The Quadric Error Metrics (QEM) algorithm is widely used for 3D mesh simplification, but it struggles with models containing discontinuous appearance attributes and often fails to preserve fine local details. To address these issues, we propose a textured mesh simplification method that incorporates multiple feature constraints. The approach introduces tailored edge collapse rules and a new error metric, the seam angle error, to handle discontinuous texture regions. In addition, vertex sharpness and texture complexity are employed to enhance the algorithm’s sensitivity to geometric and textural details. Experimental results demonstrate that the proposed method effectively reduces texture distortion at seams. Specifically, at an 80% simplification rate, our method reduces the texture root mean square (RMS) error by approximately 78% compared to T-QEM. While this approach incurs a slight increase in global geometric error, it significantly preserves local visual details and texture continuity.

An integrated analysis incorporating total organic carbon (TOC) content measurement, X-ray diffraction (XRD), scanning electron microscopy (SEM), and gas adsorption experiments was performed on core samples from Well FY1-4 of the upper-fourth Shahejie Formation (Es4) in the Minfeng Sag. To address the lack of systematic research on the pore and fractal characteristics of organic-rich low-maturity shales in the Minfeng Sag (against the preponderance of studies on high-maturity shales), this study characterized the lithofacies, reservoir space and pore fractal features of the target low-maturity shale interval and clarified the sedimentary controls on lithofacies and key factors regulating pore fractal heterogeneity. The results reveal that the shale in the Es4 of the study area exhibits low thermal maturity, with six distinct lithofacies identified. Organic-rich laminated calcareous shale lithofacies (RL-1) and organic-rich laminated calcareous/argillaceous mixed shale lithofacies (RL-2) represent the most favorable lithofacies, which are dominated by large mesopores and macropores. Their reservoir spaces were primarily composed of intergranular pores, intragranular pores, and organic pores, whereas the other lithofacies are dominated by small mesopores. The pore surface fractal dimension (D) was calculated using the Frenkel–Halsey–Hill (FHH) model based on low-temperature N2 adsorption (LTNA) data. The meso-macropore system shows higher heterogeneity than the micropore system (D2 > D1). Both D1 and D2 exhibit a weak negative correlation with TOC and carbonate content and a positive correlation with clay content. In the initial depositional stage of the Es4, the arid climate, weak terrigenous input, shallow lake depth, and high salinity resulted in the strongly reducing saline depositional environment with relatively low organic matter enrichment. As the climate became progressively humid in the middle and late stages, hydrodynamic conditions intensified, leading to a lithofacies transition from mixed shales to argillaceous calcareous shales. Increased TOC and carbonate contents reduce the pore fractal dimension of shale. Smaller fractal dimensions directly indicate a simple pore structure and regular pore surface in the shale oil reservoir of the Minfeng Sag, where reservoir space is dominated by large pores such as intercrystalline pores and dissolved pores. Such pore characteristics are more favorable for the enrichment of shale oil.

In response to the challenges in monitoring the production profile during the development of the Qingcheng shale oil field in the Changqing Oilfield, this study systematically investigates the application mechanism and practical effectiveness of Distributed Temperature Sensing (DTS) technology for dynamic monitoring in horizontal wells. By establishing a coupled model of fracture-matrix dual-porosity media flow and wellbore thermodynamics, which integrates mass, momentum, and energy conservation equations solved via the finite difference method, an interpretation method for the production profile based on the Joule-Thomson effect is proposed. The model was calibrated using shut-in temperature data and validated by comparing simulated temperature profiles with DTS measurements under constant-rate production. Field tests conducted in six horizontal wells in the Qingcheng oil field enabled the quantitative analysis of cluster-level production contributions along the horizontal section, with a water-producing zone localization accuracy of +/- 3.5 m. The results indicate that shale oil wells exhibit a non-uniform production characteristic of "high at the front and low at the rear" during the early production stage, where the production contribution from fully fractured segments can be up to 2.8 times that of adjacent segments. Inversion of the fiber-optic monitoring data reveals that differences in the conductivity of hydraulic fractures are the primary cause of flow heterogeneity. This research provides a theoretical foundation and technical support for the efficient development of shale oil, contributing to the transition of China's continental shale oil development from "experience-driven" to "data-driven."

