Abstract: The present application provides a method for adjusting the local thickness of a photoresist, and a semiconductor front layer structure. The thickness of the photoresist layer in a first region of the stack structure is more than the thickness of the photoresist layer in a second region. The photomask is configured to include an auxiliary pattern for exposing the first region and another pattern for exposing the second region. The first region and the second region of the photoresist layer are exposed simultaneously by one photomask. The exposure intensity is adjusted so that part of the surface of the first region is dissolved during the development, the auxiliary pattern is not transferred to the photoresist layer in the first region, resulting in decreased thickness of the photoresist layer in the first region after development. After development, the other pattern is transferred to the photoresist layer in the second region.

Abstract: A method may include obtaining various well logs or various core samples regarding a geological region of interest. The method may further include determining various permeability values, various porosity values, and various dolomite volume fraction values regarding the geological region of interest using the well logs or the core samples. The dolomite volume fraction values may correspond to a percentage of dolomite in a total mineral volume. The method may further include determining, using the porosity values, various permeability thresholds corresponding to various predetermined reservoir qualities. The method may further include generating, using the permeability thresholds, the permeability values, and the dolomite volume fraction values, a reservoir model including various dolomite boundaries defining the predetermined reservoir qualities. The method may further include determining a hydrocarbon trap prediction using the reservoir model.

Abstract: Methods of estimating permeability of reservoir rocks using mercury injection capillary pressure can include: receiving mercury injection capillary pressure test data and porosity data for a core sample; determining a fractal dimension (D) for the core sample based on the received mercury injection capillary pressure test data for the core sample; determining a pore throat radius (Rd) for the core sample; determining a composite parameter (?) for the core sample where ? = ? ? R d ( D - 2 ) ; and estimating permeability (K) of the core sample based on a relationship of ln(K) as a function of ln(?) determined by performing a regression analysis data from other core samples from the reservoir.

Abstract: A method of rock typing includes obtaining mercury injection capillary pressure (MICP) data regarding a region of interest. A distance matrix is computed for distributions determined from the MICP data using a statistical distance metric. A cluster tree of the distributions is generated using the distance matrix. The cluster tree is adjusted based on a petrographic characteristic to produce an adjusted cluster tree, which is used to determine a pore structure types of the region of interest.

Abstract: Methods of estimating permeability of reservoir rocks using mercury injection capillary pressure can include: receiving mercury injection capillary pressure test data and porosity data for a core sample; determining a fractal dimension (D) for the core sample based on the received mercury injection capillary pressure test data for the core sample; determining a pore throat radius (Rd) for the core sample; determining a composite parameter (?) for the core sample where ? = ? ? R d ( D - 2 ) ; and estimating permeability (K) of the core sample based on a relationship of ln(K) as a function of ln(?) determined by performing a regression analysis data from other core samples from the reservoir.

Abstract: A method of rock typing includes obtaining mercury injection capillary pressure (MICP) data regarding a region of interest. A distance matrix is computed for distributions determined from the MICP data using a statistical distance metric. A cluster tree of the distributions is generated using the distance matrix. The cluster tree is adjusted based on a petrographic characteristic to produce an adjusted cluster tree, which is used to determine a pore structure types of the region of interest.

Abstract: A method may include obtaining various well logs or various core samples regarding a geological region of interest. The method may further include determining various permeability values, various porosity values, and various dolomite volume fraction values regarding the geological region of interest using the well logs or the core samples. The dolomite volume fraction values may correspond to a percentage of dolomite in a total mineral volume. The method may further include determining, using the porosity values, various permeability thresholds corresponding to various predetermined reservoir qualities. The method may further include generating, using the permeability thresholds, the permeability values, and the dolomite volume fraction values, a reservoir model including various dolomite boundaries defining the predetermined reservoir qualities. The method may further include determining a hydrocarbon trap prediction using the reservoir model.

Abstract: This application provides an object recognition method and apparatus, and a storage medium, and relates to the technical field of image recognition. The object recognition method includes: receiving an image of an object to be recognized; inputting the image of the object to be recognized into a pre-trained image feature extractor to obtain a first feature vector of the image of the object to be recognized; and determining category information of the object to be recognized according to the first feature vector of the image of the object to be recognized as well as image feature vectors in an image database, wherein the image database includes image information of a plurality of objects.

Abstract: An embodiment of the present application provides a method and a device for predicting a pair of similar questions and an electronic equipment, wherein a pair of similar questions to be predicted are input into multiple different prediction models, and a prediction result output by each of the prediction models is obtained; a random disturbance parameter is added into an embedding layer of at least one of the prediction models; and voting operation is performed on multiple prediction results to obtain a final prediction result of the pair of similar questions to be predicted. According to the present application, the a random disturbance parameter is added into the embedding layer of the prediction model, so that over-fitting caused by over-learning of sample knowledge by the prediction model can be effectively prevented, and the prediction accuracy can be effectively improved by predicting the pair of similar questions utilizing the prediction model.