DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 21-42 are pending under this Office action.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 21, 24-25, 27-32, 34 36-39, and 41-42 are rejected under 35 U.S.C. 103 as being unpatentable over Bryman (US 20080128604 A1) in view of Cancelliere, etc. (US 20200348440 A1), further in view of Botto, etc. (US 20210156810 A1).
Regarding claim 21, Bryman teaches that a method for modelling a block cave mine, the method (See Bryman: Figs. 1-2, and [0012], "A method according to an example embodiment of the present disclosure may also include positioning and, in some instances, repositioning, a plurality of muon detectors at a plurality of subterranean locations adjacent the test region, collecting muon detection data from the detectors for a plurality of intersecting trajectories, and processing the collected muon detection data to form a three-dimensional density map of the test region"; and [0023], "FIGS. 1 and 2 generally illustrate the concept in which one or more substantially vertical holes 100 are drilled into the earth and at least one CRM detector, and preferably a series of CRM detectors, are inserted in the hole(s) for detecting CRM flux along a range of trajectories 102 and 102'. For example, a time projection chamber (TPC) may be a suitable CRM detector, although the present disclosure is not limited thereto. Once the location and/or orientation of the detectors is determined or established, the direction of travel (angles .theta. and .phi. in spherical coordinates), and optionally the approximate energy, of each detected muon may be collected and analyzed to construct an image of the material through which the muons have passed before reaching the detector(s) ") comprising:
at each of a plurality of spaced apart muon detection locations in, or in a vicinity, of the block cave mine (See Bryman: Figs. 1-2, and [0027], "FIG. 1 illustrates an example embodiment of a detector system according to the present disclosure in which cosmic ray muons (CRM) impinging on the surface of the earth penetrate to various depths depending on their energy and the amount of material encountered along their paths. The surviving CRM are may then be detected by one of a substantially vertically arranged group of detectors A, B, and C provided in a borehole 100. The variations in both the distance traveled and the material through which the CRMs have traveled to reach the detector result in corresponding variations in the CRM flux observed by detectors A, B, and C corresponding to different trajectories 102 and 102"):
detecting muons that interact with a muon detector over a time period (See Bryman: Figs. 1-2, and [0024], "In an example embodiment, the CRM detectors may be surrounded, encompassed or otherwise shielded with some relatively high density material to reduce the detectors' sensitivity to "soft" or low energy particles, such as electrons resulting from muon bremsstrahlung. The data acquired by the tracking detectors consists of the 3-dimensional position of entry of the track in the detector and the two angles of incidence, .theta. and .phi., necessary to determine the detected particles' trajectories. The rate of energy loss in the detector material may also be measured for particle identification. In addition, the presence of an induced magnetic field near the detectors could be used to obtain a measure of the CRM momentum"); and
determining, from the interactions, measured directional muon intensities for a plurality of directions intersecting at the muon detection location (See Bryman: Figs. 1-2, and [0025], "After a suitable observation period, the data set for each detector position, characterized by n(z, .theta., .phi.), the intensity (or number of events) observed at a depth z and angles .theta. and .phi. may be analyzed to indicate the relative (or locally differentiated) and absolute flux distribution of CRM along the trajectories through the earth which intersected the detector or detectors"); and
optimizing an objective function to thereby obtain optimal values for a plurality of model parameters (See Bryman: Figs. 1-2, and [0010], "An example embodiment of a system according to the present disclosure may also include a plurality of muon detectors configured for deployment in subterranean test locations, e.g., mines, wells, boreholes and caverns, for detecting muon flux data associated with a subterranean test region; at least one positioning device for determining the location of the test locations at which the muon detectors are deployed, a communication device connected to the muon detectors for receiving and transmitting the detected muon flux data, e.g., a conductive or fiber optic communication cable or an RF transmitter, a processor configured for receiving the detected muon flux data and manipulating the detected muon flux data to produce a three-dimensional density map corresponding of the subterranean test region"; and [0026], "After correction for known topological and geographical features of the study region are made, those regions having a significantly higher (or lower) density compared to nearby regions, e.g., the "POD" illustrated in FIGS. 1 and 2, will result in a deficit (or surplus, respectively) of counts being detected at the same angle .theta. (e.g., azimuth angle) for each detector position and will exhibit variations across the different angles .theta. (e.g., incident angle) corresponding to the various detectors. The detected variations will depend on the relative positioning of the detectors and the POD depending on the depth compared to those trajectories which to not intersect the POD and reflect a baseline CRM flux from which the CRM flux though the POD deviates". Note that generating the 3D map is mapped to the modeling, but other arts will be used to address the cost function features and the block cave mine features) which parameterize a model of the block cave mine,
wherein the objective function attributes cost to a difference between the measured directional muon intensities at the plurality of muon detection locations and modelled directional muon intensities at the plurality of muon detection locations (See Bryman: Figs. 1-2, and [0032], "As will be appreciated, conversely a POD having a reduced relative density or comprising a void, such as a cavern, will tend to produce a corresponding increase in the CRM flux along those trajectories that transverse the lower density POD when compared with trajectories that allow the associated CRM to avoid passing through the POD. By measuring the CRM distribution including the incidence angle and, optionally, the energy, at two or more depths, variations in the CRM flux may be correlated to construct an image of the variable density region"; and [0033], "With sufficient exposure, a complete density distribution of the region of the earth near the detectors would be obtained using techniques analogous to those employed in CT tomography as described in, for example, Klingenbeck-Regn. Multiple holes with multiple detectors and/or reconfigurable or re positionable detectors will increase detector coverage and/or data collection and may, therefore, be used to enhance the statistical precision with which density images may be obtained or the precision with which localized variations in density may be defined". Note that more arts will be searched to explicitly teach attributes cost to a difference).
However, Bryman fails to explicitly disclose that optimizing an objective function to thereby obtain optimal values for a plurality of model parameters which parameterize a model of the block cave mine, wherein the objective function attributes cost to a difference between the measured directional muon intensities at the plurality of muon detection locations and modelled directional muon intensities at the plurality of muon detection locations.
However, Cancelliere teaches that optimizing an objective function to thereby obtain optimal values for a plurality of model parameters which parameterize a model of the block cave mine, wherein the objective function attributes cost to a difference between the measured directional muon intensities at the plurality of muon detection locations and modelled directional muon intensities at the plurality of muon detection locations (See Cancelliere: Fig. 2, and [0007], "Automated history matching techniques are introduced to alleviate the drawbacks associated with conventional history matching. Automated history matching techniques are used to treat history matching as an optimization process, where a cost function is defined representative of the discrepancy between actual and simulated data, and the cost function is minimized. The minimization of the cost function can be obtained by applying an optimization algorithm. Although techniques such as optimization and non-linear programming are not new in the art, the selection of the most adequate optimization algorithm is not trivial, and the number of independent variables involved in complex reservoir simulation does not make the solution of the optimization problem a standard procedure"; [0048], "The first step in this workflow 200 as indicated by block 210 is the utilization of the previous parameters and certain state data estimates (such as static data and dynamic data) to perform the reservoir simulations to establish a set of reservoir models. The simulations are forwarded in time to predict future reservoir performance as indicated by block 220. A monitor survey is computed based on the recently updated reservoir state data at the end of the predicted period and according to the certain reservoir parameters. The time-dependent difference (that is, the difference between the monitor survey and a base survey) is then computed and used in conjunction with the predicted reservoir production data in block 230 to update the previous reservoir model estimates, as well as to serve as a starting point for the next iteration of history matching").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was effectively filed to modify Bryman to have optimizing an objective function to thereby obtain optimal values for a plurality of model parameters which parameterize a model of the block cave mine, wherein the objective function attributes cost to a difference between the measured directional muon intensities at the plurality of muon detection locations and modelled directional muon intensities at the plurality of muon detection locations as taught by Cancelliere in order to improve the quality of the match between actual production and the reservoir model (See Cancelliere: Fig. 1, and [0050], "The base survey is initially acquired before production is begun in the reservoir. Subsequent surveys can be performed later during production to quantify the fluid displacement in the reservoir. The time-dependent difference (the difference between the monitor survey and the base survey or the difference between the monitor survey and the subsequent survey) is used for history matching to improve the quality of the match between actual production and the reservoir model, thus reducing the uncertainty in the porosity and permeability field data"). Bryman teaches a method and system that may detect cosmic ray muon (CRM) flux along a variety of trajectories through a subterranean test region, collect the muon detection data, and process the data to form a three-dimensional density distribution image corresponding to the test region; while Cancelliere teaches a system and method that may establish an ensemble of models reflecting attributes of the hydrocarbon reservoir based on the reservoir model in its present state, update the reservoir model by utilizing a volumetric density image of the hydrocarbon reservoir, and construct the volumetric density image via muon tomography by minimizing the cost function that represents the difference between actual and simulated data. Therefore, it is obvious to one of ordinary skill in the art to modify Bryman by Cancelliere to construct a cost function and minimize the cost function to optimize the 3D density modeling od the geological regions. The motivation to modify Bryman by Cancelliere is "Use of known technique to improve similar devices (methods, or products) in the same way".
However, Bryman, modified by Cancelliere, fails to explicitly disclose that to thereby obtain optimal values for a plurality of model parameters which parameterize a model of the block cave mine.