Shale reservoirs provide critical storage space for unconventional oil and gas, yet their frequent vertical facies alternations and complex spatial architectures make it difficult for conventional two-point geostatistical methods to reproduce thin interbedding and reservoir-scale continuity. Multiple-point geostatistics can incorporate structural information through training images (TIs), but practical 3D shale modeling is often hindered by the limited availability of representative 3D TIs. Using the F2 Member in the Qintong Sag, Subei Basin, eastern China, as a case study, we propose a hierarchical 2D-to-3D geological modeling workflow that combines mixed-point geostatistical simulation (MIXSIM) for generating vertical 2D facies sections and a sequential 2D simulation strategy with conditioning data (s2Dcd) for propagating section-based patterns into 3D space under hard well constraints. In the workflow, vertical sections serve as TI carriers to explicitly capture bedding-scale alternations, while well data are imposed as hard conditioning information during 3D simulation. Quantitative evaluation is performed in terms of (i) conditioning-data consistency, (ii) vertical facies-transition statistics quantified by transition counts and Markov transition probability matrices, (iii) global facies proportions summarized as the mean of 10 realizations, and (iv) connectivity characterized by connected geobody analysis. The realizations honor the conditioning data exactly, reproduce vertical transition behavior with a transition-matrix discrepancy of DMAE=0.0396, and maintain global facies proportions close to well-based estimates with a maximum deviation of 2.36%. These results demonstrate that the proposed MIXSIM-s2Dcd workflow provides a practical solution for well-data-driven, high-resolution 3D shale facies modeling when 3D training images are unavailable.

Evaluating the productivity contribution of individual fracturing stages in deep coalbed methane (CBM) horizontal wells remains a critical challenge, hindering the optimization of stimulation designs. This study systematically integrates dual-phase (aqueous and gaseous) fracturing tracer monitoring with machine learning algorithms to address this issue. Based on large-scale field applications across ten deep CBM horizontal wells in the Changqing mining area of the Ordos Basin, comprising 132 monitored stages, quantitative production profile data were interpreted. Three distinct gas production archetypes—Homogeneous, Heel-Dominated, and Heterogeneous—were identified, each governed by specific geomechanical and stratigraphic controls. Pearson correlation analysis and Random Forest feature importance ranking were employed to decouple the hierarchical influence of geological parameters (Class I coal intersection length, trajectory position, coal thickness) and engineering parameters (proppant volume, pumping rate, fluid volume). A power-law correlation between Class I coal length and initial gas productivity was quantified (R2 = 0.71). For the first time, an economically viable “differentiated fracturing scale window” tailored to coal petrophysical classes and wellbore trajectory positions was defined. Subsequently, a machine learning-assisted geology-engineering closed-loop optimization methodology was established, using tracer data as a dynamic feedback bridge to iteratively refine fracturing designs. This research provides a reliable technical approach and practical template for enhancing single-well productivity and recovery efficiency in deep unconventional gas reservoirs.

Accurate geological modeling of shallow-water delta reservoirs remains challenging due to complex sedimentary architecture and strong heterogeneity. This study develops an advanced modeling technique that integrates geological process understanding with deep learning, with a focus on the accurate representation of channel geometry under multiple data constraints. Field outcrop investigations of the Chang 6 Member in the Ordos Basin were conducted to clarify key geological characteristics and geometric parameters of shallow-water distributary channels. An improved object-based method was employed to effectively generate three-dimensional training datasets capturing typical channel bifurcation and convergence patterns. A conditional progressive generative adversarial network is proposed to incorporate multi-source constraints, including global geological features, well logs, and seismic probability volumes, thereby enabling simultaneous learning of geological patterns and data fidelity. Application to a shallow-water delta reservoir in the Ordos Basin demonstrates that the method produces geologically realistic facies models that honor all available constraints, significantly improving modeling accuracy and computational efficiency. This work provides an innovative and adaptive methodology for intelligent modeling of complex reservoir systems.

