Patent Document

RELATED APPLICATIONS 
       [0001]    This patent application is a divisional of U.S. Non-Provisional patent application Ser. No. 12/722,357, titled “System, Method, and Nanorobot to Explore Subterranean Geophysical Formations” and filed on Mar. 11, 2010, which claims the benefit of and priority to U.S. Provisional Patent Application No. 61/159,943, titled “System, Method, and Nanorobot to Explore Subterranean Geophysical Formations” and filed on Mar. 13, 2009, the contents both of which are incorporated herein by reference in their entireties. 
     
    
     BACKGROUND OF THE INVENTION 
       [0002]    1. Field of the Invention 
         [0003]    This invention generally relates to the field of exploring underground rock and hydrocarbon formations. In particular, the present invention is directed to a method and apparatus for using transmitter assemblies to move through a subsurface formation to identify various geophysical characteristics. 
         [0004]    2. Description of the Related Art 
         [0005]    The overriding problem in exploring for hydrocarbons in the subsurface is the probing in, and characterizing of, an environment that cannot be seen. Similarly once a commercial hydrocarbon deposit has been discovered and is about to be developed and exploited much conjecture and many assumptions must be made by reservoir geologists and reservoir engineers in the modeling of a large volume of rock which cannot be seen. 
         [0006]    Subsurface reservoir data is currently acquired from probes lowered into boreholes and from images (seismography). In the first instance, the data is handicapped by its insufficiency, by virtue of being sourced from a single 6-inch hole, thus giving too narrow of a view. The interpreted seismic volumes, on the other hand, gives too broad of a view due to their imaging quality and resolution inadequacies. Even combining the two will not enable for the mapping of exact high permeability pathways. 
         [0007]    The integration of available geological, geophysical, petrophysical engineering, and drilling data makes interesting inroads into the detection, mapping and predictive modeling of high permeability pathways. The final uncertainty of integrated models, however, can only be marginally better than the average uncertainty inherent in the various methods used. Mix and integrate as much as one may, the broad brush strokes on reservoir map deliverables, will remain just that: broad brush. A 0.5 mm scribble drawn on a 1:200,000 scale map to represent a fracture in the subsurface, is akin to depicting a fracture with an aperture of 200 m because of the width of the scribble relative to the scale of the map. The scribble will not reveal the precise path that the fluids are likely to take. 
         [0008]    As oil fields mature, it can be expected that fluid injection for pressure support (secondary enhanced oil recovery) will increasingly tend to erratically invade, and irregularly sweep, the residual oil leg. At the close of the second millennium, petroleum concerns were seen scrambling to mobilize however possible in order to identify, detect and map pathways that may lead injected fluids prematurely updip along encroachment fingers. More often than not, the encroachment materializes faster than even the worst expectations, and commonly in quite unpredictable directions. Moreover, premature encroachment is commonly tortuous and will change direction in 3D volume, much like a rubber ball wildly bounced about in a cubic enclosure. This type of tortuousity renders high permeability pathway prediction almost impossible to satisfactorily pin down. In spite of an arsenal of cutting-edge technologies thrown at such problems, high permeability pathway prediction capability continues to suffer from high levels of uncertainty. 
         [0009]    Post mortem and predictive mapping of erratically occurring high permeability pathways is a leading issue of concern to major petroleum companies. The solution to the problem is currently sought through the manipulation of data acquired directly from the borehole and indirectly, through map view representations of faults or fracture swarms or horizontal permeability (“kh”) from pressure buildups. Permeability pathways are interwell phenomena. Unfortunately, it is interwell control that is very difficult to characterize. 
         [0010]    With current technology, it is impossible to work out the exact pathway that fluid fingering takes as it invades deep into an oil leg, much less where it will go next. Engineering data (e.g. water arrival data—i.e., water arrival detected in an oil producing well, flowmeter data, test kh build-up, pressure data, and productivity/injectivity data), although mostly acquired at the borehole, are typically correlated aerially. The resultant maps are a very indirect, unreliable and a crude way of trying to depict the reservoir geology of a reservoir. The resultant maps are interpretive, and reservoir engineers are the first to dissociate them from being accurate reflections of specific geologic features. Moreover, the map resolutions are too broad to even remotely represent most geological features that would commonly be associated with high permeability pathways. 
         [0011]    Other interwell methods to map permeability pathways are, likewise, handicapped by resolution problems. Geophysical technologies rooted in interpreting 3D, 4D, shear wave, or multi-component volumes; even when utilizing ever-developing clarity and resolution enhancing software packages, still only render a generalized mapping of a miniscule sampling of some faults in the general area where they may or may not be located. 
         [0012]    In carbonate rocks, fractures with apertures measured in millimeters, or geobodies only centimeters across, can provide the necessary plumbing to take injected fluid past matrixed oil. To further illustrate this, a 3 cm wide fracture with no displacement may, under pressure, move fluids at several Darcies. These dimensions cannot be seen by current interpretive geophysical devices. Subsequently, the fault lines drawn on reservoir structure maps cannot be considered more than broad arrows pointing out a general direction; and not a depiction of actual permeability pathways. Furthermore, geophysically-interpreted data must be augmented by a solid understanding of the regional stress-strain regimes in order to filter out fracture swarms which may not be contributing to premature fluid breakthroughs. 
         [0013]    Dyes and radioactive chemicals (tracers) introduced with injected fluids can be locally helpful, but they will not reveal the actual pathway taken by the host fluid from the entry well to the detection well. Borehole detection methods are the most exact, but they are also afflicted with major shortcomings The immediately obvious shortcoming is that, for mapping purposes, wellsite data must be extrapolated and transformed into interwell information. Extrapolation in itself is the problem. 
         [0014]    Any sedimentologist will sympathize with the deposition heterogeneities with or without a structural overprint. The slightest shifts in water depth, measured in decimeters, can create worlds of difference in depositional fabric. Moreover, rock minerals, especially carbonates, are in continuous “life long” effective diagenesis from the instant of deposition. There is no carbonate porosity that has not been dictated by deposition and then unceasingly altered by diagenesis. One can already see the problem of interwell extrapolation from well control. 
         [0015]    The geostatistical distribution of attributes, including fractures detected on borehole image logs, at the wellbore, is the best we&#39;ve got; but it is only statistical, and natural geological landscapes are too variable and rugose to respond comfortably to the smooth, clean logic of mathematics. Much like fingerprints, there are no two features in carbonate rocks that are the same. Extrapolation in the complex world of carbonate geology has a long way to go. 
         [0016]    Adding to the difficulties of borehole solutions is that the geological features contributing to abnormally high flow rates are, like some rare species, rarely captured in rock cores. Consequently reservoir geologists are, in most cases, disallowed the opportunity to properly study and characterize reservoir problems. 
       SUMMARY OF THE INVENTION 
       [0017]    A geophysical formation can include large rock formations. The rock formations are not solid (like metals), rather, they are a series of interconnected pores and pathways. Many of these pores and pathways are less than 1000 nanometers wide. The pores can contain a variety of fluids including oil, water, or natural gas. 
         [0018]    It is desirable to know the contents and the structure of the pores. It is also important to understand the structures that permit high speed fluid flow through the formation. These “pathways” are important because water used to push the hydrocarbons through the formation, whether natural water-drive water or injected water, can flow from the water source, through the pathway to the wellbore, thus bypassing pockets of hydrocarbons. 
         [0019]    Due to the depth of hydrocarbon bearing formations, often several thousand feet below ground, it is difficult to map a series of microscopic pores. Conventional devices for determining the contents of the formation, as shown in  FIG. 1 , are not effective for mapping the pore structure or learning the contents of the pores. One such method is surface seismic analysis, in which loud noises such as explosive charges are created near the surface, and an array of acoustic receivers  20  measure and record the reflected sound. Similarly, acoustic receivers  22  can be lowered into a wellbore  100  to record reflected sound. Neither of these seismic methods provide any detail about the pore structure nor the specific locations of the pores. Another method is to drill a wellbore  100  and remove core samples from the area drilled. The core samples are only a few inches wide and do not reveal the pathway structure for the entire geophysical formation. 
         [0020]    A nanoscale robot, also referred to as a transmitter assembly, with a dimension smaller than 500 nanometers, could move through the pores to map the pore and pathway structure, find hydrocarbons within the structure, find water within the structure, and analyze the fluids, minerals, and rocks within the structure. The geophysical exploration nanorobots move through the hydrocarbon reservoir and, thus, may be called “Resbots”™. 
