Zhuwen Wang

Facing volatile oil prices, strict ecological regulations and complex geological conditions, traditional multi-attribute decision-making (MADM) methods for oilfield investment overemphasize economic benefits while ignoring non-economic indicators and attribute uncertainty. To solve these deficiencies, this paper proposes an improved MADM framework for oil and gas development investment decision-making. Firstly, a five-dimensional evaluation index system covering economic, technological, geological, ecological and social attributes is established. Secondly, interval-valued fuzzy numbers (IVFNs) and interval fuzzy analytic hierarchy process (IF-AHP) are adopted to quantify index weights and address decision uncertainty. Furthermore, grey relational analysis (GRA) and technique for order preference by similarity to ideal solution (TOPSIS) are combined to rank development schemes. A case study of six oilfield blocks in China verifies the reliability of the proposed framework. The results indicate that this method can effectively reduce decision bias and comprehensively characterize multidimensional attributes. This study provides a systematic decision-making tool for oilfield investment optimization and offers references for the sustainable development of oil and gas resources.

Fractures exhibit complex spatial structures and multiscale geometric characteristics, and their accurate characterization is fundamental to reservoir evaluation, fluid migration analysis, and rock mechanics. To address the limitations of single-scale local fractal methods in simultaneously capturing fracture details and global structures, as well as the dependence of supervised learning on labeled data, this study proposes an unsupervised fracture identification method integrating Complexity-Adaptive Box-Height Differential Box-Counting (CABH-DBC) with a self-organizing map (SOM). Local fractal features are extracted using fixed multiscale windows, while the box height along the gray-level dimension is adaptively refined according to the local grayscale standard deviation. The multiscale features are then fed into the SOM for clustering, with grayscale information assisting in fracture-cluster determination. Experiments on borehole image logs from ten depth intervals of the CCSD main borehole yield mean F1 and IoU values of 0.659 and 0.493, respectively. Compared with DBC-Kmeans, the proposed method improves F1 and IoU by 39.0% and 58.0%, respectively; compared with DBC-SOM, the strongest baseline in this study, the improvements are 16.6% and 24.8%. Ablation experiments further demonstrate the complementary contributions of complexity-adaptive box-height refinement, fixed multiscale fractal features, and SOM clustering.

Formation Microresistivity Scanner Imaging (FMI) plays a crucial role in identifying lithology, sedimentary structures, fractures, and reservoir evaluation. However, during the lithology identification process of FMI images relying on transfer learning networks, the limited dataset size of existing models and their relatively primitive architecture substantially compromise the accuracy of well-log interpretation results and practical production efficiency. This study employs the VGG-19 transfer learning model as its core framework to conduct preprocessing, feature extraction, and analysis of FMI well-log images from glutenite formations in an oilfield in Xinjiang, with the objective of achieving rapid and accurate intelligent identification and classification of formation lithology. Simultaneously, this paper emphasizes a systematic comparative analysis of the recognition performance between the VGG-19 model and existing models, such as GoogLeNet and Xception, to screen for the model exhibiting the strongest region-specific applicability. The study finds that lithology can be classified into five types based on physical structures and diagnostic criteria: gray glutenite, brown glutenite, fine sandstone, conglomerate, and mudstone. The research results demonstrate the VGG-19 model exhibits superior accuracy in identifying FMI images compared to the other two models; the VGG-19 model achieves a training accuracy of 99.64%, a loss value of 0.034, and a validation accuracy of 95.6%; the GoogLeNet model achieves a training accuracy of 96.1%, a loss value of 0.05615, and a validation accuracy of 90.38%; and the Xception model achieves a training accuracy of 91.3%, a loss value of 0.0713, and a validation accuracy of 87.15%. These findings are anticipated to provide a significant reference for the in-depth application of VGG-19 transfer learning in FMI well-log interpretation.

