scholarly journals Research on the Cooperative Detection of Stochastic Resonance and Chaos for Weak SNR Signals in Measurement While Drilling

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3011
Author(s):  
Yi Yang ◽  
Fei Li ◽  
Nan Zhang ◽  
Aiqing Huo
Keyword(s):  
Stochastic Resonance ◽  
Scale Transformation ◽  
Attitude Measurement ◽  
Limit Condition ◽  
Duffing System ◽  
Detection Model ◽  
Frequency Detection ◽  
Measurement Signal ◽  
Measurement While Drilling ◽  
Well Trajectory

In the process of drilling, severe downhole vibration causes attitude measurement sensors to be erroneous; the errors will accumulate gradually during the inclination calculation. As a result, the ultimate well path could deviate away from the planned trajectory. In order to solve this problem, this paper utilized the stochastic resonance (SR) and chaos phase transition (CPT) produced by the second-order Duffing system to identify the frequency and estimate the parameters of the signal during measurement while drilling. Firstly, the idea of a variable-scale is introduced in order to reconstruct the frequency of the attitude measurement signal, and an SR frequency detection model based on a scale transformation Duffing system is established in order to meet the frequency limit condition of the SR. Then, an attitude measurement signal with a known frequency value is input into the Duffing chaos system, and the scale transformation is used again to make the frequency value meet the parameter requirement of chaos detection. Finally, two Duffing oscillators with different initial phases of their driving signal are combined in order to estimate the amplitude and phase parameters of the measurement signal by using their CPT characteristics. The results of the laboratory test and the field-drilling data demonstrated that the proposed algorithm has good immunity to the interference noise in the attitude measurement sensor, improving the solution accuracy of the inclination in a severe noise environment and thus ensuring the dynamic stability of the well trajectory.

An improved adaptive stochastic resonance with general scale transformation to extract high-frequency characteristics in strong noise

2018 ◽  
Vol 32 (15) ◽  
pp. 1850185 ◽  
Author(s):  
Dawen Huang ◽  
Jianhua Yang ◽  
Jingling Zhang ◽  
Houguang Liu
Keyword(s):  
Stochastic Resonance ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Scale Transformation ◽  
Weak Signal ◽  
Signal Features ◽  
Signal Characteristics ◽  
Periodic Signals ◽  
Sr Method ◽  
General Scale

The idea of general scale transformation is introduced in detail. Based on this idea, an improved adaptive stochastic resonance (SR) method is proposed to extract weak signal features. Different periodic signals are considered to verify the proposed method. Compared with the normalized scale transformation, the output signal-to-noise ratio (SNR) of the proposed method is increased to a greater extent. Further, the influences of some key parameters on the responses of the two methods are discussed minutely. Results show that the improved adaptive SR method with general scale transformation is obviously superior to the traditional normalized scale transformation that is used in the former literatures. For different noise intensities and time scales, the proposed approach can always obtain the optimal response of SR to enhance the weak signal characteristics.

scholarly journals Stochastic resonance of cascaded bistable duffing system

2013 ◽  
Vol 62 (7) ◽  
pp. 070503
Author(s):  
Lai Zhi-Hui ◽  
Leng Yong-Gang ◽  
Fan Sheng-Bo
Keyword(s):  
Stochastic Resonance ◽  
Duffing System

Secondary signal-induced large-parameter stochastic resonance for feature extraction of mechanical faults

2019 ◽  
Vol 33 (15) ◽  
pp. 1950157 ◽  
Author(s):  
Yunjiang Liu ◽  
Fuzhong Wang ◽  
Lu Liu ◽  
Yamin Zhu
Keyword(s):  
Feature Extraction ◽  
Stochastic Resonance ◽  
High Frequency ◽  
Low Frequency ◽  
Transformation Method ◽  
Scale Transformation ◽  
Large Parameter ◽  
Noise Energy ◽  
Mechanical Faults ◽  
System Output

Aiming to solve the problem that it is difficult to extract large parameter signals from a strong noise background, a novel method of large parameter stochastic resonance (SR) induced by a secondary signal is proposed. The SR mechanism of high-frequency signals is expounded by analyzing the density distribution curve. High-frequency signals are converted to low-frequency signals using the scale transformation method, and then large-parameter SR is induced by the secondary signal. Ultimately, the method is applied to the feature extraction of mechanical faults. Simulation and experimental results indicate that (i) the effect of SR induced by the secondary signal is significantly enhanced when the frequency of the secondary signal is twice that of the signals to be detected after the scale transformation; (ii) when the frequency of secondary signal is twice the maximum frequency of the signals to be detected after the scale transformation, choosing an appropriate amplitude of secondary signal can alleviate the problem that the noise energy is excessively concentrated in the low-frequency channel with regard to the extraction of two-frequency or three-frequency high-frequency signals; and (iii) by adding the secondary signal to the engineering example, the fault power spectrum value of system output is 101% higher than that without the secondary signal.