         [0021]    One embodiment of a system to measure properties in a geophysical includes a wellbore lining in a wellbore, a plurality of fixed radio frequency receivers spaced apart along the longitudinal extent of and associated with the wellbore lining to receive radio frequency transmissions at one or more preselected radio frequencies, and a plurality of independent and untethered robots positioned within the geophysical formation. Each of the plurality of independent and untethered robots includes a robot body formed of a plurality of carbon nanotubes adapted to withstand temperatures exceeding 300 degrees Fahrenheit and being sized so that none of the length, width, or height of the robot body is greater than 500 nanometers, a sensor associated with the robot body and positioned to detect the presence of one or more hydrocarbons within the geophysical formation, a radio frequency transmitter associated with the robot body, positioned to transmit positional data and hydrocarbon characteristic data from the geophysical formation when the robot is positioned therein, and a power supply associated with the robot body to supply power to the transmitter and the sensor. These parts of the independent and untethered robot can collectively define a geophysical nanorobots. In this embodiment, the system also includes a machine in communication with each of the plurality of geophysical nanorobots, the machine including a processor, a display in communication with the processor, and a non-transitory, computer-readable storage medium with an executable program stored therein, wherein the program instructs the processor to perform the following steps: receiving positional data from one or more of the plurality of geophysical nanorobots, the positional data indicating the location of the geophysical nanorobots at a point in time; plotting, responsive to receipt of the positional data, at least one positional data point for one or more of the plurality of geophysical nanorobots to indicate a location of a cavity accessible by a geophysical nanorobots; receiving interior surface location data from one or more of the plurality of geophysical nanorobots, the interior surface location data defining a sensed three dimensional location of at least one point on an interior surface within the geophysical formation; combining the surface location data from the one or more of the plurality of geophysical nanorobots to create a representation of a physical map of at least a portion of the geophysical formation, the physical map indicating the three dimensional location of each of the plurality of sensed three dimensional locations within an interior surface of the geophysical formation; generating an interpolated map by projecting surfaces between a plurality of the points of the physical map, the interpolated map identifying a plurality of cavities in fluid communication with adjacent cavities; receiving fluid data from one or more of the plurality of geophysical nanorobots, the fluid data indicating the type and location of fluid located at each of a plurality of locations within the geophysical formation; and creating a fluid map on the display by plotting the type and location of fluids onto the interpolated map. 
         [0022]    In another embodiment, the system includes a molecular processor associated with the robot body and responsive to the sensor to process detected hydrocarbon data from the sensor, and the radio frequency transmitter associated with the robot body is responsive to the molecular processor and positioned to transmit hydrocarbon characteristic data to one or more of the plurality of fixed radio frequency receivers. 
         [0023]    In another embodiment, the system includes a geophysical nanorobot carrier adapted to carry and transport the plurality of geophysical nanorobots into the wellbore when positioned adjacent thereto, the geophysical nanorobot carrier being a wellbore lining having a plurality of perforations therein through which the plurality of geophysical robots pass when being inserted into the geophysical formation. 
         [0024]    In another embodiment, at least one of the fixed radio frequency receivers is positioned to receive data from at least another one of the fixed radio frequency receivers when positioned in the geophysical formation and re-transmit the data from the at least another one of the fixed radio frequency receivers to the machine. 
         [0025]    In another embodiment, each of the nanorobots also includes a propulsion device associated with each of the robot bodies to propel each of the plurality of geophysical nanorobots through pathways within the geophysical formation. 
         [0026]    Another embodiment includes a plurality of fixed radio transmitters associated with the wellbore lining. Each of the plurality of geophysical nanorobots also includes a payload bay having a payload; and the geophysical nanorobot is positioned to release the payload in response to a signal from one of the plurality of fixed radio transmitters. 
         [0027]    In another embodiment, the propulsion device of each of the plurality of geophysical nanorobots can include one or more of the following: a propeller, a flagella, a membrane, a crawler, and a Brownian motor. In another embodiment, the power supply of each of the plurality of geophysical nanorobots can derive energy from a fluid within the geophysical formation. In yet another embodiment, the power supply of each of the plurality of geophysical nanorobots can include one or more of the following: a fuel cell, wherein the fuel cell derives power from in-situ hydrocarbons; a thermoelectric power supply, wherein the heat of the fluid within the geophysical formation generates electricity; a piezoelectric generator, wherein the compressive forces acting on the piezoelectric generator generate electricity; an electromechanical nanoactuator responsive to movement of the fluid; and an ATPase catalyst, wherein the ATPase catalyst causes a chemical within the fluid to decompose and wherein energy is released when the chemical within the fluid decomposes. In another embodiment, the sensor can of each of the plurality of geophysical nanorobots can sense one or more of the following: fluid type, temperature, pressure, petrophysical property, geophysical nanorobot trajectory, and geophysical nanorobot position. 
         [0028]    Another embodiment includes a plurality of fixed radio transmitters associated with the wellbore lining and each of the plurality of geophysical nanorobots also includes a nanorobot radio frequency receiver associated therewith; and one or more of the plurality of nanorobots propels in a direction different than a current trajectory in response to instructions from the machine transmitted via the plurality of fixed radio transmitters. 
         [0029]    Another embodiment includes a battery charger associated with the wellbore lining which defines a downhole charging station; and each of the plurality of geophysical nanorobots also includes a carbon nanotube based battery located in the robot body. Each of the plurality of geophysical nanorobots can propel to the proximity of the downhole charging station and the downhole charging station charges each of the carbon nanotube based batteries. 
         [0030]    Another embodiment includes a plurality of radio directional transmitters associated with the wellbore lining, each transmitting a beacon therefrom, wherein each of the plurality of geophysical nanorobots also includes a nanorobot radio frequency receiver, and wherein each of the plurality of geophysical nanorobots determines its position in response to signals from the plurality of radio direction beacons. In another embodiment, each of the plurality of geophysical nanorobots also includes a nanorobot radio frequency receiver, wherein one or more of the plurality of geophysical nanorobots is positioned to receive positional data from at least another one of the plurality of geophysical nanorobots and re-transmit the positional data from the at least another one of the plurality of geophysical nanorobots. 
         [0031]    In another embodiment, the surface location data includes the location of a point wherein one of the plurality of geophysical nanorobots contacted a surface within the geophysical formation. In another embodiment, the surface location data includes multiple location points from non-contact sensors. In another embodiment, the non-contact sensors include an ultrasonic sensor or a radio frequency sensor, or both, located on the geophysical nanorobots. 
         [0032]    In another embodiment, the program further instructs the processor to perform the step of interpolating fluid data to identify a three-dimensional region filled with a homogenous fluid to define a fluid pocket within the geophysical formation. In another embodiment, the program also instructs the processor to perform the step of identifying a plurality of cavities in communication with one another, each cavity having a cross-sectional area greater than a predetermined value, to define a pathway. 
         [0033]    In another embodiment, the program also instructs the processor to perform the step of identifying a pocket having a homogenous hydrocarbon that is generally surrounded by a fluid that is different than the homogenous hydrocarbon to define a hydrocarbon pocket within the geophysical formation. In another embodiment, the program also instructs the processor to perform the step of causing at least one of the plurality of geophysical nanorobots to move to a location different than its current location. 
         [0034]    One embodiment of a technique to identify properties of a geophysical formation includes steps of: communicating, to a machine, the machine including a processor, a display in communication with the processor, and a non-transitory, computer-readable storage medium with an executable program stored therein, interior surface location data of the geophysical formation from a plurality of geophysical robots, the interior surface location data defining a sensed three dimensional location of at least one point on each of a plurality of interior surfaces within the geophysical formation; generating an interpolated map on the machine, responsive to the interior surface location data, by projecting surfaces between representations of the at least one points on each of the plurality of interior surfaces of the geophysical formation, the interpolated map identifying a physical shape and a location of a plurality of surfaces in the geophysical formation; communicating, to the machine, fluid data responsive to a sensor located on each of the one or more of the plurality of geophysical robots, the fluid data indicating the type and location of fluid located at each of a plurality of locations within the geophysical formation; and creating a fluid map on the machine by plotting the type and location of fluids onto the interpolated map of the geophysical formation so that physical representation of fluids within the geophysical formation are displayed on the machine. 
         [0035]    In another embodiment, the technique includes interpolating, by the machine, the fluid data to identify a three-dimensional region filled with a homogenous fluid to define a fluid pocket within the geophysical formation. In another embodiment, the technique includes identifying, by the machine, a plurality of cavities in communication with one another, each cavity having a cross-sectional area greater than a predetermined value, to define a pathway. 
         [0036]    In another embodiment of the technique, the plurality of geophysical robots include a nanorobot defined as having: a robot body formed of a plurality of carbon nanotubes adapted to withstand temperatures exceeding 300 degrees Fahrenheit and being sized so that none of the length, width, or height of the robot body is greater than 500 nanometers, a hydrocarbon sensor associated with the robot body and positioned to detect the presence of one or more hydrocarbons within the geophysical formation, a radio frequency receiver associated with the robot body, positioned to receive radio frequency transmissions, a radio frequency transmitter associated with the robot body, positioned to transmit positional data and hydrocarbon characteristic data from the geophysical formation when the robot is positioned therein, and a power supply associated with the robot body to supply power to the receiver, the transmitter, and the sensor. 
