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Abstract Background Observational studies have demonstrated an association between gut microbiota and myasthenia gravis; however, the causal relationship between the two still lacks clarity. Our goals are to ascertain the existence of a bidirectional causal relationship between gut microbiota composition and myasthenia gravis, and to investigate how gut microbiota plays a role in reducing the risk of myasthenia gravis. Methods We acquired gut microbiota data at the phylum, class, order, family, and genus levels from the MiBioGen consortium (N = 18,340) and myasthenia gravis data from the FinnGen Research Project (426 cases and 373,848 controls). In the two-sample Mendelian randomization analysis, we assessed the causal relationship between the gut microbiota and myasthenia gravis. We also conducted bidirectional MR analysis to determine the direction of causality. The inverse variance weighted, mendelian randomization-Egger, weighted median, simple mode, and weighted mode were used to test the causal relationship between the gut microbiota and severe myasthenia gravis. We used MR-Egger intercept and Cochran's Q test to assess for pleiotropy and heterogeneity, respectively. Furthermore, we utilized the MR-PRESSO method to evaluate horizontal pleiotropy and detect outliers. Results In the forward analysis, the inverse-variance weighted method revealed that there is a positive correlation between the genus Lachnoclostridium (OR = 2.431,95%CI 1.047–5.647, p = 0.039) and the risk of myasthenia gravis. Additionally, the family Clostridiaceae1 (OR = 0.424,95%CI 0.202–0.889, p = 0.023), family Defluviitaleaceae (OR = 0.537,95%CI 0.290–0.995, p = 0.048), family Enterobacteriaceae (OR = 0.341,95%CI 0.135–0.865, p = 0.023), and an unknown genus (OR = 0.407,95%CI 0.209–0.793, p = 0.008) all demonstrated negative correlation with the risk of developing myasthenia gravis. Futhermore, reversed Mendelian randomization analysis proved a negative correlation between the risk of myasthenia gravis and genus Barnesiella (OR = 0.945,95%CI 0.906–0.985, p = 0.008). Conclusion Our research yielded evidence of a causality connection in both directions between gut microbiota and myasthenia gravis. We identified specific types of microbes associated with myasthenia gravis, which offers a fresh window into the pathogenesis of this disease and the possibility of developing treatment strategies. Nonetheless, more studies, both basic and clinical, are necessary to elucidate the precise role and therapeutic potential of the gut microbiota in the pathogenesis of myasthenia gravis.

BackgroundIt has emerged that disturbances of the gut microbiota (GM) are linked to insomnia. However, the causality of the observed associations remains uncertain.MethodsWe conducted a two-sample Mendelian randomization analysis based on genome-wide association study data to explore the possible causal link between GM and insomnia. The GM data were from the MiBioGen consortium, while the summary statistics of insomnia were obtained from the FinnGen consortium R9 release data. Cochran’s Q statistics were used to analyze instrumental variable heterogeneity.ResultsAccording to the inverse variance weighted estimates, the family Ruminococcaceae (odds ratio = 1.494, 95% confidence interval:1.004–2.223, p = 0.047) and the genus Lachnospiraceae (odds ratio = 1.726, 95% confidence interval: 1.191–2.501, p = 0.004) play a role in insomnia risk. In contrast, the genus Flavonifractor (odds ratio = 0.596, 95% confidence interval: 0.374–0.952, p = 0.030) and the genus Olsenella (odds ratio = 0.808, 95% confidence interval: 0.666–0.980, p = 0.031) tended to protect against insomnia. According to the reverse MR analysis, insomnia can also alter GM composition. Instrumental variables were neither heterogeneous nor horizontal pleiotropic.ConclusionIn conclusion, our Mendelian randomization study provides evidence of a causal relationship between GM and insomnia. The identified GM may be promising gut biomarkers and new therapeutic targets for insomnia. This investigation also provides a foundation for future studies examining the influence of GM on sleep disorders beyond insomnia, with potential implications for redefining the mechanisms governing sleep regulation.

