Journal of Applied Geophysics最新文献

Characterizing the subsurface distribution of crude oil after a spill in a coastal environment is challenging due to variations in the soil and fluid properties. In situ sampling is limited in capturing the lateral and vertical migration of the crude oil within the vadose and saturated zones. This study presents a laboratory sandbox framework used to assess the effectiveness of electrical resistivity imaging for investigating the spatiotemporal distribution of crude oil in coastal sandy soils. A sandbox with dimensions L = 240 cm, W = 60 cm, and H = 60 cm was constructed using a 10 mm plexiglass and filled to a 40 cm height with 2 mm medium to fine-grained sand. At each stage of the experiment, 20 kg of sand was mixed with 1 l of water to create moist sand, after which the mixture was flushed over 12 h to remove suspended fine particles. Both saturated and unsaturated conditions were simulated by setting the water table at 10 cm and draining a fully saturated system overnight. Two liters of crude oil were spilled and monitored for 30 h. A surface array of 98 electrodes, with a unit electrode spacing of 2 cm, was installed along two transects 12 cm apart. Resistivity measurements were collected using a dipole-dipole array before, during, and after the simulated crude oil spill. The time-lapse electrical resistivity results revealed an initial gravity-induced vertical migration under both saturated and unsaturated conditions; over time, lateral migration of crude oil became apparent. In the saturated zone, there was a noticeable reduction in the percentage difference in resistivity from 700 % to 400 % after 24 h, depicting a spatial and temporal redistribution of the crude oil attributed to variation in pore geometry. This highlights the sensitivity of electrical resistivity measurements to subtle but measurable anisotropy in the distribution of soil pores. Overall, electrical resistivity proved successful in imaging the non-ideal behavior of crude oil pollutants and the associated spatial changes in the pore-size distribution of subsurface sediments.

The geological structure of the goaf overlying rock is complex, a consequence of coal mining that has modified the original stratified structure of the sedimentary strata. To enhance the accuracy of microseismic source location in such intricate geological formations, a wave velocity model for the “three zones” goaf was constructed based on natural divisions within the strata using Snell's law and assuming a homogeneous medium. The model took into account the effects of rock deformation and fracture development, enabling the derivation of formulas for microseismic wave propagation path and travel time calculation. Additionally, the concept of equivalent wave velocity was defined. An indoor simulation test using similar materials was conducted to establish a geological model of the goaf. By comparing the errors between the theoretical and measured values of equivalent wave velocity, assessing the locating effects before and after implementing the wave velocity model of the goaf, and verifying the feasibility of the model, it was demonstrated that establishing a wave velocity model based on the characteristics of the strata structure was crucial for improving the accuracy of the microseismic source location. Notably, as the propagation path of microseismic waves in the goaf increased, the equivalent wave velocity decreased. The wave velocity structure in the goaf exhibited nonuniformity, with the relative error between the theoretical and measured values of equivalent wave velocity being limited to 10 %. The incorporation of this established wave velocity model into the location method resulted in a substantial 58.57 % increase in locating accuracy.

Identification of stratigraphic boundaries is a fundamental task in the seismic interpretation of oil and gas reservoir locations. When employing deep learning techniques to interpret stratigraphic boundaries, insufficient training data and sample imbalances are common challenges affecting model training. In regions with intricate geological structural changes, conventional deep-learning segmentation algorithms, such as U-Net often struggle to accurately capture the features of complex local structures. To address these limitations, we propose a model integration approach that incorporates global and local uneven-type stratigraphic data augmentation to enhance the accuracy of stratigraphic boundary identification in uneven-type regions. To address the problems of class imbalance and insufficient complex variation samples, we adopted a strategy of separately training global and local data and integrating predictions, thereby handling the disparity between uneven-type and flat-type stratigraphic data during model training. By testing the Netherlands F3 dataset with sparsely labeled profiles, it was demonstrated that the proposed method can effectively improve the delineation accuracy of stratigraphic boundaries compared to the benchmark U-Net model.

