Porosity is a crucial parameter in assessing reservoir performance, as its accurate estimation in simulation models impacts reserves evaluation, production forecasting, and field development. The inherent uncertainties associated with porosity estimation, namely core stress relief, logging errors, gas effects, and interpretational uncertainty, necessitate specialized approaches during model development. Traditional methods often address these uncertainties only partially, are weakly constrained by well data, may lack sufficient detail, and may not preserve geological consistency. This study presents an integrated methodology that systematically assesses these uncertainties by combining core analysis data, well logs, and automated parameter variation. To enhance reliability, the methodology ranks porosity estimates from well logs by correlation with core data. A comprehensive uncertainty assessment employs well-zone regionalization, treating each geological layer as an independent zone. All identified uncertainties are integrated into a unified database, which is then used to propagate them across the entire reservoir. In the reference interval, the proposed method exhibited an uncertainty range of 0.7–4.4% for porosity, compared to 3.1–10.4% for the four traditional approaches considered, indicating a considerable reduction in uncertainty. This approach preserves the geologically consistent porosity distribution along wellbores while ensuring sufficient granularity for the variation process. The proposed methodology was successfully applied to a real-world field case where interpretation was complicated by data scarcity and a complex geological setting. The developed approach yields a geologically sound three-dimensional model, providing a robust basis for informed decisions in hydrocarbon asset development and management, and may reduce manual effort while constraining automated history matching within data-driven porosity bounds.
The northern monocline belt of the Kuqa Depression, Tarim Basin, is characterized by structural complexity and limited exploration, despite its considerable hydrocarbon potential. This study subdivides the area into the Kubei and Bashi segments, separated by the Heiyingshan strike-slip fault, and investigates their differentiated hydrocarbon accumulation patterns using integrated geochemical fingerprinting, Bayesian classification, hierarchical clustering, fluid inclusion analysis, PVTx modeling, burial–thermal history reconstruction, and seismic interpretation. The results show that the Kubei segment, controlled by multi-phase thrusting, follows a “continuous hydrocarbon generation–episodic charging” model. Oil–source correlation indicates that hydrocarbons were mainly derived from the Triassic Huangshanjie Formation (T3h), with minor input from the Karamay Formation (T2–3k). Three types of hydrocarbon-bearing inclusions in well MQ1 record three charging events at approximately 55 Ma, 23 Ma, and 3 Ma. In contrast, the Bashi segment is mainly controlled by strike-slip faulting and exhibits a “late rapid burial–two-phase charging” model. Hydrocarbons in the Bashi segment are primarily sourced from T2–3k source rocks. Fluid inclusions in well KQ1 indicate two charging stages during the early stage of Kangcun Formation deposition and during Kuche Formation deposition, at approximately 5.3 Ma and 3 Ma. These results reveal a segmented petroleum system controlled by contrasting tectonic histories and source rock contributions, providing important guidance for future deep hydrocarbon exploration in the northern Kuqa Depression.
Cementing is essential for maintaining wellbore integrity, but high-pressure, high-temperature (HPHT) reservoirs can compromise the stability of conventional cement formulations. Ilmenite is commonly used as a weighting material to increase cement density for HPHT applications. However, it can cause particle settling and affect other slurry and hardened-cement properties. This study evaluated the effect of tire-waste powder on ilmenite-based cement, with emphasis on rheology, compressive and tensile strength, dynamic Young’s modulus, dynamic Poisson’s ratio, porosity, and particle settling assessed through density variation and computed tomography (CT) scans. Results showed that a low dosage of tire-waste powder improved both fresh and hardened properties of high-density ilmenite-based cement. After screening 0-4% tire waste by weight of cement (BWOC) for rheological performance, 2% BWOC was selected for detailed characterization. At this dosage, slurry fluidity improved, with plastic viscosity decreasing by 59%, yield point increasing by 28%, and 10-second gel strength increasing by 124%. The 2% dosage also increased compressive strength by 58% and tensile strength by 9%, indicating a more durable cement sheath. Ultrasonic measurements showed a 17.5% reduction in dynamic Young’s modulus and a 32% increase in dynamic Poisson’s ratio, suggesting improved wellbore stability. Porosity decreased by 4%, indicating enhanced sealing performance. Density-variation testing and CT-based analysis further confirmed a more homogeneous density distribution, with a low density variation of 0.5%. These findings support the use of tire waste as a performance-enhancing additive for heavy-weight HPHT cementing systems while valorizing an end-of-life waste stream.
