The Paleocene marked a critical structural transition period from tectonic subduction to extensional rifting in the Bohai Bay Basin. To reconstruct the spatiotemporal structural framework, evolutionary process, and basin prototype during the Paleocene Kongdian period of the Dongying Depression in the eastern Bohai Bay Basin, this study integrated seismic interpretation, core observation, ⁴⁰Ar/³⁹Ar dating, and structural restoration. The ⁴⁰Ar/³⁹Ar dating of basalts (approximately 57.58 ± 0.77 Ma) revealed that the depression entered an active rifting stage with intense volcanism in the early Paleocene Kongdian period. Regional faults, including the Chennan, Wangjiagang Shicun, and Gaocheng faults, were well developed during the first member of the Kongdian sedimentary stage. Their activity indicates rapid rift expansion and resulted in a half-graben pattern characterized by faulting in the north and onlap in the south. Structural restoration showed that the stretching ratio during the Paleocene Kongdian period was approximately 7.3%, which reflects rapid rifting. The development of the early Paleocene rift has important implications for deep hydrocarbon exploration in the Bohai Bay Basin.

Underground hydrogen storage (UHS) in saline aquifers is a promising solution for large-scale, long-duration energy storage in a low-carbon energy system. Cushion gases, such as N₂, CO₂, and CH₄, are commonly used to regulate reservoir pressure and enhance storage efficiency. However, despite recent advancements in understanding the interfacial tension (IFT) behavior of H₂–gas mixtures in contact with brine under reservoir conditions, key gaps persist in systematic data for H₂–cushion gas mixtures (e.g., with N₂, CO₂, and CH₄) across specific thermophysical ranges, despite its influenceon capillary sealing, gas mobility, and recovery. This study presents systematic experimental measurements of IFT and density for H₂–N₂, H₂–CO₂, and H₂–CH₄ gas mixtures (70:30 mol%) with both distilled water and brine (30,000 ppm NaCl) at different temperatures (25, 50, and 70 °C) and pressures (1, 5, and 10 MPa). The IFT was measured using a high-pressure, high-temperature drop tensiometer, and density was obtained via an Anton Paar DMA 4500 densitometer. Results show a consistent decrease in IFT with rising temperature and pressure across all systems. Notably, the H₂–CH₄ mixture exhibited an IFT of 69.4 mN/m in distilled water at 25 °C and 1 MPa, decreasing to 57.3 mN/m at 70 °C; in contrast, the same mixture in 30,000 ppm brine showed a larger reduction from 61.3 mN/m to 47.8 mN/m under identical conditions, highlighting the amplifying effect of salinity on IFT decline. Among the gas mixtures, CH₄ yielded the approximately lowest overall IFT values, favoring injectivity, whereas N₂ maintained the highest IFT, supporting enhanced capillary retention. These findings address a critical data gap for realistic cushion gas performance and provide foundational insight for modeling UHS in saline aquifers.

Produced water is the largest waste stream generated during oil and gas production, presenting significant environmental risks due to its complex chemical composition. Characterizing the contaminant profile of produced water is crucial for assessing its environmental impact, ensuring compliance with discharge standards, and developing effective pollution mitigation strategies. This study characterizes untreated produced water from Angolan offshore Block 15 over a 50-day sampling period, analyzing over 30 target constituents. Results reveal substantial variability in key parameters, including chemical oxygen demand (394.02–526.00 mg/L), biochemical oxygen demand (9.80–20.40 mg/L), phenol (2.70–4.12 mg/L), benzene, toluene, ethylbenzene and xylene (BTEX, 2.77–7.00 mg/L), polyaromatic hydrocarbons (0.20–1.20 mg/L), total organic compounds (248–918.45 mg/L), total oil and grease (19–206 mg/L), and total petroleum hydrocarbons (12–105.02 mg/L). Metals and anions exhibited significant fluctuations, with sodium ranging from 1167.77 to 2100.00 mg/L and chloride from 400 to 2146.84 mg/L. The biodegradability index (0.028) indicates limited potential for biological treatment due to toxic and refractory organics. The results emphasize the necessity for advanced treatment technologies and stricter environmental regulations to reduce contaminant discharge and minimize the ecological footprint of offshore oil production.