However, Botto teaches that to thereby obtain optimal values for a plurality of model parameters which parameterize a model of the block cave mine (See Botto: Figs. 1-5, and [0099], "For example and referring again to FIG. 1, the surface 101 of the heap 100 is subdivided into a grid 103, with each pixel on the grid defining the base of truncated pyramid voxels 401, 402a, and 402b, each traversed by a small subset of the overall muon flux. Because of the directional nature of the muon detectors' resolution and combined with the orientation of the muon detector, the muon flux can be mapped in a three dimensional manner"; [0100], "The size and shape of each pixel in the grid 103 is not limited and can be modified when the density map is displayed. In some instances, it may be advantageous to choose a finer or coarser grid as a function of the available statistic. For instance, the grid spacing can be chosen on the basis of the uncertainty with which the bulk density can be determined due to the intrinsic statistical nature of the measurement, including choosing a grid with a non-uniform spacing or a grid defining inverted pyramidal voxels with an increasing base area (on top of the heap) as one moves further away from the location of the muon detector located under the heap. In particular, as the muon rate decreases for larger muon angles relative to the vertical, the statistical accuracy of the method decreases and can be improved by increasing the pixel size, for example, trading position reconstruction in favor of measurement accuracy. In certain other cases, the grid 103 may become denser, and the volume of each voxel 401, 402a, and 402b may shrink over time as more and more muon tracks are measured at the detector. In such cases, one may effectively choose to trade off an increased measurement sensitivity for an increased position resolution over time as defined by choice of pixel areas across the volume. In still further embodiments, the pixels can have a non-rectangular base, for example the base of the pixel can be an arc segment. In such an embodiment, instead of a square grid, the pixel would be arrange to form concentric circles or arc segments"; [0048], "The first step in this workflow 200 as indicated by block 210 is the utilization of the previous parameters and certain state data estimates (such as static data and dynamic data) to perform the reservoir simulations to establish a set of reservoir models. The simulations are forwarded in time to predict future reservoir performance as indicated by block 220. A monitor survey is computed based on the recently updated reservoir state data at the end of the predicted period and according to the certain reservoir parameters. The time-dependent difference (that is, the difference between the monitor survey and a base survey) is then computed and used in conjunction with the predicted reservoir production data in block 230 to update the previous reservoir model estimates, as well as to serve as a starting point for the next iteration of history matching"; [0129], "In yet other configurations, not exclusive to the above, a muon detector 518 could be placed inside the tailing pond itself, offering access to a different set of muon tracks 515-517 and 519 and view angles with which to further analyze the 3-dimensional distribution of fluids and density inside the dam. This is particularly advantageous for monitoring the densification process at the basis of the pond or soil reclamation as well as determine residual water content in the pond. Furthermore, it is noted that in some situations (not shown), muon tracks may traverse the material of the tailing pond 502 without traversing the dam 500 and subsequently strike muon detector 503 after traversing hard soil 501"; and [0117], "As shown in FIG. 3 and FIG. 4, leaching pad area module 203 is in the process of being filled with new material, and therefore the leaching pad area module is inactive and is not producing pregnant leaching fluid for processing (though some pregnant fluid may be in the process of being collected for processing). The movable bucket wheel excavator 301 and moveable stacker 302 are moved and positioned according to the associated mine's production schedule. Equipment tracks 312, 313, and 314 allow for this equipment to turn 180 degrees and move to the next section of the leaching area, where leaching pad area modules 211, 212, 213, 214, 215, 216, and 230 are located. In this manner, all of the pads defining the leaching pad area modules 201-206, 210-216, and 230 of leaching area 200 are continuously loaded with ore, irrigated with leaching fluid, and have the spent ore removed").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was effectively filed to modify Bryman to have to thereby obtain optimal values for a plurality of model parameters which parameterize a model of the block cave mine as taught by Botto in order to improve the quality of the match between actual production and the reservoir model (See Botto: Fig. 1, and [0050], "The base survey is initially acquired before production is begun in the reservoir. Subsequent surveys can be performed later during production to quantify the fluid displacement in the reservoir. The time-dependent difference (the difference between the monitor survey and the base survey or the difference between the monitor survey and the subsequent survey) is used for history matching to improve the quality of the match between actual production and the reservoir model, thus reducing the uncertainty in the porosity and permeability field data"). Bryman teaches a method and system that may detect cosmic ray muon (CRM) flux along a variety of trajectories through a subterranean test region, collect the muon detection data, and process the data to form a three-dimensional density distribution image corresponding to the test region; while Botto teaches a system and method that may detect muons in various location,, model the muon density in a plurality of models, and apply those models in mining environments. Therefore, it is obvious to one of ordinary skill in the art to modify Bryman by Botto to apply the muon density model in the block cave mine. The motivation to modify Bryman by Botto is "Use of known technique to improve similar devices (methods, or products) in the same way".
Regarding claim 24, Bryman, Cancelliere, and Botto teach all the features with respect to claim 21 as outlined above. Further, Botto teaches that the method of claim 21 wherein the model parameters comprise density profile parameters, which assign a density to each of a plurality of three-dimensional voxels in the block cave mine (See Botto: Figs. 1-2, and [0089], "Measurement of muon flux is obtained with one or more muon detectors which can be positioned at different view angles with respect to the object or volume under investigation. From the measurements of muon flux through the volume, a three dimensional tomographic density map can be formed. In some embodiments, a single measurement point is provided, and the density information remains three dimensional in nature but can be represented by a two dimensional projection. In other embodiments, multiple measurement points are provided"; [0098], "FIG.1 illustrates an exemplary embodiment of the disclosure. In FIG.1, muon detectors 201 and 202 are placed around a section of a heap of material 100, which can be a heap of mining ore in a leaching pad. Heap 100 further comprises two side walls 102 and a top surface 101. It should be noted that although heap 100 is depicted in FIG. 1 as having a flat, rectangular top surface, the shape of the heap is not so limited. Heaps can have irregular shapes, including shapes with different elevations or a non-polygonal contour. Muon detector 201 is placed sideways on the heap and muon detector 202 is placed beneath the heap. A plurality of muon particles originating from space traverses the heap first by passing through the top of the heap and subsequently arriving at muon detectors 201 and 202. The muon particles traverse the heap in all possible directions. Muon detectors 201 and 202 can detect the arrival of each muon particle on their surface and determine the muon incoming direction event by event. According to FIG. 1, muon tracks 301a and 301b traverse the heap 100 and arrive at detector 201, and muon tracks 302a and 302b traverse the heap 100 and arrive at detectors 201 and 202. Muon track 302c does not traverse the heap but nonetheless arrives at detector 201. Muon tracks 310 and 311 also traverse at least a portion of the heap 100 but because such tracks do not arrive at any muon detector, their arrival and direction is not registered. Those skilled in the art recognize that in some embodiments, muon detectors 201 and 202 intrinsically have angular resolution such that muon arrival tracks can only be determined within a certain solid angle and that this angular resolution is different for muon particles that arrive from different directions. Furthermore, it is also appreciated that density and fluid information can only be obtained after a sufficiently high event statistics is accumulated over time. Thus, in certain embodiments, muon tracks are grouped over 3-dimensional voxels that are larger than the solid angle due to the muon detectors' resolution. Muon tracks that belong to a particular voxel are summed and analyzed together to calculate the average value of the bulk density and/or the amount of fluid within the volume of the voxel itself").
Regarding claim 25, Bryman, Cancelliere, and Botto teach all the features with respect to claim 24 as outlined above. Further, Botto teaches that the method of claim 24 wherein the density profile parameters also assign a density to each of a plurality of three-dimensional voxels in the earth adjacent to the block cave (See Botto: Figs. 1-2, and [0112], "In other cases, the heap leaching process is adapted to the morphology of the terrain. For instance in valley fills or valley leaching operations, leaching pads are placed at the bottom of a valley, with the natural slope providing a convenient way to collect process fluids percolating through the stack of crushed ore. In these cases, because at least some portions of the surrounding terrain are above the stack of crushed ore, muon detectors may be placed in boreholes, tunnels or caves excavated on the side of the valley fill"; and Fig. 7, and [0134], "Referring to FIG. 7, a scintillator 700 is realized by an arrangement of elongated scintillator elements 701-704 oriented along an X direction, followed by a second plane of elongated scintillator elements (705-708) oriented primarily in an orthogonal Y direction. When a muon traverses such a detector plane, its arrival position can be determined by comparing the data along the X and Y-oriented elongated scintillator elements. A muon detector including at least two such detector planes (i.e. two X- and two Y-planes) will thus be able to determine two points along the cosmic ray track and from there one can calculate the muon's original direction. Typical lengths for each of the scintillator elements are 0.5-2 min length, 0.5-2 cm in thickness and 1-5 cm in width". Note that the cosmic ray traverse the overburden and surrounding rocks before it reached the voids, so the density model recovered data include the adjacent earth).