Smart water injection has emerged as a cost-effective and environmentally sustainable enhanced oil recovery (EOR) strategy for carbonate and sandstone reservoirs. This review presents a comprehensive and contemporary synthesis of the physicochemical mechanisms governing smart-water EOR, including wettability alteration, interfacial tension (IFT) reduction, multi-ion exchange, and electrical double-layer expansion. It further discusses emerging phenomena such as salting-in and salting-out effects, pH-dependent mineral reactions, precipitation-induced mobilization, and ion-specific interactions with surface-active organic species. The review explores the synergistic potential of hybrid systems that integrate smart water with nanoparticles, surfactants, polymers, and dissolved CO2 (carbonated smart water, CSMW), providing mechanistic insight into how these combinations enhance oil displacement efficiency under varied reservoir conditions. Economic and operational assessments demonstrate that smart water offers a technically robust and lower-cost alternative to polymer flooding and CO2 injection, while maintaining compatibility with existing surface facilities. To address the increasing complexity of reservoir chemistry and production optimization, the integration of artificial intelligence (AI) and machine learning (ML) is proposed for mechanism identification, brine design, and real-time injection control. Advanced techniques such as hybrid physics–AI modeling, explainable AI, and federated learning are highlighted as future tools for intelligent, adaptive EOR management. Finally, the review identifies key challenges scaling, corrosion, and brine compatibility and outlines sustainable solutions including zero-liquid-discharge (ZLD) systems and renewable-powered injection operations. By combining mechanistic understanding, digital innovation, and sustainability principles, this study provides a unified roadmap positioning smart-water injection as a next-generation, mechanism-driven, and low-carbon EOR strategy.
To investigate the hydrogen leakage and diffusion behavior of buried hydrogen pipelines, this paper establishes a fluid-domain model specifically for buried pipelines and examines the effects of operating pressure, leak size, soil type, and leak orientation on hydrogen dispersion. The results demonstrate that both the gas diffusion velocity and leakage rate are directly proportional to the pipeline operating pressure and leak size. Hydrogen exhibits higher diffusion velocities in low-viscosity soils, with the leakage rate in sandy soil being 3.9 times greater than that in clay. Consequently, pipelines operating under high-pressure conditions in low-viscosity soils exhibit elevated risk coefficients. To predict hazardous zones under various operating conditions, a GA-BP neural network model was developed using numerical simulation data as training samples. The model achieves reliable predictive capability with a maximum mean deviation error of -2.12.
The cyclic loading and unloading of casing internal pressure is an important influencing factor causing the generation and development of micro-annulus at the wellbore cementation interface.The well deviated section bears alternating loads while facing complex conditions of deflection of in-situ stress and casing eccentricity. A numerical model of the casing-cement sheath-formation combination in the deviated well section considering the casing eccentricity and in-situ stress deflection was established. A comparative analysis was conducted on the change law of the cumulative plastic strain under conventional conditions, as well as under the conditions of in-situ stress deflection and casing eccentricity in the deviated well section. The influence of the casing's centralization degree, wellhead pressure, and mechanical parameters of the cement sheath on the the change law of the width of micro-annulus was analyzed, and the distribution law at different depths in the well deviated section was quantified. The research findings indicated that, as the degree of casing eccentricity grew, the initial plastic strain will correspondingly increased, thereby further heightening the risk of gas channeling. The width of the micro-annulus formed within the cement sheath gradually expanded as the wellhead pressure keeps rising. Under identical mechanical environment conditions, enhancing the casing centralization degree and decreasing the elastic modulus of the cement sheath are of great benefits for reducing the width of the micro-annulus. Under the condition of the same number of alternating load cycles, the width of the micro-annulus in the well deviated section will tend to increase when depth increases. Finally, a cementing method using composite slurry column structure in deviated section was proposed, and it proved to have better effect in controlling the width of micro annulus. Moreover, the research results can offer an effective theoretical reference for safeguarding the sealing integrity of the cement sheath in the deviated well section.
