The main components of the Ordovician carbonate reservoirs in the Tahe Oilfield are paleokarst fracture-cavity paleo-channel systems formed by karstification. Detailed characterization of these paleokarst reservoirs is challenging because of heterogeneities in characteristics and strong vertical and lateral non-uniformities. Traditional seismic analysis methods are not able to solve the identification problem of such strongly heterogeneous reservoirs. Recent developments in seismic interpretation have heightened the need to describe the fracture-cavity structure of a paleo-channel with more accuracy. We propose a new prediction model for fracture-cavity carbonate reservoirs based on spectral decomposition and a waveform cluster. By the Matching Pursuit decomposition algorithm, the single-frequency data volumes are obtained. The specific frequency data volume that is the most sensitive to the reservoir is chosen based on seismic synthesis traces of well-logging data and geological interpretability. The waveform cluster is then applied to delineate the complex paleokarst systems, particularly the fracture-caves in the runoff zone. This method was applied to the area around Well T615 in the Tahe oilfield, and a paleokarst fracture-cavity system with strong heterogeneity in the runoff zone was delineated and characterized. The findings of this research provide insights for predicting other similar karst systems, such as karstic groundwater and karst hydrogeological systems.

Paleokarst reservoirs are the major type of the Ordovician carbonate reservoirs in the Tahe Oilfield. Due to the strong heterogeneity in distribution, it is a real challenge to detect the spatial distribution of paleokarst reservoirs, especially those deeply buried more than 5500 m in the Tahe area. Based on the abundant core samples, this paper first described the structure of paleocaves drilled by well. Second, after time–depth conversions, the results from drilled wells were tied to three-dimensional (3D) seismic datasets, and then the threshold of host rocks and caves in wave impedance were identified. Third, the seismic-scale mapping and visualization of the paleokarst reservoirs were achieved by tracing the distribution of paleocaves. This approach was applied in the well T403 area, and the structure of the paleokarst, especially the runoff zone, was interpreted. 3D structure and spatial distribution of the paleokarst system was demonstrated by plane, vertical, and 3D models. Additionally, according to the hydrology genetic relationships, the paleocaves in the runoff zone were divided into sinkholes, main channel, and branch channel. The approach of a 3D geophysical characterization of a deeply buried paleokarst system can be applicable to Tahe and other similar paleokarst oilfields, which will guide hydrocarbon exploration in paleokarst reservoirs.

As deep carbonate fracture-cavity paleokarst reservoirs are deeply buried and highly heterogeneous, and the responded seismic signals have weak amplitudes and low signal-to-noise ratios. Machine learning in seismic exploration provides a new perspective to solve the above problems, which is rapidly developing with compelling results. Applying machine learning algorithms directly on deep seismic signals or seismic attributes of deep carbonate fracture-cavity reservoirs without any prior knowledge constraints will result in wasted computation and reduce the accuracy. We propose a method of combining geological constraints and machine learning to describe deep carbonate fracture-cavity paleokarst reservoirs. By empirical mode decomposition, the time–frequency features of the seismic data are obtained and then a sensitive frequency is selected using geological prior constraints, which is input to fuzzy C-means cluster for characterizing the reservoir distribution. Application on Tahe oilfield data shows the potential of highlighting subtle geologic structures that might otherwise escape unnoticed by applying machine learning directly.

In recent years, the rapid advancements in Internet of Things boost the utilization of Wireless Sensor Network (WSN). Through WSN, a huge amount of various kinds of data can be acquired from different environments, which further facilitate us to harness the world we live. However, the error-prone and unpredictable natures of wireless links significantly impair the quality of data transmission and the utility of acquired data in WSN. Therefore, to solve this crucial problem, this paper focuses on data access control and hybrid transmission control of WSN. Based on the mathematical models of the two control operations, an intractable optimization problem is first formulated with numerous considerations, including data utility, energy consumption, network stability and data loss rate. Because of the complexity and intractability of the originally formulated problem, a Lyapunov function-based network optimization theory is utilized to transform and decompose it into three relatively simple subproblems. Stochastic Data Scheduling Mechanism (SDSM) is designed based on the solutions to the three subproblmes. The optimality and implementation of SDSM are also analyzed. Finally, the performance of SDSM is demonstrated through extensive evaluations.

The random error of Micro Electro Mechanical Systems (MEMS) accelerometer is one of the important factors affecting its measurement accuracy. This study begins to adjust for the random error of a MEMS accelerometer in order to completely increase its use value. Firstly, Allan variance method is introduced to analyze and quantify each noise term causing random error. The linear relationship of sample output data is determined through a series of steps such as data preprocessing, randomness test, identification model, judgment order and calculation parameters. Accordingly, the Kalman filter is made to lessen the random drift error noise component that affects the measurement accuracy of MEMS accelerometers.

The moving-Coil geophone is a vibration sensor widely used in geophysical exploration and structural health monitoring; fatigue damage to its constituent components limits its service life. This study develops a structural model and conducts drop impact simulations to compare the stress responses of different components during impact. Results indicate that the maximum stress is concentrated at the rib roots of the elastic element. Multi-angle 1m drop simulations were performed for two commercial elastic elements, B314 and 5H. For B314, the maximum stress at various angles predominantly appears at the inner-ring rib roots, whereas for 5H it is mostly at the outer-ring rib roots; the outer-ring ribs determine performance parameters of the geophones. Under the 60° condition, the peak equivalent stress of 5H reaches 2.13 GPa, markedly higher than that of B314 at the same angle. Field testing involving 5,000 drops from 1 m revealed deformation and fracture at the rib root locations of the elastic element; the 5H is more prone to damage at the outer-ring rib roots, while the B314 exhibits better impact resistance-consistent with the simulations. This study provides theoretical guidance for the impact-resistant structural design of geophones.