Regarding claim 27, Bryman, Cancelliere, and Botto teach all the features with respect to claim 21 as outlined above. Further, Cancelliere teaches that the method of claim 21 wherein the measured directional muon intensities have associated uncertainties and wherein optimizing the objective function comprises accounting for the uncertainties in the measured directional muon intensities to thereby provide the optimal values of the model parameters with corresponding model parameter uncertainties (See Cancelliere: Fig. 3, and [0064], "In block 310, a static geological model is constructed by utilizing static data. Actual static data including certain geophysical and petrophysical data can be acquired at various observation locations of the reservoir. In some embodiments, the data include data sets of well logs, seismic, core, and petrophysical information of the hydrocarbon reservoir. An initial estimate of several possible realizations of a reservoir model is constructed. In some embodiments, the static model can include an ensemble of models to reduce uncertainty in the actual data. The uncertainty associated with the actual static data can be addressed by creating a set of equiprobable scenarios. For example, a set of equiprobable scenarios can be created by implementing two scenarios having different permeability distributions generated by a sequential Gaussian simulation where both distributions reflect the well data and variograms specified by the geomodeler. The variability (corresponding to uncertainty) between the two scenarios may increase when the permeability distributions significantly deviate from data points provided by quantitative data. In other embodiments, the static model can include a single model"; and [0053], "EnKF is an inverse-modelling local optimization technique originated from the Kalman Filter (KF), which has been designed originally for electrical-signal processing. EnKF provides sequential assimilation of both static and dynamic data into a model. Static and dynamic data are assimilated sequentially from only the current timestep. EnKF is based on a Bayesian framework and Monte Carlo simulation that stochastically generates reservoir models that are integrated over time to estimate probability density functions (or PDFs). A previous PDF is updated to a recent PDF incorporating recent data. This updating can be implemented independent of a reservoir simulator. The model estimate of EnKF is derived by maximizing analytically the posterior probability without any numerical optimization algorithm. Because EnKF is devised accounting for the stochastic nature of the modelling system, the system is represented by an ensemble of equally and likely to be drawn realizations of model estimates. The model covariance matrix is replaced by a sample covariance matrix, which is computed from the ensemble members. EnKF requires storing a portion of the covariance matrix that describes the model-to-data and data-to-data relationship. Instead of deriving and solving analytical equations of hydrocarbon reservoir behavior, any suitable flow simulation methodology can be coupled with EnKF to constrain the reservoir models and to predict future reservoir performance. Gradient computation is not necessary").
Regarding claim 28, Bryman, Cancelliere, and Botto teach all the features with respect to claim 21 as outlined above. Further, Cancelliere teaches that the method of claim 21 wherein the optimal values of the model parameters comprise probability densities or probability distributions of the values of the model parameters (See Cancelliere: Figs. 1-2, and [0053], "EnKF is an inverse-modelling local optimization technique originated from the Kalman Filter (KF), which has been designed originally for electrical-signal processing. EnKF provides sequential assimilation of both static and dynamic data into a model. Static and dynamic data are assimilated sequentially from only the current time step. EnKF is based on a Bayesian framework and Monte Carlo simulation that stochastically generates reservoir models that are integrated overtime to estimate probability density functions (or PDFs). A previous PDF is updated to a recent PDF incorporating recent data. This updating can be implemented independent of a reservoir simulator. The model estimate of EnKF is derived by maximizing analytically the posterior probability without any numerical optimization algorithm. Because EnKF is devised accounting for the stochastic nature of the modelling system, the system is represented by an ensemble of equally and likely to be drawn realizations of model estimates. The model covariance matrix is replaced by a sample covariance matrix, which is computed from the ensemble members. EnKF requires storing a portion of the covariance matrix that describes the model-to-data and data-to-data relationship. Instead of deriving and solving analytical equations of hydrocarbon reservoir behavior, any suitable flow simulation methodology can be coupled with EnKF to constrain the reservoir models and to predict future reservoir performance. Gradient computation is not necessary").
Regarding claim 29, Bryman, Cancelliere, and Botto teach all the features with respect to claim 21 as outlined above. Further, Cancelliere teaches that the method of claim 21 wherein optimizing the objective function comprises performing at least one of: a Bayesian optimization process and a Monte Carlo optimization process (See Cancelliere: Figs.1-2, and [0035], "As used throughout the disclosure, the term "static data" refer to data such as permeability and porosity field data that were conventionally considered not to vary with time. It should be noted that static data can be updated with time in ensemble based Bayesian filtering methods. Static data may also include well log data, seismic data, core data, geophysical data, and petrophysical data").
Regarding claim 30, Bryman, Cancelliere, and Botto teach all the features with respect to claim 21 as outlined above. Further, Cancelliere teaches that the method of claim 21 wherein optimizing the objective function is based at least in part on prior mine information (See Cancelliere: Fig. 2, and [0047], "FIG. 2 shows an example workflow 200 for history matching of production data and muographic 4D data in accordance with an embodiment of the disclosure. The workflow 200 is an iterative process as shown as the dotted arrow that updates over time as new production data, new muographic 4D data, or both, are obtained over time").
Regarding claim 31, Bryman, Cancelliere, and Botto teach all the features with respect to claim 30 as outlined above. Further, Cancelliere teaches that the method of claim 30 wherein the modelled directional muon intensities are based at least in part on the prior mine information (See Cancelliere: Figs. 1-3, and [0052], "In some embodiments, to provide accurate estimates of state variables the initial ensemble is conditioned with production data and model dynamics iteratively over time. This can be achieved by using methodologies such as EnKF"; and [0067], "Still in block 330, as part of the history matching utilizing EnKF or EnKS processing, the dynamic reservoir model realizations initially constructed in block 320 are iterated sequentially based on the production data gathered since the last iteration in block 340. In block 340, the production data includes historical data and i-field data. The dynamic reservoir model realizations initially formed in block 320 are iterated sequentially based on the muographic 4D data gathered since the last iteration in block 370. Muography assisted history matching involves a constant input flow of muographic 4D data, which can be averaged over time. The muographic 4D data input provided in block 370 includes a volumetric image of fluid saturations with a varying range of uncertainty based on the number of muon detection events over the predetermined timeframe. Both the muographic 4D data provided in block 370 and historical data and i-field data provided in block 340 are available in real time or at least periodically. This allows the ensemble-based reservoir model to continuously or at least periodically update itself as new inputs of muographic 4D data, historical data, and i-field data are introduced overtime such that the reservoir model can assimilate the data. In some embodiments, simulation time and computing power can be reduced by updating the reservoir model when significant changes in density are observed in the reservoir").
Regarding claim 32, Bryman, Cancelliere, and Botto teach all the features with respect to claim 30 as outlined above. Further, Botto teaches that the method of claim 30 wherein the prior mine information comprises geometrical information which defines one or more regions of the block cave mine, the one or more regions selected from the group comprising: an ore body, an air gap and a muck pile (See Botto: Fig. 1, and [0001], “In certain other embodiments which are referred to herein as static heap leaching, a multi-step process is employed. During static heap leaching, a first liner is positioned in a location, and a first ore volume is deposited on the first liner. The first liner is fluid impermeable and collects any fluid that seeps through any ore volume that is positioned above the first ore volume. The first ore volume is irrigated and a first pregnant leaching fluid is collected in order to extract metals. After the irrigation and collection of a first pregnant leaching fluid is completed, a second liner is optionally placed on the first ore volume, and a second ore volume is deposited on the optional second liner. While two layers of liner and ore volume are described above, it is appreciated that any number of alternating liners and ore volumes can be provided, and that additional liners are not necessarily required above the first liner. Between each ore pair of ore volumes, the liners are optional. However, as described above, there must be at least one liner present below the lowest ore volume in order to collect any fluid that seeps through any ore volume that is positioned above the line. In static heap leaching, when the irrigation of an ore volume is stopped and a new liner is placed on the ore volume, the density of the ore volume does not change substantially and thus for ease of analysis, any density changes of the heap over time can be attributed to additional ore volume(s) which are deposited”).
Regarding claim 34, Bryman, Cancelliere, and Botto teach all the features with respect to claim 30 as outlined above. Further, Cancelliere teaches that the method of claim 30 wherein the prior mine information comprises one or more of: information determined in a previous iteration of the optimization; and information determined prior to performing any iterations of the optimization (See Cancelliere: Fig. 2, and [0047], “FIG. 2 shows an example workflow 200 for history matching of production data and muographic 4D data in accordance with an embodiment of the disclosure. The workflow 200 is an iterative process as shown as the dotted arrow that updates over time as new production data, new muographic 4D data, or both, are obtained over time”).
Regarding claim 36, Bryman, Cancelliere, and Botto teach all the features with respect to claim 21 as outlined above. Further, Bryman teaches that the method of claim 21 wherein optimizing the objective function is based at least in part on additional measured information comprising one or more of: gravimetric information detected by one or more gravimetric sensors; seismic information detected by one or more seismic sensors; Radio Frequency Identification (RFID) information from one or more RFID devices; smart beacon information from one or more smart beacon devices; and image data from one or more downhole cameras (See Bryman: Figs. 4A-B, and [001], “llustrated in FIG. 4B is an example embodiment in which additional variables are indicated including an irregular surface topography, non-uniform strata and offset detector arrays. Based on the data available, each of these variables may be taken into account during the analysis of the CRM flux data from the various detectors and detector arrays. For example, topographic profiles, accurate detector positioning and orientation data and strata data provided by core sampling and/or active seismic tomography in which generated shock waves are applied to a survey site region, reflected off the underlying rock strata at variable velocities and detected by an array of geophones arrayed across the surface of the survey site. As will be appreciated, some initial substrate composition data may be obtained during the drilling of the boreholes utilized for the vertical detector installations and/or may have been collected during the excavations by which openings or chambers in which larger, e.g., >1 m.sup.2, detectors may be deployed, for example, a horizontal configuration, as suggested in FIGS. 4A and 4B, in a mine”).
