Yangtze University

The present application belongs to the technical field of petroleum extraction, and specifically relates to a method for in-situ removal of reservoir plugging using microorganisms and in-situ reservoir oil displacement, and an iron-reducing bacterium activator. The method includes the following steps: injecting a microbial mixture into a target reservoir to allow in-situ activation of microorganisms, such that magnetic iron nanoparticles are generated in-situ in the target reservoir to obtain a plugging-removed target reservoir; wherein the microbial mixture includes an iron-reducing bacterium fermentation broth and an iron-reducing bacterium activator; and injecting water into the plugging-removed target reservoir to allow the reservoir oil displacement. In summary, the method removes reservoir plugging caused by secondary precipitation of Fe(OH) 3 through an environmental-friendly biological method. Meanwhile, the microorganisms with an iron-reducing activity enable a green and low-cost biological method to produce highly-active magnetic nanoparticles in situ to allow the reservoir oil displacement.

A Tralopyril derivative and use thereof are provided. The Tralopyril derivative may be used to control plant-parasitic nematodes, such as root-knot nematodes and cyst nematodes. The biological assay results show that, using the foliar spray method at a dosage only one twentieth of that used in the root-irrigation method, Tralopyril-β-aminobutyric acid exhibits higher potted-plant activity against root-knot nematodes in tomato, cowpea, and cucumber than cyclobutrifluram applied at the same concentration via the root-irrigation method. Tralopyril-β-aminobutyric acid also exhibits excellent control efficacy against tobacco root-knot nematodes when applied by both foliar spray and root-irrigation methods. Tralopyril-β-aminobutyric acid also exhibits certain control efficacy against cyst nematodes in soybean.

Provided is a method for removing noise from a noisy Focused Ion Beam Scanning Electron Microscope (FIBSEM) image, including constructing an initial Denoising Convolutional Neural Network (DnCNN) denoising model with a DnCNN model as a framework, optimizing model parameters to obtain an improved DnCNN denoising model, and denoising a noisy FIBSEM image using the improved DnCNN denoising model, where the method specifically includes the following steps: S1: constructing the initial DnCNN denoising model; S2: model parameter optimization through S2.1 data preprocessing, S2.2 residual learning training, and S2.3 model testing; S3: iteration; and S4: denoising. This application employs an improved DnCNN model combined with residual learning and batch normalization techniques, making the denoising process more stable and adaptable to different noise levels. Specifically for noise removal in FIBSEM images, this application effectively reduces noise and curtain effects caused by surface topography variations and compositional differences in samples.

The present disclosure provides a PIKAN-based heterogeneous reservoir seepage simulation method, which belongs to the field of heterogeneous reservoir development technology, including the following steps: S 1 , using three independent KAN networks to approximate the PIKAN of pressure p, velocity component v x and velocity component v y respectively; S 2 , constructing a loss function of the mixed pressure-velocity formula including the residual of the mass conservation formula, the residual of Darcy's law and the residual of the relevant boundary conditions, and optimizing the loss function. The present disclosure adopts the above-mentioned PIKAN-based heterogeneous reservoir seepage simulation method, introduces KAN into the flow simulation of porous media for the first time, and provides a preliminary reference for the development of general seepage simulation tools based on PIKAN.

An oil-water identification method based on RPFE characteristics of undisturbed formation can identify oil and water layers, and the oil-water identification method is based on the dispersion of the original signal of array induction logging, mapping a spectrum image of the array induction logging sub-array 7, and forming a two-dimensional oil-water identification chart by using the dispersion and the product of the resistivity after borehole correction and the formation porosity, and the test of actual logging data shows that this method can accurately and effectively divide the oil and water layers, and improve the efficiency of the oil-water evaluation in reservoir.

Provided are a gel temporary plugging agent and a preparation method and use thereof. The gel temporary plugging agent has a three-dimensional network structure formed by cross-linking an animal protein, which can be degraded into linear polypeptide chains in an acid solution under a low-temperature condition, thereby achieving a temporary plugging effect; it is reinforced by introducing a reinforcing agent structural unit to ensure plugging performance of the gel temporary plugging agent, thereby enabling the gel temporary plugging agent to achieve plugging in low-temperature reservoirs.

A method for mirror imaging of gas holdup in gas-water two-phase flow based on a fiber optic probe array includes: projecting six fiber optic probes of a FIT onto a cross-section of a wellbore at a same height, gridding and normalizing an inner diameter of the wellbore and probe positions to obtain a cross-sectional grid, and determining a two-dimensional coordinate of each fiber optic probe within the cross-sectional grid and a local gas holdup at each fiber optic probe; rotating, based on a radial symmetry of gas-water flow along the wellbore center, the six fiber optic probes counterclockwise to obtain mirror probes which are radially symmetrical and coordinates thereof; and predicting gas holdup at each mirror probe to obtain predicted gas holdup values, interpolating the predicted gas holdup values in the cross-sectional grid by an interpolation algorithm to obtain a reconstructed gas-water image of the cross-section of the wellbore.

The present invention relates to sports training and testing technology and discloses a water training integrated machine for dragon boat racing. The system comprises a dragon boat, a shore data processing device, and a water device. The water device is mounted on a blade of the dragon boat and includes a sensor, a data operation unit, and a communication transmitting module, sequentially connected. These components are powered by a power supply module. The data operation unit communicates with the shore data processing device via the communication transmitting module. The shore data processing device comprises a data processing module, a display module, and multiple communication receiving modules. The display module and communication receiving modules are electrically connected to the data processing module. This technical solution enables accurate measurement and real-time monitoring of stroke force during water sports training, thereby enhancing performance analysis and training efficiency.

A system for intelligently recovering water-flooded layer of oil reservoir based on spiking neural network is provided. The system includes: a data acquiring module, a data preprocessing module, a model training module, a resistivity recovering module, and a water-flooded layer interpreting module. The data acquiring module is configured to acquire multi-modal data such as conventional and electrical well logging curves. The data preprocessing module is configured to perform range normalization and Z-Score standardization on the data. The model training module is configured to construct a resistivity recovering model based on a spiking neural network, and the resistivity recovering model is improved and verified by various manners. The resistivity recovering module is configured to recover an original resistivity. The water-flooded layer interpreting module is configured to calculate correlative parameters based on the original resistivity to determine a water-flooded layer and classify and interpret it.

Provided is a real-time inversion method of multi-measurement point and multi-parameter drilling, a device, a media and a product, relating to the technical field of oil-gas exploration. The method includes: acquiring downhole measurement data of target drilled well; where the downhole measurement data is measured in real time based on an intelligent microsphere along a drill pipe; establishing a real-time inversion interpretation model for a downhole gas kick condition based on a wellbore multiphase flow forward model and an adaptive unscented Kalman filter algorithm according to the downhole measurement data; and inputting the downhole measurement data into the real-time inversion interpretation model for the downhole gas kick condition to obtain an inversion parameter of target drilled well.