         [0037]    In another embodiment, the communicating step of the technique includes transmitting, via a radio frequency transmitter associated with the robot body, to a fixed radio frequency receiver located in a wellbore. In another embodiment, the communicating step of the technique includes transmitting data, via a fixed radio frequency transmitter associated with a wellbore, to a fixed radio frequency receiver associated with the wellbore and further communicating the data to the machine. 
         [0038]    In another embodiment, a system to measure properties in a geophysical formation includes a plurality of wellbore linings each being positioned in a separate and different one of a plurality of wellbores extending into a geophysical formation. It also includes a plurality of fixed radio frequency transmitters spaced apart along the longitudinal extent of and associated with one or more of the plurality of wellbore linings to transmit radio frequency signals at one or more preselected radio frequencies and a plurality of independent and untethered robots positioned within the geophysical formation. Each of the plurality of independent and untethered robots can include a robot body having a diameter no greater than 1000 nanometers, formed of a plurality of carbon nanotubes adapted to withstand temperatures exceeding 300 degrees Fahrenheit, and a radio frequency identification tag positioned to transmit a signal responsive to the one or more preselected radio frequency signal transmitted by one or more of the plurality of fixed transmitters. Thus, the plurality of independent and untethered robots can collectively define a plurality of geophysical nanorobots. The system can also include a plurality of fixed radio frequency receivers positioned spaced apart along the longitudinal extent of and associated with one or more of the plurality of wellbore linings to receive radio frequency signals at one or more preselected radio frequencies, a machine in communication with each of the plurality of geophysical nanorobots, the machine including a processor, a display in communication with the processor, and a non-transitory, computer-readable storage medium with an executable program stored therein. The program product can instruct the processor to perform the following steps: receiving positional data from one or more of the plurality of geophysical nanorobots, the positional data indicating the location of the geophysical nanorobots at a point in time; plotting, responsive to receipt of the positional data, at least one positional data point for a portion the plurality of geophysical nanorobots to indicate a location of a cavity accessible by one of the plurality of geophysical nanorobots; and combining the positional data points of a portion of the plurality of geophysical nanorobots to create a representation of a physical map of at least a portion of the geophysical formation, the physical map indicating the three-dimensional location of each cavity of the geophysical formation accessible by one of the plurality of nanorobots. 
         [0039]    In another embodiment, each of the plurality of geophysical nanorobots has a substantially spherical shape and the program further instructs the processor to perform the steps of: identifying a plurality of cavities in communication with one another, each cavity having a cross-sectional area located between outer walls of the cavity, transverse to a travel path of the geophysical nanorobot, greater than a predetermined value, to define a pathway responsive to the three-dimensional location of each cavity indicated on the physical map. The program can instruct the computer to cause a portion of the plurality of geophysical nanorobots, located within the pathway, to release the payload contained therein within the pathway. 
         [0040]    In another embodiment, the body of each of the plurality of geophysical nanorobots has a substantially spherical shape and the plurality of geophysical nanorobots also has a plurality of different sized diameters. The program further instructs the processor to perform the step of identifying a location within the formation accessible to a first set of the plurality of geophysical nanorobots having one of the different sized diameters, not readily accessible to a second set of geophysical nanorobots having another one of the different sized diameters. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0041]    So that the manner in which the features, advantages and objects of the invention, as well as others which will become apparent, are attained and can be understood in more detail, more particular description of the invention briefly summarized above may be had by reference to the embodiment thereof which is illustrated in the attached drawings, which drawings form a part of this specification. It is to be noted, however, that the drawings illustrate only a preferred embodiment of the invention and therefore should not be considered limiting of its scope as the invention may admit to other equally effective embodiments. 
           [0042]      FIG. 1  is a partial sectional view of surface and downhole seismic mapping operations according to the prior art. 
           [0043]      FIG. 2  is a partial sectional view of a geophysical nanorobot based geophysical exploration system according to an embodiment of the present invention. 
           [0044]      FIG. 3  is an enlarged sectional view of a wellbore in a geophysical formation having a plurality of geophysical nanorobots deployed through fissures, pathways, and porous rock structures according to still another embodiment of the present invention. 
           [0045]      FIG. 4  is a sectional view of a geophysical nanorobot having multiple propulsion devices, a processor, a radio frequency transmitter, and a sensor according to yet another embodiment of the present invention. 
           [0046]      FIG. 5  is a partial sectional view of a geophysical nanorobot having a nano-processor control system according to yet another embodiment of the present invention. 
           [0047]      FIG. 6  is a partial sectional view of a geophysical nanorobot having a radio frequency transmitter and a vibration sensor according to yet another embodiment of the present invention. 
           [0048]      FIG. 7  is a flowchart of operational identification of fluid properties responsive to a plurality of geophysical nanorobots according to another embodiment of the present invention. 
           [0049]      FIG. 8A  is a perspective view of a geophysical nanorobot having a propulsion device according to yet another embodiment of the present invention. 
           [0050]      FIG. 8B  is a perspective view of a geophysical nanorobot having a propulsion device according to yet another embodiment of the present invention. 
           [0051]      FIG. 8C  is a perspective view of a geophysical nanorobot having a propulsion device according to yet another embodiment of the present invention. 
           [0052]      FIG. 8D  is a perspective view of a geophysical nanorobot having a propulsion device according to yet another embodiment of the present invention. 
           [0053]      FIG. 9  is a functional block diagram of a geophysical nanorobot based geographical exploration system according to an embodiment of the present invention. 
           [0054]      FIG. 10  is a flowchart of operational propulsion of a plurality of geophysical nanorobots in a geophysical formation according to another embodiment of the present invention. 
           [0055]      FIG. 11A  is a sectional view of a geophysical nanorobot performing contact mapping according to yet another embodiment of the present invention. 
           [0056]      FIG. 11B  is a depiction of a map developed from the contact mapping of  FIG. 11A  according to yet another embodiment of the present invention. 
           [0057]      FIG. 12  is a sectional view of a geophysical nanorobot performing non-contact mapping according to yet another embodiment of the present invention. 
           [0058]      FIG. 13  is a perspective view of a geophysical nanorobot having a payload bay and a flagella propulsion device according to yet another embodiment of the present invention. 
           [0059]      FIG. 14  is an environmental sectional view of a plurality of different sized geophysical nanorobots, each having a spherical shape and a radio frequency identification tag, located in pathways in a geophysical formation according to another embodiment of the present invention. 
           [0060]      FIG. 15  is an environmental sectional view of a system having a plurality of geophysical nanorobots that are injected with secondary recovery pressurized water according to another embodiment of the present invention. 
           [0061]      FIG. 16  is a partial sectional view of a carrier inserting a plurality of geophysical nanorobots to pass through perforations in wellbore casing of a wellbore according to another embodiment of the present invention. 
           [0062]      FIG. 17  is a flowchart of operational insertion of a plurality of geophysical nanorobots into a geophysical formation according to another embodiment of the present invention. 
           [0063]      FIG. 18  is a sectional view of the casing of the geophysical exploration system of  FIG. 2  according to an embodiment of the present invention. 
           [0064]      FIG. 19  is an environmental sectional view of a plurality of geophysical nanorobots relaying transmissions to wellbore receivers according to yet another embodiment of the present invention. 
           [0065]      FIG. 20  is a partial sectional view of a geophysical nanorobot based geophysical exploration system according to yet another embodiment of the present invention. 
           [0066]      FIG. 21  is a flowchart of a nanorobot based geophysical mapping operation according to an embodiment of the present invention. 
           [0067]      FIG. 22  is a functional block diagram of a computer to control a plurality of geophysical nanorobots and analyzing data from geophysical nanorobots according to another embodiment of the present invention. 
           [0068]      FIG. 23  is a flowchart of a controller operating a plurality of geophysical nanorobots according to another embodiment of the present invention. 
           [0069]      FIG. 24  is a flowchart of operational mapping of a geophysical formation using data from a plurality of geophysical nanorobots according to another embodiment of the present invention. 
           [0070]      FIG. 25  is a flowchart of operational mapping fluid formations using data from a plurality of geophysical nanorobots according to another embodiment of the present invention. 
           [0071]      FIG. 26  is a flowchart of operational mapping pathways using data from a plurality of geophysical nanorobots according to another embodiment of the present invention. 
           [0072]      FIG. 27  is a flowchart of operational locating of hydrocarbon formations using data from a plurality of geophysical nanorobots according to another embodiment of the present invention. 
           [0073]      FIG. 28  is a flowchart of operational mapping gas plumes using data from a plurality of geophysical nanorobots according to another embodiment of the present invention. 
           [0074]      FIG. 29  is a flowchart of operational mapping potable water formations using data from a plurality of geophysical nanorobots according to another embodiment of the present invention. 