Regarding claim 37, Bryman, Cancelliere, and Botto teach all the features with respect to claim 21 as outlined above. Further, Bryman teaches that the method of claim 21 wherein optimizing the objective function comprises performing tomographic reconstruction based at least in part on the measured directional muon intensities (See Bryman: Fig. 1, and [0049], “The present disclosure, therefore, encompasses both the configuration of equipment and the method of utilizing such equipment to examine or evaluate the tomographic configuration of the earth for geological exploration using cosmic ray muons impinging at all possible angles on deeply positioned detectors. In this technique, CRM of all angles impinging on the detectors would be used to survey the nearby region of the earth using differential intensity variations n(z, .theta., .phi.) and, depending on the number and location of the detectors, may be used to develop a subterranean density map. This apparatus and technique may be useful, for example, in identifying and imaging mineral and/or petroleum rich regions within a larger region. Similarly, this apparatus and technique may be useful for identifying and imaging subterranean voids such as natural caverns or abandoned mines”).
Regarding claim 38, Bryman, Cancelliere, and Botto teach all the features with respect to claim 37 as outlined above. Further, Cancelliere teaches that the method of claim 37 wherein the objective function is based at least in part on the tomographic reconstruction (See Cancelliere: Fig. 3, and [0071], “In block 360, each muon detection event provides information regarding the muon trajectory and the average density along the trajectory path as shown for example in FIG. 1. In block 370, muon detection data obtained in block 360 is used for tomographic inversion to create a time-dependent volumetric density image of the hydrocarbon reservoir. Optionally, muon detection period can be adjusted based on the uncertainty or resolution of the inverted tomographic image. The adjustment of the muon detection period may alter the waiting time for each ensemble update. For example, the detection period can be extended to obtain more muon detections in block 360 before the ensemble updating process in block 330 is initiated in the next iteration. In the next iteration, a time-averaged volumetric density image can be obtained in block 370. In some embodiments, such time-averaged volumetric density image can be a pixelated image that still can be assimilated by EnKF or EnKS”).
Regarding claim 39, Bryman, Cancelliere, and Botto teach all the features with respect to claim 37 as outlined above. Further, Bryman teaches that the method of claim 37 wherein the modelled directional muon intensities are based at least in part on the tomographic reconstruction (See Cancelliere: Fig. 3, and [0071], “In block 360, each muon detection event provides information regarding the muon trajectory and the average density along the trajectory path as shown for example in FIG. 1. In block 370, muon detection data obtained in block 360 is used for tomographic inversion to create a time-dependent volumetric density image of the hydrocarbon reservoir. Optionally, muon detection period can be adjusted based on the uncertainty or resolution of the inverted tomographic image. The adjustment of the muon detection period may alter the waiting time for each ensemble update. For example, the detection period can be extended to obtain more muon detections in block 360 before the ensemble updating process in block 330 is initiated in the next iteration. In the next iteration, a time-averaged volumetric density image can be obtained in block 370. In some embodiments, such time-averaged volumetric density image can be a pixelated image that still can be assimilated by EnKF or EnKS”. Note that the inverted tomographic image reconstruction is mapped to the directional intensities).
Regarding claim 41, Bryman, Cancelliere, and Botto teach all the features with respect to claim 21 as outlined above. Further, Bryman, Cancelliere, and Botto teach that a system for modelling a block cave mine, the system (See Bryman: Figs. 1-2, and [0012], "A method according to an example embodiment of the present disclosure may also include positioning and, in some instances, repositioning, a plurality of muon detectors at a plurality of subterranean locations adjacent the test region, collecting muon detection data from the detectors for a plurality of intersecting trajectories, and processing the collected muon detection data to form a three-dimensional density map of the test region"; and [0023], "FIGS. 1 and 2 generally illustrate the concept in which one or more substantially vertical holes 100 are drilled into the earth and at least one CRM detector, and preferably a series of CRM detectors, are inserted in the hole(s) for detecting CRM flux along a range of trajectories 102 and 102'. For example, a time projection chamber (TPC) may be a suitable CRM detector, although the present disclosure is not limited thereto. Once the location and/or orientation of the detectors is determined or established, the direction of travel (angles .theta. and .phi. in spherical coordinates), and optionally the approximate energy, of each detected muon may be collected and analyzed to construct an image of the material through which the muons have passed before reaching the detector(s) ") comprising:
a plurality of spaced apart muon detectors distributed in, or in a vicinity of the block cave mine (See Bryman: Figs. 1-2, and [0027], "FIG. 1 illustrates an example embodiment of a detector system according to the present disclosure in which cosmic ray muons (CRM) impinging on the surface of the earth penetrate to various depths depending on their energy and the amount of material encountered along their paths. The surviving CRM are may then be detected by one of a substantially vertically arranged group of detectors A, B, and C provided in a borehole 100. The variations in both the distance traveled and the material through which the CRMs have traveled to reach the detector result in corresponding variations in the CRM flux observed by detectors A, B, and C corresponding to different trajectories 102 and 102"), each muon detector detecting muons that interact with the muon detector over a time period at one or more corresponding muon detection locations (See Bryman: Figs. 1-2, and [0024], "In an example embodiment, the CRM detectors may be surrounded, encompassed or otherwise shielded with some relatively high density material to reduce the detectors' sensitivity to "soft" or low energy particles, such as electrons resulting from muon bremsstrahlung. The data acquired by the tracking detectors consists of the 3-dimensional position of entry of the track in the detector and the two angles of incidence, .theta. and .phi., necessary to determine the detected particles' trajectories. The rate of energy loss in the detector material may also be measured for particle identification. In addition, the presence of an induced magnetic field near the detectors could be used to obtain a measure of the CRM momentum");
a controller in communication with the muon detectors (See Bryman: Fig. 1, and [0010], “An example embodiment of a system according to the present disclosure may also include a plurality of muon detectors configured for deployment in subterranean test locations, e.g., mines, wells, boreholes and caverns, for detecting muon flux data associated with a subterranean test region; at least one positioning device for determining the location of the test locations at which the muon detectors are deployed, a communication device connected to the muon detectors for receiving and transmitting the detected muon flux data, e.g., a conductive or fiber optic communication cable or an RF transmitter, a processor configured for receiving the detected muon flux data and manipulating the detected muon flux data to produce a three-dimensional density map corresponding of the subterranean test region”), the controller configured to:
determine, based on the interactions between the muons and the muon detectors, measured directional muon intensities for a plurality of directions intersecting at each muon detection location (See Bryman: Figs. 1-2, and [0025], "After a suitable observation period, the data set for each detector position, characterized by n(z, .theta., .phi.), the intensity (or number of events) observed at a depth z and angles .theta. and .phi. may be analyzed to indicate the relative (or locally differentiated) and absolute flux distribution of CRM along the trajectories through the earth which intersected the detector or detectors");
optimize an objective function (See Cancelliere: Fig. 2, and [0007], "Automated history matching techniques are introduced to alleviate the drawbacks associated with conventional history matching. Automated history matching techniques are used to treat history matching as an optimization process, where a cost function is defined representative of the discrepancy between actual and simulated data, and the cost function is minimized. The minimization of the cost function can be obtained by applying an optimization algorithm. Although techniques such as optimization and non-linear programming are not new in the art, the selection of the most adequate optimization algorithm is not trivial, and the number of independent variables involved in complex reservoir simulation does not make the solution of the optimization problem a standard procedure"; [0048], "The first step in this workflow 200 as indicated by block 210 is the utilization of the previous parameters and certain state data estimates (such as static data and dynamic data) to perform the reservoir simulations to establish a set of reservoir models. The simulations are forwarded in time to predict future reservoir performance as indicated by block 220. A monitor survey is computed based on the recently updated reservoir state data at the end of the predicted period and according to the certain reservoir parameters. The time-dependent difference (that is, the difference between the monitor survey and a base survey) is then computed and used in conjunction with the predicted reservoir production data in block 230 to update the previous reservoir model estimates, as well as to serve as a starting point for the next iteration of history matching") to thereby obtain optimal values for a plurality of model parameters (See Bryman: Figs. 1-2, and [0010], "An example embodiment of a system according to the present disclosure may also include a plurality of muon detectors configured for deployment in subterranean test locations, e.g., mines, wells, boreholes and caverns, for detecting muon flux data associated with a subterranean test region; at least one positioning device for determining the location of the test locations at which the muon detectors are deployed, a communication device connected to the muon detectors for receiving and transmitting the detected muon flux data, e.g., a conductive or fiber optic communication cable or an RF transmitter, a processor configured for receiving the detected muon flux data and manipulating the detected muon flux data to produce a three-dimensional density map corresponding of the