Understanding the controls of depositional and diagenetic processes on pore system evolution is essential for predicting fluid flow behavior in sedimentary reservoirs. The sandstones of Mangahewa Formation were deposited during a marine transgressive phase in the Taranaki Basin, New Zealand, during the Late Eocene. Despite their potential as hydrocarbon reservoirs, the pore system evolution, as well as the extent of reservoir heterogeneity within Mangahewa transgressive facies, remains poorly constrained. This study applies an integrated sedimentological, petrographic, and petrophysical approach to investigate these processes. The transgressive sandstones comprise stratified lithofacies that grade upward into intensely bioturbated sandstones interbedded with siltstones and mudstones. The sandstones are arkosic to subarkosic arenites affected by compaction, feldspar dissolution, and selective carbonate and clay cementation. Reservoir heterogeneity is controlled by the combined influence of depositional facies, sandstone composition, and diagenetic modification, which govern pore system architecture and connectivity. Petrophysical analysis reveals a wide range of porosity (1%–24.9%) and permeability (0.01–10,000 mD), indicating pronounced heterogeneity. Statistical heterogeneity indicators and hydraulic flow unit (HFU) classification identify six HFUs with contrasting porosity–permeability relationships and flow capacities. Stratified sandstones form the main flow conduits, whereas bioturbated and fine-grained transgressive deposits act as flow baffles and barriers due to restricted pore-throat connectivity. These results highlight the critical role of depositional architecture and diagenetic evolution in controlling reservoir quality and fluid flow behavior in transgressive sandstone systems, improving the prediction of high-quality reservoir intervals in the Upper Eocene Mangahewa Formation and analogous settings.
Although there are abundant shale gas resources in China, shale gas is generally buried much deeper than foreign countries. For large-scale development, there are still many core technologies that have not yet been solved. In this paper, we have analyzed microstructure of formation rock and its mineral composition, and the mechanism of wellbore instability is revealed by studying the petrophysical properties and characteristics of the wellbore instability strata in the Changning-Weiyuan area. The laboratory innovatively constructs the "solid phase intercalation-liquid phase expansion stripping method" and obtains high-quality graphene nanosheets. The results of the performance test and the HTHP filtration test on the graphene nanosheets show that it achieves the lowest HTHP filtration loss (8.0 mL) among tested additives, outperforming super-fine CaCO3(8.6 mL) and conventional agents (9.2 mL), while minimally affecting rheology. Other performance tests indicate that high-quality graphene nanosheets made by our laboratory can be well dispersed in solvent with almost no obvious aggregation and it has the median particle size ranges from 212 nm to 228 nm, which means it has perfect ability to plug micro-crack in the Changning-Weiyuan area. The oilfield application shows that adding 0.75% graphene nanosheets to the field mud significantly enhanced wellbore stability, achieving zero mud loss and a 94.6% drilling rate in the target zone, while reducing HTHP filtration to below 2.4 mL. (c) 2026 Southwest Petroleum University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
Severe fluid loss in fractured formations poses a major technical challenge in deep oil and gas drilling, significantly constraining wellbore integrity and drilling efficiency. This study establishes a comprehensive multi-scale research system combining indoor experiments and CFD-DEM numerical simulations to investigate particle transport and sealing mechanisms. A dynamic sealing model incorporating the Di Felice drag force and modified JKR contact theory was innovatively developed to rigorously analyze multi-physics interactions, including van der Waals forces, electrostatic forces, and fluid shear under complex seepage conditions. Results demonstrate that for single-particle sealing, the optimal particle size is 1/2 to 2/3 of the fracture width, satisfying the one-third bridging theory. Among gradation schemes, a triple-particle distribution exhibits the highest pressure-bearing capacity, particularly when dominant particles comprise 70%–80% and the filling particle size ratio is 0.2–0.4. Furthermore, an optimal concentration window of 8% was identified; lower concentrations result in sealing failure, while higher concentrations cause false plugging due to jamming effects . Additionally, increasing drilling fluid viscosity to 50 mPa · s enhances sealing layer density, while an optimal fluid density of 1.4 g/cm3 achieves a critical balance between particle accumulation efficiency and buoyancy. With experimental and simulation results agreeing within a 5% relative error, this research provides a robust theoretical basis for optimizing leak-stopping fluid formulations in fractured strata.
