Corrosion remains a persistent challenge in drilling operations, particularly under high-pressure, high-temperature (HPHT) conditions, resulting in economic losses and safety risks. While conventional corrosion inhibitors have been effective to varying extents, their limitations, particularly in terms of toxicity, biodegradability, and compatibility with drilling fluid properties, necessitate exploring sustainable alternatives. Recent advances have focused on green inhibitors, notably plant-derived extracts, due to their biodegradability and low toxicity. However, a critical gap remains: few studies holistically evaluate both corrosion inhibition efficiency and the resultant impact on drilling fluid performance. To address this, the present review introduces an Integrated Screening and Corrosion Inhibition Modelling (I-SCIM) framework. This review comprehensively synthesizes recent research on corrosion inhibitors used in water-based, oil-based, and synthetic-based fluids. It categorizes inhibitors, evaluates their performance, and discusses their integration into complex mud systems. Key findings highlight that while conventional inhibitors achieve 70-85% efficiency, emerging green and hybrid formulations often exceed 90% inhibition. Furthermore, the paper explores the emerging class of smart and responsive inhibitors that adapt to environmental stimuli such as pH and temperature, highlighting their potential in dynamic wellbore conditions. By adopting the I-SCIM framework, this paper bridges the fragmented assessment approaches in current literature and proposes a unified strategy for evaluating corrosion inhibitors beyond inhibition efficiency alone. The review culminates in future research recommendations for inhibitors that are sustainable, efficient, and compatible with the evolving demands of HPHT drilling.

The pressure fluctuation caused by the axial movement of the string easily causes the bottomhole pressure to exceed the safe pressure window, which seriously restricts the horizontal open-hole extension capacity (OHEC) of the offshore extended-reach horizontal wells (ERHWs). In this work, in order to improve the horizontal OHEC for ERHWs, an optimal mud density model in extended-reach horizontal drilling (ERHD) was established based on the prediction and analysis of horizontal OHEC of ERHWs under string tripping operations. The results show that with the increase of mud density, the horizontal OHEC has a trend of first increasing and then decreasing in both tripping operations and casing tripping operations, and there exists an optimal mud density to maximize the horizontal OHEC. The optimal mud density of the two operations is obtained when the horizontal OHEC of the two operations is equal, and it is positively correlated with pull out of hole (POOH) speed, diameter of the wellbore and drill string, whereas it is negatively correlated with diameter of the casing and casing running speed. Controlling the tripping speed and optimizing the rheological properties are crucial to improve the horizontal OHEC. This model can provide scientific basis for optimization design of ERHD and the improvement of horizontal OHEC of ERHWs.

To address the absence of fire resistance evaluation methods for drilling wellhead structures under gas well blowout fires, this study firstly conducted experiments to investigate the mechanical properties of wellhead materials under high-temperature conditions. Subsequently, a temperature model and a mechanical response model of the wellhead structure under fire were established and validated respectively based on thermo-mechanical coupling methods. Furthermore, a fire resistance limit evaluation method of wellhead structures in well blowout fires was developed. The results indicated that with clearance operations at the well site, a thermal radiation threshold existed; Only when the thermal radiation exceeded this threshold, the wellhead reached its fire resistance limit and failed. Without clearance operations at the well site, the wellhead structure was certain to reach its fire resistance limit and failed. The upper surface of the wellhead had the largest displacement and reached its fire resistance limit first. The convective heat transfer coefficient, pressure load on the upper surface of the metal sealing ring, and casing gravity load had minimal influence on the fire resistance limit. Without clearance operations at the well site, the wellhead surface temperature approached the flame temperature, and the rubber seal reaches its decomposition point, leading to seal failure. The research findings could provide valuable references for the safety assessment of wellheads during gas well blowout fires and contribute to the development of effective response strategies for blowout fire control.