BackgroundGeneralized convulsive epilepsy (GCE), an important subtype of epilepsy, is a syndrome of neuronal dysfunction characterized by diffuse abnormal discharge of neurons within the brain. Compounding evidence suggests a correlation between epilepsy and inflammatory factors, for instance, cyclooxygenase-2, interleukin-1β, and interleukin-6. Elevated levels of inflammatory factors have been observed in patients with epilepsy and several animal models. Therefore, inflammation may be closely associated with the pathogenesis and progression of GCE. However, the cause-and-effect relationship between the two is difficult to determine because of small sample sizes and confounding factors.MethodsTo test for causality of the 41 cytokines on GCE, we conducted a two-sample Mendelian randomization (MR) based on the largest and latest genome-wide association study (GWAS) involving 290 cases and 453,521 European controls and a GWAS meta-analysis consisting of 41 cytokines from 8,293 individuals.ResultsR confirmed a bidirectional causal link between cytokines and GCE. Genetically predicted increased levels of hepatocyte growth factor and decreased levels of eotaxin and interleukin-18 are associated with an increased risk of GCE (OR = 1.904, 95% CI = 1.019–3.561, p = 0.044; OR = 0.641, 95% CI = 0.417–0.984, p = 0.042; OR = 0.482, 95% CI = 0.251–0.927, p = 0.046). Furthermore, the presence of GCE is related to an increase in levels of multiple cytokines, such as macrophage inflammatory protein-1α, interleukin-12p70, interleukin-17, interleukin-1 receptor antagonist, and basic fibroblast growth factor (OR = 1.038, 95% CI = 1.005–1.073, p = 0.024; OR = 1.031, 95% CI = 1.009–1.054, p = 0.006; OR = 1.027, 95% CI = 1.002–1.053, p = 0.037; OR = 1.037, 95% CI = 1.003–1.072, p = 0.032; OR = 1.032, 95% CI = 1.000–1.066, p = 0.048; OR = 1.025, 95% CI = 1.003–1.048, p = 0026).ConclusionA bidirectional causal link existed between inflammation and GCE. Detecting significantly altered factor concentrations may be of great significance for screening GCE and predicting their occurrence. Moreover, available pharmacological treatments for GCE are focused primarily on suppressing seizures. In future, altering the concentration of these cytokines in the body through targeted anti-inflammatory therapy to modify the epileptogenic mechanism and prevent the recurrence and refractoriness of GCE may become the key to new treatments.

This paper introduces a novel method for enhancing underground pipeline inspection, specifically addressing limitations associated with traditional closed-circuit television (CCTV) systems. These systems, commonly used for capturing visual data of sewer system deformations, heavily rely on subjective human expertise, leading to limited accuracy in detection. Furthermore, their inability to perform quantitative analyses of deformation extent hampers overall inspection effectiveness. Our proposed method leverages laser point cloud data and employs a 3D scanner for objective detection of geometric deformations in underground pipe corridors. By utilizing this approach, we enable a quantitative assessment of blockage levels, offering a significant improvement over traditional CCTV-based methods. The key advantages of our method lie in its objectivity and quantification capabilities, ultimately enhancing detection reliability, accuracy, and overall inspection efficiency.