The oil and gas industry increasingly employs advanced engineering solutions to optimize enhanced oil recovery (EOR). A systematic and effective screening process, supported by multi-criteria decision-making (MCDM) techniques, is essential for selecting appropriate reservoirs and EOR strategies toward production optimization. This study introduces a screening framework designed to identify the most suitable EOR alternative. The proposed approach integrates a coupled objective-subjective weighting method to assign criteria weights, followed by a refined, data-driven, non-linear scoring procedure and an improved approach for prioritizing EOR alternatives. A distance-based scoring method is developed to evaluate alternatives against a desired screening interval, utilizing the Full Consistency Method (FUCOM) and Simultaneous Evaluation of Criteria and Alternatives (SECA) model for weighting criteria and subsequently integrating them into a unified assessment. The ranking of alternatives is performed using a modified version of the approach introduced by Dickson et al. (2010). The applicability of the proposed framework is demonstrated through two CO2-EOR case studies, while its reliability is assessed through Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The Spearman’s rank correlation coefficient for three ranking methods was over 0.982 for Iran's CO2-EOR screening case and above 0.943 for Canada's, showing the robustness and consistency of the ranking results. The findings confirm the effectiveness of the developed hybrid decision-support framework in optimizing EOR strategy selection, thereby contributing to more efficient hydrocarbon recovery in line with carbon capture and storage objectives.
Permeability prediction in tight sandstone reservoirs is strongly influenced by diagenesis, multiscale pore structures and multiphase flow effects, resulting in pronounced spatial heterogeneity. Traditional empirical models based on homogenization assumptions suffer from inherent physical limitations, making their predictive accuracy insufficient for practical applications. Although existing data-driven deep learning methods offer advantages in nonlinear modeling, their performance is constrained by few-shot samples, imbalanced distributions, low-density information and the absence of physical constraints, which hinder the extraction of effective representations of inter-features and lead to biased learning and physical deviations. To address these challenges, this study proposes a framework including a data enhancement strategy based on high-order feature construction for quality improving and adversarial generation for quantity expanding, and a deep learning model termed DAFM-PI, which integrates bidirectional attention fusion with physical information constraints. Experimental results show that the test accuracy R2 of DAFM-PI improves from 0.444 before enhancement to 0.969 after that, representing a 52.2% improvement. Furthermore, compared with conventional POR-LR, ResNet, and Transformer Encoder models, DAFM-PI achieves accuracy improvements of 61.9%, 18.1%, and 6.2%, demonstrating its effectiveness in enhancing both prediction accuracy and generalization for permeability prediction in tight sandstone reservoirs.
