Oil-based drilling fluids are widely used in challenging wells such as those with large displacements, deepwater and ultra-deepwater wells, deep wells, and ultra-deep wells due to their excellent temperature resistance, inhibition properties, and lubrication. However, there is a challenging issue of rheological deterioration of drilling fluids under high-temperature conditions. In this study, a dual-amphiphilic segmented high-temperature-resistant gelling agent (HTR-GA) was synthesized using poly fatty acids and polyether amines as raw materials. Experimental results showed that the initial decomposition temperature of HTR-GA was 374 °C, indicating good thermal stability. After adding HTR-GA, the emulsion coalescence voltage increased for emulsions with different oil-to-water ratios. HTR-GA could construct a weak gel structure in oil-based drilling fluids, significantly enhancing the shear-thinning and thixotropic properties of oil-based drilling fluids under high-temperature conditions. Using HTR-GA as the core, a set of oil-based drilling fluid systems with good rheological properties, a density of 2.2 g/cm³, and temperature resistance up to 220 °C were constructed. After aging for 24 h at 220 °C, the dynamic shear force exceeded 10 Pa, and G′ exceeded 7 Pa, while after aging for 96 h at 220 °C, the dynamic shear force exceeded 4 Pa, and G″ reached 7 Pa. The synthesized compound HTR-GA has been empirically validated to significantly augment the rheological properties of oil-based drilling fluids, particularly under high-temperature conditions, showcasing impressive thermal stability with a resistance threshold of up to 220 °C. This notable enhancement provides critical technical reinforcement for progressive exploration endeavors in deep and ultra-deep well formations, specifically employing oil-based drilling fluids.

Drilling fluid is pivotal for efficient drilling. However, the gelation performance of drilling fluids is influenced by various complex factors, and traditional methods are inefficient and costly. Artificial intelligence and numerical simulation technologies have become transformative tools in various disciplines. This work reviews the application of four artificial intelligence techniques—expert systems, artificial neural networks (ANNs), support vector machines (SVMs), and genetic algorithms—and three numerical simulation techniques—computational fluid dynamics (CFD) simulations, molecular dynamics (MD) simulations, and Monte Carlo simulations—in drilling fluid design and performance optimization. It analyzes the current issues in these studies, pointing out that challenges in applying these two technologies to drilling fluid gelation performance research include difficulties in obtaining field data and overly idealized model assumptions. From the literature review, it can be estimated that 52.0% of the papers are related to ANNs. Leakage issues are the primary concern for practitioners studying drilling fluid gelation performance, accounting for over 17% of research in this area. Based on this, and in conjunction with the technical requirements of drilling fluids and the development needs of drilling intelligence theory, three development directions are proposed: (1) Emphasize feature engineering and data preprocessing to explore the application potential of interpretable artificial intelligence. (2) Establish channels for open access to data or large-scale oil and gas field databases. (3) Conduct in-depth numerical simulation research focusing on the microscopic details of the spatial network structure of drilling fluids, reducing or even eliminating data dependence.

This study proposes an interpretable deep learning framework to address the high-dimensional and inherently unpredictable challenges associated with oil and gas drilling and completion operations. By comparing TabNet, Tab Transformer, Hopular, and TabDDPM through computational experiments under identical conditions, TabNet was selected as the optimal approach. The framework integrates Bayesian optimization (BO) with TabNet to model complex oilfield tabular datasets. Fair Cut Tree (FCT) and Synthetic Minority Over-sampling Technique (SMOTE) are incorporated to mitigate data missingness and imbalance, thereby enhancing dataset integrity and robustness. Empirical validation was conducted using 270 data entries collected from 15 distinct oil fields, specifically focusing on reservoir water sensitivity damage in natural core samples. The proposed framework exhibited superior predictive accuracy for the water sensitivity index on an independent test set, achieving a mean absolute percentage error (MAPE) of 4.4495% and a root mean square error (RMSE) of 4.05, underscoring its strong generalization capability. Moreover, this methodological approach enables a quantitative assessment of the influence of critical factors, including reservoir water salinity, initial permeability, and the mineralogical composition of rock formations, on water sensitivity predictions. This represents a significant advancement from traditional qualitative analyses to a more rigorous quantitative factor analysis, with the interpretability findings corroborating established mechanistic insights. The proposed framework offers a versatile and reliable solution for precise predictive modeling in complex drilling and completion scenarios reliant on tabular data, thereby providing a robust theoretical foundation and algorithmic support for accurate forecasting in the oil and gas industry.

