1. A method of dynamic reservoir simulation comprising:
a) partitioning, via a computing processor, a reservoir geometry into one or more grid-blocks in 1D, 2D or 3D space;
b) assigning fluid and rock properties to one or more grid-blocks;
c) assigning boundary conditions and well properties to one or more grid-blocks;
d) solving pressure, material balance, and energy balance equations wherein the pressure equation and material balance equation are solved by finite difference (FD) method and the energy balance equation is solved by discontinuous Galerkin (DG) method to determine temperature of the one or more grid-blocks; and
e) simulating fluid flow across one or more grid-blocks by using results from the solved pressure, material balance, and energy balance equations.
2. The method of claim 1, wherein said one or more grid-blocks are selected from the group consisting of: Cartesian, corner-point-geometry, static, dynamic, radial, curvilinear, and any combination thereof.
3. The method of claim 1, wherein the one or more pressure, material balance, or energy balance equations are applied in Implicit Pressure-Explicit Saturation (IMPES), fully implicit models, adaptive implicit model, or any combination thereof.
4. The method of claim 1, wherein said reservoir properties is simulated using a thermal model, steam-flooding model, steam-assisted gravity drainage (SAGD) model, black-oil model, compositional model, finite-difference simulator, or the like.
5. The method of claim 1, wherein average temperature and the temperature differences at the grid-block interface are calculated for each grid.
6. The method of claim 1, wherein 2 degrees of freedom in a 1D model, 3 degrees of freedom in a 2D model, or 4 degrees of freedom in a 3D model.
7. The method of claim 1, wherein \u03b1-slope limiter is a value between 0 and 1 including 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0.
8. A method of dynamic reservoir simulation comprising:
a) partitioning, via a computing processor, a reservoir geometry into one or more grid-blocks in 1D, 2D or 3D space;
b) assigning fluid and rock properties to one or more grid-blocks;
c) assigning boundary conditions and well properties to one or more grid-blocks; i) calculate average temperature at the center of the grid blocks and temperatures at the grid blocks interfaces, ii) apply a slope limiter to improve stability of the analysis, iii) use interface temperatures to calculate thermal fluxes among grid-blocks;
d) solving pressure, material balance, and energy balance equations wherein the pressure equation and material balance equation are solved by finite difference (FD) method and the energy balance equation is solved by discontinuous Galerkin (DG) method to determine temperature of the one or more grid-blocks; and
e) simulating fluid flow across one or more grid-blocks by using results from the solved pressure, material balance, and energy balance equations.
9. The method of claim 8, wherein said one or more grid-blocks are selected from the group consisting of: Cartesian, corner-point-geometry, static, dynamic, radial, curvilinear, and any combination thereof.
10. The method of claim 8, wherein one or more of the pressure, material balance, or energy balance equations are applied in Implicit Pressure-Explicit Saturation (IMPES), fully implicit models, adaptive implicit model, or any combination thereof.
11. The method of claim 8, wherein said reservoir properties are simulated using a thermal model, steam-flooding model, steam-assisted gravity drainage (SAGD) model, black-oil model, compositional model, finite-difference simulator, or any combination thereof.
12. The method of claim 8, wherein the average temperature and the temperature differences at the grid-block interface are calculated for each grid.
13. The method of claim 8, wherein 2 degrees of freedom in a 1D model, 3 degrees of freedom in a 2D model, or 4 degrees of freedom in a 3D model.
14. The method of claim 8, wherein the slope limiter is a value between 0 and 1 including 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method for registering a three-dimensional model of a body volume to a real-time image of a sensor, the method comprising:
inserting the sensor into the body lumen to acquire location data;
processing the location data to form a three-dimensional shape including a plurality of cavity voxels;
defining a plurality of parameters having predefined thresholds;
selecting cavity voxels of the plurality of cavity voxels satisfying the predefined thresholds of the plurality of parameters; and
comparing the three-dimensional shape including the cavity voxels satisfying the predefined thresholds of the plurality of parameters to a three-dimensional model to establish a feature-based registration.
2. The method of claim 1, further comprising assigning a value to each cavity voxel of the plurality of cavity voxels encountered by the sensor.
3. The method of claim 2, further comprising correlating the value of each cavity voxel to a frequency with which each cavity voxel encounters the sensor.
4. The method of claim 3, further comprising adjusting a density of the plurality of cavity voxels in accordance with the value of each cavity voxel.
5. The method of claim 4, further comprising creating clouds of cavity voxels having varying densities that match interior anatomical cavity features of the body volume.
6. The method of claim 1, wherein the processing step further includes de-cluttering, digitizing, and filtering the location data.
7. The method of claim 1, further comprising assigning cavity voxels with higher densities a higher weight in registration than cavity voxels with lower densities.
8. The method of claim 1, wherein defining parameters comprises:
defining a density range required for each cavity voxel of the plurality of cavity voxels;
defining a proximity from an already-designated cavity voxel with the cavity voxels satisfying the predefined thresholds of the plurality of parameters; and
defining a parameter template including multiple parameters.
9. The method of claim 8, wherein defining the parameter template involves defining the predefined thresholds of the plurality of parameters as follows:
requiring the cavity voxels to have a certain density corresponding to air;
requiring the cavity voxels to be located adjacent another cavity voxel having the certain density corresponding to air; and
requiring the cavity voxels to be adjacent to cavity voxels having densities corresponding to blood vessels.
10. The method of claim 1, wherein the comparing step further includes:
developing an initial guess;
using the initial guess to establish the feature-based registration; and
calculating a difference between the three-dimensional model and the three-dimensional shape including cavity voxels satisfying the predefined thresholds of the plurality of parameters.
11. A method for providing feature-based registration for a three-dimensional model, the method comprising:
forming a computed tomography (CT) volume from image data, the CT volume corresponding to space within a tortuous channel, the CT volume defined as a plurality of cavity voxels representing values of a three-dimensional shape;
defining a plurality of parameters having predefined thresholds that cavity voxels of the plurality of cavity voxels of the CT volume are required to meet to be designated as candidate cavity voxels to be utilized in the three-dimensional shape;
selecting cavity voxels of the plurality of cavity voxels that satisfy the predefined thresholds of the plurality of parameters; and
comparing the three-dimensional shape including the cavity voxels satisfying the predefined thresholds of the plurality of parameters to the three-dimensional model to establish the feature-based registration.
12. The method of claim 11, further comprising assigning a value to each cavity voxel of the plurality of cavity voxels encountered by a sensor inserted through the tortuous channel.
13. The method of claim 12, further comprising correlating the value of each cavity voxel to a frequency with which each cavity voxel encounters the sensor.
14. The method of claim 13, further comprising adjusting a density of the plurality of cavity voxels in accordance with the value of each cavity voxel.
15. The method of claim 14, further comprising creating clouds of cavity voxels having varying densities that match interior anatomical cavity features of the tortuous channel.
16. The method of claim 12, wherein the processing step further includes de-cluttering, digitizing, and filtering the plurality of cavity voxels encountered by the sensor.
17. The method of claim 16, further comprising assigning cavity voxels with higher densities a higher weight in registration than cavity voxels with lower densities.
18. The method of claim 11, further comprising creating a binary voxel model of the CT volume by assigning a value of zero to all voxels having densities higher than a threshold value set for the cavity voxels that satisfy the predefined thresholds of the plurality of parameters.
19. The method of claim 18, further comprising assigning all other voxels a value of 1 and considering those voxels having a value of 1 as representing air.
20. The method of claim 19, wherein comparing the three-dimensional shape to the three dimensional model includes using a binary matching method.