Abstract The development of unconventional oil and gas resources fundamentally relies on multi-stage hydraulic fracturing for horizontal wells. Operators also concerned deep reservoirs and those with complex in-situ stress fields. Concurrently, the large-scale fracturing operations significantly increased, leading to increasingly severe issues of casing damage and deformation encountered in the field. Existing instruments such as multi-finger caliper tools and downhole video cameras cannot meet the stringent requirements for wellbore measurement during fracturing. Coiled-tubing (CT) operations or other traditional intervention technology are widely used for deploying Bottom Hole Assembly (BHA), which involves significant investment and operational costs. An alternative wellbore inspection solution to above issues was proposed in this article. This solution integrates both high-frequency wellhead pressure sensors and electromagnetic-based measuring assembly using wireline and hydraulic pumping process. Surface pressure gauges can be quickly connected to the wellhead with maximum sampling frequency of 4000 Hz. Through performing cepstrum and spectral analytics on acquired pressure-wave signals, key characteristic extraction is enabled to assess wellbore integrity. Novel electromagnetic (EM)-based inspection BHA incorporates a magneto-resistive sensors array, effectively probing magnetic-field variations along the casing wellbore. One model for magnetic-field signal processing was provided, which quantitatively characterize wellbore integrity issues such as casing deformation, damage and corrosion. One 45-stage fracturing operation with 288-cluster perforations was conducted in one shale-oil well to validate this solution. Analysis of the water-hammer wave and pressure-drop dynamics after pump shutdown for one certain stage indicated poor fracture propagation as well as damage to the hydrocarbon reservoir. Comprehensive analysis demonstrated a high probability of casing deformation caused by complicated formations. For the another field case, casing deformation and fracture interference always occurred at shale-gas pads. The operator preventively conducted EM-based inspection towards BHA pumping failure and stuck trouble. It was indicated that suspected casing bending occurred in the 4210-4250 m interval and has no negative impact on wireline-used pump operations. Two industrial pilots successfully validated the low-cost and efficient-response solution, which contributes to better wellbore integrity and decision making during operations.

Flexible pressure sensors are critical in electronic skin, healthcare monitoring, and robotics. In this work, a flexible pressure sensor based on PDMS/MWCNTs/ZnO with three-dimensional porous structures is designed. Results demonstrate that the sensor upper detection limit reaches 266 kPa, with a resistance change sensitivity of 0.73 kPa-1 in the high-pressure region. This performance is attributed to the synergistic response between the tunneling junction effect of the porous sponge structure and the multi-level sensitive units, overcoming the detection range limitations of single-structure sensors. Additionally, the designed grid-patterned array-structured electrodes significantly increase the contact points between the electrodes and the hierarchical structure, enhancing signal transmission efficiency. This further improves signal stability and the signal-to-noise ratio, thereby boosting the overall sensor performance. To meet the requirements of multifunctional sensors, PVA/ MWCNTs/Graphene temperature-sensitive layer is introduced. This temperature sensor demonstrates an exceptionally high maximum temperature coefficient of resistance (TCR) of-0.068 degrees C-1, outstanding linearity (R2 = 0.9925), and extends measurement range up to 210 degrees C, exhibiting superior temperature-sensing characteristics. This work advances the design of high-performance, scalable, and low-cost flexible electronics, expanding the potential of porous materials in sensing applications.

Capacitive tomography offers significant advantages such as visualization and non-invasiveness, demonstrating broad application potential in petrochemical and aerospace fields. Due to the strong nonlinearity and ill-posed nature of its inverse problem, traditional algorithms reconstructing manifold images commonly suffer from issues like detail loss and edge blurring. This paper proposes Attention UNet++ (AttnUNet++), a nested encoder-decoder network integrating a convolutional attention mechanism. Based on the UNet++ backbone architecture, it incorporates a dedicated capacitive modality encoder and embeds convolutional attention blocks (CBAM) within nested convolutional layers. Channel attention filters critical feature channels, while spatial attention focuses on core flow regions, enhancing the model's sensitivity to subtle flow variations. A static/dynamic flow pattern hybrid dataset was constructed via finite element electrostatic field modeling and coupled electrostatic field-flow field simulations, and comparative experiments were conducted against traditional algorithms and deep learning benchmark models under different signal-to-noise ratio (SNR) conditions. Results demonstrate that under noise-free conditions, the AttnUNet++ model achieves an RMSE of 0.0985 and an SSIM of 0.9664. Compared to the standard UNet++ model, this represents a 41.66% reduction in RMSE and a 13.16% improvement in SSIM. Industrial field validation further confirms that this method accurately reconstructs the structural features of bubble flow, annular flow, core flow, and stratified flow, providing a highly robust imaging solution for real-time monitoring of industrial multiphase flows.

