Magnetic Leakage Internal Detection Device and Series-Parallel Detection Method for Small Diameter Ferromagnetic Spiral Heat Exchanger Tubes

Based on the calculation of magnetic circuit of the detection probe, this article designs a kind of magnetic leakage detection device and series-parallel detection method of small diameter spiral heat exchange tube to realize the safety detection of small diameter spiral heat exchange tube. The detection device includes a detection probe and a probe drive mechanism, which drives the detection probe to move in the spiral heat exchange tube. The detection probe includes an intermediate connector, a magnetizer which is arranged coaxially at both ends of the intermediate connector in turn, a steel ball support body and an end connector. The designed detection probes are mixed in series and parallel, and multiple detection probes are put together in series to be placed in a spiral heat exchange tube to form a group of detection probes in series. Then, multiple groups of detection probes in series are placed in each spiral heat exchange pipe, forming a series-parallel combination, which can detect multiple spiral heat exchange pipes at the same time and improve the detection efficiency greatly

Dynamic coupling modelling and application case analysis of high-slip motors and pumping units

To solve the issues and difficulties in the high-coupling modelling of beam pumping units and high-slip motors, external characteristic experiments of high-slip motors were performed where the external database and characteristic correlation equations of the motors were obtained through data regression analysis. Based on the analysis of the kinematics, dynamics and driving characteristics of the beam pumping unit, a fully coupled mathematical model of a motor, pumping unit, sucker rod and oil pump was established. The differential pumping equation system of the pumping unit used a cyclic iteration method to solve the problem of high coupling among the motor, pumping unit, sucker rod and the pumping pump. The model was verified by experimental data of field l pumping wells. Theoretical calculations and experimental tests showed that the soft characteristic of the high-slip motor can reduce the peak suspension load of the sucker rod, peak net torque of the gearbox and peak power of the motor. In addition, the results show that the soft characteristic can also decrease the high-frequency fluctuation of the motor power curve and the torque curve of the gearbox. The highslip motor can improve the smoothness and safety of the pumping well system

Review of variable speed drive technology in beam pumping units for energy-saving

Beam pumping units have been widely used in oilfields worldwide due to its simple structure, strong field adaptability, and convenient maintenance. Different energy-saving technologies have also been broadly applied in various beam pumping units. Among these energy-saving methods, the variable speed drive is one of the most acceptable techniques in the oil and gas industry. In this paper, the energy-saving technology of variable speed drives is discussed in detail for beam pumping units pointing out existing difficulties and current research status in kinematics. Three application examples of a variable speed drive in Daqing oilfield, the largest oilfield in China, is shown

Reservoir Permeability Prediction Based on Analogy and Machine Learning Methods: Field Cases in DLG Block of Jing’an Oilfield, China

AbstractReservoir permeability, generally determined by experimental or well testing methods, is an essential parameter in the oil and gas field development. In this paper, we present a novel analogy and machine learning method to predict reservoir permeability. Firstly, the core test and production data of other 24 blocks (analog blocks) are counted according to the DLG block (target block) of Jing’an Oilfield, and the permeability analogy parameters including porosity, shale content, reservoir thickness, oil saturation, liquid production, and production pressure difference are optimized by Pearson and principal component analysis. Then, the fuzzy matter element method is used to calculate the similarity between the target block and analog blocks. According to the similarity calculation results, reservoir permeability of DLG block is predicted by reservoir engineering method (the relationship between core permeability and porosity of QK-D7 in similar blocks) and machine learning method (random forest, gradient boosting decision tree, light gradient boosting machine, and categorical boosting). By comparing the prediction accuracy of the two methods through the evaluation index determination coefficient (R2) and root mean square error (RMSE), the CatBoost model has higher accuracy in predicting reservoir permeability, with R2 of 0.951 and RMSE of 0.139. Finally, the CatBoost model is selected to predict reservoir permeability of 121 oil wells in the DLG block. This work uses simple logging and production data to quickly and accurately predict reservoir permeability without coring and testing. At the same time, the prediction results are well applied to the formulation of DLG block development technology strategy, which provides a new idea for the application of machine learning to predict oilfield parameters

Combination Therapy Strategy of Quorum Quenching Enzyme and Quorum Sensing Inhibitor in Suppressing Multiple Quorum Sensing Pathways of P. <i>aeruginosa</i>