Shale oil reservoirs often experience severe hydraulic fracture stress sensitivity and conductivity damage, leading to rapid production decline and low oil recovery. Understanding fracture damage mechanisms is crucial for optimizing stimulation and production strategies. This study established a novel conductivity damage model of hydraulic fracture incorporating stress sensitivity, gel-breaking residue deposition, and the process of shale particle hydration, expansion, shedding, and blockage. A sensitivity analysis was conducted by numerical simulation to evaluate the impact of different damage mechanisms on fracture conductivity and oil production. The simulation results indicate that different damage mechanisms have varying impacts on fracture conductivity and production. Stress sensitivity significantly reduces main fracture conductivity, but broken gel residue is the primary cause of oil production decline. In branch fractures, both stress sensitivity and broken gel residue decrease conductivity, with stress sensitivity having the greatest impact on production. Shale particle expansion and deposition can further impair fracture conductivity, while particle detachment and output help restore it. Oil production of branch fractures is more sensitive to conductivity damages. These insights can guide fracture design, fracturing fluid optimization, and production control. It is recommended to increase proppant concentration to mitigate the adverse effect of stress sensitivity on oil production, enhance the concentration of anti-swelling agents in the pre-slug of fracturing fluidto better protect branch fractures, and control drawdown pressure to prevent shale particles from detaching and depositing in the fractures.

The integrity of welded X65 carbon steel (CS) pipelines in oil and gas systems is significantly compromised by preferential weldment corrosion (PWC) and pitting in sour conditions. This study clarifies that the effect of pre-corrosion conditioning and inorganic deposits (SiO₂ or CaCO₃) on welded CS pipelines, with SiO₂ deposits leading to the most substantial damage at weld metal (WM) and heat-affected zone (HAZ) relative to parent metal (PM). Due to localized acidification and differences in potential, corrosion rates (CRs) hierarchies are SiO₂-deposited > CaCO₃-deposited > non-deposited. Microstructural analysis reveals that deposited CS-WM has carbide redistribution and increase residual stress from the welding thermal process, with increased aggressive species and stabilized iron oxides, leading to intensified PWC. However, amine-based inhibitor CRW11 suppresses the CRs below 0.1 mmpy through chemisorbed films that disrupt microgalvanic coupling and protective corrosion product layers with inhibition efficiency (IE > 85%). CS-PM-CaCO₃ has a high pit depth (68.3 ± 2.7 μm) and propagation rate (1.2 ± 0.2 mmpy), making it the most vulnerable region to pitting relative to CS-WM-CaCO₃ (52.5 ± 1.1 μm; 0.9 ± 0.1 mmpy) and CS-HAZ-CaCO₃ (29.3 ± 0.6 μm; 0.5 ± 0.1 mmpy). This proves that CaCO₃ deposit on welded CS creates a physical barrier for the surface nucleation effect and improves anodic dissolution. Machine learning models (Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost)) accurately predict IE (R² = 0.99). However, RF performs best for data without deposits (RMSE = 0.9), while DT is most appropriate model for those with SiO₂ and CaCO₃ deposits (lowest RMSE = 0.5 and 0.2). These findings provide coupled electrochemical, microstructural, and machine learning frameworks for PWC mitigation, allowing for adaptive corrosion management methods in sour service pipes and material designs optimized for topology.