The gel plugging technology of wellbore is a new type of operate under pressure technology developed in recent years, which effectively solves the problem of poor adaptability of traditional mechanical devices by plugging the wellbore with gel, but the plugging mechanism of gel has rarely been discussed. In this study, the plugging mechanism of gels was investigated in depth through indoor experiments, numerical simulations and theoretical model. The results indicate that during the plugging process, pressure-compressed gel increases friction, thereby enhancing the gel plugging performance. However, when the gel reaches its compression limit, the effect of friction begins to diminish. Furthermore, the casing radius and gel compressibility significantly impact plugging performance. By adjusting the gel formulation, the compressibility of the gel can be modified, thereby regulating its plugging capabilities. In addition, the theoretical model effectively describes the stress and compression changes of the gel during the plugging process and enables the calculation of gel length based on bottomhole pressure. This work is of great significance for the application and popularization of gel plugging technology.

High-resolution LiDAR-derived three-dimensional (3D) digital outcrop models are crucial for detailed geological analysis. However, their massive data volumes often exceed the rendering and memory capacities of standard computer systems, posing significant visualization challenges. Although Level of Detail (LOD) techniques are well-established in Geographic Information Systems (GISs) and computer graphics, they still require customized design to address the unique characteristics of geological outcrops. This paper presents an automated method for constructing and visualizing LOD models specifically tailored to high-resolution LiDAR outcrops. The workflow begins with segmenting the single-body model based on texture coverage, followed by building an adaptive LOD tile pyramid for each segment using a pseudo-quadtree approach. The proposed LOD construction method incorporates several innovative components: segmentation based on texture coverage, an adaptive LOD tile pyramid using a pseudo-quadtree, and a feature-preserving mesh simplification algorithm that includes vertex sharpness constraint and boundary freezing strategy to maintain critical geological features. For visualization, a dynamic multi-scale loading and rendering mechanism is implemented using an LOD index with the OpenSceneGraph (OSG) engine. The results demonstrate that the proposed method effectively addresses the bottleneck of rendering massive outcrop models. The models loading time and average memory usage were reduced by more than 90%, while the average display frame rate reached around 60 FPS. It enables smooth, interactive visualization and provides a robust foundation for multi-scale geological interpretation.

The variogram function is a crucial tool for quantifying spatial correlation and a key component of Kriging interpolation, directly influencing the accuracy of interpolation results. However, traditional variogram fitting methods, such as least squares and weighted polynomial regression, often face limitations when handling complex or non-stationary spatial structures, which can lead to unstable fitting outcomes. To address these challenges, this paper proposes an automatic fitting method for experimental variogram functions based on deep learning. The variogram fitting process is inherently a nonlinear optimization problem, where the goal is to optimize the alignment between experimental and theoretical variogram functions. In this study, we construct a deep learning model designed to capture the nonlinear characteristics of experimental variograms with the objective of minimizing fitting errors and improving both the accuracy and stability of the fitting process. Extensive experimental results demonstrate that the proposed neural network model can effectively learn to fit a variety of experimental variograms and accurately represent different theoretical variogram functions. Compared to traditional methods, this approach consistently achieves superior fitting performance across multiple datasets, validating its effectiveness and robustness under diverse conditions. The stronger generalization capability and improved accuracy of the proposed method significantly enhance the automatic fitting of experimental variogram functions.

The reservoir in focus has braided river delta front deposition, with multiple periods of submerged distributary channels within the reservoir. It also displays frequent cutting and stacking with local-connecting characteristics. Forecasting the sand distribution characteristics between wells in this type of reservoir brings a significant challenge for modeling. The data- and knowledge-driven modeling method proposed is applied to the Sangtamu Oilfield as an example. Obtain channel-scale information from the geological knowledge database. Geological expertise is applied to interpret the characteristics of channel distribution. These results are employed as conditional data in the process of geological modeling. By combining the expertise of experts with multi-sources of geoscientific data, this method can obtain accurate and reliable spatial information about the channel. This information is crucial for stochastic simulation between wells and enables to minimize uncertainty in predicting results. The method is useful for 3D modeling of similar sedimentary bodies or well-sparse areas.

