Bo Dang

Real-time monitoring of sand production is of great significance for disaster prediction, control of sand production, and optimization of production in offshore heavy oil wells. The design and location determination of sensors in real-time monitoring systems are particularly important. In this article, a real-time monitoring sensor for sand signals is designed by combining piezoelectric ceramics with acoustic wedge materials. Using the embedded boundary method to simulate the motion process of sand and gravel particles in the fluid. The trajectory of solid particles is obtained by solving Newton's Equations of motion. Conduct three-dimensional finite element simulation analysis on the pressure field of sand particles moving inside the vertical, bending, and horizontal straight section of the pipe, and determine the placement position of the sensor as the bottom layer of the bent pipe. Through extensive laboratory experiments, it has been found that the frequency of the signal is concentrated in the range of 70kHz to 500kHz. Real time sand production monitoring has been carried out in heavy oil wells such as GOGDXXX-X5 and GOX-XX-XX6 in Shengli Oil Field. Theoretical analysis and early laboratory test verification are consistent with field test results.

This paper proposes a dual-band receiver-transmitter partially reflective metasurface (RTPRM) based Fabry-Pérot resonator antenna (FPRA) for generating dual circularly polarized (CP) dual-beam radiations in low frequency (LF) and high frequency (HF) simultaneously based on spin-decoupled methods. The dual-band RTPRM consists of top-layer gradient square patches and bottom-layer periodic square patches, which are separated by metal ground and connected by metal probes. Integrating dual-band RTPRM with dual-polarized feed, the FPRA will generate LP resonance inside the resonator cavity, and its polarization state will change from LP to dual-CP in LF and HF by arranging top-layer square patches of the RTPRM in the specific gradient when the electromagnetic wave propagates to the outside of the resonator cavity. Finally, the FPRA is designed, simulated, and fabricated for verifying the performance of dual-band dual-CP dual-beam radiation. The proposed design, performing the beam splitting and polarization conversion in LF and HF simultaneously, should pave the way for building up more advanced FPRA for the specific radiating manipulations of electromagnetic fields in radar and satellite communications.

Borehole electromagnetic (EM) technology facilitates rapid and accurate underground geophysical prospecting. However, analytical solutions of the Helmholtz equation involving a layered cylindrical borehole model couple the spatial position information of sensors with the target information, which tends to distort the measurement signals obtained when multiple sensors are used to improve the signal-to-noise ratio (SNR). Moreover, applying Gauss-Legendre quadrature approximation to convert the spatially coupled sensor and target terms into a product between the target vector and an observation matrix containing the spatial position information of each sensor array element leads to an ill-posed problem, while applications of the compressive-sensing (CS) technique to address this ill-posed problem have failed to address equally serious problems associated with signal quality and the lack of signal processing to enhance the SNR. The present work addresses these issues by introducing a sparsity constraint based on the dimensionality reduction property of the CS technique to optimize the sparsity of the target vector containing the target information, which provides a solution to the ill-posed problem obtained when borehole EM measurement signals are spatially decoupled via Gauss-Legendre quadrature approximation, while simultaneously enhancing the SNR obtained from multiple sensors. The ability of the proposed CS optimization model to solve the ill-posed reconstruction problem is established based on the results of sparsity analysis for the target vector and correlation analysis for the observation matrix of a standard case as an example, and the effectiveness of the proposed approach is demonstrated based on the results of simulations and experiments.

As the final crucial line of defense against well blowout accidents, the implementation of the relief well scheme facilitates the swiftest possible rescue of the target well, thereby minimizing economic losses and enhancing production efficiency. Among them, the relative distance and orientation determination between the relief well and the accident well becomes the key to the rapid intersection and connection of the two wells. However, the inaccessibility of the accident well due to safety factors makes it challenging to accurately determine the relative position of the two wells. The current injection method has now been proved to be able to achieve relief well detection, and some signal processing methods are necessary for optimizing the detection performance. In the process of signal characterization, analytical methods show many advantages due to their direct relevance to the principles of electromagnetic theory. To provide an effective theoretical basis for the optimization of relief well detection methods, this paper proposes an analytical solution to the corresponding electromagnetic field distribution for current injection detection scheme. Based on the principle of the current injection detection, the signal model is constructed, and the electromagnetic distribution expression is deduced. Then, the adaptability of the analytical solution obtained under different physical parameters is investigated. Moreover, the reliability and validity of the proposed analytical solution are validated by comparing the results with those obtained from finite element numerical simulations, thus providing a theoretical basis for subsequent signal processing and method optimization.

Borehole pulsed eddy-current (PEC) systems based on uniform linear multicoil arrays (ULMAs) perform efficient nondestructive evaluations (NDEs) of metal casings. However, the limited physical space of the borehole restricts the degrees of freedom (DoFs) of ULMAs to be less than the number of constraints, which leads to the difficulty of compensating for the differences in signals acquired by different receivers with different transmitting-to-receiving distances (TRDs), and thus limits the effectiveness of the ULMA system. To solve this problem, this paper proposes sparse linear constraint minimum variance (S-LCMV) for NDEs of downhole casings employing ULMAs. By transforming and characterizing the original PEC signal, it was observed that the signal power dramatically decreased with increasing Legendre polynomial stage, confirming that the signal was sparsely distributed over the Gauss–Legendre stages. Using this property, the S-LCMV cost function with reduced constraints was constructed to provide enough DoFs to accurately calculate the weight coefficients, thus improving the detection performance. The effectiveness of the proposed method was verified through field experiments on an 8-element ULMA installed in a borehole PEC system for NDEs of oil-well casings. The results demonstrate that the proposed method could improve the weighting effect by reducing the number of constraints by 70% while ensuring the approximation accuracy, which effectively improved the signal-to-noise ratio of the measured signals and reduced the computational cost by about 87.9%.

Gas–liquid counter-current flow in vertical annulus is involved in several industrial fields such as petroleum engineering. For example, in coalbed methane wells with liquid pump drainage, obtaining the real-time flow rate of gas–liquid two-phase in the annulus is crucial for the development management of coalbed methane wells. However, due to complex flow conditions, this demand is difficult to achieve through traditional flow metering means. Therefore, this paper proposes a flow prediction method based on multi-group differential pressure signals and machine learning technology. Air-water two-phase flow experiments were carried out on a vertical annulus pipeline with inner and outer diameters of 75 mm/125 mm and adjustable eccentricity. The probability density function and power spectrum density of 3 groups of differential pressure signals collected at different height intervals in the annulus were used as model inputs. A gas–liquid two-phase flow prediction model was constructed based on the artificial neural network model, and the model hyper-parameters were optimized using Bayesian optimization. The results show that on the 440-group test dataset under the combined conditions of air superficial velocity of 0.06–5.04 m s⁻¹, water superficial velocity of 0.03–0.25 m s⁻¹, and pipeline eccentricity of 0, 0.25, 0.5, 0.75, 1, etc. This model can achieve average prediction errors of 9.12% and 29.34% for gas and water flow rates respectively. This method can be applied to the non-throttling, non-invasive measurement of phase flow rate under the condition of annulus air-liquid counter-current flow.