Salt dome detection in seismic images is a critical aspect of hydrocarbon exploration and production. Salt domes are subsurface structures formed from the accumulation of salt deposits and can trap oil and gas reservoirs. Seismic imaging techniques are used to visualize the subsurface structures and identify the presence of salt domes. Historically, the process of detecting salt domes in seismic images was done manually, which was time-consuming and required the input of domain experts. However, in recent years, automated methods using seismic attributes and machine learning algorithms have been developed to improve the efficiency of salt dome detection. Deep learning-based methods have shown promising results in salt body segmentation, and several techniques have been proposed in recent years. This review examines recent deep-learning architectures for salt body segmentation in seismic images, offering a concise overview of the various models proposed in the literature. It delves into established benchmark datasets, highlighting potential limitations and emphasizing the importance of data quality for robust models. It explores performance evaluation metrics used in the literature to capture a more comprehensive picture of segmentation performance. This paper identifies several promising areas for further research and development opportunities to refine and enhance the current state-of-the-art salt body segmentation in seismic images. This comprehensive analysis provides a valuable roadmap for researchers and practitioners interested in understanding how deep learning can be utilized for salt body classification in seismic exploration.

We applied two machine learning models to detect earthquakes from records observed with seismometers temporarily installed on a volcanic island. The two models are based on different principles: one regards seismic waveforms as images, using a convolutional neural network (CNN) to determine the first arrival times of P-waves, S-waves. The other model regards seismic waveforms as series data. The model processes seismic waveforms as data in a specific order of noise, P-wave, and S-wave, similar to natural language.

The purpose of this study is to present the results of using machine learning first arrival times identification models with two principles for noisy seismic waveforms, caused by sea waves and strong winds in volcanic islands, and to evaluate the effectiveness of machine learning models for noisy observation records.

We created a Confusion Matrix using first arrival times determined by an expert and evaluated the detection performance of these two models using some metrics of the matrix. Additionally, we assessed accuracy of the model-identified first arrival times by generating a frequency distribution of the difference from the expert's detecting time.

The study discovered that the model treating data as series had superior detection ability for noisy data compared to the one treating data as images and the accuracy of the first arrival time detection was also better for the series data model too.

We compared the results obtained on this island with those obtained at the permanent station, which is considered to have less noise interference, described in Mousavi et al., 2020. It was found that the difference in detection ability between the two models is slight for data obtained at permanent stations with low noise interference, but that the difference in detection ability between the algorithms of the two models is significant in noisy environments.

This research was dedicated to examining regions rich in schist rock near the Singhbhum shear zone in Ghatshila, Jharkhand. The aim was to detect schist rocks that were sheared, fractured, and highly foliated in both shallow and deeper layers. Electrical resistivity tomography (ERT) measurements were conducted using a 2 × 21 electrode configuration, with nine profiles covering inter-electrode spacings ranging from 3 m to 10 m. A recently developed software called Anisotropic DC resistivity Forward and Inverse (ADCFI) was employed to conduct 2D isotropic and anisotropic inversion of the collected data. The 2D interpreted sections along the profiles indicated non-continuous resistivity values at their intersections. Furthermore, areas demonstrating irregular resistivity values showcased anisotropy coefficients exceeding unity, indicating significant anisotropy in these particular zones. The irregular resistivity patterns additionally provided further evidence for the existence of substantial anisotropic behavior within the region.

The outcomes of the 2D anisotropic inversion conducted in Ghatshila unveiled significant anisotropy coefficients beyond a depth of 20 m. This depth correlated with the presence of layers containing chalcopyrite, suggesting stratified deposition originating from a volcanogenic setting. Furthermore, the existence of schist rocks in shallow borehole depths contributed to the observed anisotropic tendencies. Notably, regions with heightened anisotropy demonstrated thicker layers in the isotropic section compared to the anisotropic section across all profiles. Anisotropy coefficient values derived from areas abundant in schist rock in Ghatshila were approximately 2.00. This substantial anisotropy was attributed to the inherent foliation and schistosity of the dominant rock type, namely schist.

While there is a connection between petrophysical logs and reservoir porosity, finding analytical solutions for this relationship is still difficult. This paper presents a novel approach for forecasting porosity and lithofacies by using a convolutional neural network (CNN) model in conjunction with a bi-directional long short-term memory (BLSTM) network. The BLSTM network uses a self-organizing map (SOM) technique to form connections between input and destination data. The SOM is used to organize depth intervals with similar facies into four separate clusters, each exhibiting internal consistency in petrophysical parameters. The CNN is responsible for extracting spatial characteristics, while the BLSTM network gathers comprehensive spatiotemporal components, guaranteeing that the model accurately represents the spatiotemporal aspects of log data. The accuracy of the model was verified by analyzing simulation logging data. The findings indicate that the BLSTM network model successfully recovers significant characteristics from logging data, resulting in improved estimate accuracy. In addition, Facies-01 has lower gamma ray levels in comparison to other facies. Facies-01 is also suggestive of pristine sandstone formations, which are greatly sought as reservoir rocks. The BLSTM network model is effective in predicting physical characteristics of reservoirs, offering a new method for precise reservoir characterization parameter prediction.