In the current context of the global energy landscape, China is facing a growing challenge in oil and gas exploration and development. It is difficult to evaluate the log data because of the lithological composition of igneous rocks, which displays an unparalleled degree of complexity and unpredictability. Against this backdrop, this study deploys advanced multifractal detrended fluctuation analysis (MF-DFA) to comprehensively analyze key parameters within igneous rock logging data, including natural gamma-ray logging, resistivity logging, compensated neutron logging, and acoustic logging. The results unequivocally demonstrate that these logging data possess distinct multifractal characteristics. This multifractality serves as a powerful tool to elucidate the inherent complexity, heterogeneity, and structural and property variations in igneous rocks caused by diverse geological processes and environmental changes during their formation and evolution, which is crucial for understanding the subsurface reservoir behavior. Subsequently, through a series of rearrangement sequences and the replacement sequence on the original logging data, we identify that the probability density function and long-range correlation are the fundamental sources of the observed multifractality. These findings contribute to a deeper theoretical understanding of the data-generating mechanisms within igneous rock formations. Finally, multifractal detrended cross-correlation analysis (MF-DCCA) is employed to explore the cross-correlations among different types of igneous rock logging data. We uncover correlations among different igneous rocks’ logging data. These parameters exhibit different properties. There are negative long-range correlations between natural gamma-ray logging and resistivity logging, natural gamma-ray logging and compensated neutron logging in basalt, and resistivity logging and compensated neutron logging in diabase. The logging data on other igneous rocks have long-range correlations. These correlation results are of great significance as they provide solid data support for the formulation of oil and gas exploration and development plans.

Shallow three-electrode laterolog is widely used in the field of coal field logging because of its strong economy. Coal seams in Xinjiang region have the characteristics of large thickness variation, high dip angle, easy diameter enlargement and fracture development, but the law of these characteristics in the three-electrode laterolog curve is not clear. In this paper, through the finite element simulation software COMSOL Multiphysics, 2D and 3D models are constructed based on shallow three-electrode laterolog, and the characteristics of using shallow three-electrode laterolog in coal field logging are studied. The results show that the electrode system coefficient of shallow three-electrode laterolog instrument should be 0.94 times of the theoretical electrode system coefficient. The discrimination ability of shallow three-electrode laterolog to coal seams is about 0.3 m. There is a power function relationship between the determination coefficient of coal seam boundary and the ratio of coal seam-surrounding rock resistivity ratio. An increase in the dip angle of the coal seam leads to an increase in the apparent thickness of the coal seam following a quadratic function relationship, while an increase in the wellbore radius results in a quadratic function decrease in the apparent resistivity of the coal seam. An increase in both the width and length of the fractures leads to a decrease in apparent resistivity following a power function relationship. The research results provide a more accurate reference standard for the analysis and evaluation of coal seams using shallow three-electrode laterolog.

Volcanic rocks, as a widely distributed rock type on the earth, are mostly buried deep within basins, and their internal structures possess characteristics by irregularity and self-similarity. In the study of volcanic rocks, accurately identifying the lithology of volcanic rocks is significant for reservoir description and reservoir evaluation. The accuracy of lithology identification can improve the success rate of petroleum exploration and development as well as the safety of engineering construction. In this study, we took the electron microscope images of four types of volcanic rocks in the Liaohe Basin as the research objects and comprehensively used the differential box-counting dimension (DBC) and the gray-level co-occurrence matrix (GLCM) to identify the lithology of volcanic rocks. Obtain the images of volcanic rocks in the research area and conduct preprocessing so that the images can meet the requirements of calculations. Firstly, calculate the different box-counting dimension. Divide the grayscale image into boxes of different scales and determine the differential box-counting dimension based on the variation of grayscale values within each box. The differential box-counting dimension of basalt ranges from 1.7 to 1.75, that of trachyte ranges from 1.82 to 1.87, that of gabbro ranges from 1.76 to 1.79, and that of diabase ranges from 1.78 to 1.82. Then, the gray-level co-occurrence matrix is utilized to extract four image texture features of volcanic rock images, namely contrast, energy, entropy, and variance. The recognition of four types of volcanic rock images is achieved by combining the different box-counting dimension and the gray-level co-occurrence matrix. This method has been experimentally verified by volcanic rock image samples. It has a relatively high accuracy in identifying the lithology of volcanic rocks and can effectively distinguish four different types of volcanic rocks. Compared with single-feature recognition methods, this approach significantly improves recognition accuracy, offers reliable technical support and a data basis for volcanic rock-related geological analyses, and drives the further development of volcanic rock research.