subterranean test region"; and [0026], "After correction for known topological and geographical features of the study region are made, those regions having a significantly higher (or lower) density compared to nearby regions, e.g., the "POD" illustrated in FIGS. 1 and 2, will result in a deficit (or surplus, respectively) of counts being detected at the same angle .theta. (e.g., azimuth angle) for each detector position and will exhibit variations across the different angles .theta. (e.g., incident angle) corresponding to the various detectors. The detected variations will depend on the relative positioning of the detectors and the POD depending on the depth compared to those trajectories which to not intersect the POD and reflect a baseline CRM flux from which the CRM flux though the POD deviates". Note that generating the 3D map is mapped to the modeling, but other arts will be used to address the cost function features and the block cave mine features) which parameterize a model of the block cave mine (See Botto: Figs. 1-5, and [0099], "For example and referring again to FIG. 1, the surface 101 of the heap 100 is subdivided into a grid 103, with each pixel on the grid defining the base of truncated pyramid voxels 401, 402a, and 402b, each traversed by a small subset of the overall muon flux. Because of the directional nature of the muon detectors' resolution and combined with the orientation of the muon detector, the muon flux can be mapped in a three dimensional manner"; [0100], "The size and shape of each pixel in the grid 103 is not limited and can be modified when the density map is displayed. In some instances, it may be advantageous to choose a finer or coarser grid as a function of the available statistic. For instance, the grid spacing can be chosen on the basis of the uncertainty with which the bulk density can be determined due to the intrinsic statistical nature of the measurement, including choosing a grid with a non-uniform spacing or a grid defining inverted pyramidal voxels with an increasing base area (on top of the heap) as one moves further away from the location of the muon detector located under the heap. In particular, as the muon rate decreases for larger muon angles relative to the vertical, the statistical accuracy of the method decreases and can be improved by increasing the pixel size, for example, trading position reconstruction in favor of measurement accuracy. In certain other cases, the grid 103 may become denser, and the volume of each voxel 401, 402a, and 402b may shrink over time as more and more muon tracks are measured at the detector. In such cases, one may effectively choose to trade off an increased measurement sensitivity for an increased position resolution over time as defined by choice of pixel areas across the volume. In still further embodiments, the pixels can have a non-rectangular base, for example the base of the pixel can be an arc segment. In such an embodiment, instead of a square grid, the pixel would be arrange to form concentric circles or arc segments"; [0048], "The first step in this workflow 200 as indicated by block 210 is the utilization of the previous parameters and certain state data estimates (such as static data and dynamic data) to perform the reservoir simulations to establish a set of reservoir models. The simulations are forwarded in time to predict future reservoir performance as indicated by block 220. A monitor survey is computed based on the recently updated reservoir state data at the end of the predicted period and according to the certain reservoir parameters. The time-dependent difference (that is, the difference between the monitor survey and a base survey) is then computed and used in conjunction with the predicted reservoir production data in block 230 to update the previous reservoir model estimates, as well as to serve as a starting point for the next iteration of history matching"; [0129], "In yet other configurations, not exclusive to the above, a muon detector 518 could be placed inside the tailing pond itself, offering access to a different set of muon tracks 515-517 and 519 and view angles with which to further analyze the 3-dimensional distribution of fluids and density inside the dam. This is particularly advantageous for monitoring the densification process at the basis of the pond or soil reclamation as well as determine residual water content in the pond. Furthermore, it is noted that in some situations (not shown), muon tracks may traverse the material of the tailing pond 502 without traversing the dam 500 and subsequently strike muon detector 503 after traversing hard soil 501"; and [0117], "As shown in FIG. 3 and FIG. 4, leaching pad area module 203 is in the process of being filled with new material, and therefore the leaching pad area module is inactive and is not producing pregnant leaching fluid for processing (though some pregnant fluid may be in the process of being collected for processing). The movable bucket wheel excavator 301 and moveable stacker 302 are moved and positioned according to the associated mine's production schedule. Equipment tracks 312, 313, and 314 allow for this equipment to turn 180 degrees and move to the next section of the leaching area, where leaching pad area modules 211, 212, 213, 214, 215, 216, and 230 are located. In this manner, all of the pads defining the leaching pad area modules 201-206, 210-216, and 230 of leaching area 200 are continuously loaded with ore, irrigated with leaching fluid, and have the spent ore removed"),
wherein the objective function attributes cost to a difference between the measured directional muon intensities at the plurality of muon detection locations and modelled directional muon intensities at the plurality of muon detection locations (See Bryman: Figs. 1-2, and [0032], "As will be appreciated, conversely a POD having a reduced relative density or comprising a void, such as a cavern, will tend to produce a corresponding increase in the CRM flux along those trajectories that transverse the lower density POD when compared with trajectories that allow the associated CRM to avoid passing through the POD. By measuring the CRM distribution including the incidence angle and, optionally, the energy, at two or more depths, variations in the CRM flux may be correlated to construct an image of the variable density region"; and [0033], "With sufficient exposure, a complete density distribution of the region of the earth near the detectors would be obtained using techniques analogous to those employed in CT tomography as described in, for example, Klingenbeck-Regn. Multiple holes with multiple detectors and/or reconfigurable or re positionable detectors will increase detector coverage and/or data collection and may, therefore, be used to enhance the statistical precision with which density images may be obtained or the precision with which localized variations in density may be defined". Note that more arts will be searched to explicitly teach attributes cost to a difference).
Regarding claim 42, Bryman, Cancelliere, and Botto teach all the features with respect to claim 21 as outlined above. Further, Bryman, Cancelliere, and Botto teach that a method for modelling a block cave mine, the method (See Bryman: Figs. 1-2, and [0012], "A method according to an example embodiment of the present disclosure may also include positioning and, in some instances, repositioning, a plurality of muon detectors at a plurality of subterranean locations adjacent the test region, collecting muon detection data from the detectors for a plurality of intersecting trajectories, and processing the collected muon detection data to form a three-dimensional density map of the test region"; and [0023], "FIGS. 1 and 2 generally illustrate the concept in which one or more substantially vertical holes 100 are drilled into the earth and at least one CRM detector, and preferably a series of CRM detectors, are inserted in the hole(s) for detecting CRM flux along a range of trajectories 102 and 102'. For example, a time projection chamber (TPC) may be a suitable CRM detector, although the present disclosure is not limited thereto. Once the location and/or orientation of the detectors is determined or established, the direction of travel (angles .theta. and .phi. in spherical coordinates), and optionally the approximate energy, of each detected muon may be collected and analyzed to construct an image of the material through which the muons have passed before reaching the detector(s) ") comprising:
determining modelled directional muon intensities based on a density model of the block cave mine (See Bryman: Figs. 1-2, and [0025], "After a suitable observation period, the data set for each detector position, characterized by n(z, .theta., .phi.), the intensity (or number of events) observed at a depth z and angles .theta. and .phi. may be analyzed to indicate the relative (or locally differentiated) and absolute flux distribution of CRM along the trajectories through the earth which intersected the detector or detectors");
detecting muons traversing through the block cave mine to generate muon trajectory data (See Bryman: Figs. 1-2, and [0024], "In an example embodiment, the CRM detectors may be surrounded, encompassed or otherwise shielded with some relatively high density material to reduce the detectors' sensitivity to "soft" or low energy particles, such as electrons resulting from muon bremsstrahlung. The data acquired by the tracking detectors consists of the 3-dimensional position of entry of the track in the detector and the two angles of incidence, .theta. and .phi., necessary to determine the detected particles' trajectories. The rate of energy loss in the detector material may also be measured for particle identification. In addition, the presence of an induced magnetic field near the detectors could be used to obtain a measure of the CRM momentum");
determining measured directional muon intensities based on the muon trajectory data (See Bryman: Fig. 1, and [0036], “Referring to FIG. 5, a TPC 500 may include of a relatively large volume drift region 510 with a relatively high voltage plane 520. A centrally-situated high voltage plane 520 (as illustrated) may provide two TPCs which use the same high voltage plane 520 to generate drifts in opposite directions. Alternatively, a single TPC may be provided with a drift in only one direction. A muon traversing the drift region 510 may generate a charged track 540. Consequently, ionization electrons 530 from the charged track 540 may drift to one or both ends of the drift region 510 where they are detected by a two-dimensional measuring system (e.g., proportional wire system) at the end cap 550, which measures the x and y coordinates. The z coordinate (along the drift direction) is measured using the arrival time of the ionization electrons 530 relative to the trigger time provided by, for instance, plastic scintillators”; and [0044], “An alternate example embodiment shown in FIG. 4A of a system useful for geologic tomography utilizing the example method described above may include one or more large area (e.g., 2 m.times.2 m) TPC detector systems with plastic scintillator trigger detectors placed horizontally in a mine or other underground cavity. Time projection chambers (TPC), as described by, for example, C. Hargrove et al., NIM 219 (1984) 461, the disclosure of which is hereby incorporated, in its entirety, by reference, having a length of about 1 m surrounded by 1 cm thick plastic scintillation counters are placed at various depths or at various horizontal locations. Each TPC, triggered by the scintillation counters (perhaps, in time coincidence on opposite sides of the TPC) samples the trajectory of the detected muon using the ionization trail produced in the gas of the detector by the muon as described above to determine the direction of the detected muon. As will be appreciated, data from horizontal detector arrays and vertical detector arrays may be combined to improve the accuracy of the scan as permitted by the site limitations and thereby improve the system flexibility and adaptability”); and