Permeability is a critical parameter for evaluating reservoir properties, and accurate prediction is an important basis for identifying high-quality reservoirs and geological modeling. However, the strong heterogeneity, complex lithology and diagenesis in the reservoirs of this region pose a major challenge for the accurate assessment of reservoir permeability. In recent years, the use of machine learning (ML) to solve problems in geophysical well logging and related fields has gained much attention thanks to advances in data science and artificial intelligence. ML is any predictive algorithm or combination of algorithms that learns from data and makes predictions without being explicitly coded with a deterministic model. The most immediate example is deep neural networks (DNN) that are trained with data to minimize a cost function and make predictions. The tight reservoirs in the Chang 7 Member of the Ordos Basin host significant oil and gas resources and have recently emerged as the main focus of unconventional oil and gas exploration and development. In this work, we performed DNN-based permeability prediction for the tight reservoirs in the Ordos Basin area. From 19 well logs, we selected effective data points from 17 wells for DNN training after preprocessing and used the remaining two wells for testing. First, we trained the DNN with all collected parameters as inputs, resulting in permeability prediction R² values of 0.64 and 0.72 for the two wells, indicating a good fit. We then optimized the input parameters by performing a crossplot analysis between these parameters and the permeability. Using the same network structure (with all hyperparameters set the same), we trained the DNN again to obtain a new DNN-based model. The prediction results showed that removing input parameters with poor correlation to permeability improved the prediction accuracy with R² values of 0.70 and 0.87 for the two wells.

Attenuation is a main factor limiting the resolution of seismic data. Earth works as a low-pass filter, which has strong attenuation of the high-frequency data. The loss of high-frequency energy can be compensated by the inverse Q filtering strategy. However, this method will also increase the energy of random noise which limits its application. The inverse Q filtering algorithm also needs the Q-factor as the input parameter, which is not easy to obtain. In this paper, we proposed a three-stage process to correct the attenuation of poststack data. In the first stage, a robust structure-oriented filtering is applied to remove random noise while protecting the structure information to avoid high-frequency noise burst. In the second stage, the local centroid frequency shift (LCFS) method is used to estimate the Q factor along the seismic trace. This method combined shaping regularization and centroid frequency shift (CFS) method to improve the robustness and accuracy of Q estimation to some extent. The final stage is to apply a stable inverse Q-filtering. Synthetic and field data examples demonstrate that time-varying Q-value can be accurately estimated by using the local centroid frequency shift (LCFS) method, and the proposed workflow can compensate the attenuation without bursting of high-frequency random noise.

The uniaxial compressive strength is one of the most important basic parameters of rock, which is essential for surrounding rock stability analysis and support scheme design of underground engineering. At present, it is time consuming and costly to take a large amount of core samples for laboratory testing, and the mechanical properties of the cores may be affected by mining disturbance, which can easily lead to inaccurate result. The measurement while drilling (MWD) technology provides a new approach to solve the above challenge. The key to implementing this technology is to establish a correlation model between drilling parameters and rock mechanics parameters. Based on the characteristics of polycrystalline diamond compact (PDC) bits in drilling and rock breakage, this paper analyzes the mechanical state of the bit in breaking rock. A theoretical correlation model between the torque, feed force of the bit and the uniaxial compressive strength of the rock has been developed. To verify the accuracy of the theoretical model, the uniaxial compressive strength of five different types of rocks (red sandstone, green sandstone, limestone, marble and shale) was obtained through laboratory mechanical tests. The torque M_b, feed force F_b and other parameters in the drilling process of these five rocks were tested through the newly developed MWD test system. The correlation between the drilling parameters and the uniaxial compressive strength of rock was established. The results showed that the feed force F_b and torque M_b measured at five different types of rocks indicate a linear increasing trend with an increase in depth of cut h. Meanwhile, a strong linear relationship between the feed force F_b and torque M_b is evident. This paper proposes an MWD-based method to rapidly conduct the in-situ measurement of the uniaxial compressive strength of various rocks.