Horizontal well technology is a promising method for oil and gas development. During cementing operations in horizontal wells, it was found that conventional casing centralizers could not meet the requirements for casing cementing in expanded wellbores. Therefore, a new type of casing centralizer needs to be designed for horizontal well sections that have undergone enlargement. By analyzing the most common materials currently used, 45 steel was selected for the spring leaf of the novel casing centralizer. To evaluate the centralization effect of the horizontal well casing centralizer, a casing centralization degree evaluation function was established, and a wellbore-centralizer mechanical model was proposed using the finite element method to simulate the working conditions of the centralizer spring leaf in ϕ215.9 and ϕ311.2 mm well sections. On this basis, a wellbore-centralizer-casing coupling model that does not consider the effect of wellbore fluid on the casing was established to simulate the centralization characteristics of the new casing centralizer and traditional centralizer under different wellbore sizes. Simulation results show that the average casing centralization degree of the new centralizer is 85.53%, while that of the traditional centralizer is 55.58%. That is, the horizontal well casing centralizer can maintain a good centralization effect on the casing string.

The use of Horizontal wells is a common method of underground natural gas storage (UGS), but there is still a need to discuss whether they are more suitable for slotted liner or perforation completions. To address this issue, a numerical model is developed to predict the gas injection rate of horizontal wells while considering the skin factor. Here, a novel uncoupled iteration method is employed to determine the skin factor deriving from turbulence in each time step when the bottom hole pressure is fixed. The uncoupled method begins with an estimate of the initial gas injection rate, which is then used to calculate a turbulent skin factor. This turbulent skin factor is then used to update the gas injection rate, iterating continuously until convergence is achieved. The effects of slotted liner and perforation design parameters, formation damage, and injection pressure on the skin factor are analyzed. The main findings suggest that the error in the gas injection rate calculated by the non-coupled model compared with the coupled model is only 0.6%, yet it can reduce the number of sub-iterations to 1/10 of that required by the coupled model. Moreover, the uncoupled model can provide results within four steps, even when the convergence condition is 10⁻¹⁴. The open area and perforation density play a significant role in determining the connection degree between the horizontal well and the reservoir, with a larger perforation density resulting in a negative skin factor. Perforations are more suitable than slotted liners for reservoirs with severe formation damage, and the difference in skin factor between the two can reach a value of 40.87 when the ratio of the damage zone’s permeability to that of the normal reservoir zone is 0.05. It is easier to reduce turbulence damage in slotted liner completions than perforation completions, with the turbulence damage of the slotted liner being only 15.9% of that of the perforation. However, to avoid damage it is crucial to prevent the screen tube from being plugged in, as it might otherwise rise to three to ten times the original level. This study provides a theoretical basis and practical reference for the application of slotted liner and perforation method in UGS horizontal wells.

Conglomerate reservoirs present significant technical challenges during drilling operations due to their complex mineral composition and heterogeneous characteristics, yet the quantitative relationships between mineral composition and microscopic mechanical behavior remain poorly understood. To elucidate the variation patterns of conglomerate micromechanical properties and their mineralogical control mechanisms, this study develops a novel multi-scale characterization methodology. This approach uniquely couples nanoindentation technology, micro-zone X-ray diffraction analysis, and machine learning algorithms to systematically investigate micromechanical properties of conglomerate samples from different regions. Hierarchical clustering algorithms successfully classified conglomerate micro-regions into three lithofacies categories with distinct mechanical differences: hard (elastic modulus: 81.90 GPa, hardness: 7.83 GPa), medium-hard (elastic modulus: 54.97 GPa, hardness: 3.87 GPa), and soft lithofacies (elastic modulus: 25.21 GPa, hardness: 1.15 GPa). Correlation analysis reveals that quartz (SiO₂) content shows significant positive correlation with elastic modulus (r = 0.52) and hardness (r = 0.51), while clay minerals (r = −0.37) and plagioclase content (r = −0.48) exhibit negative correlations with elastic modulus. Mineral phase spatial distribution patterns control the heterogeneous characteristics of conglomerate micromechanical properties. Additionally, a random forest regression model successfully predicts mineral content based on hardness and elastic modulus measurements with high accuracy. These findings bridge the gap between microscopic mineral properties and macroscopic drilling performance, enabling real-time formation strength assessment and providing scientific foundation for optimizing drilling strategies in heterogeneous conglomerate formations.

