Abstract: Disclosed are an image matching method and apparatus. The image matching method is inclusive of steps of obtaining a panoramic image of at least one subspace in a 3D space and a 2D image of the 3D space; acquiring a 2D image of the at least one subspace in the 3D space; performing 3D reconstruction on the panoramic image of the at least one subspace, and procuring a projection image corresponding to the panoramic image of the at least one subspace; and attaining a matching relationship between the panoramic image of the at least one subspace and the 2D image of the at least one subspace, and establishing an association relationship between the panoramic image of the at least one subspace and the 2D image of the at least one subspace between which the matching relationship has been generated.

Abstract: An electric tool includes an output member, a motor, and a housing. The motor includes a rotor shaft, a rotor core, and a stator component. The rotor shaft is configured to rotate around a first axis. The stator component includes a coil winding. The stator component surrounds the first axis to separate the accommodation cavity into a first space and a second space. The first space includes a first end and a second end. The motor further includes a first end cover and a second end cover. The first end cover is disposed at the first end of the first space. The second end cover is disposed at the second end of the first space. A passage is formed between the second end cover and a structure surrounding the first space, and the passage is configured to place the first space into communication with the second space.

Abstract: A method, system, and computer program product for resource management are described. The method includes selecting trouble regions within the service area, generating clustered regions, and training a trouble forecast model for the trouble regions for each type of damage, the training for each trouble region using training data from every trouble region within the clustered region associated with the trouble region. The method also includes applying the trouble forecast model for each trouble region within the service area for each type of damage, determining a trouble forecast for the service area for each type of damage based on the trouble forecast for each of the trouble regions within the service area, and determining a job forecast for the service area based on the trouble forecast for the service area, wherein the managing resources is based on the job forecast for the service area.

Abstract: A method, system, and computer program product for resource management are described. The method includes selecting trouble regions within the service area, generating clustered regions, and training a trouble forecast model for the trouble regions for each type of damage, the training for each trouble region using training data from every trouble region within the clustered region associated with the trouble region. The method also includes applying the trouble forecast model for each trouble region within the service area for each type of damage, determining a trouble forecast for the service area for each type of damage based on the trouble forecast for each of the trouble regions within the service area, and determining a job forecast for the service area based on the trouble forecast for the service area, wherein the managing resources is based on the job forecast for the service area.

Abstract: A method, system, and computer program product for resource management are described. The method includes selecting trouble regions within the service area, generating clustered regions, and training a trouble forecast model for the trouble regions for each type of damage, the training for each trouble region using training data from every trouble region within the clustered region associated with the trouble region. The method also includes applying the trouble forecast model for each trouble region within the service area for each type of damage, determining a trouble forecast for the service area for each type of damage based on the trouble forecast for each of the trouble regions within the service area, and determining a job forecast for the service area based on the trouble forecast for the service area, wherein the managing resources is based on the job forecast for the service area.

Abstract: A method, system, and computer program product for resource management are described. The method includes selecting trouble regions within the service area, generating clustered regions, and training a trouble forecast model for the trouble regions for each type of damage, the training for each trouble region using training data from every trouble region within the clustered region associated with the trouble region. The method also includes applying the trouble forecast model for each trouble region within the service area for each type of damage, determining a trouble forecast for the service area for each type of damage based on the trouble forecast for each of the trouble regions within the service area, and determining a job forecast for the service area based on the trouble forecast for the service area, wherein the managing resources is based on the job forecast for the service area.

Abstract: A method for determining water-filled porosity and water salinity in a well includes obtaining complex dielectric permittivity of earth formations, either from dielectric measurements representative of well cores, or from dielectric well logs; selecting a dielectric mixing law for the index number m; plotting a m-th root of complex dielectric permittivity at a specified frequency in the complex domain, wherein m is an index number; determining a matrix permittivity, a water salinity, and a water-filled porosity based on the complex dielectric permittivity and the dielectric mixing law; and displaying the water salinity and the water-filled porosity.

Abstract: A method, system, and computer program product for resource management are described. The method includes selecting trouble regions within the service area, generating clustered regions, and training a trouble forecast model for the trouble regions for each type of damage, the training for each trouble region using training data from every trouble region within the clustered region associated with the trouble region. The method also includes applying the trouble forecast model for each trouble region within the service area for each type of damage, determining a trouble forecast for the service area for each type of damage based on the trouble forecast for each of the trouble regions within the service area, and determining a job forecast for the service area based on the trouble forecast for the service area, wherein the managing resources is based on the job forecast for the service area.

Abstract: Systems and methods for providing a predictive framework are provided. The predictive framework comprises plural neural layers of adaptable, executable neurons. Neurons accept one or more input signals and produce an output signal that may be used by an upper-level neural layer. Input signals are received by an encoding neural layer, where there is a 1:1 correspondence between an input signal and an encoding neuron. Input signals for a set of data are received at the encoding layer and processed successively by the plurality of neural layers. An objective function utilizes the output signals of the topmost neural layer to generate predictive results for the data set according to an objective. In one embodiment, the objective is to determine the likelihood of user interaction with regard to a specific item of content in a set of search results, or the likelihood of user interaction with regard to any item of content in a set of search results.

Abstract: To measure the phase behavior of a fluid in a porous medium such as a tight gas shale, one illustrative method involves: (a) loading the fluid into a sample cell containing the porous medium; (b) setting a pressure and a temperature for the fluid in the sample cell; (c) applying an RF pulse sequence to the fluid in the sample cell to acquire an NMR signal; (d) deriving from the NMR signal an NMR parameter distribution that depends on the pressure and the temperature; (e) determining whether a fluid phase is present based on the NMR parameter distribution; (f) repeating operations (c) through (f) to determine the presence or absence of the fluid phase at multiple points along a pressure-temperature path that crosses a phase boundary; and (g) providing an estimated location of the phase boundary based on the presence or absence of the fluid phase at said points.