Significant Surge and Swab Offshore Brazil Induced by Rig Heave during Drillpipe Connections

2021 ◽  
pp. 1-8
Author(s):  
John-Morten Godhavn ◽  
Banzi Olorunju ◽  
Dmitri Gorski ◽  
Martin Kvernland ◽  
Mateus Sant`Ana ◽  
...  
Keyword(s):  
North Sea ◽  
Sampling Rate ◽  
Drilling Mud ◽  
Long Distance ◽  
Pressure Oscillations ◽  
The North ◽  
Order Of Magnitude ◽  
Measurement While Drilling ◽  
Well Trajectory ◽  
The North Sea

Summary In this paper, we describe measured and simulated downhole pressure variations (“surge and swab”) during drillpipe connections when drilling an ultradeepwater well offshore Brazil on Bacalhau (former Carcará) Field. Floating rig motion caused by waves and swell (“rig heave”) induces surge and swab when the drillstring is suspended in slips to make up or break a drillpipe connection and topside heave compensation is temporarily deactivated. This is a known issue in regions with harsh weather, such as the North Sea, where pressure oscillations of up to 20 bar have been reported during connections. Recorded downhole drilling data from Bacalhau Field reveals significant pressure oscillations downhole (in the same order of magnitude as in the North Sea) each time the drillstring was suspended in slips to make a connection in the subsalt 8½-in. section of the well. Mud losses were experienced around the same well depth, and they might have been caused by surge and swab. Measured surge and swab pressure variations have been reproduced in an advanced proprietary surge and swab simulator that considers rig heave, drillpipe elasticity, well friction, non-Newtonian drilling mud, well trajectory, and geometry. Moreover, findings in this paper suggest that surge and swab was in fact significantly higher than recorded by the measurement while drilling (MWD) tool. The true magnitude of surge and swab is not captured in the recorded MWD data due to low sampling frequency of the downhole pressure recording (one measurement every 6 seconds, a standard downhole pressure sampling rate used on many operations today). This work shows that surge and swab during drillpipe connections on floaters may challenge the available pressure window for some wells, even in regions with calm weather such as Brazil. Managed pressure drilling (MPD) is a technique that improves control of the downhole pressure. It is, however, not possible to compensate fast downhole pressure transients, such as heave-induced surge and swab, using MPD choke topside. This is due to the long distance between the choke and the bit, which translates into a time delay in the same order of magnitude as typical wave and heave periods. A downhole choke combined with continuous circulation is one of the potential solutions. Surge and swab during drillpipe connections can result in a loss or an influx and should be considered in the well planning phase when mud weight, section lengths, etc. are selected.

Experimental Analysis of Stochastic Resonance in a Duffing System

2002 ◽  
Vol 20 (1) ◽  
pp. 28-30 ◽  
Author(s):  
Wang Fu-Zhong ◽  
Chen Wei-Shi ◽  
Qin Guang-Rong ◽  
Guo De-Yong ◽  
Liu Jun-Ling
Keyword(s):  
Stochastic Resonance ◽  
Experimental Analysis ◽  
Duffing System

Adaptive Stochastic Resonance in Second-Order System with General Scale Transformation for Weak Feature Extraction and Its Application in Bearing Fault Diagnosis

2018 ◽  
Vol 17 (01) ◽  
pp. 1850009 ◽  
Author(s):  
Qiang Ma ◽  
Dawen Huang ◽  
Jianhua Yang
Keyword(s):  
Stochastic Resonance ◽  
Noise Intensity ◽  
Reference Value ◽  
Harmonic Signal ◽  
Second Order ◽  
Bistable System ◽  
Scale Transformation ◽  
Order System ◽  
Weak Signal ◽  
General Scale

The theory of general scale transformation (GST) is put forward and used in the second-order underdamped bistable system to extract weak signal features submerged into strong noise. An adaptive stochastic resonance (SR) with GST is proposed and realized by the quantum particle swarm optimization (QPSO) algorithm. The harmonic signal and experimental signal are considered to compare GST with normalized scale transformation (NST) in the second-order system. The results show that detection effect of the adaptive SR with GST is better than the NST SR. In addition, the output signal-to-noise ratio (SNR) is significantly improved in the GST method. Meanwhile, the dependence of the signal extraction efficiency on the noise intensity is researched. The output SNR is decreased with the increase of the noise intensity in two methods. However, the proposed method is always superior to the NST. Moreover, the superiority of the Brown particle oscillation in the single well is discussed. The proposed method has certain reference value in the extraction of the weak signal under the strong noise background.

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