           [0075]      FIG. 30  is a flowchart of approximating surface locations from the positions of geophysical nanorobots according to another embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
       [0076]    The present invention will now be described more fully hereinafter with reference to the accompanying drawings which illustrate embodiments of the invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the illustrated embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Like numbers refer to like elements throughout, and the prime notation, if used, indicates similar elements in alternative embodiments. 
         [0077]    One or more wellbores  100  are drilled  510  into a geophysical formation  102  (hereinafter “geophysical formation,” “formation,” or “rock”), as shown in  FIGS. 2 and 3 . A wellbore  100  can be an exploratory well used to locate hydrocarbons  110  such as oil or gas  116 , water, or other fluids  112 . The term “fluids” refers to any type of gas or liquid fluid, including water, hydrocarbons, and gas. If desirable fluids are found, a wellbore  100  can be completed as a production well. Wellbore completion frequently includes lining  512  the wellbore with a wellbore lining such as, for example, casing  104 , which is generally a metallic pipe or tube. The casing  104  can be cemented in place. Additional production wells  106  can be drilled in the same geophysical formation. Wellbores can also be used for secondary recovery operations ( FIG. 15 ). In a secondary or tertiary recovery operation, an injection fluid  108  such as water, steam, carbon dioxide, or chemicals are injected, under pressure, into the geophysical formation  102 . The injection fluid serves as a drive mechanism to push the well fluids out through a production well  100 . The production or injection well can be used to insert nanorobots into the geophysical formation. 
         [0078]    A geophysical nanorobot  114 , or “Resbot™,” is a nanoscale probe that is able to travel deep within underground rock strata along pathways permeable to fluids and transmit back and/or collect data that can be used to map and characterize the pathways. In the instant specification, the term “robot” means a mechanical device that is capable of performing one or more tasks on command or by being programmed in advance; a machine or device that can be operated by remote control or automatically. A nanorobot, thus, is a robot on a nano scale. In an exemplary embodiment, the nanorobots have at least one dimension less than 500 nanometers. Individual components in a nanorobot  114  can generally have dimensions of 1 to 100 nanometers. In some embodiments, all of the dimensions (length, width, height) are less than 500 nanometers. One nanometer (nm) is one billionth, or 10 −9 , of a meter. An exemplary embodiment of a nanorobot  114  is shown in  FIG. 4 . The nanorobots  114  are small enough to fit through the pathways, pores, and fissures in the formations. 
         [0079]    As shown in  FIG. 3 , the geophysical nanorobots  114  travel through cavities  118 , which includes pores and pathways  120 , within the geophysical formation  102 . The cavities  118  depicted in  FIG. 3  are enlarged to show detail. The pores and pathways inside the oil bearing rocks are very small, typically less than 1000 nanometers. A pathway  120  can also be is less than 1000 nanometers wide, but could be larger than 1000 nanometers wide. A nanorobot with a dimension less than 500 nm can fit through most of the pores and pathways within the formation. 
         [0080]    The nanorobots  114 , are deployed  518  into the geophysical formation  102  (rock formation) to map the formation, find fluids  110  such as hydrocarbons  110 , find bypassed pockets of fluids, water  116 , mineral solids, and voids. Once in the cavity  118  system of the targeted host rock, the nanorobot  114  is propelled  522  along with the natural flow of the fluid medium within which it is traveling, and, in some embodiments, it as also able to thrust itself along using its own power. If the nanorobot is in the desired location to be mapped  524 , it will proceed with its analysis. In one embodiment, if the nanorobot is not in the correct location  524 , an onboard controller  124  or above ground nanorobot control computer  126  can instruct the nanorobot to move to a different location  526 . The nanorobot  114  can communicate with the control computer  126  by using an onboard transmitter  128  and receiver  130 . The nanorobot can communicate with the control computer by sending and receiving data through fixed receivers  134  and transmitters  136  located in the wellbore  100 , on the surface, or embedded in the geophysical formation  102 . The nanorobot  114  uses sensors  138  to identify  528  and describe the fluids  112  it comes in contact with. The nanorobot  114  also supports characterizing rock formations, by measuring  530  dimensions and locations of subterranean features, including the size of cavities  118  and the pathways  120  formed by interconnected cavities  118 . The overall process for the insertion, deployment, control, data transmission, and analysis is shown in  FIG. 21 , and referenced throughout this document. A more detailed description of this geophysical nanorobot exploration system follows. 
         [0081]    As illustrated in  FIGS. 4-14 , the nanorobots  114  can have sensors  138  and an onboard computer  124 . The onboard computer  124  controls the actions of the nanorobot  114  and can record data regarding the nanorobot&#39;s position and sensor  138  readings. In some embodiments, the nanorobot  114  is able to determine its coordinates and calculate its velocity at any given time. Some nanorobots have transmitters  128  for sending information and receivers  130  for receiving information. Some embodiments have a payload bay  140  and can deliver a payload  142 , such as a surfactant, to a location within the geophysical formation. In some nanorobots, the receiver  130  can receive data signals from fixed transmitters  136  or from other nanorobots  114 . Furthermore, the receiver  130 , or another receiver, can detect radio frequency or ultrasonic signals. Each of these components will be described in greater detail. The nanorobots  114  can operate independently and without being tethered to any other component. 
         [0082]    The nanorobot is housed in a body  132  or shell, as shown in  FIG. 4 . The body is adherence-resistant and can be generally spherical ( FIG. 14 ) or capsule-shaped, but could be other shapes. The body  132  can have a tapered shape, as shown in  FIG. 6 , or a cylindrical shape, as shown in  FIGS. 8A-8D . The adherence resistance, among other things, prevents the viscous formation fluids from adhering to the nanorobot. The body  132  can be hermetically sealed to protect the components inside the body  132  from wellbore fluids  112 . The body  132  houses the elements of the nanorobot and can serve as a frame for the elements. The body can be made of any material that is suitable to the small scale of the nanorobot and that provides the required protection from the intended operating environment. The body material can be based upon, for example, carbon nanotubes (“CNT”) or Boron-Nitride. The carbon nanotubes can be bonded or otherwise fused together to form the body. The nanorobot  114  can encounter temperatures in excess of 300 degrees Fahrenheit. The body  132  is able to withstand temperatures in excess of 300 degrees and serve as a thermal protection shield for the other components of the nanorobot. The body  132 , thus, allows the nanorobot to operate in environments in excess of 300 degrees Fahrenheit. 
         [0083]    In one embodiment, shown in  FIG. 14 , a nanorobot  115  has a spherical shape. In one embodiment, nanorobot  115  has a reactive identification tag  133  that responds to an external signal. A wellbore fixed transmitter  136 , for example, could emit a signal that causes reactive identification tag  133  to emit a different signal, and the different signal can be received by one or more wellbore fixed receivers  134 . The reactive identification tag  133  could be, for example, a radio frequency identification tag that emits a unique radio frequency in response to receiving a radio frequency. In one embodiment, the reactive identification tag  133  includes magnetic particles. The magnetic particles respond to electromagnetic energy emitted by the wellbore fixed transmitters  136 , and the presence of the magnetic particles is detected by wellbore fixed receivers  134 . In one embodiment, nanorobots  115  have different sizes. Some could have a diameter, for example, less than 500 nanometers, and some could have a diameter, for example, of 750 nanometers or 1000 nanometers. 
         [0084]    As illustrated in  FIGS. 4-14 , the nanorobot  114  has one or more sensors  138  for sensing its environment. In an exemplary embodiment, the nanorobot  114  has positional sensors that indicate its position within the rock formation, including its vertical position. The positional sensor, or data from the positional sensor, can also detect the nanorobot&#39;s  114  velocity as it moves through the formation. In some embodiments, an onboard computer  124  uses positional information to calculate velocity and/or trajectory of the nanorobot  114 . In other embodiments, the onboard computer  124  can use data from directional and velocity sensors to calculate position. In some embodiments, the positional sensors can use external signals such as directional radio frequency beacons to determine the position. 
         [0085]    The nanorobot sensor  138  can include chemical and gas sensors and can be carbon nano tube (“CNT”) based. The chemical and gas sensors are capable of determining the composition of rocks and fluids. The onboard computer  124 , such as a nano-computer or molecular computer, can be used to interpret sensor data and identify elements. In some embodiments, the raw sensor data is transmitted to the wellbore receiver and sent to a nanorobot control computer  126  for interpretation. 
         [0086]    The sensor  138  can include a fluid properties sensor and, thus, can sense fluid properties including the presence of a fluid, fluid type, temperature, pressure, and viscosity  528 . In some embodiments, the fluid sensor can identify the presence of a hydrocarbon, identify the type of hydrocarbon, or identify a particular liquid. Furthermore, in one embodiment, the fluid type sensor can detect the presence and type of natural water drive fluids, injection fluids, and other fluids that may be present in the rock formation. The fluid data can also indicate fluid saturation within the geophysical formation. In one embodiment, sensor  138  includes ligands that are chemically reactive to, for example, different fluid types, salinity, pH, and temperature.  FIG. 7  is a flow chart showing examples of techniques to determine fluid properties using sensors  138  on a nanorobot. The steps of  FIG. 7  are referred to throughout the discussion of sensors. 
