An improved method for predicting CO2 minimum miscibility pressure based on artificial neural network

 The CO2 enhanced oil recovery (EOR) method is widely used in actual oilfields. It is extremely important to accurately predict the CO2 minimum miscibility pressure (MMP) for CO2-EOR. At present, many studies about MMP prediction are based on empirical, experimental, or numerical simulation methods, but these methods have limitations in accuracy or computation efficiency. Therefore, more work needs to be done. In this work, with the results of the slim-tube experiment and the data expansion of the multiple mixing cell methods, an improved artificial neural network (ANN) model that predicts CO2 MMP by the full composition of the crude oil and temperature is trained. To stabilize the neural network training process, L2 regularization and Dropout are used to address the issue of over-fitting in neural networks. Predicting results show that the ANN model with Dropout possesses higher prediction accuracy and stronger generalization ability. Then, based on the validation sample evaluation, the mean absolute percentage error and R-square of the ANN model are 6.99 and 0.948, respectively. Finally, the improved ANN model is tested by six samples obtained from slim-tube experiment results. The results indicate that the improved ANN model has extremely low time cost and high accuracy to predict CO2 MMP, which is of great significance for CO2-EOR.Cited as: Dong, P., Liao, X., Chen, Z., Chu, H. An improved method for predicting CO2 minimum miscibility pressure based on artificial neural network. Advances in Geo-Energy Research, 2019, 3(4): 355-364, doi: 10.26804/ager.2019.04.0

APyCE: A Python module for parsing and visualizing 3D reservoir digital twin models

Engineers, geoscientists, and analysts can benefit from fast, easy, and real-time immersive 3D visualization to enhance their understanding and collaboration in a virtual 3D world. However, converting 3D reservoir data formats between different software programs and open-source standards can be challenging due to the complexity of programming and discrepancies in internal data structures. This paper introduces an open-source Python implementation focused on parsing industry reservoir data formats into a popular opensource visualization data format, Visual Toolkit files. Using object-oriented programming, a simple workflow was developed to export corner-point grids to Visual Toolkit-hexahedron structures. To demonstrate the utility of the software, standard raw input files of reservoir models are processed and visualized using Paraview. This tool aims to accelerate the digital transformation of the oil and gas industry in terms of 3D digital content generation and collaboration.Document Type: Short communicationCited as: Tosta, M., Oliveira, G. P., Wang, B., Chen, Z., Liao, Q. APyCE: A Python module for parsing and visualizing 3D reservoir digital twin models. Advances in Geo-Energy Research, 2023, 8(3): 206-210. https://doi.org/10.46690/ager.2023.06.0

Ramsey interferometry through coherent X2Σg+−A2Πu−B2Σu+X^2\Sigma_g^+ - A^2\Pi_u - B^2\Sigma_u^+ coupling and population transfer in N2+^+_2 air laser

The laser-like coherent emission at 391nm from N$_2$ gas irradiated by strong 800nm pump laser and weak 400nm seed laser is theoretically investigated. Recent experimental observations are well simulated, including temporal profile, optical gain and periodic modulation of the 391nm signal from N$_2^+$. Our calculation sheds light on the long standing controversy on whether population inversion is indispensable for the optical gain. We demonstrate the Ramsey interference fringes of the emission intensity at 391nm formed by additionally injecting another 800nm pump or 400nm seed, which are well explained by the coherent modulation of transition dipole moment and population between the $A^2\Pi_u(\nu=2)$-$X^2\Sigma_g^+$ states as well as the $B^2\Sigma_u^+ (\nu=0)$-$X^2\Sigma_g^+$ states. This study provides versatile possibilities for the coherent control of $\text{N}_2^+$ air laser.Comment: 5 pages, 5 figure

Learning to Branch in Combinatorial Optimization with Graph Pointer Networks

Branch-and-bound is a typical way to solve combinatorial optimization problems. This paper proposes a graph pointer network model for learning the variable selection policy in the branch-and-bound. We extract the graph features, global features and historical features to represent the solver state. The proposed model, which combines the graph neural network and the pointer mechanism, can effectively map from the solver state to the branching variable decisions. The model is trained to imitate the classic strong branching expert rule by a designed top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. Our approach also outperforms the state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances

Serum from patients with ankylosing spondylitis can increase PPARD, fra-1, MMP7, OPG and RANKL expression in MG63 cells

OBJECTIVES: To explore the effects of serum from patients with ankylosing spondylitis on the canonical Wnt/β-catenin pathway and to assess whether the serum has an osteogenic effect in MG63 cells. METHODS: MG63 cells were cultured with serum from 45 ankylosing spondylitis patients, 30 healthy controls, or 45 rheumatoid arthritis patients. The relative PPARD, fra-1, MMP7, OPG and RANKL mRNA levels were measured using quantitative real-time polymerase chain reaction. Associations between gene expression and patient demographics and clinical assessments were then analyzed. RESULTS: MG63 cells treated with serum from ankylosing spondylitis patients had higher PPARD, fra-1, MMP7 and OPG gene expression than did cells treated with serum from controls or rheumatoid arthritis patients (all