As cluster wells have become increasingly widely used in oil production, anti-collision while drilling technology, as a key technology in cluster well development, plays a crucial role in preventing the drill bit of the well being drilled from colliding with the casing of existing wells during the oilfield drilling stage. To solve the while-drilling anti-collision problem, this paper proposes a dual-coil model for well anti-collision detection under arbitrary attitudes. The method takes advantage of the signal difference received by two upper and lower receiving coils symmetrically distributed about the central receiving coil. Based on such signal difference, the included angle between the casing of existing drilled wells and the ongoing drilling well can be calculated and identified, which enables while-drilling anti-collision detection for cluster wells under arbitrary attitudes. To verify the effectiveness of the proposed method, a downhole while-drilling anti-collision model is established by using COMSOL. The signal responses and characteristics of receiving coils arranged at different positions around the drill pipe are systematically analyzed. The results show that the proposed dual-coil structure can accurately identify the three-dimensional spatial relationship between adjacent wells and achieve reliable while-drilling anti-collision performance. It is proved that this method can effectively assist field operators in reasonably planning wellbore obstacle avoidance schemes.

The structural integrity of oil and gas well casings is directly related to production safety and environmental risk prevention and control. However, under the coupled effects of high temperature, high pressure, chemical corrosion, and complex in-situ stress, it is highly prone to induce hidden damages such as pipe wall thickness reduction and microcrack propagation. Traditional damage detection methods based on fixed thresholds have significant limitations under complex downhole noise interference, often leading to an uncontrollable increase in the false alarm rate, which severely restricts detection reliability. To address this issue, this study proposes a variational mode decomposition-constant false alarm rate (VMD-CFAR) joint detection framework: First, the original logging signals are subjected to adaptive frequency band decomposition via VMD to suppress background noise interference; then, the optimally selected intrinsic mode function (IMF) components are input into multi-type CFAR detectors to achieve threshold decision-making with dynamic background perception. Through systematic simulation experiments, the combined performance of different IMF components and CFAR algorithms is quantitatively analyzed, and the optimal VMD-CFAR parameterization scheme is finally determined.

This paper proposes a novel borehole to airborne survey mode for electromagnetic method by integrating the advantages of borehole excitation and airborne observation, which is applicable to the exploration of oil and gas reservoirs. The new approach is expected to offer potential exploration benefits such as large depth penetration, wide coverage area, high precision, and efficiency. Currently, the transient electromagnetic method (TEM) in the borehole to airborne survey mode has not been implemented domestically or internationally, lacking theoretical guidance for equipment development and exploration operations. This paper focuses on the analysis of electromagnetic response characteristics of the borehole to airborne TEM using the vertical electric source in the case of the vertical well condition. The characteristics including induced current diffusion, spatial distribution of multi-component electromagnetic responses, and signal attenuation at different measuring points, are investigated. The study identifies the optimal electromagnetic field components for observation, discusses the technical challenges and feasibility of detection equipment. In summary, the findings of this paper provide essential theoretical groundwork for advancing the new method in terms of detection equipment, operational techniques, data processing, and interpretation.

We propose a quad-polarization multi-beam folded transmitarray (QPMBFTA) by integrating dual-linearly polarized transmission metasurface (DLPTM) and polarization conversion metasurface (PCM) in Ku-band. The DLPTM is a multi-layer structure alternately stacked by square metal patches and annular metal slots, which has the ability to realize transmission and conversion of different polarized waves. The PCM is employed to provide proper phase distributions for reducing the profile height of the QPMBFTA. By integrating PCM and DLPTM, the QPMBFTA is capable of realizing quad-polarization multi-beam radiation with uniform energy distribution by independently manipulating the phase distribution of DLPTM in the x-direction and y-direction based on phase weighting method and aperture field superposition method. Finally, the QPMBFTA is fabricated and measured experimentally, and the tested results show the peak gains of XP, YP, LHCP and RHCP are 17.5dBi at 13.8GHz, 16.9 dBi at 13.9 GHz, 17.5 dBic at 13.9GHz, and 17.0 dBic at 14.0GHz, and the 3dB axial ratio bandwidths are 12.9-14.5 GHz for LHCP and 13.0-14.3 GHz for RHCP. Our proposed design that possesses quad-polarization multi-beams radiation performance should pave the way for multichannel wireless communication system to improve the channel capacity.

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.