         [0087]    The nanorobot  124  must be in contact with a fluid  172 . If it is not, the onboard computer  124  or control computer  126  can direct the nanorobot  114  to a different location  174 . In one embodiment, as shown in  FIG. 4 , the sensor  138  includes electrodes  146 ,  176  for determining fluid properties. Electric current can be passed between the electrodes  178 . The amount of resistance provided by the fluid can indicate the presence of a fluid and the fluid type  180 . Similarly, the resistance  192  from a thermistor  148  ( FIG. 5 ) can indicate the temperature  188  of a fluid in contact with the nanorobot  114 ,  190 . The electrodes  146  and thermistor  148  can be connected to the onboard computer  124 . The electrodes  146  can also be part of a pH sensor  182  for determining the pH of fluids. Electric current can be passed through the fluid between pH sensor electrodes  184 , and the amount of current passed through the fluid can indicate the hydrogen ion concentration in the fluid  186  and, thus, indicate the pH of the fluid. 
         [0088]    In one embodiment, the nanorobot  114  includes a laboratory on a chip (“LOC”)  150  ( FIG. 6 ). The LOC  150 , also known as a “micro-total-analysis-system,” integrates several chemical or bio-chemical analysis steps on a single chip, wherein the chip is small enough to fit inside a nanorobot. LOC analysis could include, for example, measurement of reservoir (dynamic) fluid properties, production allocation, formation stresses, pressures and borehole stability, formation damage assessment, mud rheology and mud logging, and formation evaluation. LOC analysis can also determine whether water is potable. LOC  150  may include ligands. 
         [0089]    In some embodiments, one of the sensors  138  can include a nano camera to record and ultimately transmit images from inside the formation. Other sensors can include a pressure sensor or a viscosity sensor. For example, the deflection of a pressure sensor  204  ( FIG. 8A ) can be measured  196  to determine the pressure of the fluid. The viscosity  198  of a fluid can be measured by measuring the velocity of the nanorobot and comparing the velocity to the required propulsion power  200 . Indeed, propulsion components, including, for example, propellers  166 , vibrating membranes  168 , and flagella  170  can also be used to determine several fluid properties. For example, the current required to drive any of the propulsion devices can be indicators of viscosity  202 . Furthermore, the resistance encountered by the vibrating membrane  168  can be an indicator of pressure. 
         [0090]    In some embodiments, the sensor  138  includes a rock composition sensor that is able to determine the type of rock in contact with the nanorobot. The sensor can also determine, for example, the relative permeability, pore throat size, porosity, permeability, and mineral structure of the rocks  530 . Other rock characteristics can also be measured. For example, sensors  138  on the nanorobot  114  can include sensors to measure physical dimensions such as the aperture of a cavity  118 , or pore, in a rock formation. A variety of sensors could be used including, for example, positional, contact sensors, and non-contact sensors. Porosity, relative permeability, and mineral structure of the geophysical formation  102  can be determined from sensed data. 
         [0091]    In some embodiments, the pore width is measured by recording the position of the nanorobot as it moves across the diameter of the pore  118  along a path  156  (see  FIG. 11A ). The path  156  can be random or deliberate movements within the pore  118 . The surfaces  152  of the pore are identified each time the nanorobot contacts the surface  152  at a point  154 . The relative locations of the points  154  can be stored in the memory of the nanorobot computer  124 , or can be transferred to the control computer  126  for analysis.  FIG. 11B  shows a plot that can be created from the contact points  154  of the nanorobot. Each point  154  is plotted relative to the other known contact points  154 . The aperture or pore size can be calculated from the distance between the contact points  154 , and the overall cross-sectional area of the pore can be calculated from the known points. The positional data of the nanorobot, thus, is analyzed to determine the physical dimensions of the pore  118 . In some embodiments, the nanorobot sensor  138  can detect contact with the rock to determine each contact point. In other embodiments, the nanorobot  114  is propelled through the pore and stops or changes directions each time it contacts a surface in the pore. The location of the nanorobot  114  is recorded each time it stops or changes direction to identify the contact point  154 . In one embodiment, the mere presence of the nanorobot  114  can act as a sensor. By determining the location of the nanorobot, the computer  126  can determine that the robot is in a pathway that has a diameter or cross-section that is at least as large as the nanorobot. Thus, the pathways can be mapped even if the precise location of surfaces defining the pathway are not known. Furthermore, the locations of surfaces defining the pathway can be approximated from the locations of the nanorobots  114 . 
         [0092]    The features of a rock formation can also be measured by an ultrasonic sensor  158  ( FIG. 8C ), wherein the sensor emits an ultrasonic frequency and then interprets the signals reflected back to the nanorobot  114 , as shown in  FIG. 12 . Similarly, a radio frequency generator  160  on the nanorobot  114  can emit a radio frequency  162  that is reflected by the surface  152  of the formation back to the nanorobot. The ultrasonic or radio frequency sensor each allows the nanorobot to map geological features in its immediate area without directly contacting each geological feature. In some embodiments, the radio frequency generator used for non-contact surface mapping can share components with the radio frequency transmitter  136  used for communication. 
         [0093]    In one embodiment, the nanorobot  114  has position sensors for determining its own position or movement. For example, vibration sensor  205  can determine vibrations associated with movement and, thus determine the velocity and direction of movement of the nanorobot  114  ( FIG. 5 ). Other motion or position sensors  206 , such as, for example, a nano-sized accelerometer, can be used to determine the location or relative movement of the nanorobot  114  within the formation. 
         [0094]    The nanorobot can require power to perform its tasks. Some embodiments, however, do not require a power supply or power source to be located on or in the nanorobot  114 . For embodiments that require power, numerous power sources are available to power the nanorobot  114 , examples of which are illustrated in  FIGS. 5 ,  6 , and  9 . Various types of power supplies  208  can capture power from these power sources. For example, power can come from thermoelectric power created by the high temperatures of the subterranean environment. Power can also come from piezoelectric generators, which generate power in response to compression or vibration of a surface. The piezoelectric generator can include a crystal that gains an electrical charge when a force is applied to the crystal. Well fluid can cause the piezoelectric generator to vibrate and thus create electricity to power the nanorobot. Furthermore, the same crystal can vibrate in response to an electric charge applied to the crystal. The vibration may be sufficient to give off an ultrasonic signal, which could be used to drive a propulsion device. Therefore, stored power in the nanorobot could be used to provide power and thus the single piezoelectric generator can provide both electricity and propulsion for a nanorobot. 
         [0095]    Similarly, fluid movement in the vicinity of the nanorobot  114  can cause an electromechanical nanoactuator to move and thus generate electricity. In another embodiment, power is generated by CNT based fuel-cells. The fuel-cells generate power from in-situ hydrocarbon. Some power can be produced by friction with rock surfaces. In some embodiments, ATPases are used to power or partially power some sensors. An ATPase is a class of enzymes that catalyze the decomposition of various chemicals, causing the chemicals to release energy as they decompose. 
         [0096]    Power from these various power supplies  208  can be stored in batteries  212  such as, for example, CNT-based batteries. Furthermore, power can be stored in batteries prior to inserting the nanorobots  114  into the ground. In one embodiment, nanorobots are able to recharge at a downhole charging station  210  ( FIG. 17 ). The downhole charging station could be, for example, a battery charger located in or on the casing  104  in the wellbore  100 . The nanorobots  114  can propel themselves or be propelled to the charging station  210 . In this embodiment, the power supply  208  receives electrical power from the charging station  210 . The power supply can operate by contact or non-contact techniques. After the power supply  208  recharges the battery  212  or batteries  212 , the nanorobot  114  can move away from the power station  210  to continue its task. 
         [0097]    Various propulsion devices  164 , as shown in  FIGS. 4-10 , can be used to propel nanorobots through the rock formation. The nanorobot  114  is able to move through pores  118  within the formation without becoming stuck inside the pore (and thus blocking fluid flow through the pore). Some of the propulsion devices are able to move the nanorobot through the rock formation even when not aided by downhole fluid flow. Furthermore, the propulsion devices are able to overcome the viscous and gravitational forces present within the formation. Some of the propulsion devices can propel the nanorobot at a practical speed against the reservoir fluid flow. Finally, the propulsion devices are able to propel the nanorobot in any direction, including changing direction laterally and vertically. 