Genome-wide comparative analysis of digital gene expression tag profiles during maize ear development

Background: Development of the maize (Zea mays L.) female inflorescence (ear) has an important impact on corn yield. However, the molecular mechanisms underlying maize ear development are poorly understood. Results: We profiled and analyzed gene expression of the maize ear at four developmental stages: elongation phase (I), spikelet differentiation phase (II), floret primordium differentiation phase (III), and floret organ differentiation phase (IV). Based on genome-wide profile analysis, we detected differential mRNA of maize genes. Among the ~6,800 differentially expressed genes (DEGs), 3,325 genes were differentially expressed in stage II, 3,765 genes in III, and 1,698 genes in IV, compared to its previous adjacent stages, respectively. Furthermore, some of DEGs were predicted to be potential candidates in maize ear development, such as AGAMOUS (GRMZM2G052890) and ATFP3 (GRMZM2G155281). Meanwhile, some genes were well-known annotated to the mutants during maize inflorescence development such as compact plant2 (ct2), zea AGAMOUS homolog1 (zag1), bearded ear (bde), and silky1 (si1). Some DEGs were predicted targets of microRNAs such as microRNA156. K-means clustering revealed that the DEGs showed 18 major expression patterns. Thirteen transcriptional factors from 10 families were differentially expressed across three comparisons of adjacent stages (II vs. I, III vs. II, IV vs. III). Antisense transcripts were widespread during all four stages, and might play important roles in maize ear development. Finally, we randomly selected 32 DEGs to validate their expression patterns using quantitative reverse-transcription polymerase chain reaction (qRT-PCR). The results were consistent with those from Solexa sequencing. Conclusions: DEGs technique had shown an advantage in detecting candidates, and some transcription factors during maize ear development. RT-PCR data were consistent with our sequencing data and supplied additional information on ear developmental processes. These results provide a molecular foundation for future research on maize ear development

Structural evolution of GeMn/Ge superlattices grown by molecular beam epitaxy under different growth conditions

GeMn/Ge epitaxial 'superlattices' grown by molecular beam epitaxy with different growth conditions have been systematically investigated by transmission electron microscopy. It is revealed that periodic arrays of GeMn nanodots can be formed on Ge and GaAs substrates at low temperature (approximately 70°C) due to the matched lattice constants of Ge (5.656 Å) and GaAs (5.653 Å), while a periodic Ge/GeMn superlattice grown on Si showed disordered GeMn nanodots with a large amount of stacking faults, which can be explained by the fact that Ge and Si have a large lattice mismatch. Moreover, by varying growth conditions, the GeMn/Ge superlattices can be manipulated from having disordered GeMn nanodots to ordered coherent nanodots and then to ordered nanocolumns

Multicenter validation of the value of BASFI and BASDAI in Chinese ankylosing spondylitis and undifferentiated spondyloarthropathy patients

The objectives of this study were to evaluate the reliability of Bath ankylosing spondylitis functional index (BASFI) and Bath ankylosing spondylitis disease activity index (BASDAI) in Chinese ankylosing spondylitis (AS) and undifferentiated spondyloarthropathy (USpA) patients. 664 AS patients by the revised New York criteria for AS and 252 USpA patients by the European Spondyloarthropathy Study Group criteria were enrolled. BASDAI and BASFI questionnaires were translated into Chinese. Participants were required to fill in BASFI and BASDAI questionnaires again after 24 h. Moreover, BASDAI and BASFI were compared in AS patients receiving Enbrel or infliximab before and after treatment. For AS group, BASDAI ICC: 0.9502 (95% CI: 0.9330–0.9502, α = 0.9702), BASFI ICC: 0.9587 (95% CI: 0.9521–0.9645, α = 0.9789). For USpA group, BASDAI ICC: 0.9530 (95% CI: 0.9402–0.9632, α = 0.9760), BASFI ICC: 0.9900 (95% CI: 0.9871–0.9922, α = 0.9950). In the AS group, disease duration, occipital wall distance, modified Schober test, chest expansion, ESR, and CRP showed significant correlation with BASDAI and BASFI (all P < 0.01). In the USpA group, onset age, ESR, and CRP were significantly correlated with BASDAI (all P < 0.05), while modified Schober test, ESR, and CRP were significantly associated with BASFI (all P < 0.05). The change in BASDAI and BASFI via Enbrel or infliximab treatment showed a significant positive correlation (P < 0.01). The two instruments have good reliability and reference value regarding the evaluation of patient’s condition and anti-TNF-α treatment response