The size of sulfur particle aggregates directly influences the critical sulfur particle carrying velocity, flow resistance, sulfur blockage thickness, corrosion rate, and scaling rate in high-sulfur gas wells. this can significantly impact the normal production of the gas wells. Although considerable research has been conducted on the mechanisms and behaviors of particle aggregation, studies specifically addressing the diameter of sulfur particle aggregates remain limited. In this paper, utilizes numerical simulation and multiphase pipe flow experiments to investigate the gas-particle sulfur two-phase pipe flow. It focuses on the effects of gas velocity, particle flow velocity, initial particle diameter, and initial particle concentration on the size of sulfur particle aggregates. The results indicate that the maximum diameter of sulfur particle aggregates diminishes as gas velocity and particle flow velocity increase, whereas it escalates with larger initial particle diameters and higher particle concentrations. A predictive model for the diameter of sulfur particle aggregates has been established based on the principles of mechanical equilibrium, incorporating factors such as particle collision frequency, aggregation probability, and the influences of van der Waals forces and liquid bridge forces between particles. This model has been validated with experimental data (six sets), numerical simulations (twenty sets), and relevant literature (ten sources), demonstrating high concordance between predicted and measured diameters, with an average absolute error of 6.35%. By predicting sulfur particle aggregate diameters in the wellbore, the model aids in optimizing gas well production, enhancing flow efficiency, and reducing wellbore blockages, providing a strong theoretical basis for optimizing multiphase flow.
Carbon dioxide (CO2) injection is a promising strategy for enhancing shale oil recovery while enabling geological carbon storage. In this study, non-equilibrium molecular dynamics (NEMD) simulations were employed to investigate CO2-driven shale oil transport in kerogen nanopores with complex surface characteristics. The distribution and migration behaviors of multiphase shale oil were analyzed, and the effects of pore size, temperature, and injection pressure on fluid flow and CO2 sequestration were systematically evaluated. The results show that shale oil forms three distinct adsorption layers within kerogen nanopores, with the first layer exhibiting a peak density of approximately 1.13 g/cm3, which is about 1.85 times higher than that in the free region. The irregular kerogen surface increases the resistance to shale oil desorption, resulting in slower CO2-driven displacement compared with smooth graphene pores. Increasing pore size, temperature, and injection pressure significantly promotes shale oil desorption and enhances CO2 storage capacity. For example, when the pore size increases from 2 nm to 5 nm, the shale oil adsorption fraction decreases markedly while CO2 storage increases significantly. Temperature also plays an important role in regulating adsorption and sequestration behavior. Overall, this study reveals the molecular-scale mechanisms governing CO2-enhanced shale oil mobilization and carbon sequestration in kerogen nanopores, providing theoretical insights for optimizing CO2 injection strategies in shale reservoirs.
The discovery of ultra-deep strike–slip fault–controlled hydrocarbon reservoirs in the central Tarim Basin has renewed interest in the structural evolution and reservoir-controlling mechanisms of intracratonic strike–slip systems. Based on integrated drilling data, high-resolution 3D seismic reflection interpretation, and structural analog modeling, this study investigates the FI12 and FI17 fault zones in the Fuman area as representative examples. The results show that ultra-deep strike–slip faults exhibit combined lateral segmented growth and vertical stratified propagation, with secondary shear faults overlapping and stepping in both horizontal and vertical directions. To characterize fault activity in a reproducible manner, a semi-quantitative slip intensity framework is established using fault-zone width, structural relief, and segmentation complexity. Comparative analysis demonstrates that slip intensity is the first-order control on the scale and effectiveness of fault-controlled carbonate reservoirs: fault zones with higher slip intensity develop wider damage zones, stronger fracture connectivity, and larger reservoir volumes. Within individual fault zones, slip intensity is preferentially concentrated at lateral step-overs and relay zones of secondary shear faults, where large-scale fracture corridors form and hydrocarbon productivity is significantly enhanced. In addition, for reservoirs characterized by a lower-source–upper-reservoir configuration, hydrocarbon productivity is positively correlated with the proximity of vertical fault step-overs to the target reservoir interval. Shallower vertical overlap facilitates more efficient upward hydrocarbon migration, resulting in higher hydrocarbon abundance. These results establish a three-dimensional structural control model linking slip intensity, fault architecture, and reservoir effectiveness in ultra-deep carbonate strike–slip systems, providing a robust geological basis for reservoir prediction and exploration risk reduction in complex ultra-deep settings.
