High-precision sand thickness data are fundamentally important for optimizing exploration strategies in petroleum geology. In the Chengbei work area of the Jiyang Depression, the stratigraphic channels are chaotically developed, with channels of varying sizes in different strata overlapping, intersecting, and exhibiting narrow widths. The actual well-seismic relationship is poor. Therefore, individual seismic attributes in this area exhibit extremely low correlation with channel sandstone thickness. Conventional attributes such as root mean square amplitude show no distinct channel characteristics, necessitating the integration of multiple seismic attributes for effective prediction. Moreover, the high multicollinearity among seismic attributes introduces significant interference in prediction results. Therefore, this study integrates the Pearson correlation coefficient and variance inflation factor (VIF) to optimize seismic attribute selection, effectively eliminating redundant attributes and those with low correlation. To further enhance prediction accuracy and address the significant bias inherent in single-model predictions, this study introduces the ensemble learning XGBoost model, which integrates predictions from multiple weak learners to improve the precision of sandstone thickness estimate. The Newton-Raphson-based optimization algorithm was employed to fine-tune the XGBoost parameters. Results from test wells demonstrate a remarkable improvement in prediction accuracy, achieving reliable sandstone thickness estimation despite poor well-seismic correlations. This research provides valuable insights and offers a widely applicable methodology for predicting the thickness of complex channel sand bodies.

The Upper Paleozoic Shihezi Formation in Block L of the eastern Ordos Basin harbors extensive tight sandstone gas reservoirs. However, these reservoirs exhibit strong heterogeneity, thin sand bodies, and overlapping elastic properties between gas- and water-bearing layers, which significantly limit the effectiveness of conventional pre-stack inversion methods in delineating thin sand bodies and predicting gas saturation. To address these challenges, we propose an integrated high-resolution gas prediction technique combining geostatistical inversion with deep learning. First, within a Bayesian sequential inversion framework, we jointly inverted well-log data, seismic data, and geological constraints to obtain high-resolution elastic parameters, substantially improving the identification of thin sand bodies (<5 m). Second, we employed a long short-term memory network to extract temporal features from inverted elastic parameter sequences and establish a non-linear mapping between gas/water-sensitive attributes and water saturation; this step incorporates horizon constraints and an attribute optimization strategy to enhance prediction accuracy. Field applications demonstrated that our method achieved superior performance compared to conventional approaches, with an 85% consistency rate between predicted gas saturation and drilling results. The integration of geostatistical inversion and deep learning provides a robust workflow for characterizing thin, heterogeneous tight gas reservoirs, offering significant potential for optimizing exploration and development strategies in the Ordos Basin.

Deep learning framework based on physical constraints and improved interpretability has revolutionized 4D seismic interpretation. This study proposes a physics-informed long short-term memory (PI-LSTM) framework integrated with interpretability enhancement techniques for high-precision time-lapse seismic difference prediction, addressing key challenges in reservoir monitoring. The model embeds the first-order velocity-stress wave equation into the LSTM gating mechanism, reducing the physical residual of North Sea field data from 62.3 kPa to 15.2 kPa—a 75.6% decrement. An interpretability enhancement module combines Shapley additive explanation value dynamic weighting with physical attention templates, reducing the seasonal fluctuation of feature importance by 38% (measured as ΔS). Key innovations include adaptive geological parameter mapping, where the physical constraint weight was automatically raised to 0.89 ± 0.04 when porosity exceeded 15%. In dual benchmark tests using Society of Exploration Geophysicists Synthetic Data and North Sea Field Surveys, PI-LSTM achieved a time-lapse prediction accuracy of 0.71-2.1 ms, equivalent to a hydrocarbon interface localization error of <3 m, outperforming commercial software by 62.9%. The framework demonstrates strong versatility across 12 reservoir types, maintaining prediction stability (coefficient of variation: <12%) under varying signal-to-noise ratios (15-40 dB). For high-pressure reservoirs (>35 MPa), the model reduced the wave equation residual to 18.6 kPa, 67.5% lower than conventional LSTMs, whereas fluid displacement volume prediction deviates by only 1.8% from well data. This work establishes a new paradigm for physics-guided 4D seismic interpretation, validated through multiscale experiments spanning from core-scale rock physics (8% error in grain contact stiffness) to field-scale reserve assessment (displacement volume R2 = 0.94).

Reverse time migration is widely recognized as one of the most advanced seismic depth migration techniques because of its ability to generate a high-quality seismic image even for complex structures. However, its practical implementation for large-scale applications can be hindered by tremendous computational overhead and memory demands associated with handling wavefields. To address these challenges, we propose a wave equation-based, Kirchhoff-style migration method incorporating the excitation amplitude imaging condition. In our migration scheme, both the forward and backward wavefields are represented using excitation information obtained by interpolating a limited set of excitation information. This representation allows us to avoid not only storing the forward wavefield but also performing backward wavefield simulation. Numerical experiments with both synthetic and field data demonstrate that the proposed migration approach can deliver high-quality migration images with significantly improved computational efficiency.

Deep geothermal reservoirs are expected to serve as a sustainable resource for clean energy production, contributing to the achievement of global dual-carbon targets. This study analyzes the seismic acquisition method for soft-structure fracture zones in deep geothermal reservoirs through forward modeling analysis. Based on geological data from the Baoying area, China, a 2D geological model—integrating formation velocities, densities, and stochastic fracture media within the Upper Sinian-Middle Ordovician strata—was constructed for the forward modeling. To enhance the accuracy of seismic simulations and reduce numerical dispersion, high-order finite-difference methods were employed. A detailed theoretical analysis of seismic dispersion characteristics indicates that higher-order spatial and temporal differences can effectively mitigate numerical dispersion. Numerical seismic forward simulations were performed using a 10th-order difference accuracy, with a detailed analysis of acquisition survey parameters such as trace spacing, shot spacing, maximum offset, and record length. Simulated records for the geological model with and without fracture zones were compared, revealing distinct differences, particularly when fracture zones are located within high-velocity layers. Further analysis of pre-stack depth migration profiles with varying offsets, trace spacings, and shot intervals indicates that a maximum offset above 7000 m, a trace spacing of 5 m (or 10 m as a cost-effective option), and a shot interval of 40 m provide optimal imaging accuracy for fracture zones. These findings offer guidance for improving seismic imaging and interpretation of soft structures within fracture zones, thereby enhancing seismic exploration of deep geothermal reservoirs.

Onshore non-repeatable time-lapse (TL) seismic exploration is a challenging yet convenient technique for enhancing production in mature oil and gas fields. Data repeatability across two or more acquisition phases is fundamental for reliable TL analysis. However, differences in acquisition geometries - from variations in geological targets, acquisition technologies, and acquisition parameters - can cause significant inconsistencies between two data vintages. Drawing on survey design parameters, this study proposes a dual-constraint method for data reconstruction and quality control, integrating common midpoint (CMP) similarity with the sum of shot-receiver geometric distances. Unlike conventional techniques, the proposed approach simultaneously controls shot and receiver position errors through a dynamic threshold, indirectly preserving offset and azimuth consistency. Compared with typical methods, it avoids cross-domain transformations and multi-parameter adjustments, offering high applicability. Applied to conventional (2004) and high-density (2008) datasets from a Chinese onshore oilfield, the method achieved data utilization rates of 77.5% and 39.8%, respectively. The reconstructed data demonstrated higher offset distribution uniformity and improved CMP fold consistency compared with the CMP-constrained receiver deviation method. This study provides a practical reference for TL studies in onshore mature oilfields.