- Article
19 Pages
Shale oil reservoirs exhibit strong heterogeneity and complex rock mechanical properties. Accurate geomechanical modeling is crucial for optimizing horizontal well trajectories and designing fracturing parameters. Traditional mechanical experiments are costly and provide limited data, while empirical formulas suffer from poor regional applicability. Machine learning models designed to predict reservoir mechanical parameters commonly overlook lithofacies-specific variations. To overcome this limitation, this paper presents and adopts a novel workflow that combines micromechanical characterization, machine learning prediction, and mechanical upscaling. Nanoindentation tests are first performed in Well W1 in the Ordos Basin to obtain the mechanical properties of different micro-constituents in shale. Then, a machine learning model is used to establish the relationship between conventional well logs and micro-constituent contents, thereby predicting the distribution of micro-constituents along the entire wellbore. Finally, based on micromechanical derivation, the Mori–Tanaka model is applied for mechanical upscaling to obtain the Young’s modulus. The feasibility of this method is validated by comparing the results with those calculated from conventional well log data. This approach combines the high-precision micromechanical characterization of nanoindentation with the predictive capability of machine learning. It overcomes the limitations of elemental capture spectroscopy logging (namely, its high cost and insufficient organic matter information) and provides a new pathway for establishing one-dimensional geomechanical models.
Nanomaterials
29 September 2026




![Evidence-based physicochemical and biological stress trajectory relevant primarily to few-strand tetrahedral DNA nanostructures (TDNs/tFNAs). The schematic summarizes key conditions encountered during biological transit, including ionized magnesium, pH transitions, physiological temperature, serum and nuclease exposure, and cellular entry. Numerical values are shown only when supported by defined physiological or experimentally reported ranges, whereas structural and biological outcomes remain dependent on sequence, size, formulation, ionic environment, and experimental conditions. The scheme is not intended to imply direct head-to-head superiority over other DNA nanostructures or to extrapolate these behaviors to scaffolded/wireframe tetrahedral origami without direct evidence. Solid arrows indicate the sequential biological trajectory, dotted connectors link each stress condition to the central TDN, and the color-coded panels and schematic shapes distinguish the principal physicochemical and biological environments encountered during transit. The ionized-magnesium context is supported by Refs. [20,21,38]; pH conditions and pH-dependent TDN responses by Refs. [42,43,44]; thermal behavior by Refs. [21,46,47]; serum and nuclease exposure by Refs. [16,17,23]; and cellular entry by Refs. [11,12]. TDN, tetrahedral DNA nanostructure; tFNA, tetrahedral framework nucleic acid; Mg2+, magnesium ion. Original graphical representation created by the authors with graphical assistance from ChatGPT (GPT-5.6 Sol, OpenAI, 2026), with OpenAI image-generation tools.](https://mdpi-res.com/cdn-cgi/image/width=281%2Cheight=192/https://mdpi-res.com/nanomaterials/nanomaterials-16-01227/article_deploy/html/images/nanomaterials-16-01227-ag-550.jpg)