optimizing an objective function (See Cancelliere: Fig. 2, and [0007], "Automated history matching techniques are introduced to alleviate the drawbacks associated with conventional history matching. Automated history matching techniques are used to treat history matching as an optimization process, where a cost function is defined representative of the discrepancy between actual and simulated data, and the cost function is minimized. The minimization of the cost function can be obtained by applying an optimization algorithm. Although techniques such as optimization and non-linear programming are not new in the art, the selection of the most adequate optimization algorithm is not trivial, and the number of independent variables involved in complex reservoir simulation does not make the solution of the optimization problem a standard procedure"; [0048], "The first step in this workflow 200 as indicated by block 210 is the utilization of the previous parameters and certain state data estimates (such as static data and dynamic data) to perform the reservoir simulations to establish a set of reservoir models. The simulations are forwarded in time to predict future reservoir performance as indicated by block 220. A monitor survey is computed based on the recently updated reservoir state data at the end of the predicted period and according to the certain reservoir parameters. The time-dependent difference (that is, the difference between the monitor survey and a base survey) is then computed and used in conjunction with the predicted reservoir production data in block 230 to update the previous reservoir model estimates, as well as to serve as a starting point for the next iteration of history matching") that attributes cost to a difference between the measured directional muon intensities and the modelled directional muon intensities to thereby obtain a plurality of optimal values for a plurality of model parameters (See Bryman: Figs. 1-2, and [0010], "An example embodiment of a system according to the present disclosure may also include a plurality of muon detectors configured for deployment in subterranean test locations, e.g., mines, wells, boreholes and caverns, for detecting muon flux data associated with a subterranean test region; at least one positioning device for determining the location of the test locations at which the muon detectors are deployed, a communication device connected to the muon detectors for receiving and transmitting the detected muon flux data, e.g., a conductive or fiber optic communication cable or an RF transmitter, a processor configured for receiving the detected muon flux data and manipulating the detected muon flux data to produce a three-dimensional density map corresponding of the subterranean test region"; and [0026], "After correction for known topological and geographical features of the study region are made, those regions having a significantly higher (or lower) density compared to nearby regions, e.g., the "POD" illustrated in FIGS. 1 and 2, will result in a deficit (or surplus, respectively) of counts being detected at the same angle .theta. (e.g., azimuth angle) for each detector position and will exhibit variations across the different angles .theta. (e.g., incident angle) corresponding to the various detectors. The detected variations will depend on the relative positioning of the detectors and the POD depending on the depth compared to those trajectories which to not intersect the POD and reflect a baseline CRM flux from which the CRM flux though the POD deviates". Note that generating the 3D map is mapped to the modeling, but other arts will be used to address the cost function features and the block cave mine features) which parameterize a model of the block cave mine (See Botto: Figs. 1-5, and [0099], "For example and referring again to FIG. 1, the surface 101 of the heap 100 is subdivided into a grid 103, with each pixel on the grid defining the base of truncated pyramid voxels 401, 402a, and 402b, each traversed by a small subset of the overall muon flux. Because of the directional nature of the muon detectors' resolution and combined with the orientation of the muon detector, the muon flux can be mapped in a three dimensional manner"; [0100], "The size and shape of each pixel in the grid 103 is not limited and can be modified when the density map is displayed. In some instances, it may be advantageous to choose a finer or coarser grid as a function of the available statistic. For instance, the grid spacing can be chosen on the basis of the uncertainty with which the bulk density can be determined due to the intrinsic statistical nature of the measurement, including choosing a grid with a non-uniform spacing or a grid defining inverted pyramidal voxels with an increasing base area (on top of the heap) as one moves further away from the location of the muon detector located under the heap. In particular, as the muon rate decreases for larger muon angles relative to the vertical, the statistical accuracy of the method decreases and can be improved by increasing the pixel size, for example, trading position reconstruction in favor of measurement accuracy. In certain other cases, the grid 103 may become denser, and the volume of each voxel 401, 402a, and 402b may shrink over time as more and more muon tracks are measured at the detector. In such cases, one may effectively choose to trade off an increased measurement sensitivity for an increased position resolution over time as defined by choice of pixel areas across the volume. In still further embodiments, the pixels can have a non-rectangular base, for example the base of the pixel can be an arc segment. In such an embodiment, instead of a square grid, the pixel would be arrange to form concentric circles or arc segments"; [0048], "The first step in this workflow 200 as indicated by block 210 is the utilization of the previous parameters and certain state data estimates (such as static data and dynamic data) to perform the reservoir simulations to establish a set of reservoir models. The simulations are forwarded in time to predict future reservoir performance as indicated by block 220. A monitor survey is computed based on the recently updated reservoir state data at the end of the predicted period and according to the certain reservoir parameters. The time-dependent difference (that is, the difference between the monitor survey and a base survey) is then computed and used in conjunction with the predicted reservoir production data in block 230 to update the previous reservoir model estimates, as well as to serve as a starting point for the next iteration of history matching"; [0129], "In yet other configurations, not exclusive to the above, a muon detector 518 could be placed inside the tailing pond itself, offering access to a different set of muon tracks 515-517 and 519 and view angles with which to further analyze the 3-dimensional distribution of fluids and density inside the dam. This is particularly advantageous for monitoring the densification process at the basis of the pond or soil reclamation as well as determine residual water content in the pond. Furthermore, it is noted that in some situations (not shown), muon tracks may traverse the material of the tailing pond 502 without traversing the dam 500 and subsequently strike muon detector 503 after traversing hard soil 501"; and [0117], "As shown in FIG. 3 and FIG. 4, leaching pad area module 203 is in the process of being filled with new material, and therefore the leaching pad area module is inactive and is not producing pregnant leaching fluid for processing (though some pregnant fluid may be in the process of being collected for processing). The movable bucket wheel excavator 301 and moveable stacker 302 are moved and positioned according to the associated mine's production schedule. Equipment tracks 312, 313, and 314 allow for this equipment to turn 180 degrees and move to the next section of the leaching area, where leaching pad area modules 211, 212, 213, 214, 215, 216, and 230 are located. In this manner, all of the pads defining the leaching pad area modules 201-206, 210-216, and 230 of leaching area 200 are continuously loaded with ore, irrigated with leaching fluid, and have the spent ore removed").
Claims 22 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Bryman (US 20080128604 A1) in view of Cancelliere. etc. ( US 20200348440 A1), further in view of Botto, etc. (US 20210156810 A1) and Hunt, etc. (US 20200089823 A1).
Regarding claim 22, Bryman, Cancelliere, and Botto teach all the features with respect to claim 21 as outlined above. Further, Bryman teaches that the method of claim 21 wherein the modelled directional muon intensities are based at least in part on the model parameters (See Cancelliere: Fig. 2, and [0048], "The first step in this workflow 200 as indicated by block 210 is the utilization of the previous parameters and certain state data estimates (such as static data and dynamic data) to perform the reservoir simulations to establish a set of reservoir models. The simulations are forwarded in time to predict future reservoir performance as indicated by block 220. A monitor survey is computed based on the recently updated reservoir state data at the end of the predicted period and according to the certain reservoir parameters. The time-dependent difference (that is, the difference between the monitor survey and a base survey) is then computed and used in conjunction with the predicted reservoir production data in block 230 to update the previous reservoir model estimates, as well as to serve as a starting point for the next iteration of history matching". Note that using the parameters to establish the model and using the model to predict the future directional muon data is mapped to the current limitation), wherein the model parameters comprise: cave back surface model parameters which parameterize a model of a cave back surface of the block cave mine; and upper muck pile surface model parameters which parameterize a model of an upper muck pile surface of the block cave mine.
Bryman, modified by Cancelliere and Botto, fails to explicitly disclose that wherein the model parameters comprise: cave back surface model parameters which parameterize a model of a cave back surface of the block cave mine; and upper muck pile surface model parameters which parameterize a model of an upper muck pile surface of the block cave mine.