The target reservoir is a typical blocky bottom water reservoir. There are several interlayers of varying scales inside it, which impact the characteristics of the oil production and water breakthrough curves of the wells in the water flooding development oilfield, resulting in strong heterogeneity within the reservoir. The Sangtamu Oilfield has an average well spacing of approximately 600 m, causing a challenge in accurately identifying the range of small-scale interlayer spreading. This, in addition, challenges the subsequent 3D geological modeling process. This paper proposes a method to accurately characterize the size of various interlayers in a reservoir by using production dynamics data from bottom water reservoirs to invert the interlayer scale. By classifying the water breakthrough curve styles derived from theoretical testing, the range of reservoir internal interlayer scales is compared and inverted based on the actual water breakthrough styles of wells in the Sangtamu oil field. In the process, many interlayer scales derived from various types of geologic data acquisition were combined to form a quantitative geologic knowledge database of interlayers that synthesizes both dynamic and static data. This method has been applied in the Tarim Basin’s Sangtamu Oilfield, where the reservoir numerical simulation history was used to fit the model. This resulted in an overall fitting coincidence rate of 96% in the Sangtamu Oilfield and a single-well fitting coincidence rate of over 90% in well LN14. This method offers a new viewpoint on characterizing the interlayer’s scale in the area of the non-dense well network.

This study quantifies morphological features of global braided river deltas using Google Earth imagery, analyzing eight systems (e.g., Yukon–Kuskokwim, Poyang Lake, Lena River deltas). Methods include listwise deletion for missing data (retaining 87% of Poyang Lake delta samples) and sensitivity analysis (threshold changes ≤2.4%). Nonparametric tests (Kruskal–Wallis, H = 12.73, p = 0.005) show significant differences in bifurcation angles across deltas, with the wave-dominated Po River (59.2°) having an 18% higher 80% threshold the than tide-dominated Poyang Lake (50.1°, p = 0.003). Key quantitative results include the following: 1.65% of bifurcation angles cluster at 30–60°, differing from fan deltas (p < 0.01); wavelength–amplitude relationships are nonlinear (R2 = 0.537–0.913), with positive slopes indicating a high sediment supply (e.g., Yukon–Kuskokwim) and negative slope channel avulsion (e.g., Poyang Lake); bifurcation spacing correlates with the sediment supply—54% of Poyang Lake spacings < 2000 m (dense networks) vs. 80% of Lena River spacings < 15,000 m (stable channels). The resulting dataset enables global, remote-sensing-based comparisons, providing thresholds for sedimentary modeling and reservoir prediction. Moderate missing data (≤13%) minimally affect results, though high-missingness cases need further analysis. This study replaces empirical rules with statistical validation, showing that morphometric differences reflect depositional dynamics, which are critical for reservoir heterogeneity assessments.

High sand-ratio and low-connectivity reservoirs are commonly developed in deep-water depositional environments. Well-developed muddy interlayers reduce reservoir connectivity and form multiple discrete sandbody units, thereby offering good potential for layered development. However, due to limited research and insufficient data, modeling such complex reservoir structures remains challenging. The existing multiple-point statistics (MPS) method can utilize limited training images for geological modeling, but under high sand-ratio conditions, it often produces models with excessively high connectivity, failing to accurately represent reservoir characteristics. To address this issue, this study proposes a self-attention-enhanced single-image generative adversarial network (SA-SinGAN). Based on the original 2D SinGAN, the method was extended to 3D modeling by incorporating a self-attention mechanism and trained layer by layer to capture multi-scale geological features. Experimental results show that the FID score of SA-SinGAN is 143.75, compared with 175.61 for the MPS method. In terms of average connectivity error, MPS yields 0.42, which is significantly higher than 0.13 for SA-SinGAN, while the average NTG error is similar. SA-SinGAN can more accurately reproduce the low-connectivity characteristics of reservoirs while maintaining randomness, outperforming MPS in modeling performance. This demonstrates the applicability of SA-SinGAN for modeling complex reservoirs.