Lost circulation(LC), as one of the high-risk accidents in drilling, usually occurs when the wellbore pressure is greater than the formation pressure, causing a large amount of drilling fluid to seep into the formation and resulting in a significant decrease in the flow rate at the wellhead. Currently, existing LC prediction studies mainly rely on logging parameters for time series prediction, ignoring the problem of the inherent imbalance in the ratio of positive and negative samples in the LC dataset and geological information. To address these issues, this study proposes a clustering-guided feature enhancement and Bayesian optimization Long Short-Term Memory(BO-LSTM) method for LC time series anomaly detection. This method, based on the mud logging and geological structural information of faults and lithology risks, leverages clustering algorithms to uncover the latent structural information within samples to construct enhanced features, which combine with the selected key features, forming a joint representation that is fed into the LSTM model. At the same time, the BO algorithm is introduced to adaptively optimize the LSTM hyperparameters, and finally outputs the LC occurrence's probability. The results show that the proposed method performs well in LC prediction, with an average false negative rate of 0.825%, an average false positive rate of 1.525%, and an average lead time of 4.825 minutes for effective early warnings, significantly improving the accuracy and timeliness of early LC identification.
Accurately forecasting the sealing failure behavior of the casing-cement sheath-formation assembly under various steam injection operational parameters and cyclic steam stimulation conditions remains challenging. Such unpredictability heightens the risk of seal integrity compromise in the wellbores of thermal heavy oil production. This paper develops a numerical simulation and analytical approach based on heat-fluid-solid interactions within the casing-cement sheath-formation system. It assesses the impact of potential cement sheath and interfacial failures in response to differing steam injection pressures, temperatures, injection rates, and cyclic steam volumes. This assessment leverages the established relationships between the temperature-dependent elastic modulus and Poisson's ratio of cementitious materials, as well as the dynamics of wellbore pressure, temperature, and steam quality during steam injection. Experimental findings suggest that maintaining steam injection temperatures between 340 degrees C and 380 degrees C can effectively mitigate the risk of shear-induced damage and enhance the safety of the cementing interface. The injection rate appears to have a minimal impact on interface integrity. The extent of plastic strain in the cement sheath is proportionate to the size of the micro-annular gap at the interface. When the initial formation has an elastic modulus of 15 GPa, variations in the cement sheath's elastic modulus do not influence the micro-annular gap size. However, in formations with an initial elastic modulus of 5 GPa, a lower elastic modulus of the cement sheath corresponds to a smaller micro-annular gap. The study concludes that a higher initial geostress and temperature, coupled with a high elastic modulus of the surrounding strata, are conducive to maintaining the integrity of the cement sheath interface. (c) 2026 Southwest Petroleum University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
A significant challenge in advancing nanoparticle-based enhanced oil recovery (EOR) is the gap between synthesis and application. Many reviews catalog surface modification techniques but fail to explain why specific molecular features lead to success in EOR mechanisms. Our work addresses this gap by providing a mechanism-centric framework focused on structure-to-function relationships. This approach is vital because while nano-EOR promises to overcome the drawbacks of traditional recovery, the base nanoparticles themselves are often ineffective. Their low stability and poor interfacial performance in challenging reservoir environments necessitate surface functionalization. Instead of focusing on modification methods, we analyze how the deliberate addition of chemical groups like sulfonates, carboxylates, hydroxyls, amines, and alkyl chains directly impacts core EOR functions, including reducing interfacial tension (IFT), altering rock wettability, stabilizing emulsions and foams, and enhancing viscoelasticity. By elucidating the underlying molecular interactions (electrostatic, hydrogen bonding, hydrophobic), we build a clear pathway from chemical design to functional outcome. This review extends to advanced applications, including trigger-responsive smart nanoparticles and asphaltene inhibition. Ultimately, we provide a comparative analysis and a selection guide to empower researchers to rationally design nanoparticles for specific reservoir challenges, thereby accelerating the field’s transition from empirical testing to predictive engineering.
