Ferrocene has been the most used organometallic moiety introduced in organic and bioinorganic drugs to cure cancers and various other diseases. Following several pioneering studies, two real breakthroughs occurred in 1996 and 1997. In 1996, Jaouen et al. reported ferrocifens, ferrocene analogs of tamoxifen, the chemotherapeutic for hormone-dependent breast cancer. Several ferrocifens are now in preclinical evaluation. Independently, in 1997, ferroquine, an analog of the antimalarial drug chloroquine upon the introduction of a ferrocenyl substituent in the carbon chain, was reported by the Biot-Brocard group and found to be active against both chloroquine-sensitive and chloroquine-resistant strains of Plasmodium falciparum. Ferroquine, in combination with artefenomel, completed phase IIb clinical evaluation in 2019. More than 1000 studies have been published on ferrocenyl-containing pharmacophores against infectious diseases, including parasitic, bacterial, fungal, and viral infections, but the relationship between structure and biological activity has been scarcely demonstrated, unlike for ferrocifens and ferroquines. In a majority of ferrocene-containing drugs, however, the production of reactive oxygen species (ROS), in particular the OH. radical, produced by Fenton catalysis, plays a key role and is scrutinized in this mini-review, together with the supramolecular approach utilizing drug delivery nanosystems, such as micelles, metal–organic frameworks (MOFs), polymers, and dendrimers. 

Ferrocene-Based Drugs, Delivery Nanomaterials and Fenton Mechanism: State of the Art, Recent Developments and Prospects


 Addressing gas migration in a static mud column during the shut-in period is a major concern in Pressurized Mud Cap Drilling (PMCD). Significant discrepancies have been found between the field data and existing correlations for gas migration velocity, since the latter are based on either small-scale experiments or overly simplified assumptions, resulting in overly conservative estimations. To meet the Light Annular Mud (LAM) requirement for managing gas migration and to monitor the transient pressure experienced throughout the PMCD operation, an improved gas migration velocity model was developed by combining the equation of motion (bubble flow) and Taylor-bubble correlation (slug flow). In the bubble flow model, the effects of non-Newtonian fluid properties and drill pipe rotation are considered through a modified drag coefficient (CD) that incorporates the bubble Reynolds number (Reb) and dimensionless shear rate (Sr). The effect of bubble swarm is taken into account through a void fraction (αg) term. The slug flow model is based on a Taylor bubble correlation in terms of Eötvös number (Eo) and inverse viscosity number (Nf). For the first time, the dependence of Taylor bubble velocity on drill pipe rotation has been shown and correlated as a function of Sr. Predictions of the gas migration velocities in PMCD operations are made and successfully compared with the existing models and test-well experimental data. The drift flux model embedded in the new gas migration velocity model was applied to simulate the gas migration in a test well. Good agreement between the model and measured pressure results in the full-scale test-well experiments can be obtained. Its companion work (Liu et al., 2023) provides the design and calculation method of key parameters in bullheading/PMCD operations.

Gas Migration Model for Non-Newtonian Fluids Under Shut-In Well Conditions

A deep learning approach for abnormal pore pressure prediction based on multivariate time series of kick


 Early detection and characterization of anomalous events during drilling operations are critical to avoid costly downtime and prevent hazardous events, such as a stuck pipe or a well control event. A key aspect of real-time drilling data analysis is the capability to make precise predictions of specific drilling parameters based on past time series information. The ideal models should be able to deal with multivariate time series and perform multi-step predictions. The recurrent neural network with a long short-term memory (LSTM) architecture is capable of the task, however, given that drilling is a long process with high data sampling frequency, LSTMs may face challenges with ultra-long-term memory. The transformer-based deep learning model has demonstrated its superior ability in natural language processing and time series analysis. The self-attention mechanism enables it to capture extremely long-term memory.
 In this paper, transformer-based deep learning models have been developed and applied to real-time drilling data prediction. It comprises an encoder and decoder module, along with a multi-head attention module. The model takes in multivariate real-time drilling data as input and predicts a univariate parameter in advance for multiple time steps. The proposed model is applied to the Volve field data to predict real-time drilling parameters such as mud pit volume, surface torque, and standpipe pressure. The predicted results are observed and evaluated.
 The predictions of the proposed models are in good agreement with the ground truth data. Four Transformer-based predictive models demonstrate their applicability to forecast real-time drilling data of different lengths. Transformer models utilizing non-stationary attention exhibit superior prediction accuracy in the context of drilling data prediction.
 This study provides guidance on how to implement and apply transformer-based deep learning models applied to drilling data analysis tasks, with a specific focus on anomaly detection. When trained on dysfunction-free datasets, the proposed model can predict real-time drilling data with high precision, whereas when a downhole anomaly starts to build, the significant error in the prediction can be used as an alarm indicator. The model can consider extremely long-term memory and serve as the alternative algorithm to LSTM. Furthermore, this model can be extended to a wide range of sequence data prediction problems in the petroleum engineering discipline.

Reducing NPT Using a Novel Approach to Real-Time Drilling Data Analysis

Redox-sensitive self-assembled micelles based on low molecular weight chitosan-lipoic acid conjugates for the delivery of doxorubicin: Effect of substitution degree of lipoic acid.

Redox-sensitive hyaluronic acid-ferrocene micelles delivering doxorubicin for enhanced tumor treatment by synergistic chemo/chemodynamic therepay

Enzyme-loaded glycogen nanoparticles with tumor-targeting Activatable host-guest supramolecule for augmented chemodynamic therapy.

Tumor-targeted glycogen nanoparticles loaded with hemin and glucose oxidase to promote tumor synergistic therapy.

Redox-Sensitive Targeted Polymer Micelles for Enhanced Chemodynamic Therapy and Chemotherapy

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