Image segmentation has a remarkable influence on the classification accuracy of object-based image analysis. Accordingly, how to raise the performance of remote sensing image segmentation is a key issue. However, this is challenging, primarily because it is difficult to avoid over-segmentation errors (OSE) and under-segmentation errors (USE). To solve this problem, this article presents a new segmentation technique by fusing a region merging method with an unsupervised segmentation evaluation technique called under- and over-segmentation aware (UOA), which is improved by using edge information. Edge information is also used to construct the merging criterion of the proposed approach. To validate the new segmentation scheme, five scenes of high resolution images acquired by Gaofen-2 and Ziyuan-3 multispectral sensors are chosen for the experiment. Quantitative evaluation metrics are employed in the experiment. Results indicate that the proposed algorithm obtains the lowest total error (TE) values for all test images (0.3791, 0.1434, 0.7601, 0.7569, 0.3169 for the first, second, third, fourth, fifth image, respectively; these values are averagely 0.1139 lower than the counterparts of the other methods), as compared to six state-of-the-art region merging-based segmentation approaches, including hybrid region merging, hierarchical segmentation, scale-variable region merging, size-constrained region merging with edge penalty, region merging guided by priority, and region merging combined with the original UOA. Moreover, the performance of the proposed method is better for artificial-object-dominant scenes than the ones mainly covering natural geo-objects.

This paper presents a newly developed segmentation algorithm for high-resolution remote sensing imagery. The new method is based on binary merge tree (BMT) and mainly comprises two parts, which are BMT construction and tree node selection. The first part is initialized by super-pixels, which are iteratively merged through using the well-known multi-resolution segmentation approach to complete BMT construction. The primary contribution of this paper resides in the second part, in which a new tree node selection criterion is developed. The new criterion is formulated by using spectral variance and class label cues in order to better describe intra-segment homogeneity. To validate the proposed strategy, four multispectral scenes captured by two Chinese satellites, ZiYuan-3 and GaoFen-2, were adopted for segmentation experiments. The results indicated that our method out-performed some state-of-the-art algorithms in terms of segmentation accuracy.

This paper proposes an efficient paddy field mapping method using object-based image analysis and a bitemporal data set acquired by Landsat-8 Operational Land Imager. In the proposed approach, image segmentation is the first step and its quality has a serious impact on the accuracy of paddy field classification. In order to improve segmentation quality, a new segmentation algorithm based on a frequently used method, fractal net evolution approach, is developed, with improvement mainly in merging criteria. In order to automate the process of scale parameter determination, an unsupervised scale selection method is utilized to determine the optimal scale parameter for the proposed image segmentation approach. After segmentation, four types of object-based features including geometric, spectral, textural, and contextual information are extracted and input into the subsequent classification procedure. By using a random forest classifier, paddy fields and nonpaddy fields are separated. The proposed image segmentation method and the final classification result are both quantitatively evaluated. Our segmentation method outperformed two popular algorithms according to three supervised evaluation criteria. The classification result with overall accuracy of 91.00% and kappa statistic of 0.82 validated the effectiveness of the proposed framework. Further analysis on feature importance indicated that spectral features made the most contribution as compared to the other three types of object-based features.

In remote sensing, active learning (AL) is considered to be an effective solution to the problem of producing sufficient classification accuracy with a limited number of training samples. Though this field has been extensively studied, most papers exist in the pixel-based paradigm. In object-based image analysis (OBIA), AL has been comparatively less studied. This paper aims to propose a new AL method for selecting object-based samples. The proposed AL method solves the problem of how to identify the most informative segment-samples so that classification performance can be optimized. The advantage of this algorithm is that informativeness can be estimated by using various object-based features. The new approach has three key steps. First, a series of one-against-one binary random forest (RF) classifiers are initialized by using a small initial training set. This strategy allows for the estimation of the classification uncertainty in great detail. Second, each tested sample is processed by using the binary RFs, and a classification uncertainty value that can reflect informativeness is derived. Third, the samples with high uncertainty values are selected and then labeled by a supervisor. They are subsequently added into the training set, based on which the binary RFs are re-trained for the next iteration. The whole procedure is iterated until a stopping criterion is met. To validate the proposed method, three pairs of multi-spectral remote sensing images with different landscape patterns were used in this experiment. The results indicate that the proposed method can outperform other state-of-the-art AL methods. To be more specific, the highest overall accuracies for the three datasets were all obtained by using the proposed AL method, and the values were 88.32%, 85.77%, and 93.12% for “T1,” “T2,” and “T3,” respectively. Furthermore, since object-based features have a serious impact on the performance of AL, eight combinations of four feature types are investigated. The results show that the best feature combination is different for the three datasets due to the variation of the feature separability.