However, Hunt teaches that wherein the model parameters (See Hunt: Fig. 9, and [0048], "Turning now to FIG. 9, after creating the 3D vector field, the present method allows the user to edit the movement profile at any given location. This allows the user to make changes based on the topographic survey, misfires, or any other credible observation as required") comprise: cave back surface model parameters which parameterize a model of a cave back surface of the block cave mine (See Hunt: Fig. 6, and [0027], "Referring now to FIG. 6, the 3-D block modelling method of a resource boundary in a post-blast muck pile to optimize desired delineation, for example, grade control polygons, is provided. An in-situ pre-blast model of a resource deposit in a blast volume to be mined, blast design information, movement data if available, and post-blast topographic data are input in to the memory of a general purpose computer. The foregoing being user-supplied information and formulas operative on the user-supplied information. The pre-blast block model is a centroid export with a grid and having attribute features. If the mine takes a single sample from a blast hole and lets it represent an entire flitch, this value is assumed to be a composite representing the full bench depth, unless other data is available"; [0028], "Using the pre-blast block model, blast design information, movement data if available, and post-blast topographic data, a three-dimensional vector field is generated. The method uses the three-dimensional vector field to move a plurality of centroids of the in-situ block model to populate a three-dimensional post-blast location. The method optimizes the populated three-dimensional post-blast locations to determine a plurality of sets of optimal dig boundaries"; [0035], "The relationship between the pre and post surface, allows for automatic calculation of the vertical trajectory of any point in the blast, to include any transmitter to monitor movement, pre blast block model centroids, and/or any structural information with a pre-blast XYZ location"; and [0027], "Referring now to FIG. 6, the 3-D block modelling method of a resource boundary in a post-blast muck pile to optimize desired delineation, for example, grade control polygons, is provided. An in-situ pre-blast model of a resource deposit in a blast volume to be mined, blast design information, movement data if available, and post-blast topographic data are input in to the memory of a general purpose computer. The foregoing being user-supplied information and formulas operative on the user-supplied information. The pre-blast block model is a centroid export with a grid and having attribute features. If the mine takes a single sample from a blast hole and lets it represent an entire flitch, this value is assumed to be a composite representing the full bench depth, unless other data is available". Note that the 3D model of muck pile including the movement of material boundaries and the resulting geometry/surface of the post blast pile is mapped to the back surface of the block cave); and
upper muck pile surface model parameters which parameterize a model of an upper muck pile surface of the block cave mine (See Hunt: Figs. 1-5, and [0010], "It is therefore an object to provide a method for determining a post-blast shape of a resource in a muck pile to optimize grade control polygons for enhanced efficiency in the mining operation"; [0037], "Referring now to FIGS. 1-5, there is shown the prior art method of creating 2-D polygons pre-blast. The exact same dataset is provided to three ore control geologists and the results at 11, 13 and 15 show three different results. As a result, the subjective polygon selection leaves open opportunities for improvement. FIG. 2 demonstrates how the polygons 19, 21 are moved horizontally with blast movement at 19' and 21'. FIG. 3 demonstrates a real case of narrow vein blast movement in views 1-4. View 2 shows the lack of movement of the floor and view 4 demonstrates the consequences of the prior art practice with the ore loss and dilution". Note that parameterizing the muck pile model from the observed movement data is directly transferrable to the analogous free surface of the block cave muck pile, and this is mapped to the upper surface of the muck pile model).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was effectively filed to modify Bryman to have that wherein the model parameters comprise: cave back surface model parameters which parameterize a model of a cave back surface of the block cave mine; and upper muck pile surface model parameters which parameterize a model of an upper muck pile surface of the block cave mine as taught by Hunt in order to ensure the accurate estimation of the area of mineralization during extraction (See Hunt: Fig. 1, and [0004], "In open pit mines, a bench blasting method is often used to allow the removal of a determined volume of a given rock mass. These mine deposits are highly heterogeneous with the resource, such as ore, disseminated in pockets of varying grade with an economic cut-off grade determined for the mine operation and as such, any material with less mineralization may be designated as waste or sent to leach pads for additional extraction. The ore is excavated and hauled to the mineral processing plant while the waste is transported to a suitable dumping location. Blasting of these rocks involves drilling a series of holes with a calculated spacing-burden ratio necessary to fragment and loosen the rock mass. However, the movement of the rock caused by blasting has an unfavorable effect on the separation of the ore and waste region in the muck pile, causing either ore loss (the ore is wrongly categorized as waste and sent to the waste dump) and/or ore dilution (waste is wrongly categorized as ore and sent to the processing plant). The dilution or loss of mineral are two important factors in grade control of a mine. Misclassification can also occur, where ore is sent to an incorrect processing destination or stockpile"). Bryman teaches a method and system that may detect cosmic ray muon (CRM) flux along a variety of trajectories through a subterranean test region, collect the muon detection data, and process the data to form a three-dimensional density distribution image corresponding to the test region; while Hunt teaches a system and method that may include parameters defining the cave-back surface and the upper muck pile surface in the model parametrizations. Therefore, it is obvious to one of ordinary skill in the art to modify Bryman by Hunt to incorporate the cave back surface and the upper muck pile surface in the block cave mine modeling. The motivation to modify Bryman by Hunt is "Use of known technique to improve similar devices (methods, or products) in the same way".
Regarding claim 26, Bryman, Cancelliere, Botto, and Hunt teach all the features with respect to claim 22 as outlined above. Further, Bryman teaches that the method of claim 22 comprising determining a height (z-dimension) profile of an air gap of the block cave mine, the height profile comprising a height (z-dimension) of the air gap at a plurality of transverse (x, y) locations (See Bryman: Fig. 1, and [0032], "As will be appreciated, conversely a POD having a reduced relative density or comprising a void, such as a cavern, will tend to produce a corresponding increase in the CRM flux along those trajectories that transverse the lower density POD when compared with trajectories that allow the associated CRM to avoid passing through the POD. By measuring the CRM distribution including the incidence angle and, optionally, the energy, at two or more depths, variations in the CRM flux may be correlated to construct an image of the variable density region"; and [0092], "The present disclosure, therefore, encompasses both the configuration of equipment and the method of utilizing such equipment to examine or evaluate the tomographic configuration of the earth for geological exploration using cosmic ray muons impinging at all possible angles on deeply positioned detectors. In this technique, CRM of all angles impinging on the detectors would be used to survey the nearby region of the earth using differential intensity variations n(z, .theta., .phi.) and, depending on the number and location of the detectors, may be used to develop a subterranean density map. This apparatus and technique may be useful, for example, in identifying and imaging mineral and/or petroleum rich regions within a larger region. Similarly, this apparatus and technique may be useful for identifying and imaging subterranean voids such as natural caverns or abandoned mines". Note that once the low-density volume is available, the vertical thickness of that low-density region can be read put at any set of (x, y) location, yielding precisely the height profile).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Bryman (US 20080128604 A1) in view of Cancelliere. etc. (US 20200348440 A1), further in view of Botto, etc. (US 20210156810 A1), Hunt, etc. (US 20200089823 A1), and Whittaker. etc. (US 7069124 B1).
Regarding claim 23, Bryman, Cancelliere, Botto, and Hunt teach all the features with respect to claim 22 as outlined above. However, Bryman, modified by Cancelliere, Botto and Hunt, fails to explicitly disclose that the method of claim 22 wherein: the cave back surface model parameters comprise three-dimensional locations of a plurality of vertices of a polygonal surface mesh that models the cave back surface; and the upper muck pile surface model parameters comprise three-dimensional locations of a plurality of vertices of a polygonal surface mesh that models the upper muck pile surface.
However, Whittaker teaches that the method of claim 22 wherein: the cave back surface model parameters (See Hunt: Fig. 9, and [0048], "Turning now to FIG. 9, after creating the 3D vector field, the present method allows the user to edit the movement profile at any given location. This allows the user to make changes based on the topographic survey, misfires, or any other credible observation as required") comprise three-dimensional locations of a plurality of vertices of a polygonal surface mesh that models the cave back surface (See Whittaker: Fig. 12, and Col. 15 Lines 50-62, "To navigate, local 3D scans are mapped into terrain maps of surfaces such as floors, walls and ceilings, by analyzing surface gradients and vertical clearance in the 3D scans. The result is subsequently transformed into cost functions expressed in the robot's three-dimensional configuration space, by convolving the surface terrain maps with kernels that describe the robot's footprints in different orientations. Fast A-star planning is then employed in configuration space to generate paths executed through proportional-differential control. It is understood that alternate planning schemes such as D-star and alternate control schemes such proportional-integral-differential control are also viable for this invention". Note that the floor surface model is mapped to the back surface mesh); and
the upper muck pile surface model parameters comprise three-dimensional locations of a plurality of vertices of a polygonal surface mesh that models the upper muck pile surface (See Whittaker: Fig. 1 Col. 2 Lines 7-17, "In order to ensure that new mines do not penetrate into existing mines, the government generally requires that the excavator of the new mine obtain a permit before any excavation can begin. Part of the process of obtaining a permit for digging a new mine includes demonstrating that the proposed new mine will not intersect an existing mine. This is accomplished primarily by inspecting copies of existing maps of all mines in the area surrounding the proposed new mine and planning the layout of the new mine so that there is a safe distance between the new mine and any pre-existing mine"; and Col. 2 Lines 53-64, "Use of robotics for mapping mines offers the possibility of generating survey quality mapping of those mines, as opposed to the results of competing technologies which only provide approximations of the location of voids which may or may not be mines. Not only would a two-dimensional (2D) layout of the mine be obtainable from the use of such robots, but such robotics could model three-dimensional (3D) surfaces such as the roof, walls and floors of such a mine. In addition, small robots would be capable of accessing confined voids that might be completely undetectable by complementary approaches". The roof surface model is mapped to the upper cave surface mesh).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was effectively filed to modify Bryman to have the method of claim 22 wherein: the cave back surface model parameters comprise three-dimensional locations of a plurality of vertices of a polygonal surface mesh that models the cave back surface; and the upper muck pile surface model parameters comprise three-dimensional locations of a plurality of vertices of a polygonal surface mesh that models the upper muck pile surface as taught by Whittaker in order to generate the map for plotting course through and around void without direct human supervision (See Whittaker: Fig. 1, and Col. 3 Lines 52-58, "As such, there is a need in the art to provide a self-contained, autonomous robot capable of generating a map on its own or to plot a course through and around a void without direct human supervision. The present invention, in at least one preferred embodiment, addresses one or more of the above-described and other limitations to prior art systems"). Bryman teaches a method and system that may detect cosmic ray muon (CRM) flux along a variety of trajectories through a subterranean test region, collect the muon detection data, and process the data to form a three-dimensional density distribution image corresponding to the test region; while Whittaker teaches a system and method that may generate 3D geometric models of subterranean voids, mines, caves and tunnels with surface representation implemented routinely as polygon meshes with vertices as the model parameters. Therefore, it is obvious to one of ordinary skill in the art to modify Bryman by Whittaker to implement the 3D cave model with surface mesh model and geometric polygons and vertices as the mesh model parameters. The motivation to modify Bryman by Whittaker is "Use of known technique to improve similar devices (methods, or products) in the same way".