         [0098]    The simplest propulsion device is a fluid-flow device, wherein fluids  112  within the rock formation  102  propel the nanorobot. In this embodiment, the nanorobots are injected into the reservoir with normal injection water, as shown in  FIG. 15 . As the water  116  pushes hydrocarbons through the pores and pathways within the formation, the nanorobots  114  move with the water and hydrocarbons. The nanorobots in this embodiment can have any shape, including, for example, a spherical shape, as shown in  FIG. 14 . In one embodiment, spherical nanorobots  115  having various sizes are used in a single formation. The larger diameter nanorobots  115  are only able to move along pathways  120  in the formation  102 , while smaller diameter nanorobots  115 ′ are able to travel along smaller pathways  121 . The pathways  120  accessible to all nanorobots  115 ,  115 ′, are at least as large as the largest nanorobot  115 . Pathways  121 , being accessible to the smaller nanorobots  115 ′ but not nanorobots  115 , are identified as being smaller than nanorobot  115  but larger than nanorobot  115 ′. The cross-section of each pathway is defined as the distance, transverse to the path of the nanorobot  115 , between the walls, or surfaces, of the cavity. Finally, pathways  122  may be so small that they are not accessible to any nanorobots. As one of skill in the art will appreciate, the nanorobots  115 ,  115 ′, thus, serve as a type of “go/no-go” gage to measure the size of pathways within the formation  102 . 
         [0099]    The powered propulsion devices  164 , as shown in  FIGS. 8A-8D  can be powered directly from the power supply  208 , or the power supply power can be routed through the onboard computer  124 . A single nanorobot  114  can have more than one powered propulsion device, and can use gravity and fluid flow in combination with the powered propulsion device. 
         [0100]    In some embodiments, the propulsion device  164  includes one or more propellers  166 . The propeller  166  can be a molecular propeller with blades formed by planar aromatic molecules and a shaft comprising a carbon nanotube. One of ordinary skill in the art will appreciate the nano-motor required to rotate propeller  166 . Any of the propulsion techniques, including the propeller  166 , can be used in conjunction with one or more rudders  234  to steer the nanorobot. Rudders  234  can be moved by, for example, signals from an onboard computer  124  to cause the nanorobot  114  to alter its trajectory. 
         [0101]    In another embodiment, the propulsion device  164  can include flagella  170 . In this embodiment, the nanorobot has a leg-like or fin-shaped appendage similar to that of bacteria or paramecia. The flagella  170  can use a biomimetic synthetic flagella composed of multiwalled carbon nanotubes. 
         [0102]    In another embodiment, a rapidly vibrating membrane  168  can provide the necessary thrust to propel the nanorobot. The vibrating membrane  168  can be alternately tightened and relaxed to produce thrust. Because the nanorobot is so small, the thrust produced by the vibrating membrane  168  can be sufficient to propel the nanorobot. Vibrating membranes  168  can be located on more than one surface of the body  132  and, thus, used to steer the nanorobot  114 . For example, a nanorobot can have vibrating membrane  168  on a rear surface to propel the robot  114  forward, and can also have one or more vibrating membranes  168 ′ to cause lateral movement or to cause the nanorobot  114  to turn and move in a different direction than its current trajectory, as shown in  FIG. 8C . 
         [0103]    As shown in  FIG. 10 , propulsion devices  164  such as propellers  166 , membranes  168 , and flagella  170  can each produce thrust  216  against a wellbore fluid  112 . In one embodiment, the nanorobot  114  moves in random directions  218  until it makes contact with a surface  152 . Upon contact, the nanorobot moves in a different direction  220 . In another embodiment, the nanorobot  114  can receive a signal to change direction  222  from, for example, the control computer  126 , or the onboard computer  124  of the nanorobot can determine that it is necessary to change direction. In response to the signal  222  or determination  236  to change direction, the nanorobot  114  can use a thruster such as its flagella  170  or lateral vibrating membrane  168 ′ to cause it to change direction off of its current axis of movement  224 . Some embodiments can have a propeller  166  that is offset from the center of the nanorobot body  132 , which can cause a change in direction  226 . In embodiments having a rudder  234 , the nanorobot can move the rudder  234  in response to the signal to change direction  236 ,  222  and thus cause a change in direction. 
         [0104]    In still another embodiment, the propulsion device  164  can include crawlers  214  wherein mechanical legs, such as carbon nano tube legs, are driven by nano-motors to enable the nanorobot to “walk” within the rock formation, even in the absence of liquid fluids. In these embodiments, the nanorobot comes into contact with a surface  230  in the formation  102 , and the crawler  214  propels the nanorobot  114  along the surface  232 . Other variations of the propulsion device can include wriggling, rolling, and worm-like or gecko-like movement, all of which can be performed within a fluid or in the absence of a fluid. There can be overlap between a crawler  214  and other propulsion devices. For example, flagella  170  can propel the nanorobot through fluid  216 , and can, at other times, contact a surface  230  and cause the nanorobot  114  to move along the surface  232 . 
         [0105]    As one of skill in the art will appreciate, the propulsion devices can be powered by various kinds of motors  238 , including, for example, nano-motors and Brownian motors. Brownian motors are nano-scale or molecular devices by which thermally activated processes (chemical reactions) are controlled and used to generate directed motion in space and to do mechanical or electrical work. In one embodiment, a radio frequency powered motor  240  is used to drive the propulsion device  164 . In this embodiment, a radio frequency transmitter, which could be the same transmitter used for communication, generates a signal that causes the RF motor  240  to actuate. 
         [0106]    The nanorobot  114  can have an onboard computer  124 , as shown in  FIGS. 5 ,  6 , and  9 . In some embodiments, the computer  124  includes a processor  244 , memory  246 , and an input/output device  248 . The computer  124  could be a quantum computer, a nanotube computing system, a nanomachine, a molecular computer, or a combination thereof. The onboard computer processor  124  can have parallel processing capabilities. 
         [0107]    The onboard computer  124  can serve as a controller for the nanorobot  114 . The controller can initiate and manage functionality within other onboard components based on, for example, the data collected by the sensors. In an exemplary embodiment, sensor readings cause a response from the nanorobot. In one example, when the sensor  138  detects a hydrocarbon, the controller  124  actuates the transmitter  128  and causes the transmitter  128  to transmit the current location and the presence of the hydrocarbon to the wellbore fixed receiver  134 . In another example, when the sensor  138  reading does not show a hydrocarbon, the controller actuates the propulsion device  164 , causing the nanorobot  114  to move to a new location. 
         [0108]    The onboard computer  124  can also serve as a memory device. In the event that the nanorobot  114  is unable to transmit data regarding, for example, its position or the presence of a hydrocarbon, the data is stored in the onboard computer memory  246  until the nanorobot  114  is able to transmit or until the data is otherwise downloaded to a data collector. A data collector (not shown) includes a device to collect nanorobots from a collection point, such as in production fluid, extract the nanorobots  114 , and then download the memory of the nanorobots into a computer memory. 
         [0109]    The nanorobot can have communication abilities, such as a radio frequency transmitter  128  and a radio frequency receiver  130 . The transmitter  128  and receiver  130  are best shown in  FIGS. 5 ,  6 , and  9 . The nanorobot computer can control the transmitter  128  and direct signals to the transmitter for transmission. The nanorobot computer can also receive data through the receiver  130 . An antenna  131  may be connected to or integral with the transmitter  128  and receiver  130 . In one embodiment, the receiver  130  and transmitter  128  are the same component—a reactive identification tag  133 , such as a radio frequency identification tag or radio frequency identification device (“RFID”). The radio frequency identification tag receives a signal and, in response to the signal, transmits a signal. 
         [0110]    The nanorobot uses the radio frequency transmitter  128  to transmit various data to receivers  532 , such as fixed receivers  134  located in the wellbore  100 . The radio frequency transmitter transmits, and the fixed receiver receives, radio frequency transmissions at preselected frequencies. The transmitted data could include, for example, the presence or absence of hydrocarbons, the type of hydrocarbon encountered by the nanorobot, the pressure and temperature inside the formation, and the position of the nanorobot  114 . 
         [0111]    In an exemplary embodiment, the nanorobot  114  has a radio frequency receiver  130 . The nanorobot receiver  130  receives signals, for example, from fixed transmitters  136  located in the wellbore  100 . The transmitted signals could include, for example, instructions directing the nanorobot  114  to move in a different direction or to a different specified location. 
         [0112]    In some embodiments, the nanorobot can have a payload bay  140  for delivering a payload  142  to a location inside the geophysical formation  102 , as shown in  FIG. 13 . The payload  142  could be, for example, a surfactant used to change the surface tension of the fluid inside the formation. Alternatively, the payload  142  can be a matrix acidizing or damage removal fluid, a formation consolidation chemical for sand control, or a polymer for conformance control. In one embodiment, the payload  142  includes a swelling hydrophilic polymer for obstructing undesirable pathways. The payload bay  140  can have one or more doors  250  which protect the payload during travel. When the nanorobot reaches the delivery point, the payload doors  250  can open to release the contents. In one embodiment, the payload door  250  forms a hermetic seal to prevent fluids from contacting the payload  142  prior to opening the door  250 . 