The friction factor plays a critical role in drag and torque calculations. In the drag and torque model, the friction factor is typically treated as an average value, or the wellbore is roughly divided into casedhole and openhole segments with different values assigned. Such simplified model parameters fail to account for variations in the friction factor across different well segments, impacting the accuracy of the computations. Precise drag and torque calculations are significant, and the prerequisite for accurate drag prediction is the inverse analysis of realistic friction factor parameters. This study proposes a friction factor inverse analysis method based on morphological characteristics of measured data. The Fisher optimal segmentation method is employed to divide the field-measured data into multiple reasonable subsegments based on curve morphology features, indirectly determining the segmentation of the friction factor. The 3D stiff-string drag and torque model is used to establish friction factors inverse model, with corresponding friction factors introduced separately for the segmented well sections. The sparrow search algorithm, enhanced by opposition-based learning strategy, is utilized to optimize the segmented friction factors, with the optimization evaluation criteria simultaneously assessing the deviation and similarity between simulated and actual curves. Case studies demonstrate that the friction factor inverse analysis method proposed in this study, leveraging the curve morphology features of realtime drilling data, can efficiently obtain high-precision segmented friction coefficients. This method significantly reduces the error between simulated and field-measured data, improving calculation accuracy by about 50%, thus providing robust data support for accurate drag and torque prediction. Furthermore, the division approach can be applied during a round trip to optimize drilling parameters, enhancing the efficiency of drilling parameter control.

Soil contamination by total petroleum hydrocarbons (TPH) poses a major risk to soil health and crop productivity. This study aims to investigate the effects of TPH on soil properties and rice growth factors, as well as the potential of sodium persulfate (SP) to mitigate negative impacts. The study includes four treatments: control (no SP or TPH), SP (0.0, 1.50 and 3.0%), TPH (TPH-contaminated soil, 4.0%, 6.0%, and 8.0%), and SP + TPH (SP and TPH levels). Results show that TPH contamination led to reductions in pH, organic matter, nutrient availability, root and shoot growth, chlorophyll content, plant height, grain and biomass yield. SP facilitates the restoration of soil physicochemical properties, mitigates toxicity, and enhances plant growth conditions by promoting both chemical oxidation and microbial degradation of TPH contaminants. Higher doses of SP are required to counteract higher TPH levels. The findings suggest that SP can be an effective amendment for remediating TPH-contaminated soils, but dosage and application should be tailored to site-specific conditions. This study highlights the potential of SP for sustainable management of TPH-contaminated agricultural land, but further work is needed to fully understand its impact on different plant species and to optimize remediation strategies for specific site conditions.

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 flooding 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 classification 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.

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.

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 well-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.

This study uses 13 proprietary data sets from sedimentary basins around the globe to constrain the permeability and porosity ranges occupied by clastic rocks (sandstones and shale). The combined data sets represent a total sample size of 21,767 data pairs of measured porosity and permeability. The data are combined in various ways and analyzed in considerable detail, in what is presently assumed the most comprehensive permeability analysis of clastic data sets. First, comprehensive porosity-permeability transforms are plotted with the aim to understand how well the two quantities actually correlate, and what may be the root cause(s) of the huge variation in their correlation. The analysis of the empirical data is embedded in a review of prior work related to permeability transforms. The Kozeny-Carman relationship is revisited and a modified scaling approach is proposed. First, it is shown how hydraulics of pore tubes of various shapes and tortuosity relate to macroscopic permeability. The key scaling factor, called here the permeability-reduction factor, β/τ, is the ratio of the coefficient of hydraulically effective pore space, β, and the tortuosity, τ. Whereas it is concluded that the porosity is a very poor predictor of the permeability, there appears to exist a close relationship between the permeability-reduction factor and the permeability, confirming the fundamental physical nature of β/τ as an excellent predictor of hydraulic transmissibility in porous media made up of sedimentary mineral aggregates. The inferred relationship provides the basis for a new method to construct permeability transforms, from bootstrapped data sets, using Monte-Carlo simulation.

At present, the world is experiencing an unprecedented period of change, and the third wave of energy conversion is also emerging. Natural gas is widely regarded as an ideal bridge between traditional fossil fuels and new energy transformations due to its significant low-carbon advantages. Forecasting shale gas output each day is key for secure natural gas provision. However, the high dimension and nonlinear characteristics of shale gas production data present significant challenges for prediction. Therefore, this study proposes a segmented modeling method that divides the production cycle into unstable and stable periods and combines the IPSO-CNN-BiGRU-Attention hybrid model to model these two cycles respectively. Comparative results indicate segmented modeling offers superior predictive performance compared to whole production cycle modeling. Furthermore, when modeling different periods, the IPSO-CNN-BiGRU-Attention hybrid model demonstrates a superior predictive effect compared to traditional single time series models.