Abstract The threat of antibiotic resistant bacteria has called for alternative antimicrobial strategies that would mitigate the increase of classical resistance mechanism. Many bacteria employ quorum sensing (QS) to govern the production of virulence factors and formation of drug-resistant biofilms. Targeting the mechanism of QS has proven to be a functional alternative to conventional antibiotic control of infections. However, the presence of multiple QS systems in individual bacterial species poses a challenge to this approach. Quorum sensing inhibitors (QSI) and quorum quenching enzymes (QQE) have been both investigated for their QS interfering capabilities. Here, we first simulated the combination effect of QQE and QSI in blocking bacterial QS. The effect was next validated by experiments using AiiA as QQE and G1 as QSI on Pseudomonas aeruginosa LasR/I and RhlR/I QS circuits. Combination of QQE and QSI almost completely blocked the P. aeruginosa las and rhl QS systems. Our findings provide a potential chemical biology application strategy for bacterial QS disruption

Cloning and Characterization of Genes Involved in Nostoxanthin Biosynthesis of Sphingomonas elodea ATCC 31461

Most Sphingomonas species synthesize the yellow carotenoid nostoxanthin. However, the carotenoid biosynthetic pathway of these species remains unclear. In this study, we cloned and characterized a carotenoid biosynthesis gene cluster containing four carotenogenic genes (crtG, crtY, crtI and crtB) and a β-carotene hydroxylase gene (crtZ) located outside the cluster, from the gellan-gum producing bacterium Sphingomonas elodea ATCC 31461. Each of these genes was inactivated, and the biochemical function of each gene was confirmed based on chromatographic and spectroscopic analysis of the intermediates accumulated in the knockout mutants. Moreover, the crtG gene encoding the 2,2′-β-hydroxylase and the crtZ gene encoding the β-carotene hydroxylase, both responsible for hydroxylation of β-carotene, were confirmed by complementation studies using Escherichia coli producing different carotenoids. Expression of crtG in zeaxanthin and β-carotene accumulating E. coli cells resulted in the formation of nostoxanthin and 2,2′-dihydroxy-β-carotene, respectively. Based on these results, a biochemical pathway for synthesis of nostoxanthin in S. elodea ATCC 31461 is proposed

A Continuous-Time-Based MILP Model for Production and Transportation Scheduling in Nonpipelined Wells in Low-Permeability Oil Fields

AbstractThe marginal wells in low-permeability oilfields are characterized by small storage size, scattered distribution, large regional span, low production, intermittent production, etc. The production mode of these wells is nonpipeline mode. In our previous work (Zhang et al., 2019), a novel mixed-integer linear programming (MILP) model using a discrete-time representation was presented for the operation scheduling of nonpipelined wells. However, too many discretization time points are required to ensure the accuracy of the model. Even for moderately sized problems, computationally intractable models can arise. The present paper describes a new continuous-time representation method to reformulate this schedule optimization problem. By introducing the continuous-time representation, the binary variables are largely reduced. The solution effect for different model sizes is also investigated. When the model size increases to a certain degree, a feasible solution cannot be obtained within a limited time. The results of a case study originated from a real oilfield in China show that the continuous-time model requires less time to obtain the optimal solution compared to the discrete-time model. In details, considering a same scale problem, the solution based on the continuous-time model saves 52.25% of the time comparing with the discrete-time model. The comparison validates the new model’s superiority

Casing Damage Prediction Model Based on the Data-Driven Method

Casing damage caused by sand production in unconsolidated sandstone reservoirs often results in oil wells unable to produce normally. However, due to the complex mechanism of sheath damage caused by sand production, there is no more mature technology for predicting the risk of casing damage in advance. Data-driven method can better integrate various factors and use a large amount of historical data to solve complex classification prediction problems. In this paper, XGBoost and LightGBM algorithms are used to establish casing damage prediction models, and 13 model application experiments are carried out to optimize the set of casing damage factors. These two algorithms are used to calculate the feature importance of each factor and determine the final set of factors. The evaluation results of five key metrics show that both prediction models show good performance, and the prediction accuracy is 0.99 for the XGBoost model and 0.94 for the LightGBM model. Applying the established prediction model can determine reasonable range of the maximum daily liquid production of a single layer (Qlmax) to reduce the probability of casing damage. In addition, at certain Qlmax, increasing the perforation density can significantly reduce the probability of casing damage. Therefore, increasing the perforation density can achieve high production without causing casing damage

Offshore oil production planning optimization: An MINLP model considering well operation and flow assurance

With the increasing energy requirement and decreasing onshore reserves, offshore oil production has attracted increasing attention. A major challenge in offshore oil production is to minimize both the operational costs and risks; one of the major risks is anomalies in the flows. However, optimization methods to simultaneously consider well operation and flow assurance in operation planning have not been explored. In this paper, an integrated planning problem both considering well operation and flow assurance is reported. In particular, a multi-period mixed integer nonlinear programming (MINLP) model was proposed to minimize the total operation cost, taking into account of well production state, polymer flooding, energy consumption, platform inventory and flow assurance. By solving this integrated model, each well's working state, flow rates and chemicals injection rates can be optimally determined. The proposed model was applied to a case originated from a real-world offshore oil site and the results illustrate the effectiveness