Fracability evaluation is a crucial basis for fracturing and production enhancement in tight reservoirs. Due to the complexity of the geological environment, factors influencing reservoir fracability exhibit significant uncertainty. Ignoring the uncertainty of relevant parameters and conducting fracability evaluation based on deterministic parameters may lead to deviations from actual fracability results. To address this issue, this paper proposes a comprehensive evaluation method for the fracability of unconventional oil and gas reservoirs, considering reservoir description uncertainty. Based on fundamental evaluation methods, a reservoir fracability evaluation model is constructed, incorporating the Monte Carlo stochastic simulation method to determine the comprehensive probability distribution of the reservoir fracability evaluation index. This approach enables a more scientific and reliable evaluation of reservoir fracability. The research results indicate that the assumed distribution of input parameters has a certain impact on fracability evaluation results, with normal distribution demonstrating significant disturbance resistance. Additionally, brittleness index is found to be the most sensitive factor affecting fracability evaluation. The proposed evaluation method and insights can provide theoretical references for the fracability assessment of highly heterogeneous tight reservoirs. (c) 2026 Southwest Petroleum University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
Wellbore instability in chalk formations poses significant challenges during drilling and production due to low rock strength, fluid sensitivity, and creep. This study investigates the efficacy of diammonium phosphate (DAP) as a chemical consolidating agent to enhance chalk mechanical properties under simulated reservoir conditions. Austin Chalk core samples were treated with a 1 M DAP solution at 75 degrees C and 1000 psi confining pressure for 72 h to promote hydroxyapatite precipitation. Triaxial loading tests compared the treated and untreated specimens. Results demonstrated that the DAP treatment improved the confined compressive strength by 16%-8% across confining pressures (400-1600 psi). Mohr-Coulomb failure envelopes revealed a cohesion increase from 600 psi (untreated) to 1350 psi (treated), with unconfined compressive strength doubling to 3200 psi. These enhancements, attributed to hydroxyapatite cementation, indicate DAP's potential to mitigate wellbore failure by strengthening the formation itself. The findings advance chemical stabilization strategies for chalk, offering a novel solution to reduce non-productive time and improve long-term well integrity in carbonate reservoirs. (c) 2026 Southwest Petroleum University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
Persistent CO2 intrusion during drilling operations in deep carbonate formations frequently leads to excessive thickening of water-based drilling fluids, resulting in significantly increased pump pressure and posing substantial well control risks. Herein, based on field contamination incident tracking and high-temperature high-pressure CO2 intrusion simulation experiments, the generation and accumulation characteristics of CO2-derived contaminants (CO32- and HCO3-) were thoroughly analyzed. The impact of contaminants on the rheological properties of water-based drilling fluids was investigated using a Haake rheometer, a six-speed viscometer, and the glass rod method. The mechanisms of CO2-induced thickening in the drilling fluid were elucidated through a combination of zeta potential measurements, particle size distribution analysis, X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), environmental scanning electron microscopy (ESEM) and energy-dispersive spectroscopy (EDS). The results indicate that CO32- accumulation is the primary cause of drilling fluid thickening, driven most rapidly through pH adjustments (using sodium hydroxide) back to ∼11 from 10–10.5 following each CO2 intrusion. The adsorption of CO32- onto bentonite leads to "feathering" and "fine-grainization" transformations within its crystal structure. This adsorption enhances the bonding forces between bentonite particles and water molecules via hydrogen bonds and ion-dipole interactions, which outweigh the repulsive forces between the negatively charged particles. As a result, bentonite particles aggregate and adhere more readily, fundamentally driving the excessive thickening of the drilling fluid, particularly characterized by a significant increase in gel strength. This work provides a theoretical foundation for optimizing drilling fluid formulations against CO2 contamination through pH management and selective ion control.