The Cretaceous Sarvak Formation in the Zagros Basin exhibits complex reservoir heterogeneity controlled by depositional facies and sequence stratigraphy-constrained diagenesis. This study integrates petrographic and petrophysical analyses of an Abadan Plain oilfield to establish predictive relationships between microfacies, diagenesis, and automated hydraulic flow units (HFUs). Eight microfacies were identified, representing four depositional environments: open marine, shoal complex, patch-reefs, and lagoon, supporting a ramp model. Reservoir quality varies systematically across four HFU classes, ranging from poor (HFU1: 0–0.1 FZI, 2.76 mD permeability, 10.85% porosity) to excellent (HFU4: >0.7 FZI, 45.99 mD permeability, 17.4% porosity). Diagenetic processes show distinct relationships with sequence stratigraphy: marine cementation dominates transgressive systems tracts, while meteoric dissolution and fracturing prevail in regressive systems tracts and at sequence boundaries. Maximum floding surfaces record chemical compaction and authigenic mineralization. Facies-selective diagenesis primarily controls reservoir quality. Grain-supported facies develop dissolution-enhanced porosity, whereas mud-dominated facies experience porosity reduction through cementation and compaction. Dolomitization and fracturing provide secondary permeability enhancement, particularly in mid-ramp settings. HFU clasification reveals optimal reservoir performance in zones where dissolution and fracturing enhance pore networks (HFU3–HFU4). In contrast, non-reservoir zones (HFU1–HFU2) are affected by extensive cementation and compaction. These findings highlight the predictive potential of integrating sequence stratigraphy with diagenetic analysis for reservoir characterization in carbonate systems. The established facies-to-diagenesis relationships provide a robust framework for understanding reservoir heterogeneity and fluid flow behavior in the Sarvak Formation and analogous carbonate reservoirs.

Understanding the failure mechanisms affecting cement sheath integrity under ultra-high temperature (UHT) conditions is essential for ensuring wellbore integrity and supporting the safe and sustainable extraction of deep oil and gas resources. However, difficulties in characterizing moisture migration have limited understanding of the physicochemical mechanisms and cementing processes that drive failure of cementing materials in such environments. To address these challenges and enhance understanding of cement sheath failure mechanisms, this study systematically evaluates the adaptability and advantages of Neutron–X-ray Dual-modal Imaging (NXDI) technology. NXDI’s extensive applications in materials science, mineralogy, civil engineering, and energy research provide representative evidence of its potential. Building on this foundation, the study proposes, for the first time, a conceptual framework, technical roadmap, and future prospects for applying NXDI to cement sheath research under UHT conditions. Our findings indicate that intensified moisture migration critically affects the microstructure and physicochemical stability of cement sheaths. NXDI offers unique potential to overcome the limitations of conventional imaging methods by simultaneously characterizing moisture dynamics and microstructural evolution. Nevertheless, its application still faces challenges, including neutron imaging quality, dual-modal image fusion, sample representativeness, and practical implementation. To address these, we outline key research priorities and feasible steps. With continued advances in imaging technologies and experimental methods, NXDI is expected to play an increasingly important role in enhancing the efficiency, safety, and sustainability of deep oil and gas extraction.

Layering is common in hydrocarbon reservoirs due to stratified sedimentary deposition, with each layer exhibits distinct characteristics. Multilayer reservoirs are typically clasifiedas systems with interlayer/formation crosflow and systems without interlayer crosflow (commingled systems). This study focuses on the latter, where fluid exchange between layers occurs solely through the wellbore. Accurately interpreting pressure transient data from commingled systems to extract individual layer properties remains a persistent challenge. Most existing interpretation techniques rely on assumed reservoir models (typically homogeneous, isotropic, and infinite in extent) that involve numerous unknown parameters (such as permeabilities and skin factors) and require nonlinear history matching. These assumptions not only introduce subjectivity but can also yield unreliable estimates. The most reliable way to characterize individual layers has traditionally required isolating and testing each layer separately, which is a process that is both technically complex and costly to implement. This study introduces a testing and analysis methodology developed specificaly for commingled multilayer reservoirs. The proposed approach utilizes deconvolution to remove rate variation effects from bottomhole pressure data and generate pressure responses equivalent to constant-rate conditions. This transformation enables the recovery of distinct pressure signatures for each individual layer without requiring assumptions. The recovered pressure signals are wellbore storage (WBS) free and extend over the entire test duration, offering a clearer window into the dynamic behavior of each contributing formation. Deconvolution needs complete sandface rate data for each layer, data that are typically unavailable in field practice. To address this, the study develops a simple yet effective power-law model that reconstructs layer-specific rate profiles using only a few discrete production logging tool (PLT) measurements, making the overall approach feasible for real-world applications. The method was implemented and validated on three simulated oil reservoir scenarios, each exhibiting different reservoir/boundary characteristics. The results show that the estimated rate profiles, when input into a stable deconvolution algorithm, produce layer-specific pressure responses. The technique successfully distinguishes the flow behavior and boundary conditions of each individual layer, even under complex conditions such as heterogeneity, no-flow boundaries, dual-porosity formations, and wellbore crosflow during shut-in periods. This advancement enables efficient reservoir description and production optimization, by eliminating the need for mechanical isolation or extended shut-in periods, offering a practical alternative that is aligned with the industry demands for minimum well intervention and cost-effective surveillance.