Semi-airborne transient electromagnetic method (SATEM) is an innovative geophysical exploration technique with high efficiency, low cost and strong adaptability to complex terrains, particularly suitable for China's diverse geological conditions. Due to the complex electromagnetic detection theory and large volume of observational data associated with SATEM, there is a critical demand for reliable inversion method with higher efficiency to obtain subsurface geoelectric structure. Therefore, this study introduces supervised descent learning technique in machine learning, integrating it with Gauss Newton method to form a fast and practical inversion scheme for SATEM data. The proposed inversion framework consists of three stages: offline training, online prediction and model modification. During the offline training, the average descending direction of implicit model characteristics is obtained by integrating prior information into training set. In the online prediction, the parameters of geoelectric model are reconstructed rapidly by using physical modeling function and descending direction obtained by training. Subsequently, the predicted models are modified by Gauss Newton method to minimize data residuals further. Synthetic and field data examples demonstrate that the hybrid inversion method effectively combines the strengths of both techniques. Specifically, the inversion efficiency significantly surpasses that achieved by employing the Gauss Newton method alone, while the accuracy of the inversion results exceeds those obtained solely through the supervised descent method. Moreover, the model modification process ensures the reliability of the inversion scheme by reprocessing any failed predicted models, thereby enhancing the overall robustness and applicability of the method.

Single phase grounding faults occur frequently in distribution networks. The challenge of fault line selection arises due to the weak and indistinct characteristics of the fault signals. To solve this problem, a single-phase grounding fault line selection method based on the Marine Predators Algorithm (MPA) optimized Variational Mode Decomposition (VMD) and multi-scale dispersion entropy is proposed in this paper. Firstly, the zero-sequence current signals from each line are collected by simulating the fault point. Secondly, the Marine Predators Algorithm is utilized to optimize and obtain the best parameters for decomposing the zero-sequence current signals using VMD. Subsequently, the multi-scale dispersion entropy values of the decomposed zero-sequence current components for each line are calculated to construct feature vectors. Finally, the faulty line is identified by analyzing the modulus values of these feature vectors. Experimental results demonstrate that the proposed method can accurately select the faulty line under various complex operating conditions, exhibiting high accuracy and robustness.

Aiming at the difficulty of detecting casing dislocation in the middle and late stages of oil and gas field development, this paper conducts three-dimensional modeling and simulation of dislocated wells based on the transient electromagnetic method. Based on the theory of downhole casing damage detection using transient electromagnetics, a three-dimensional detection model of dislocated wells is established with COMSOL. The influences of radial displacement, axial displacement, dislocation orientation, and probe eccentricity on electromagnetic response signals are systematically analyzed, providing a theoretical basis for accurately determining the location, type, and orientation of casing dislocation.

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.

TMR sensors, based on magnetic tunnel junctions and offering advantages in planar sensitivity, are promising for magnetic field detection in confined spaces. However, the hysteresis of their ferromagnetic materials causes a nonmonotonic H-B relationship, forming a hysteresis loop that affects detection accuracy. To analyze this, a combined experimental and simulation approach was used. Helmholtz coils generated a uniform external magnetic field ($\mathrm{H} \propto \mathrm{I}$) to test the ALT021 TMR sensor, with sensor output voltage and coil current synchronously recorded. MATLAB was used for data processing and hysteresis curve plotting. The results show that the ALT021 sensor exhibits clear hysteresis, while the MAG03 sensor displays a nearly ideal linear B-H relationship, highlighting TMR's hysteresis issue. This study lays the groundwork for performance optimization and error calibration of TMR sensors, emphasizing the role of hysteresis analysis in enhancing high-precision magnetic sensing systems.

Aiming at the problem of downhole instrument movement, the position of the signal collected by the probe is shifted backward relative to the launching position, a signal synthesis processing method based on the pulsed eddy current method with multiple cycles and multiple sampling moments is proposed. COMSOL multiphysics field simulation software is used to establish a downhole casing damage model, analyze different moments of magnetic field changes around the defects, and use MATLAB to carry out the signal synthesis processing of multiple sampling moments in multiple cycles to compensate for the influence of the instrument's speed, so as to achieve an accurate match between the measured data and the actual depth of the stratum and to improve the accuracy of the inversion, which can effectively solve the problem of signal distortion due to the movement of the instrument.

To understand the flow characteristics and kinetic modelling of oil–water two-phase flow(OWTPF), this paper employs thermal array sensors to identify the flow pattern (FP) in OWTPF horizontal pipelines. Firstly, the thermal array sensor installation was constructed. Secondly, the time-varying characteristics of multi-feature signals were qualitatively analyzed using the adaptive optimal kernel time–frequency representation (AOK-TFR) method and unthresholded recurrence plot (URP) method. Finally, the sparrow search algorithm-support vector machine (SSA-SVM) machine learning algorithm is used for quantitative prediction of multi-feature classification, which leads to high prediction accuracy. The experiment proves that the correct rate of the thermal method in recognizing six types of OWTPF is above 91%, the correct rate of five types of flow is above 97.33%. This paper adopts the thermal method to identify the multiphase FP, which is a major innovation in this paper and also lays the foundation for recognizing the flow characteristics of multiphase flow.