         [0113]    The payload delivery point can be determined by a variety of devices. In one embodiment, the sensor  138  or on board computer  126  can open the door when the sensor  138  detects a predetermined condition. For example, if the sensor  138  detects crude oil having a viscosity higher than a predetermined amount, the sensor sends a signal to the payload door  250  actuator, causing the payload door to open. Alternatively, the onboard computer  124  could open the payload door  250  when the nanorobot  114  reaches a predetermined location. In still another embodiment, a signal transmitted from the above-ground nanorobot control computer  126 , via the wellbore fixed transmitters  136 , directs the payload door  250  to open. In one embodiment, an electromagnetic signal from the wellbore fixed transmitters can actuate the payload door  250 . One of skill in the art will appreciate the usefulness of being able to deliver various payloads into the pores of a geophysical formation. 
         [0114]      FIGS. 5 ,  6 , and  9  show exemplary embodiments of the interconnections and wiring between various components within the nanorobot body  132 . Furthermore,  FIG. 9  shows wireless signal connections between the nanorobot and the wellbore transmitters  136  and receivers  134 . The power supply  208  can provide power to the computer  124 . In some embodiments, the computer  124  provides and controls the application of power to other components, such as the onboard receiver  130 , the propulsion device  164 , and the sensor  138 . In other embodiments, the power supply can provide power directly to the components such as the propulsion device  164 . 
         [0115]    The nanorobots  114  can be inserted into the geophysical formation  518  and inserted into the rock pores by a variety of devices. For example, the nanorobot can be placed in water  116  or fluid used for secondary recovery operations ( FIG. 15 ). The nanorobot-containing water is injected into the reservoir or rock formation  102 , thus carrying the nanorobots  114  along the same pathways used by the pressure-injected water. One skilled in the art will appreciate that the nanorobots can be inserted through a discovery well, a production well, a water-injection well, a well drilled for the sole purpose of inserting probes, or any other routes into the geophysical formation. This technique is used anticipating that the injected nanorobots will flow into, and along, permeability pathways  120 , as shown in  FIG. 3  (the enlarged section of  FIG. 3  is drastically enlarged—the nanorobots  114  are less than 500 nanometers wide). 
         [0116]    Alternatively, the nanorobots can be placed in a carrier  252 , such as a cylinder or a running tool attached to the drill string or lowered on a cable  254  through the wellbore  100 . The carrier can have doors that open to release or deploy the nanorobots  114  at various locations within the wellbore  520 . The wellbore can be perforated as appropriate so that the nanorobots  114  can move through the perforations  256  through the sides of the wellbore. If the existing wells are not in the correct location  514  for inserting nanorobots  114 , an additional insertion well or exploratory well may be drilled  516 . 
         [0117]    An alternative insertion method is to place the nanorobots  114  in the drilling mud (not shown). Drilling mud is used to lubricate the earth-boring drill bit. Drilling mud also carries spoil (earth and rock dislodged by the bit) up to the surface. Nanorobots can be placed in the drilling mud before the mud is injected into the wellbore. The nanorobots then travel through the sides of the wellbore into the rock formation. 
         [0118]    The nanorobots can also travel ahead of the drill bit (not shown), into the rock that is going to be drilled. In this application, the nanorobot transmits data regarding the rock that is about to be drilled back to the surface. Real time downhole mud properties, formation stress, and borehole stability data can be transmitted during drilling operations. This data could be helpful for geosteering and well placement. In some embodiments, the nanorobots are sent ahead of the drill bit to collect “true formation data” before the drill bit and mud alter the formation characteristics. 
         [0119]      FIG. 17  illustrates embodiments of several techniques to release  258  nanorobots  114  into a formation  102 . If the release technique uses water drive insertion  260 , the nanorobots are first placed in the drive water  262 . Water is used for illustration only. Other types of drive fluid can be used. The drive water is injected into the formation  264 , the nanorobots  114  being injected with it. Additional water (or drive fluid) can be injected  266  after the nanorobots  114  are released to cause the nanorobots to move further into the formation  102 . 
         [0120]    If the wellbore carrier  252  is used for insertion  268 , the nanorobots are first placed into the carrier  270  and then the carrier is lowered through the wellbore  100 , or another borehole into the formation  102 , to the desired depth  272 . The carrier then ejects the nanorobots from the carrier  252  into the wellbore  274 . If drilling mud is used to insert the nanorobots  276 , the nanorobots are first placed into the drilling mud  278  and then the drilling mud is pumped into the wellbore  280 . Once released, the nanorobots from the carrier or the drilling mud can be caused to move into the formation  102 , by, for example propelling themselves through cavities in the formation  282 . 
         [0121]    As shown in  FIG. 18 , the wellbore  100  is lined with a casing  104 , such as a metal tube. Multiple fixed receivers  134  can be attached to the casing  104  or embedded within the casing  104 . The fixed receivers  134  can be spaced apart longitudinally along the casing  104 . The fixed receivers  134  can also be spaced apart around the circumference of the casing  104 . The wellbore  100  can also be lined with fixed transmitters  136  for transmitting data to the nanorobots  114 . The fixed transmitters  136  are longitudinally spaced apart along the casing  104 . The fixed transmitters  136  can be co-located with the fixed receivers  134  or be combined in the same housing with the fixed receivers  134 . Fixed receivers  134  and fixed transmitters  136  can also be located on the surface, as shown in  FIG. 2 . The fixed receivers  134  and fixed transmitters  136  can be powered by, for example electricity from batteries or wires passing through or embedded in casing  104 . 
         [0122]    As shown in  FIGS. 2 and 9 , each nanorobot onboard transmitter  128  can transmit data to one or more fixed receivers  134  located in the wellbore or on the surface. The fixed receivers  134 , in turn, can transfer the data to control computer  126  for processing and analysis. The control computer  126  can be located on the surface. Similarly, the control computer  126  can send information to the nanorobots  114 . The information from the control computer can be broadcast by the fixed transmitters  136  located in the wellbore or on the surface. In one embodiment, if the nanorobot  114  is unable to transmit to an fixed receiver  134 , the nanorobot  114  can store the information for later transmission. 
         [0123]    In some embodiments, signal cables such as wires or fiber optic cables transfer data from the fixed wellbore receivers  134  to the control computer  126 . In other embodiments, the fixed wellbore receivers can wirelessly transmit data to the control computer  126  using, for example, radio frequencies. Some wireless fixed receivers may be unable to directly communicate with the control computer  126  because, for example, the fixed receiver  134  is located too far below the surface. In one embodiment, fixed receivers  134  have a relay transmitter and are able to transmit data to another fixed receiver  134 ′, as shown in  FIG. 20 . The second fixed receiver  134 ′ is then able to relay the data to the control computer  126 , or to subsequent fixed receivers  134 . Thus the fixed receiver  134  that is in communication with a nanorobot  114  can relay data through other fixed receivers  134 ′ to the surface. Similarly, in the event a fixed transmitter is unable to communicate with the control computer  126 , other fixed transmitters  136 ′ can relay the signal to the fixed transmitter  136  that is in communication with a nanorobot  114 . 
         [0124]    As shown in  FIGS. 2 and 9 , the fixed transmitters  136  can transmit instructions and data to the nanorobots  114  such as instructions to change direction or move to a specific location. The fixed transmitters  136  can send information that is received by the onboard receiver  130  of the nanorobot  114 . The transmitters can also transmit a locating beacon which a nanorobot  114  can use to determine its own position. 
         [0125]    The nanorobot deployment can use swarm characteristics, wherein hundreds, thousands, or even billions of nanorobots work together to map the formation  102 , as shown in  FIG. 2 . The nanorobots can disperse throughout the formation  102 , or can concentrate as a swarm  286  in one area of interest. The nanorobots  114  can all be the same, or different types of robots with different types of sensors can be employed. In some embodiments, the nanorobots  114  communicate with each other. 
         [0126]    In some embodiments, an individual nanorobot  114  may not be able to communicate with any fixed transmitters  136  or receivers  134 . In one embodiment, nanorobots are able to relay data from other nanorobots, as shown in  FIGS. 2 and 19 . In one embodiment, if a nanorobot  114  is too far from a receiver  234  to transmit a signal, the nanorobot  114  can send its data to another nanorobot  114 ′, as shown in  FIG. 19 . Nanorobot  114 ′, in turn, transmits the data to the fixed receiver  134 . In one embodiment, nanorobots can form a chain where the signal is transmitted through multiple relay nanorobots  114 ′ back to the wellbore receiver  134 . Similarly, multiple nanorobots  114 ′ can relay a message from a wellbore fixed transmitter  136  to a distant nanorobot  114 . 