Claims 33 and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Bryman (US 20080128604 A1) in view of Cancelliere. etc. (US 20200348440 A1), further in view of Botto, etc. (US 20210156810 A1), Hunt, etc. (US 20200089823 A1), Whittaker. etc. (US 7069124 B1), and Yaish, etc. (US 10585208 B1).
Regarding claim 33, Bryman, Cancelliere, and Botto teach all the features with respect to claim 30 as outlined above. However, Bryman, modified by Cancelliere, Botto, Hunt and Whittaker, fails to explicitly disclose that the method of claim 30 wherein the prior mine information comprises density profile information which defines densities in one or more regions of the block cave mine, the one or more regions selected from the group comprising: an ore body, an air gap, a muck pile and earth adjacent to the ore body.
However, Yaish teaches that the method of claim 30 wherein the prior mine information comprises density profile information which defines densities in one or more regions of the block cave mine, the one or more regions selected from the group comprising: an ore body, an air gap, a muck pile and earth adjacent to the ore body (See Yaish: Fig. 1, and Col. 8 Lines 48-51, “There is provided a method that may perform mapping of underground soil and rock densities using muons. These maps can then be used to extract information on the depths of geological layers, on cavities, on ore deposits etc.”; Col. 17 Lines 3-11, “As an explicit example, let us assume an a-priori 3D model m({right arrow over (r)}) (derived for example, from GPR) with the Gaussian uncertainties u({right arrow over (r)}). For example, due to (a) above, u({right arrow over (r)}) will be small (e.g. 0.01 g/cm.sup.3) for {right arrow over (r)} values (that is, locations) near the surface and near the boreholes, but far larger (e.g. 0.2 g/cm.sup.3) for {right arrow over (r)} values in regions that were not directly sampled”; and Col. 17 Lines 44-64, “We can then present the mapping as a maximization problem seeking to maximize the likelihood L(C;T)=D(C;T)+R(T), where C is the muon data, D(C;T) is the data-likelihood term and R (T) is the regularization term. The data-likelihood term factorizes to D(C; T)=ΠL(m.sub.ijk;T), where the multiplication is over all muon data (that is, the ijk indices) and L(m.sub.ijk;T)=G(m.sub.ijk−x.sub.ijk, x.sub.ijk.sup.1/2) where G(x, σ) is the value of a Gaussian distribution function centered at 0 at location x. The regularization term T can have a form such as T=λΠT.sub.abc, where λ≥0 is the regularization strength, T.sub.abc=G (t.sub.abc−m.sub.abc;u.sub.abc), where m.sub.abc and u.sub.abc are the averages of m({right arrow over (r)}) and u({right arrow over (r)}) over the voxel abc. The regularization strength can be optimized through the standard techniques, such as the L-curve technique. This ensures that in the voxels where the ground density was well-known from previous measurements, and thus u({right arrow over (r)}) and u.sub.abc are small, the resulting map T will be constrained to be close to the a-priori well-known density m({right arrow over (r)}), while in voxels where the density was not well-known from other measurements, the muon data will dominate”. Note that the prior model and the prior density is mapped to this current cited limitation).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was effectively filed to modify Bryman to have the method of claim 30 wherein the prior mine information comprises density profile information which defines densities in one or more regions of the block cave mine, the one or more regions selected from the group comprising: an ore body, an air gap, a muck pile and earth adjacent to the ore body as taught by Yaish in order to reduce its flow resistance and gas contamination due to permeation through the gas line (See Yaish: Fig. 10, and Col. 14 Lines 33-44, " An exhaust pump can be used to evacuate the gas from the detector. The pump may be a. Attached to the detector itself, minimizing the length of the gas line between them, thus reducing its flow resistance and gas contamination due to permeation through the gas line. b. On the ground surface, as detailed below. c. In an intermediate depth. This may be useful to ensure that the gas outlet is above any groundwater and/or to protect this equipment from theft when it is left unattended for long periods of time "). Bryman teaches a method and system that may detect cosmic ray muon (CRM) flux along a variety of trajectories through a subterranean test region, collect the muon detection data, and process the data to form a three-dimensional density distribution image corresponding to the test region; while Yaish teaches a system and method that may explore underground using cosmic rays muons to generate the block cave mine model with the muon detections and the prior measurement data. Therefore, it is obvious to one of ordinary skill in the art to modify Bryman by Yaish to implement the 3D cave model with the current muon detections and the prior data and model. The motivation to modify Bryman by Yaish is "Use of known technique to improve similar devices (methods, or products) in the same way".
Regarding claim 35, Bryman, Cancelliere, and Botto teach all the features with respect to claim 30 as outlined above. Further, Yaish teaches that the method of claim 30 wherein the prior mine information has associated uncertainty and wherein optimizing the objective function comprises accounting for the uncertainty in the prior mine information to thereby provide the optimal values of the model parameters with corresponding model parameter uncertainties (See Yaish: Fig. 1, and Col. 5 Lines 9-11, “The data fusion may be responsive to uncertainties of a reference model and to dependence of said uncertainties on location”; and Col. 5 Lines 23-24, “The data fusion may include using information about uncertainties related to the geo-physical information”).
Claim 40 is rejected under 35 U.S.C. 103 as being unpatentable over Bryman (US 20080128604 A1) in view of Cancelliere. etc. (US 20200348440 A1), further in view of Botto, etc. (US 20210156810 A1), Hunt, etc. (US 20200089823 A1), Whittaker. etc. (US 7069124 B1), Yaish, etc. (US 10585208 B1), and Kim, etc. (US 20090198476 A1).
Regarding claim 40, Bryman, Cancelliere, and Botto teach all the features with respect to claim 21 as outlined above. However, Bryman, modified by Cancelliere, Botto, Hunt Whittaker and Yaish, fails to explicitly disclose that the method of claim 21 comprising repeating the steps of claim 1 over a plurality of time periods to provide a temporal model of the block cave mine, the temporal model characterized by the optimal values of the model parameters determined for each time period.
However, Kim teaches that the method of claim 21 comprising repeating the steps of claim 1 over a plurality of time periods to provide a temporal model of the block cave mine, the temporal model characterized by the optimal values of the model parameters determined for each time period (See Kim: Figs. 4-6, and [0031]. “The present invention provides a new 4-D inversion capable of inverting a plurality of monitoring data at the same time, and also providing reliable images using not only a plurality of iterative measured data but also only one measured data though subsurface structure is rapidly changed even during the measurement”; [0047], “The inversion of geophysics can be characterized in non-uniqueness and ill-posedness of solution, and thus it has serious instability such as divergence. To solve this problem, constraints are commonly applied to the inversion. In the present invention, a constraint of time domain is additionally introduced in addition to the constraint of space domain that is an inversion constraint commonly adopted to develop an inversion algorithm of geologic structure in space-time domain. The constraint of time domain is allowed since definition of the subsurface model is expanded to space-time domain in the present invention. The 4-D inversion of the present invention is based on the least-squares inversion that minimizes squares of error and gives constraints in space-time domain, so it is defined as a question of minimizing an objective inversion function .PHI. expressed in the Equation 6 (Math FIG. 6)”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was effectively filed to modify Bryman to have the method of claim 21 comprising repeating the steps of claim 1 over a plurality of time periods to provide a temporal model of the block cave mine, the temporal model characterized by the optimal values of the model parameters determined for each time period (See Kim: Fig. 1, and [0021], " According to the present invention described above, it is possible to provide a new 4-D inversion and an imaging method using it, which allows to accurately calculate geologic structure changing in time by inverting a plurality of monitoring data at the same time, and provide reliable images even when the geologic structure changes fast during data collection, using even only one measurement data, not a plurality of iterative measurement data"). Bryman teaches a method and system that may detect cosmic ray muon (CRM) flux along a variety of trajectories through a subterranean test region, collect the muon detection data, and process the data to form a three-dimensional density distribution image corresponding to the test region; while Kim teaches a system and method that may repeat the geophysical acquisition and inversion process over successive time period to produce a temporal model of an evolving underground structure (cave, void or mine). Therefore, it is obvious to one of ordinary skill in the art to modify Bryman by Kim to repeat the same steps over a period of time reconstruct the temporal model of the block cave mine. The motivation to modify Bryman by Kim is "Use of known technique to improve similar devices (methods, or products) in the same way".
Conclusion
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/GORDON G LIU/ Primary Examiner, Art Unit 2618
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