This study investigates the gas–liquid two-phase counter-current flow through a vertical annulus, a phenomenon prevalent across numerous industrial fields. The presence of an inner pipe and varying degrees of eccentricity between the inner and outer pipes often blur the clear demarcation of flow regime boundaries. To address this, we designed a vertical annulus with adjustable eccentricity (outer and inner diameters of 125 mm and 75 mm, respectively). We conducted gas–liquid counter-current flow experiments under specific conditions: gas superficial velocity ranging from 0.06 to 5.04 m/s, liquid superficial velocity from 0.01 to 0.25 m/s, and five levels of eccentricity (e = 0, 0.25, 0.5, 0.75, 1). We collected differential pressure data at two distinct height distances (DP1: 50 mm and DP2: 1000 mm). We used vectors, composed of both the probability density functions (PDFs) of the differential pressure signals and the power spectral density (PSD) reduced via Principal Component Analysis, as features. Using the CFDP clustering algorithm—based on local density—we clustered the flow regimes of the experimental data, thereby achieving an objective and consistent identification of the flow regime of gas–liquid two-phase counter-current flow in a vertical annulus. Our analysis reveals that for DP1, the main differences in the PSD of various flow regimes occur within the 0.5–1 Hz range. Among the three flow regimes involved, the slug flow exhibits the highest power intensity, followed by the bubbly flow, with the churn flow having the least. In terms of differential pressure distribution, the bubbly and churn flows have a concentrated distribution, while the slug flow is more dispersed. For DP2, the PSD differences primarily exist within the 0.5–2 Hz range. The churn flow has the highest power intensity, followed by the slug flow, with the bubbly flow being the weakest. Here, the bubbly flow's differential pressure distribution is concentrated, while the slug and churn flows are more dispersed. Based on the results of the flow regime classification, we generated a flow regime map and analyzed the influence of annulus eccentricity on the flow regime. We found that in most cases, pipe eccentricity does not significantly affect the flow regime. However, in the transition region—such as the bubbly to slug flow transition zone—flows with medium eccentricity values (e = 0.5, 0.75) are more likely to transition to slug flow. We compared the visual recognition results of flow regimes with the clustering results. 4.04% of the total samples showed different results from visual recognition and clustering, primarily located in the flow regime transition area. Since visually distinguishing flow regimes in these areas is typically challenging, our methodology offers an objective classification approach for gas–liquid two-phase counter-current flow in a vertical annulus.

The emerging hybrid analog-digital system has drawn significant attention for its potential use in future millimeter-wave communications. In this paper, the problem of direction of arrival (DOA) estimation in hybrid analog-digital system with one-bit analog-digital converter (ADC) is considered. By virtue of arcsine law, it is shown that the autocorrelation function (ACF) of the array signal can be approximated reconstructed by the one-bit signal. With the obtained ACF of the array signal, the eigen-decomposition is implemented and the signal subspace and noise subspace can be extracted to realize DOA estimation. Simulation experiments verify the performance of the proposed DOA estimation method in hybrid analog-digital system with one-bit ADC. It is found in this paper that using the one-bit ADC in hybrid analog-digital system can attain good DOA estimation performance with reducing power consumption and hardware cost.

During the measurement of multiphase flow in low yield oil wells, the liquid volume will vary with the operating characteristics of the pumping unit. Using the pulsating characteristics of the up and down strokes of a pumping unit, the flow rate is measured when there is a flow rate on the up stroke, and the water content is measured when the fluid is stationary on the down stroke. In this paper, the heat transfer method is used to measure the water content of the oil water mixture during the down stroke process. At this time, the water content can be expressed as the instantaneous water content of the oil well. Firstly, the feasibility of measuring water content using heat transfer method is demonstrated theoretically, and then the temperature change of the heating probe PT300 is simulated. Finally, the actual temperature of PT300 is measured experimentally. Comparing the experimental value with the simulation value, the calculated measurement error is within 1.27%, which indicates that the heat transfer method is feasible for measuring water content. Using the same single sensor to measure oil water two-phase flow using the pulsation characteristics of the up and down strokes of a pumping unit is a major innovation in this paper. And lays a foundation for the detection of multiphase flow using heat transfer methods. The successful implementation of the text heat transfer method for measuring water content has broken the previous situation of multiple sensor detection, simplified the structure of multiphase flow instruments, and extended the life of the instrument.