This is an article in the field of mineral processing engineering. The comprehensive recovery test of silver, gold and copper bearing sulfide concentrate was carried out. With isobutyl xanthate and sodium diethyl dithiocarbamate as a combined collector, calcium oxide as the depressant, 2# oil as a frother, which produced a concentrate approaching 16.66% Cu grade at 86.96% recovery, Au content of 278.95 g/t at 75.56% Au recovery and Ag content of 1848.74 g/t at 78.55% Ag recovery by a flotation process consisting of one stage of roughing，three stages of cleaning and two stages of scavenging was used to collect lead minerals. The comprehensive recovery of copper from sulfur concentrate has been effectively realized. At the same time, gold and silver are well enriched in copper concentrate, which is convenient for recovery and utilization, and the mineral processing index is ideal.

Abstract Leakage is one of the most serious challenges for the safe production of high‐pressure gas wells for its high risks, including abnormal annular pressure, natural gas accumulation, and environment pollution, but available methods can hardly accurately measure the leakage type and depth, which are the key parameters for the rigless leakage repair and risk assessment. Therefore, this paper proposes a method to measure the leakage based on the characteristics, which combines qualitative and quantitative measurement together. Qualitative measurement considers the annular pressure, tubing pressure, liquid level, cement quality, and workover history. Quantitative measurement is determined by noise logging, electromagnetic logging, pressure logging, and temperature logging. The logging should be optimized according to the qualitative measurement. The method was successfully applied in high‐pressure gas well belonging to Tarim Oilfield. Two potential leakage types are provided based on the annular pressure, liquid level, cement quality, and workover history, including tubing leakage and linger hanger leakage. Based on the potential leakage types, the pressure difference, logging devices string, stopping length, and time are optimized to make the engineering logging reliable. Through measurement, two leakage points are found in tubing string. One is tubing body crack at the depth of 2724 m and the other is tubing thread leakage at the depth of 5211.7 m, which well matches the production data.

In this paper, the temperature field variation and cumulative oil production rate over time with fracture number, fracture width and gas injection flow rate were investigated using Fluent software. A mathematical model of heat transfer within oil shale and a three-dimensional mathematical model of its in situ heating and fracturing were established. The simulation results showed that Model II had the highest oil shale heating rate at different fracture numbers, being 26.32% higher than that of Model I. When exploring fracture width, Model I completed all fractures in the oil shale region in 190 days. Model IV was 15 days slower than Model I with the heating rate being 7.89% lower, while Model V was 10 days faster than Model I with the heating rate being 5.26% higher. Increasing the fracture width of the oil shale region appropriately could help to increase the oil shale in-situ heating and fracturing rate. Considering gas injection flow rate, the higher the gas flow rate, the faster the increase of the oil shale area temperature and the shorter the time to reach the 10-day peak oil production rate. The peak was also larger and the fracturing of the oil shale area took place more quickly.

