Detection of lost circulation in drilling wells employing sensor data using machine learning technique

Khodnenko, Ivan; Ivanov, Sergey V.; Perets, D. S.; Simonov, Maksim

doi:10.1016/j.procs.2019.08.206

Cited by 10 publications

(5 citation statements)

References 6 publications

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“…The machine learning prediction method is a new direction rising in recent years. Its purpose is to use a machine learning model to predict lost circulation and further put forward prevention suggestions and remedial decisions for drilling engineers according to the predicted lost circulation information, such as lost circulation type and the estimated amount of lost circulation. − Jiang et al has combined the wellbore temperature transient pressure coupling model established with unscented Kalman filter to predict the location and the amount of lost circulation . Abbas et al developed a new model using an artificial neural network (ANN) and a support vector machine (SVM) to predict the lost circulation of vertical and deviated wells .…”

Section: Introductionmentioning

confidence: 99%

Research on an Lost Circulation Zone Location Method Based on Transient Pressure Wave

et al. 2021

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Accurately identifying the location of loss zone after lost circulation is the key to subsequent plugging operation. In view of the difficulty of identifying the location of lost circulation zone, a method of identifying the location of loss zone by transient pressure wave signal is proposed. When lost circulation occurs, transient back pressure is applied to the wellhead at the surface choke manifold to produce transient pressure wave. The transient pressure wave propagates downward from the wellhead. The propagation process of transient pressure wave in an annulus system is analyzed, and the position of loss zone is determined according to the change of pressure signal at the choke manifold. Based on the simulation of this method, relevant experiments are also carried out. Aiming at the problem of excessive noise of the pressure wave signal collected in the experiment, variational modal decomposition (VMD) is used to decompose the signal into multiple band-limited intrinsic mode function (BIMF) components. Combined with a Hilbert spectrum, the time–frequency characteristics and energy distribution of each BIMF component are analyzed in turn. The main frequency component is selected to reconstruct the signal to achieve the denoising effect. On this basis, a wavelet modulus maxima method is used to decompose the denoised signal, extract the characteristic points of the signal, identify the loss circulation information in the signal, and then identify the thief zone position by a time–domain method. Through experimental verification, the existence of loss zone will affect the change trend of pressure wave; a VMD–wavelet modulus maxima algorithm can effectively remove the noise of the pressure wave signal and locate the pressure change point. The experimental recognition error range of this method is 0.10–9.22%, which has certain guiding significance for field application.

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Section: Introductionmentioning

confidence: 99%

Research on an Lost Circulation Zone Location Method Based on Transient Pressure Wave

et al. 2021

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“…Furthermore, it is necessary to choose and fine-tune the deep learning method to determine the target value, controlling possible overfitting. Good examples of this approach are known [56] when the only operational data from oil wells collected initially for other process purposes made it possible to develop an algorithm for determining lost circulations, diagnostics for which by traditional methods are known to be slow and pricey. In either event the demonstrated approach lays the foundation for modern method development for bioreactor bubble flow analysis, which is the core for understanding and controlling both its productivity and safety, and ultimately economic efficiency when applied on an industrial scale of biotechnological production.…”

Section: Methodology and Results Of Approach Comparisonmentioning

confidence: 99%

Assessing the Mass Transfer Coefficient in Jet Bioreactors with Classical Computer Vision Methods and Neural Networks Algorithms

et al. 2023

Self Cite

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Development of energy-efficient and high-performance bioreactors requires progress in methods for assessing the key parameters of the biosynthesis process. With a wide variety of approaches and methods for determining the phase contact area in gas–liquid flows, the question of obtaining its accurate quantitative estimation remains open. Particularly challenging are the issues of getting information about the mass transfer coefficients instantly, as well as the development of predictive capabilities for the implementation of effective flow control in continuous fermentation both on the laboratory and industrial scales. Motivated by the opportunity to explore the possibility of applying classical and non-classical computer vision methods to the results of high-precision video records of bubble flows obtained during the experiment in the bioreactor vessel, we obtained a number of results presented in the paper. Characteristics of the bioreactor’s bubble flow were estimated first by classical computer vision (CCV) methods including an elliptic regression approach for single bubble boundaries selection and clustering, image transformation through a set of filters and developing an algorithm for separation of the overlapping bubbles. The application of the developed method for the entire video filming makes it possible to obtain parameter distributions and set dropout thresholds in order to obtain better estimates due to averaging. The developed CCV methodology was also tested and verified on a collected and labeled manual dataset. An onwards deep neural network (NN) approach was also applied, for instance the segmentation task, and has demonstrated certain advantages in terms of high segmentation resolution, while the classical one tends to be more speedy. Thus, in the current manuscript both advantages and disadvantages of the classical computer vision method (CCV) and neural network approach (NN) are discussed based on evaluation of bubbles’ number and their area defined. An approach to mass transfer coefficient estimation methodology in virtue of obtained results is also represented.

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“…The cross-sectional area of the annulus remains the same. Therefore, 3), the output impedance of the annular is shown in Equation ( 5) when there is no leakage in the annulus; when there is leakage in the annulus, the output impedance of the annulus is shown in Equation (6). Equations ( 5) and ( 6) are defined as the transfer functions of the wellbore annulus system…”

Section: Transient Pressure and Flow Transfer Model In The Annulusmentioning

confidence: 99%

“…For years, leakage prevention and plugging have been the research focus in drilling engineering. [4][5][6][7][8] The key to successful plugging is to quickly and accurately determine the location of the lost circulation layer, which is conducive to shortening the plugging time and reducing the total cost of drilling.…”

Section: Introductionmentioning

confidence: 99%

Research on spectral method of lost circulation layer located by transient pressure wave

Wang

Zhu

Lei

et al. 2023

Energy Science & Engineering

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Leakage in the wellbore annulus during drilling operations can affect normal production operations, resulting in a severe waste of resources and economic loss, so it is crucial to adopt a fast and effective leak identification method for subsequent plugging operations. For the complex problem of judging the location of the leakage layer, we proposed the method of excitation pressure wave to identify the location of the leakage layer. By analyzing the transfer of pressure waves within the annular pipe system and the pressure head response spectrum, the leak's location is identified based on the location of the resonance point and the change in resonance amplitude. The pressure wave signal contains too much noise. The variational mode decomposition (VMD) algorithm and the Hilbert joint spectrum were used to extract the main frequency components to reconstruct the signal to achieve the denoising effect. On this basis, the reconstructed signal is processed by fast Fourier transform (FFT) to obtain the pressure wave response spectrum, analyze the frequency domain features, and then determine the location of the leakage layer. The experimental results verify that: ① When a leak occurs in the wellbore annulus, the pressure wave will generate additional resonance points in the frequency domain due to the presence of the leak point. ② The combination of the VMD algorithm, FFT, and Hilbert joint spectrum can effectively remove the noise of the pressure wave signal. ③ The method effectively avoids the difficulty of identifying negative pressure waves in the time domain analysis of pressure wave signals. It can effectively locate the leaky layer in the frequency domain analysis. It is concluded that the principle of the method is feasible and has practical significance for field application.

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Detection of lost circulation in drilling wells employing sensor data using machine learning technique

Cited by 10 publications

References 6 publications

Research on an Lost Circulation Zone Location Method Based on Transient Pressure Wave

Research on an Lost Circulation Zone Location Method Based on Transient Pressure Wave

Assessing the Mass Transfer Coefficient in Jet Bioreactors with Classical Computer Vision Methods and Neural Networks Algorithms

Research on spectral method of lost circulation layer located by transient pressure wave

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