Building extraction is significant for the intelligent interpretation of high-resolution remote sensing images (HRSIs). However, in some complex scenarios where the features of the building and its adjacent ground objects are similar, the current segmentation model cannot distinguish them effectively. Therefore, we propose a complex scenarios adaptive network (CSA-Net) for building extraction. CSA-Net is comprised of the hierarchical-context feature extraction (HFE) module, the global-local feature interaction (GFI) module, and the multiscale-adaptive feature fusion (MFF) structure. The HFE obtains high-level semantic information at different levels and fuses it with low-level detailed information by skipping connections to enhance the reasoning and perception ability of building structure in complex scenes. Then, the GFI acquires global-local features of buildings and their surrounding environment via dense multiscale dilated convolution. The information can be shared through efficient interaction among features, and irrelevant backgrounds can be suppressed. Then, in the up-sampling process, the MFF alleviates the feature loss and enhances the robustness of the network by using feature fusion after layer-by-layer adaptive weight allocation. Experiments show that CSA-Net outperforms other comparable methods, with intersection over union values of 79.99%, 89.75%, and 73.59%, respectively, on the Google Arlinton, WHU, and Massachusetts building datasets. The visual comparison results demonstrate that our method can enhance the accuracy of building extraction in complex scenes. Meanwhile, the efficiency results indicate our approach strikes a balance between calculation parameters and time and achieves high levels of efficiency.

The evaluation of shale reservoir quality is of great significance for the exploration and development of shale oil. To more effectively study the distribution characteristics of shale reservoir quality, thin-section observation, scanning electron microscopy and pressure-controlled porosimetry were used to obtain the pore structure characteristics of shale in Chang 7, including pore types, pore size distribution, etc. In addition, the fractal dimensions of the shale samples were calculated based on pressure-controlled porosimetry data. The results show that residual interparticle pores, dissolution pores and clay-dominated pores were the main pore types. The overall pore size was mainly distributed between 3 nm and 50 μm. The pore system was divided into four types using fractal features, and the shale reservoir was divided into four types based on the proportion of different types of pore system. In different types of reservoirs, the production capacity of exploration wells varies significantly, as does the production capacity of horizontal wells. The classification of shale reservoirs using mercury intrusion fractal analysis proved to be suited for the efficient development of Chang 7 shale oil reservoirs.

Multiple-point geostatistics (MPS) has more advantages than two-point geostatistics in reproducing the continuity of geobodies in subsurface reservoir modeling. For fluvial reservoir modeling, the more continuous a channel, the more consistent it is with geological knowledge in general, and fluvial continuity is also of paramount importance when simulating fluid flow. Based on the pixel-based MPS algorithm Snesim, this study proposes a method that utilizes multiple search trees (MSTs) to enhance simulation continuity in 2D fluvial reservoir modeling. The objective of the MST method is to capture complete data events from a training image (TI), which aims to achieve enhanced continuity in fluvial reservoir sublayer modeling. By resorting to search neighborhoods based on their proximity to the central node of the data template, multiple data templates that correspond to the MSTs will be generated. Here, four data templates were generated by arranging the relative search neighborhood coordinates in ascending and descending order with respect to the central node. Parallel computing was tried for the construction of the search trees. This work calculated the conditional probability distribution function (CPDF) of the simulating nodes by averaging the CPDFs derived from the MSTs, and double retrieval was employed to filter out the search trees that possessed an inaccurate local CPDF for the simulating nodes. In addition, the connected component labeling (CCL) method was introduced to evaluate the simulation continuity in MPS. The results indicated that the MST method can enhance the simulation continuity of the Snesim algorithm by reproducing the fine connectivity of channel facies in 2D fluvial reservoir modeling.

After many years of exploitation in the petroleum field, most of the oil fields are in advanced stages of development, with a strong non-homogeneity of the reservoir, more residual oil, and low recovery efficiency. Therefore, research on various methods has been carried out by scholars to improve the rate of recovery and to understand the distribution pattern of residual oil in reservoirs. Among the whole clastic reservoirs, fluvial reservoirs occupy a large proportion, so fluvial reservoirs will be the priority for future reservoir research in China. The key to the fine characterization of fluvial-phase reservoirs is to able to reproduce the continuous curvature of the channel, and one important parameter is the width of the channel. The width of the channel sand body is one of the key factors in designing well programs, and accurately identifying the channel boundary is the key to identifying a single channel. Traditional research methods cannot accurately characterize the continuous bending and oscillating morphology of underwater diversion channels, and it is not easy to quantitatively characterize the spatial structure. Therefore, in this paper, a deep learning method is applied to quantitatively identify the width of a single channel within an underwater diversion channel at the delta front edge. Based on the sedimentary background of the block and modern depositional studies, we established candidate models for underwater diversion channels with channel widths of 100, 130, 160, 190, 220, and 250 m based on target simulation and human–computer interactions. The results show that when the width of the underwater diversion channel is 160 m, it has the highest matching rate with the conditional data and corresponds to the actual situation. Therefore, it can be determined that it is the common width of underwater diversion channel in the study area. And it is shown that the method can accurately identify the width of underwater diversion channels, and the results provide a basis for reservoir fine characterization studies.