Abstract Background Genetic mutations of IKZF1 have been frequently delineated in B‐lineage acute leukaemia (B‐ALL) but rarely elucidated in acute myeloid leukaemia (AML). IKZF1 mutations confer a poor prognosis in AML, and hotspot mutations of IKZF1, N159Y and N159S tend to occur in B‐ALL and AML respectively. However, the pathogenesis of IKZF1 N159S in AML and IKZF1 lineage susceptibility are largely unknown. Methods The genetic and clinical characteristics of IKZF1‐mutated AML patients were evaluated. Multi‐omics analysis and functional assays were performed in vitro using IKZF1 mutations knock‐in AML cell lines. Results 23 (4.84%) small sequence variants of IKZF1 were identified in 475 newly diagnosed AML (non‐M3) patients. Based on RNA sequencing, three classes of IKZF1‐related AML were defined, including 9 patients (39.13%) with IKZF1 N159S mutations, 10 (43.47%) with CEBPA mutations and 4 others (17.39%). IKZF1 N159S may define a unique subgroup with higher HOXA/B expression and native B‐cell immune fractions. Gene expression data of multiple knock‐in cell lines indicate that the lymphocyte differentiation‐related MME and CD44 kept high expression in IKZF1 N159Y but were downregulated in N159S. CUT&TAG sequencing showed that IKZF1 N159S reshaped the binding profiles of IKZF1. Integration analysis suggested that the pathogenesis of IKZF1 N159S may depend on the deregulation of several cofactors, such as oncogenic MYC and CPNE7 targets. Conclusions Collectively, we dissected the molecular spectrum and clinical features of IKZF1‐related AML, which may promote an in‐depth understanding of the pathogenesis, lineage susceptibility and clinical research of AML.

We study efficient solution methods for stochastic eigenvalue problems arising from discretization of self-adjoint PDEs with random data, where the underlying operators depend linearly on the random parameters. With the stochastic Galerkin approach, the solutions are represented as generalized polynomial chaos expansions. When these solutions can be approximated well by lowrank objects, we introduce a low-rank variant of the inverse subspace iteration algorithm for computing one or several minimal eigenvalues and corresponding eigenvectors of parameter-dependent matrices. In the algorithm, the iterates are approximated by low-rank matrices, which leads to significant cost savings. The algorithm is tested on two benchmark problems: a stochastic diffusion problem with some poorly separated eigenvalues and an operator derived from a discrete stochastic Stokes problem whose minimal eigenvalue is related to the inf-sup stability constant. Numerical experiments show that the low-rank algorithm produces accurate solutions compared to the Monte Carlo method, and it uses much less computational time than the original algorithm without low-rank approximation. [ABSTRACT FROM AUTHOR]

A design idea of fidelity sampling cylinder while drilling based on surface nitrogen precharging and supplemented by downhole pressurization was proposed, and the working mode and optimization method of sampling parameters were discussed. The nitrogen chamber in the sampling cylinder functions as an energy storage air cushion, which can supplement the pressure loss caused by temperature change in the sampling process to some extent. The downhole pressurization is to press the sample into the sample chamber as soon as possible, and further increase the pressure of sample to make up for the pressure that the nitrogen chamber cannot provide. Through the analysis of working mode of the sampling fidelity cylinder, the non-ideal gas state equation was used to deduce and calculate the optimal values of fidelity parameters such as pre-charged nitrogen pressure, downhole pressurization amount and sampling volume according to whether the bubble point pressure of the sampling fluid was known and on-site emergency sampling situation. Besides, the influences of ground temperature on fidelity parameters were analyzed, and corresponding correction methods were put forward. The research shows that the fidelity sampling cylinder while drilling can effectively improve the fidelity of the sample. When the formation fluid sample reaches the surface, it can basically ensure that the sample does not change in physical phase state and keeps the same chemical components in the underground formation.

Abstract In marine fossil energy production, annular pressure buildup (APB) caused by temperature increase is a widely concerned problem and needs to be solved urgently in subsea wells. To study and predict APB and avoid consequent safety accidents and economic losses, a novel model is proposed to predict APB considering the effect of cement. The model has a wider application range by considering the dynamic change of thermal properties and mass change of the annular fluid. Laboratory experiments were conducted to verify the accuracy of the proposed model. Then a case study and mitigation evaluation were conducted to make the further analysis. Research results show that cement has a significant effect on APB, ignoring the cement may bring potential danger. The height of cement affects APB differently when the top of cement (TOC) is above and below the previous casing shoes. Making the TOC below the previous casing shoe could provide a leakage surface for annular fluid. It is an effective method for APB mitigation but a balance needs to be struck between wellbore integrity and mitigation effect. Gas or foam injection is another effective method to mitigate APB. The necessary gas volume varies with the annular pressure and initial gas pressure and needs to be determined according to the actual condition, especially in deepwater wells.

