Abstract The deep sidetrack wells of Tahe oilfield require to meet requirements of avoiding water, and drill to the leakage zone and collapse area of carboniferous system, two pressure systems need to be drilled, which traditional drilling fluids and chemical plugging method can't ensure drilling safety, and if running the conventional casing into the hole, drilling and completion face many difficulties because of following slim hole. All above making the old deeply areas remaining oil can't be liberated. Expandable casing technology is a new technology, which acts as intermediate casing to block complex formation during drilling. It can optimize the structure of the well bore, block abnormal high pressure layer, collapsed layer and leaking layer, providing a technical plan to drill successfully to the target for complex deep wells. According to the developing difficulties of F177.8mm casing deep sidetrack horizontal wells in Tahe oilfield, it produces a solution of expandable casing, analysis the operating difficulties of expandable casing, proposes an expandable casing technology solution, adopts unconventional diameter 130mm borehole drilling after expanded. It also describes in detail the drilling block application of expandable casing technology for the first time in the sidetrack horizontal well. Field application shows that expandable casing drilling technology program designed can solve the drilling problem of the deep sidetrack horizontal well of the Tahe oilfield, and gains good technical practice. The effect of application confirms that the expandable casing implementation can meet the drilling block and follow unconventional slim hole drilling requirements. In conclusion, the expandable casing drilling technology is the first time for technical casing of sidetrack horizontal wells, which proves the practicability of expandable casing drilling solution, and provides a new way to solve the development problems in Tahe oilfield. Meanwhile we successfully solves the unconventional diameter 130mm borehole drilling problem, using the non-standard small-diameter MWD instrument and non-standard diameter 89mm drill pipes for the first time, perfects slim hole drilling process. Furthermore, this application successfully provides the technical foundation of expandable casing technology in complex stratigraphic drilling.
A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.
Recent study shows that the accuracy of the k-shell method in determining node coreness in a spreading process is largely impacted due to the existence of core-like group, which has a large k-shell index but a low spreading efficiency. Based on analysis of the structure of core-like groups in real-world networks, we discover that nodes in the core-like group are mutually densely connected with very few out-leaving links from the group. By defining a measure of diffusion importance for each edge based on the number of out-leaving links of its both ends, we are able to identify redundant links in the spreading process, which have a relatively low diffusion importance but lead to form the locally densely connected core-like group. After filtering out the redundant links and applying the k-shell method to the residual network, we obtain a renewed coreness for each node which is a more accurate index to indicate its location importance and spreading influence in the original network. Moreover, we find that the performance of the ranking algorithms based on the renewed coreness are also greatly enhanced. Our findings help to more accurately decompose the network core structure and identify influential nodes in spreading processes.
Recent empirical studies have confirmed the key roles of complex contagion mechanisms such as memory, social reinforcement and decay effects in information diffusion and behavior spreading. Inspired by this fact, we here propose a new agent–based model to capture the whole picture of the joint action of the three mechanisms in information spreading, by quantifying the complex contagion mechanisms as stickiness and persistence and carry out extensive simulations of the model on various networks. By numerical simulations as well as theoretical analysis, we find that the stickiness of the message determines the critical dynamics of message diffusion on tree-like networks, whereas the persistence plays a decisive role on dense regular lattices. In either network, the greater persistence can effectively make the message more invasive. Of particular interest is that our research results renew our previous knowledge that messages can spread broader in networks with large clustering, which turns out to be only true when they can inform a non-zero fraction of the population in the limit of large system size.
Designing an efficient routing strategy is of great importance to alleviate traffic congestion in multilayer networks. In this work, we design an effective routing strategy for multilayer networks by comprehensively considering the roles of nodes' local structures in micro-level, as well as the macro-level differences in transmission speeds between different layers. Both numerical and analytical results indicate that our proposed routing strategy can reasonably redistribute the traffic load of the low speed layer to the high speed layer, and thus the traffic capacity of multilayer networks are significantly enhanced compared with the monolayer low speed networks. There is an optimal combination of macro- and micro-level control parameters at which can remarkably alleviate the congestion and thus maximize the traffic capacity for a given multilayer network. Moreover, we find that increasing the size and the average degree of the high speed layer can enhance the traffic capacity of multilayer networks more effectively. We finally verify that real-world network topology does not invalidate the results. The theoretical predictions agree well with the numerical simulations.
The structure of underlying contact network and the mobility of agents are two decisive factors for epidemic spreading in reality. Here, we study a model consisting of two coupled subpopulations with intra-structures that emphasizes both the contact structure and the recurrent mobility pattern of individuals simultaneously. We show that the coupling of the two subpopulations (via interconnections between them and round trips of individuals) makes the epidemic threshold in each subnetwork to be the same. Moreover, we find that the interconnection probability between two subpopulations and the travel rate are important factors for spreading dynamics. In particular, as a function of interconnection probability, the epidemic threshold in each subpopulation decreases monotonously, which enhances the risks of an epidemic. While the epidemic threshold displays a non-monotonic variation as travel rate increases. Moreover, the asymptotic infected density as a function of travel rate in each subpopulation behaves differently depending on the interconnection probability.
We consider the effects of time-varying packet generation rates in the performance of communication networks. The time variations could be a result of the patterns in human activities. As a model, we study the effects of a degree-dependent packet generation rate that includes a sinusoidal term. Applying a modified traffic awareness protocol (TAP) previously proposed for static packet generation rates to the present situation leads to an altered value of the optimization parameter, when compared to that obtained in the static case. To enhance the performance and to cope with the time-varying effects better, we propose a class of self-adjusting traffic awareness protocols that makes use of instantaneous traffic information beyond that included in the modified TAP. Two special cases that make use of global and local information, respectively, are studied. Comparing results of our proposal schemes with the modified TAP, it is shown that the present self-adjusting schemes perform more effectively.
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