In recent years, the Chang 7 member of the Mesozoic Triassic Yanchang Formation in the Ordos Basin has emerged as a significant repository of abundant and distinctive unconventional oil resources. The Heshui area boasts substantial shale oil reserves, with reported third-level reserves surpassing 600 million tons. However, the region in the southern part of Heshui is marked by pronounced variability in reservoir quality, intricate oil–water dynamics, low formation energy, and suboptimal fluid properties, leading to divergent development outcomes for horizontal wells. There is an imperative need to devise and refine new geological models to underpin the efficient exploitation of shale oil in the southern Heshui area. This study focuses on the shale oil reservoir of the Chang 7 member in the southern Heshui area of the Ordos Basin, conducting detailed stratigraphic correlation and establishing a refined isochronous stratigraphic framework. Utilizing PetrelTM modeling software (version 2018), we integrate deterministic and stochastic modeling approaches, adhering to the principles of isochronous and phased modeling. By assessing the thickness of sand and mudstone layers and the overall stratigraphic sequence, we derive a geological probability surface. Subsequently, this surface is harnessed to constrain the lithofacies, yielding a constrained lithofacies model. Employing sequential indicator simulation and sequential Gaussian stochastic simulation, we develop a reservoir attribute model that is anchored in the lithofacies model and its controls, culminating in a robust and dependable static model. Employing the geological probability surface constraint method, we meticulously construct the reservoir matrix model, amalgamating individual well data with the inherent certainty and randomness of reservoir plane thickness. This approach further enhances the model’s accuracy and mitigates the uncertainty and randomness associated with inter-well interpolation to a significant degree.

Traditional sedimentary facies modeling using generative adversarial networks (GANs) usually requires extensive datasets for network training. However, obtaining large datasets that align with reservoir depositional characteristics is often complex and costly. This letter introduces a conditional generative adversarial network (CSinGAN) based on a single training image. CSinGAN does not use conditional data in the training phase. In the model generation stage, conditional facies simulation is achieved by adjusting the intermediate model using image editing techniques. The conditional realizations of the three sets of training images successfully matched the well data. The variogram function and connectivity function indicate that CSinGAN can generate heterogeneous structures that conform to the statistical characteristics of the training image. We used multiscale sliced Wasserstein distance to verify that the realizations of the CSinGAN outperform the classical multipoint geostatistical algorithms. This study demonstrates the viability of using a single training image in GANs for conditional sedimentary facies modeling.

In map generalization, displacement is the most frequently used operator to reduce the proximity conflicts caused by reducing scales or other generalization operations. Building displacement can be formalized as a combinatorial optimization problem, and a heuristic or intelligent search algorithm can be borrowed to obtain the solution. In this way, we can explicitly resolve minimum distance conflicts and control positional accuracy during the displacement. However, maintaining spatial relations and patterns of buildings can be challenging. To address spatial conflicts as well as preserve the significant spatial relations and patterns of buildings, we propose a new spatial contextual displacement algorithm based on an immune genetic algorithm. To preserve important spatial relations and global patterns of map objects and avoid topology errors, displacement safety zones are constructed by overlapping the Voronoi tessellation and buffer areas of the buildings. Additionally, a strategy to shift the buildings in a building group synchronously is used to maintain local building patterns. To demonstrate the effectiveness of our algorithm, two data sets with different building densities were tested. The results indicate that the new algorithm has obvious advantages in preventing topology errors and preserving spatial relations and patterns.