Carbapenem-resistant Klebsiella pneumoniae (CRKP) seriously threaten the efficacy of modern medicine with a high associated mortality rate and unprecedented transmission rate. In this study, we isolated a clinical K. pneumoniae strain DY1928 harboring blaNDM-1 from a neonate with blood infection. Antimicrobial susceptibility testing indicated that DY1928 was resistant to various antimicrobial agents, including meropenem, imipenem, ceftriaxone, cefotaxime, ceftazidime, cefepime, piperacillin-tazobactam, and amoxicillin-clavulanate. S1 nuclease-pulsed field gel electrophoresis (S1-PFGE), southern blot and conjugation experiment revealed that the blaNDM-1 gene was located on a conjugative plasmid of IncA/C2 type with a 147.9 kb length. Whole-genome sequencing showed that there was a conservative structure sequence (blaNDM-1-ble-trpF-dsbD) located downstream of the blaNDM-1 gene. Multilocus sequence typing (MLST) classified DY1928 as ST25, which was a hypervirulent K. pneumoniae type. Phylogenetic analysis of genomic data from all ST25 K. pneumoniae strains available in the NCBI database suggested that all blaNDM-1 positive strains were isolated in China and had clinical origins. A mouse bloodstream infection model was constructed to test the virulence of DY1928, and 11 K. pneumoniae strains homologous to DY1928 were isolated from the feces of infected mice. Moreover, we found that DY1928 had a tendency to flow from the blood into the intestine in mice and caused multiple organ damage. To our knowledge, this is the first study to report an infection caused by blaNDM-1-positive ST25 K. pneumoniae in the neonatal unit. Our findings indicated that stricter surveillance and more effective actions were needed to reduce the risk of disseminating such K. pneumoniae strains in clinical settings, especially in neonatal wards.

We study a multigrid method for solving large linear systems of equations with tensor product structure. Such systems are obtained from stochastic finite element discretization of stochastic partial differential equations such as the steady-state discusion problem with random coeffcients. When the variance in the problem is not too large, the solution can be well approximated by a low-rank object. In the proposed multigrid algorithm, the matrix iterates are truncated to low rank to reduce memory requirements and computational effort. The method is proved convergent with an analytic error bound. Numerical experiments show its effectiveness in solving the Galerkin systems compared to the original multigrid solver, especially when the number of degrees of freedom associated with the spatial discretization is large. [ABSTRACT FROM AUTHOR]

In this research, the pump-probe imaging technology and the time-resolved transmittance measurements are used to investigate the transient time scale of the oxidation area, the dewetted melt pool, and the ablation-induced mass transport, and their dependence on the incident laser. Moreover, the evolution time of various morphologies have been studied through a certain delay time from 10 to 30 ns with different dynamic processes. Meanwhile, the underlying formation mechanism and the phase change pathway have been studied. In this regard, a nanosecond (ns) pulsed laser with 532 nm wavelength was used to fabricate nanostructures on the surface of the thin Copper (Cu) films. The observations confirmed that the oxidation process of Cu plays a significant role in the mechanism of laser fluence-dependent oxidation, melting, and ablation dynamics. The results revealed that the different characteristics of the Cu film are related to the incident laser pulse energy. Furthermore, the experimental evidence was in good agreement with the SEM images and the Raman spectra. It is found that the Cu2O surface-rim with a central hole could be fabricated with the laser fluences above the ablation threshold. Although, for the laser fluences below the ablation threshold, the Cu2O phase can be formed in the central region.