Abstract: A method, system, and computer program product for resource management are described. The method includes selecting trouble regions within the service area, generating clustered regions, and training a trouble forecast model for the trouble regions for each type of damage, the training for each trouble region using training data from every trouble region within the clustered region associated with the trouble region. The method also includes applying the trouble forecast model for each trouble region within the service area for each type of damage, determining a trouble forecast for the service area for each type of damage based on the trouble forecast for each of the trouble regions within the service area, and determining a job forecast for the service area based on the trouble forecast for the service area, wherein the managing resources is based on the job forecast for the service area.

Abstract: A sensor and a method are disclosed for analyzing fluid and/or rock samples from a subsurface formation. Embodiments of the sensor and method utilize an array of magnets arranged in a specific way. The array and its arrangement may allow for NMR analysis of multiple samples or analysis of fluid samples which were not possible with existing technology. Further details and advantages of various embodiments of the method are described in more detail herein.

Abstract: A method, system, and computer program product for resource management are described. The method includes selecting trouble regions within the service area, generating clustered regions, and training a trouble forecast model for the trouble regions for each type of damage, the training for each trouble region using training data from every trouble region within the clustered region associated with the trouble region. The method also includes applying the trouble forecast model for each trouble region within the service area for each type of damage, determining a trouble forecast for the service area for each type of damage based on the trouble forecast for each of the trouble regions within the service area, and determining a job forecast for the service area based on the trouble forecast for the service area, wherein the managing resources is based on the job forecast for the service area.

Abstract: An electric tool includes an output member, a motor, and a housing. The motor includes a rotor shaft, a rotor core, and a stator component. The rotor shaft is configured to rotate around a first axis. The stator component includes a coil winding. The stator component surrounds the first axis to separate the accommodation cavity into a first space and a second space. The first space includes a first end and a second end. The motor further includes a first end cover and a second end cover. The first end cover is disposed at the first end of the first space. The second end cover is disposed at the second end of the first space. A passage is formed between the second end cover and a structure surrounding the first space, and the passage is configured to place the first space into communication with the second space.

Abstract: An NMR sensor and method is disclosed for analyzing a core sample from a subsurface formation. Embodiments of the method utilize two or more magnets disposed proximate to each other. The configuration of the magnets allows for increased detection frequency, and creates a strong field with much finer resolution than existing designs. In addition, embodiments of the sensor may be used at the well site due to its small size and simple hardware. Further details and advantages of various embodiments of the method are described in more detail herein.

Abstract: A disclosed method for characterizing gas adsorption on a rock sample includes: measuring a nuclear magnetic resonance (NMR) response of the rock as a function of surrounding gas pressure along an isotherm; transforming the NMR response to obtain a Langmuir pressure distribution of gas adsorption on the rock sample; and displaying the Langmuir pressure distribution. The Langmuir pressure distribution may be shown in one dimension (e.g., contribution to signal response versus Langmuir pressure), or may be combined with additional pressure-dependencies such as spin-lattice relaxation time (T1), spin-spin relaxation time (T2), and chemical shift (?) to form a multi-dimensional distribution. The method can further include: identifying peaks in the Langmuir pressure distribution; and associating a gas storage mechanism and capacity with each peak.

Abstract: A method for determining water-filled porosity and water salinity in a well includes obtaining complex dielectric permittivity of earth formations, either from dielectric measurements representative of well cores, or from dielectric well logs; selecting a dielectric mixing law for the index number m; plotting a m-th root of complex dielectric permittivity at a specified frequency in the complex domain, wherein m is an index number; determining a matrix permittivity, a water salinity, and a water-filled porosity based on the complex dielectric permittivity and the dielectric mixing law; and displaying the water salinity and the water-filled porosity.

Abstract: To measure the phase behavior of a fluid in a porous medium such as a tight gas shale, one illustrative method involves: (a) loading the fluid into a sample cell containing the porous medium; (b) setting a pressure and a temperature for the fluid in the sample cell; (c) applying an RF pulse sequence to the fluid in the sample cell to acquire an NMR signal; (d) deriving from the NMR signal an NMR parameter distribution that depends on the pressure and the temperature; (e) determining whether a fluid phase is present based on the NMR parameter distribution; (f) repeating operations (c) through (f) to determine the presence or absence of the fluid phase at multiple points along a pressure-temperature path that crosses a phase boundary; and (g) providing an estimated location of the phase boundary based on the presence or absence of the fluid phase at said points.

Abstract: Systems and methods for providing a predictive framework are provided. The predictive framework comprises plural neural layers of adaptable, executable neurons. Neurons accept one or more input signals and produce an output signal that may be used by an upper-level neural layer. Input signals are received by an encoding neural layer, where there is a 1:1 correspondence between an input signal and an encoding neuron. Input signals for a set of data are received at the encoding layer and processed successively by the plurality of neural layers. An objective function utilizes the output signals of the topmost neural layer to generate predictive results for the data set according to an objective. In one embodiment, the objective is to determine the likelihood of user interaction with regard to a specific item of content in a set of search results, or the likelihood of user interaction with regard to any item of content in a set of search results.

Abstract: An NMR sensor and method is disclosed for analyzing a core sample from a subsurface formation. Embodiments of the method utilize two or more magnets disposed proximate to each other. The configuration of the magnets allows for increased detection frequency, and creates a strong field with much finer resolution than existing designs. In addition, embodiments of the sensor may be used at the well site due to its small size and simple hardware. Further details and advantages of various embodiments of the method are described in more detail herein.