Accurate reservoir characterization is essential for optimizing hydrocarbon exploration and production, particularly in complex deep-water environments. The Simsat Field, Offshore Nile Delta, features a Pliocene deep-water turbidite reservoir, primarily composed of interbedded sand channels and shale, which poses significant challenges for reservoir delineation due to lateral lithological variations, limited well control, and complex stratigraphic architecture. Traditional seismic interpretation methods often struggle to capture the heterogeneity and connectivity of these reservoirs, leading to uncertainties in hydrocarbon prospect evaluation. To address these challenges, this study integrates seismic attributes, post-stack seismic inversion, and a multi-layer feed-forward neural network (MLFN) to enhance quantitative reservoir characterization. This integrated approach outperforms the limitations of individual techniques by combining the spatial resolution of seismic attributes, the lithology-fluid sensitivity of inversion, and the non-linear predictive capabilities of machine learning. The workflow provides a synergistic solution that improves property prediction accuracy and reduces interpretation uncertainty, particularly in data-limited, structurally complex settings. Spectral decomposition improves the visualization of channel morphology and stratigraphic variations, while seismic inversion generates acoustic impedance volumes that aid in lithology differentiation and fluid detection. The MLFN model, trained using well log data and multiple seismic attributes, provides high-accuracy predictions of shale volume (Vsh), porosity, and water saturation, significantly improving the assessment of reservoir quality. The results confirm a wel-defined gas-bearing sandstone reservoir with high porosity (>18%) and low water saturation, indicating strong hydrocarbon potential. This integrated approach demonstrates the effectiveness of combining advanced seismic interpretation with machine learning techniques to reduce interpretation uncertainties, improve reservoir connectivity analysis, and optimize field development strategies in the West Delta Deep Marine (WDDM) concession. The findings provide valuable insights into reservoir heterogeneity and contribute to more effective hydrocarbon exploration and production in the Offshore Nile Delta.

In oil and gas exploration, kick is a typical high-risk downhole incident. Currently, most intelligent kick detection methods belong to supervised learning, which depends on labeled samples and is prone to class imbalance issues in the training set, leading to reduced model accuracy and higher false alarm rates in practical applications. To address this, this paper focuses on the application of unsupervised learning in kick detection, proposing a kick detection method based on Long Short-Term Memory Autoencoder (LSTM-AE) combined with parameter trend change rules. The study integrates LSTM-AE with expert knowledge to develop an intelligent kick early warning system suitable for well sites, validated using field data from five wells. The results show that the average reconstruction mean squared error of the LSTM-AE is 0.017, with an average false alarm rate of 4.56% for kick detection, detecting kicks an average of 9.2 min earlier than manual detection. This outcome confirms that kick detection can utilize unsupervised learning methods, avoiding relying on labeled samples and class imbalance issues in the training set, thereby effectively improving model accuracy and generalization ability. Moreover, the proposed LSTM-AE demonstrates superior performance in kick detection, and the detection method combining the model and judgment rules has significant implications for monitoring and early warning of other types of downhole incidents.