Fracture-induced loss poses severe challenges to drilling operations, particularly under high-temperature and high-salinity conditions encountered in deep wells. Conventional plugging materials, characterized by relatively large particle sizes and poor structural integrity, often exhibit insufficient thermal stability and salt tolerance under extreme drilling conditions, making them prone to structural degradation and loss of adhesion, which ultimately leads to drilling fluid deterioration and downhole complications. To address this issue, a core–shell-structured microgel, ANDT-70 (named after the acronyms of 2-acrylamido-2-methylpropane sulfonic acid, N-vinyl-2-pyrrolidinone, N, N-dimethylacrylamide, dimethyl diallyl ammonium chloride, and titanium dioxide nanoparticles), was synthesized and systematically evaluated for its thermal stability, salt resistance, and interfacial adhesion capabilities. The structural evolution, dispersion behavior, and colloidal stability of the microgel were thoroughly characterized using scanning electron microscopy (SEM), transmission electron microscopy (TEM), atomic force microscopy (AFM), Raman spectroscopy, and Zeta potential analysis. Experimental results indicate that ANDT-70 exhibits excellent thermal stability and resistance to salt-induced degradation at 260 °C, maintaining its fundamental structure and performance under harsh high-temperature and high-salinity conditions, with a viscosity retention of 81.10% compared with ambient conditions. Compared to representative materials reported in the literature, ANDT-70 exhibited superior tolerance to ionic erosion in saline conditions. AFM analysis confirmed that ANDT-70 significantly improves bentonite slurry dispersion and reduces salt sensitivity risks. ANDT-70 stably adsorbs onto bentonite lamellae via the synergistic action of electrostatic interactions and hydrogen bonding, thereby forming a dense cementation network that markedly enhances the structural stability and adhesion of the system. This network significantly enhances the cohesion and structural integrity of drilling fluid systems under extreme conditions. In conclusion, ANDT-70 demonstrates strong potential as a high-performance functional microgel for enhancing the stability and effectiveness of advanced drilling fluids under complex geological environments.

Conventional polymer-based plugging materials often fail in deep-well environments due to passive thermal softening and network relaxation, which significantly compromise mechanical integrity and interfacial retention. To address this challenge, a novel smart Upper Critical Solution Temperature (UCST)-responsive hybrid microgel (SUPA) was synthesized for adaptive plugging in complex formations. The distinctive UCST responsiveness was conferred by incorporating N-(2-amino-2-oxoethyl)acrylamide (NAGA) and N-(2-hydroxypropyl) methacrylamide (HPMA) functional units into a robust dual-crosslinked network. Particle size analysis and oscillatory rheology in saline solution revealed the thermal activation mechanism: surpassing the critical temperature triggers the dissociation of intramolecular hydrogen bonds, driving polymer chain extension and volumetric expansion. This conformational transition induces dynamic network reinforcement, quantified by a significant ~7.5-fold increase in the storage modulus (G′). Consequently, the SUPA-enhanced fluid exhibited superior rheological performance, including a 4.4-fold increase in low-shear viscosity and rapid thixotropic recovery (ratio of 1.06). Crucially, lost circulation tests confirmed reliable and highly efficient sealing performance under harsh conditions of 150 °C and 5 MPa, even in fractured models. This study validates a design strategy centered on UCST-activated network reinforcement, offering a robust, mechanism-driven solution for severe lost circulation control in deep-well drilling.