Wireless networks have been generating a plethora of unstructured and highly-correlated big data with hidden anomalies. The anomalies may bring inaccurate predictions of network behaviors, which further lead to inefficient system designs such as proactive caching placement. Current Random Matrix Theory (RMT) approaches are unable to detect hidden anomalies with a satisfying tolerance of data correlation. We present a novel data Decomposition aided Random Matrix Theory (DC-RMT) framework, which enables a real-time anomaly detection of large scale multi-dimensional and highly-correlated data. The detection results show that the proposed DC-RMT methodology can detect anomalies with an accuracy of 28 times better than RMT applied without data decomposition. The prediction results present a 6 times higher accuracy than data with anomaly, which will facilitate the identification of regions of interests, and contribute to the improvement of resource allocation efficiency and user QoE.

Physical layer security (PLS) is a promising technology to enhance security performance of wireless communication systems, while the analyzing of PLS abnormal user detection is an important aspect of PLS research. Considering random matrix theory (RMT) is a time-efficient and theory-mature method, we will utilize RMT to analyze the abnormal user detection problem from the perspective of physical layer data analysis, where the carrier frequency offset (CFO) data is used as an indicator for abnormal user detection. Specially, the ring law theory and empirical spectral analysis of RMT are adopted for the analysis of CFO data, which is time-efficient and can be implanted in other detection methods. The proposed abnormal user detection method can provide a guidance for the appropriate choice of security enhancement technologies, so as to improve the utilization efficiency of different PLS technologies.

A novel complex absorbent composed of polyethylene glycol 200 (PEG200) and ionic liquids (ILs) was prepared for the absorption of volatile organic compounds (VOCs) such as dichloromethane (DCM) and benzene. We prepared complex absorbents composed of [EMIM][Cl], [BMIM][Cl], [HMIM][Cl], [BMIM][BF4], [BMIM][PF6], [BMIM][NTF2], and PEG200, respectively, and studied the absorption properties of these six complex absorbents for DCM and benzene. The results show that under the optimized situation, the absorptivity of [HMIM][Cl]–PEG200 complex absorbent for DCM is 85.46% in the first 5 min, and 87.15% for benzene. No obvious decay in the absorptivity of [HMIM][Cl]–PEG200 for DCM and benzene was observed in five cycles, indicating an impressive regeneration performance. Furthermore, the mechanism of ionic liquid absorption for VOC is explored by thermodynamic analysis and quantum chemical calculations. The theoretical calculation results show that the [HMIM][Cl]–DCM interaction is stronger than the [HMIM][Cl]–benzene interaction, which is consistent with the results of the absorption experiment. Moreover, the strong hydrogen bonds can be formed between both [HMIM][Cl]–DCM and [HMIM][Cl]–benzene.

In the offshore industry, especially heavy oil thermal recovery wells, due to the great temperature difference between the low-temperature seawater and high-temperature heavy oil, it is easy to cause the temperature increase of annular fluid in the operation process which will result in the annular pressure buildup phenomenon (APB). The increase of annulus pressure may lead to the failure of the casing and wellbore integrity, which will seriously affect the normal production and lead to great economic loss. In order to study the formation of APB and provide a basis for the field operation design, a radial full-size physical experiment of APB was carried out in this work and an annular pressure prediction model in the presence of dissolved gas was proposed based on the experimental results. The verification and comparison analyses of the full-liquid model and the dissolved gas model were conducted with the experimental data. Furthermore, the sensitivity analysis of the influence of the dissolved gas volume fraction and casing deformation on APB was carried out. The results show that the prediction results calculated by the dissolved gas model are in good agreement with the experimental data and the prediction accuracy is higher than that of the full-liquid model. When the annular dissolved gas volume fraction is less than 0.1%, the full-liquid model can be used to simplify and approximate calculations. Ignoring casing deformation will produce prediction error in each annulus, which means this simplification should be used with extreme caution. This work provides a valuable experimental reference for the study of APB, as well as a novel model for APB prediction in the field.