The formation and development of fractures increase reservoir heterogeneity and improve reservoir performance. Therefore, it is of great research value to accurately identify the development of fractures. In this paper, two- and three-dimensional models are constructed based on the finite element method and compared with the real axis integration method. The influence of different geometric parameters on the Stoneley wave amplitude is studied to assess the propagation of Stoneley waves in the fracture zone in the well. The results show a significant positive correlation between the width and number of fractures and the attenuation coefficient of Stoneley waves. The fracture angle has a negative correlation with the attenuation coefficient and lesser impact on Stoneley waves. In addition, Stoneley waves are less sensitive to changes in fracture location, while the sensitivity to fracture spacing is significant in the range of 50 cm to 75 cm. The main propagation depth of Stoneley waves occurs 20 cm from the wall of the well. Quantitative analyses of the fracture width, number, location, spacing, depth, and angle are conducted to determine the influence of the fracture parameters on the Stoneley wave attenuation coefficient, clarify Stoneley wave propagation in wells, and provide a theoretical basis for the accurate evaluation of fractures.

Capillary pressure curves are usually obtained through mercury injection experiments, which are mainly used to characterize pore structures. However, mercury injection experiments have many limitations, such as operation danger, a long experiment period, and great damage to the sample. Therefore, researchers have tried to predict capillary pressure data based on NMR data, but NMR data are expensive and unstable to obtain. This study aims to accurately predict capillary pressure curves. Based on rock particle size data, various machine learning methods, such as traditional machine learning and artificial neural networks, are used to build prediction models and predict different types of capillary pressure curves, aiming at studying the best prediction algorithm. In addition, through adjusting the amount of particle size characteristic data, the best amount of particle size characteristic data is explored. The results show that three correlation coefficients of the four optimal algorithms can reach more than 0.92, and the best performance is obtained using the Levenberg–Marquardt method. The prediction performance of this algorithm is excellent, with the three correlation coefficients being all higher than 0.96 and the root mean square error being only 5.866. When partial particle size characteristics are selected, the training performance is gradually improved with an increase in the amount of feature data, but it is far less than the performance of using all the features. When the interpolation increases the particle size characteristics, the best performance is achieved when the feature data volume is 50 groups and the root mean square error is the smallest, but the Kendall correlation coefficient decreases. This study provides a new way to obtain capillary pressure data accurately.

The inversion of the relative content and spatial distribution characteristics of radioactive elements on the lunar surface, as inferred from gamma-ray spectrometer data, holds substantial importance for forecasting lunar surface compositions. During the inversion process of gamma-ray spectrometer data, galactic cosmic rays (GCRs) have engendered disruptive “stripe noise” in the distribution map of radioactive elements. This phenomenon significantly hampers the interpretation of data and the extraction of lunar surface information. The proposed approach adeptly separates the influence of GCR from the counting rate distribution map of the lunar surface by employing the empirical mode decomposition method. It achieves the deduction of GCR background from the Chang’e-2 gamma-ray spectrometer data with precision. Compared to conventional GCR background deduction methods employed by predecessors, this model does not need to process a large amount of original data repeatedly. Moreover, it achieves an accurate deduction of the GCR background without intricate formulaic derivations. The procedural simplicity and reduced time investment make this approach significantly superior.

Applying the staggered grid finite difference method (SGFDM) for simulating acoustic responses in large-scale, complex, 3-D models poses substantial challenges in geophysics, especially in high-resolution stratigraphic model, due to the high computational burden. To address this problem, we have proposed a low overhead parallel optimization method (LOPOM) suitable for heterogeneous architectures, using high-resolution borehole models as examples. The LOPOM enhances computational intensity and optimizes memory bandwidth utilization. This is achieved through the technique of data reuse along the discontinuities of the model and by minimizing such discontinuities within the halo region. LOPOM was implemented and tested on the CUDA platform and the Sunway supercomputer. In each instance, LOPOM demonstrated an optimal acceleration ratio, thus proving its robust performance. Furthermore, the method’s effectiveness was confirmed through its application to fracture-vuggy formation models and digital core models. Finally, the numerical simulation results and the actual logging data are combined to illustrate the application value of LOPOM.