Search

Accurately predicting downhole risk before drilling in new exploration areas is one of the difficulties. Using intelligent algorithms to explore the complex relationship between multi-source data and downhole risk is a hot research topic and frontier in this field. However, due to the small number and uneven distribution of drilled wells in new exploration areas and the lack of sample data related to risk, the training model has insufficient generalization ability, and thus the prediction is not effective. In this paper, a drilling risk profile (depth domain) rich in geological and engineering information is constructed by introducing a quantitative evaluation method for drilling risk of drilled wells, which can provide sufficient risk sample data for model training and thus solve the small sample problem. For the problem of uneven distribution of drilling wells in new exploration areas, the concept of virtual wells and their deployment methods were proposed. Besides, two methods for calculating rock mechanical parameters of virtual wells were proposed, and the accuracy and applicability of the two methods are analyzed. The LSTM deep learning model was optimized to tap the quantitative relationship between drilling risk profiles and multi-source data (e.g., seismic, logging, and rock mechanical parameters). The model was validated to have an average relative error of 9.19%. The quantitative prediction of the drilling risk profile of the virtual well was achieved using the trained LSTM model and the calculation of the relevant parameters of the virtual well. Finally, based on the sequential Gaussian simulation method and the risk distribution of drilled and virtual wells, a regional 3D drilling risk model was constructed. The analysis of real cases shows that the addition of virtual wells can significantly improve the identification of regional drilling risks and the prediction accuracy of pre-drill drilling risks in unexplored areas can be improved by up to 21% compared with the 3D risk model constructed based on drilled wells only. [ABSTRACT FROM AUTHOR]

A new nanoparticle-based chemiluminescent (CL) method has been developed for the ultrasensitive detection of DNA hybridization. The assay relies on a sandwich-type DNA hybridization in which the DNA targets are first hybridized to the captured oligonucleotide probes immobilized on polystyrene microwells and then the silver nanoparticles modified with alkylthiol-capped oligonucleolides are used as probes to monitor the presence of the specific target DNA. After being anchored on the hybrids, silver nanoparticles are dissolved to Ag+ in HNO3 solution and sensitively determined by a coupling CL reaction system (Ag+-Mn2+-K2S2O8-H3PO4-luminol). The combination of the remarkable sensitivity of the CL method with the large number of Ag+ released from each hybrid allows the detection of specific sequence DNA targets at levels as low as 5 fM. The sensitivity increases 6 orders of magnitude greater than that of the gold nanoparticle-based colorimetric method and is comparable to that of surface-enhanced Raman spectroscopy, which is one of the most sensitive detection approaches available to the nanoparticle-based detection for DNA hybridization. Moreover, the perfectly complementary DNA targets and the single-base mismatched DNA strands can be evidently differentiated through controlling the temperature, which indicates that the proposed CL assay offers great promise for single-nucleotide polymorphism analysis. [ABSTRACT FROM AUTHOR]

BaTiO3 nanocrystals were synthesized by using a low-pressure spray pyrolysis (SP) method. A new strategy was proposed to improve nanocrystal formation and phase evolution by ethanol addition and Ba/Ti molar ratio variation, respectively. A mixture of submicron and nanoparticles was found with SP of precursors without ethanol addition, while only nanoparticles could be obtained by ethanol addition. BaTiO3 crystal growth was improved by increasing the Ba/Ti ratio. In comparison, SP of the same precursor under atmospheric conditions resulted in only submicron particles with smooth surfaces and irregular morphology. [ABSTRACT FROM AUTHOR]