Axial–torsional coupled impact drilling (ATCID) technology represents a promising solution for overcoming the drilling challenges posed by conglomerate formations, which are characterized by strong heterogeneity, high abrasiveness, and poor drillability. However, the optimal parameter matching relationships and their influence patterns on mechanical specific energy (MSE) remain unclear. This study employed self-developed true triaxial impact rotary rock breaking equipment with conglomerate cores from the Junggar Basin to systematically investigate the effects of weight on bit (WOB), rotational speed (RPM), axial impact frequency, and torsional impact frequency on MSE through orthogonal experimental design. The results demonstrate that the parameter influence ranking on MSE is as follows: torsional impact frequency > WOB > RPM > axial impact frequency, with torsional impact frequency exhibiting the largest range value (87.5 MPa). ANOVA reveals that the interaction between axial and torsional impact frequencies is the dominant controlling factor, contributing 22.8% to MSE variation with high statistical significance. The optimal parameter combination yields the minimum MSE (103 MPa): 19 kN WOB, 20 r/min RPM, 20 Hz axial impact frequency, and 20 Hz torsional impact frequency, representing a 69.1% reduction compared to the maximum value. Response surface analysis revealed that increasing WOB significantly reduces MSE, RPM exhibits positive correlation with MSE, and synergistic effects occur when both impact frequencies reach high values simultaneously. A nonlinear MSE prediction model incorporating main effects, quadratic terms, and interaction effects was established with R² = 0.8240 and a mean absolute percentage error of 9.26%. The research findings provide an essential theoretical foundation for parameter optimization and engineering applications of ATCID technology, offering significant implications for enhancing drilling efficiency in conglomerate and other challenging hard rock formations.

In China, a majority of the proven crude oil reserves are found in clastic rock reservoirs, which typically exhibit low natural energy levels. Water injection has become the most widely adopted technique for maintaining reservoir pressure and enhancing oil recovery in such formations. However, conventional water injection strategies heavily rely on empirical knowledge, often failing to accurately characterize the dynamic inter-well connectivity between injection and production wells. This limitation hinders the effective management of fluid injection and production processes. To address this challenge, we propose an intelligent optimization method for water allocation in high-water cut, low-permeability reservoirs. Our approach employs a Bidirectional Long Short-Term Memory (BiLSTM) neural network to learn the complex patterns from historical injection data in a data-driven manner. Furthermore, we design a well distance and time joint attention mechanism, which is integrated after the dual BiLSTM layers to enhance the model’s ability to capture the critical dynamic relationships among wells. This mechanism decouples temporal pattern recognition and the spatial physical constraints, laying the foundation for interpretable injection strategy optimization. We name this architecture “AttBiLSTM”, which is designed for optimizing injection strategies for individual layers in separate-layer water injection wells (The layer refers to the basic geological unit or flow unit within a vertically heterogeneous reservoir that is delineated and requires independent water injection regulation). Using field data from the Xinjiang Oilfield, we validate the proposed method and compare its performance against traditional water injection schemes and mainstream data-driven models. The experimental results demonstrate that the AttBiLSTM model effectively establishes a nonlinear mapping between the injection volumes and oil production rates, showing strong performance in both production prediction and injection optimization. An independent numerical reservoir simulation verification confirms that the optimized scheme increases well group oil production by over 3.6%, with no premature water breakthrough risk in a 5-year development cycle. This study provides a novel and practical technical framework for efficiently developing low-porosity, low-permeability, and highly heterogeneous reservoirs.