bacteria in the five matrices tested. Bacillus and Dermacoccus may also play vital roles in the dechlorination of DDTs as they were highly enriched during the incubations. The results of this study provide robust evidence for the application of earthworms in remediating soils polluted with DDT and highlight the importance of using combinations of cultivation-independent techniques together with process-based measurements to examine the function of microbes degrading organic pollutants in drilosphere matrices

Enhanced secretion of hepatocyte growth factor in human umbilical cord mesenchymal stem cells ameliorates pulmonary fibrosis induced by bleomycin in rats

Umbilical cord mesenchymal stem cells (UCMSCs) are a reportedly promising choice in the treatment of irreversible pulmonary fibrosis and lethal interstitial lung disease with limited drug treatment options. In this study, we investigated the therapeutic efficacy of UCMSCs overexpressing hepatocyte growth factor (HGF), which is considered one of the main anti-fibrotic factors secreted by MSCs. Adenovirus vector carrying the HGF gene was transfected into UCMSCs to produce HGF-modified UCMSCs (HGF-UCMSCs). Transfection promoted the proliferation of UCMSCs and did not change the morphology, and differentiation ability, or biomarkers. Rats were injected with HGF-UCMSCs on days 7 and 11 after intratracheal administration of bleomycin (10 mg/kg). We performed an analysis of histopathology and lung function to evaluate the anti-fibrotic effect. The results showed that HGF-UCMSCs decreased the Ashcroft scores in hematoxylin and eosin-stained sections, the percentage positive area in Masson trichrome-stained sections, and the hydroxyproline level in lungs. Forced expiratory volume in the first 300 m/forced vital capacity was also improved by HGF-UCMSCs. To explore the possible therapeutic mechanism of HGF-UCMSCs, we detected inflammatory factors in the lungs and performed mRNA sequencing in UCMSCs and HGF-UCMSCs. The data indicated that inhibition of interleukin-17 in the lung may be related to the anti-fibrosis of HGF-UCMSCs, and overexpressed HGF probably played a primary role in the treatment. Collectively, our study findings suggested that the overexpression of HGF may improve the anti-fibrotic effect of UCMSCs through directly or indirectly interacting with interleukin-17-producing cells in fibrotic lungs