         [0127]    In some embodiments, multiple wellbore radio frequency fixed receivers  134  can receive a signal from the nanorobot  114 , in which case the control computer  126  can use the received signals to triangulate the position of the nanorobot, as shown in  FIG. 20 . In this embodiment, each wellbore receiver  134  can determine the direction of the signal from the nanorobot  114 . By mapping the intersection of two or more direction signals  288 , the control computer  126  can determine the location of the nanorobot  114 . Preferably, three direction signals are used to determine an accurate three-dimensional location of the nanorobot  114 . In embodiments having a reactive identification tag  133 , the wellbore fixed transmitter  136  can transmit a signal that causes the reactive identification component to emit a signal. A wellbore fixed receiver  134  can detect the emitted signal from the reactive identification tag  133 , and use the signal to determine the direction to the nanorobot  114 . When two or more wellbore fixed receivers  134  detect the emitted signal, they can triangulate to determine the position of the nanorobot  114 . Because each reactive identification tag  133  can emit a unique signal, the control computer  126 , upon receiving the signal data from the wellbore fixed receivers, can determine the location of a particular nanorobot  114 . The control computer  126 , thus, can track the location of a particular nanorobot  114  over time to determine the path traveled by the nanorobot  114 . For triangulation, the fixed transmitters  136  and fixed receivers  134  may all be located in the same wellbore  100 , or a portion of the fixed transmitters  136  and fixed receivers  134  could be located in a different wellbore  100  or on the surface. As shown in  FIG. 20 , signal  137  can pass from an a fixed transmitter  136  in one wellbore to fixed receiver  134  in a different wellbore. The location of nanorobot  114  is determined by the point that signal  137  contacts nanorobot  114 . In one embodiment, the triangulation can work using beacon signals from the wellbore fixed transmitters  136 . Each transmitter  136  emits a unique signal. The nanorobots  114  receive the unique signals using the onboard receivers  130  and are able to triangulate their own position, from the beacons, using the onboard computer  126 . 
         [0128]    As shown in  FIG. 20 , a nanorobot  290  may be too far from the wellbore to transmit a signal to the wellbore receivers  134 . In one embodiment, however, the nanorobot  290  can transmit to other nanorobots  114 . Because the location of the other nanorobots  114  is known, the other nanorobots  114 , thus, can triangulate to determine the location of nanorobot  290 , and then transmit the location of nanorobot  290  back to the wellbore receiver  234 . 
         [0129]    One or more control computers  126  are used to receive data from the nanorobots, interpret the data from the nanorobots, and control and direct the nanorobots. An exemplary embodiment of a control computer  126  is shown in  FIG. 22 . The one or more computers providing these functions are referred to collectively as the “control computer.” In some embodiments, the control computer includes an operational control computer and a geophysical mapping computer. In other embodiments, the control, analysis, and mapping functions are performed by a single computer. The nanorobot control computer  126  collects data from the nanorobots  114 . The control computer  126  can use this data to identify fluid properties  535  and the location of pathways  538 . The data can come from the fixed receivers  134  located in the wellbore or above ground, or the data can be offloaded from the nanorobot  114  after the nanorobot is recovered. 
         [0130]    The nanorobot control computer  126  is a machine that can include a display  292 , a processor  294 , an input/output device  296 , a memory unit  298 , and a set of instructions  300  stored in a non-transitory, computer-readable storage medium with an executable program, as shown in  FIG. 22 . The non-transitory computer readable storage medium can be the machine memory  298 , or it can be a separate storage medium for loading onto the machine. When executed by the machine, the program product  300  can cause the machine to perform the following tasks: Nanorobot Director  302 ; Formation 3D Mapper  304 ; Pathway Mapper  306 ; Fluid Mapper  308 ; Hydrocarbon Locator  310 ; Gas Plume Mapper  312 ; Potable Water locator  314  and Surface Approximator  491 . Functions in any of the sets of instructions can be included in other sets of instructions. 
         [0131]    The Nanorobot Director  302  set of instructions sends information and directions to the nanorobots  114 . Preliminary data from the nanorobots can indicate an area of particular interest within the formation (“area of interest”). The nanorobot control computer can send instructions, via transmitters, to nanorobots in the formation, directing the nanorobots to move to the areas of particular interest. The nanorobot control computer can also interpret data regarding hydrocarbon characteristics and formation structure, and then instruct payload-carrying nanorobots to a specific location and then order the nanorobots to discharge their payload at that location. 
         [0132]    In one embodiment, shown in  FIG. 23 , the control computer  126  first determines  302  whether it is receiving data from a nanorobot  114  or from a particular nanorobot  114 . If it is not, it will send a signal, or ping, the nanorobot to establish communication or wait until it receives data  304 . (The term geophysical nanorobot is abbreviated as “GNR” in some drawings). Any time that any program product receives data from a nanorobot, the data can come from any technique. For example, the data can be in real time or near real time, wherein the data is transmitted from the nanorobot  114  to a wellbore receiver  134  and relayed from the wellbore receiver  134  to the control computer  126 . Alternatively, the data can be stored in the nanorobot computer as it is acquired and uploaded to the control computer at a later time. Upon receiving data from the nanorobot, the computer can determine the location of the nanorobot  306 . The location can be determined by, for example, triangulation from wellbore receivers  134  or from position data stored in the nanorobot. The computer  126  can determine whether the nanorobot is in contact with a surface within the formation  308 . If so, the computer will record the location of the nanorobot&#39;s position at the time of surface contact to establish a location of a point on the surface  310 . The computer  126  can then determine whether the nanorobot  114  is moving in the correct direction  312 , such as towards a predetermined area of interest. If so, the computer will allow the nanorobot to keep going. If not, the computer  126  will send a change direction instruction signal through the fixed radio transmitters  314  and then wait until it again receives data from the nanorobot  302 . The nanorobot will propel in a direction different than its current trajectory responsive to the instruction from the machine transmitted via the fixed radio transmitters and thus, for example, move toward an area of interest. The computer can also record the type of fluid in contact with the nanorobot  316 . The computer can receive raw sensor data, such as the amount of resistance measured by the nanorobot&#39;s electrode, or it can receive more specific fluid-type data from the nanorobot. When recording the type of fluid at step  316 , the nanorobot can also record the location of the fluid based on the nanorobots location at the time of contact with the fluid (from step  306 ). At step  308 , if the nanorobot is not in contact with a surface in the formation, the computer  126  will still record the nanorobot&#39;s position at step  318 . If the nanorobot is carrying a payload  320 , the computer determines whether the nanorobot is in the correct position to dump the payload  322 . If so, the computer instructs the nanorobot to dump the payload  324 . In one embodiment, the computer first identifies a plurality of cavities in communication with one another, each cavity having a cross-sectional area greater than a predetermined value to define a pathway. Then, upon determining that a pathway exists, the computer causes a portion of the nanorobots located with in the pathway to release their payload within the pathway. This could be done, for example, if the payload is a swelling hydrophilic polymer and it is desirable to obstruct the pathway. If not, or if the nanorobot is not carrying a payload, the computer determines whether the nanorobot is moving in the correct direction at step  312 . If the nanorobot has a non-contact surface sensor, such as an ultrasonic sensor  158  or RF sensor  160 , the computer  126  records information from the reflected sensor signal in step  326 . 
         [0133]    The Formation 3-D Mapper  304  set of instructions creates a three dimensional map of the geological formation. The nanorobot control computer combines the data from one or more nanorobots and uses it to create a three dimensional map of the formation. The map will indicate the edges of the reservoir and the fluid contacts for the field. The Formation 3-D Mapper  97  is able to update the map in real time. Features on the map can include hydrocarbon location, water location, pore size, etc. The mapping program  304  can include instructions for flood monitoring, which can map the progress of water through the reservoir during hydrocarbon extraction operations. 
         [0134]    In one embodiment, shown in  FIG. 24 , the formation 3-D Mapper  304  performs the following steps. The computer first receives data from the nanorobot at step  330 . If no data is available, it waits for data at step  332 . If data is available, the nanorobot determines whether it is non-contact scanner data  334 . If so, it receives the scanner data  336  and plots the data on a 3D map  338 . Scanner data, being data actually reflected from interior points in the formation, is a sensed three dimensional location. If there is sufficient data density to create a 3D map  340 , the computer plots the scanned data  342 . If there is not sufficient data at  340 , the computer requests and waits for more information  332 . If the forthcoming data is contact sensor data at  334 , the computer determines whether the nanorobot is in contact with a surface at  346 . If not, the computer determines the nanorobot is floating within a cavity and thus identifies open cavity space at  346 . If the nanorobot is in contact with a surface, the control computer plots the surface contact point on the 3D map  350 . Each actual contact point is a sensed three dimensional location of a surface within the interior of the geophysical formation, in that the location was sensed by the nanorobot. The sensed three dimensional location of at least one point on each of a plurality of interior surfaces within the geophysical formation, thus, is interior surface location data. The surface contact points and open cavity space data are combined to create a 3D map at  352 . In one embodiment, wherein the nanorobots do not signal actual contact with the surface, the point on an interior surface can be determined by the presence of the nanorobot and a go/no-go indication from the nanorobot. If a nanorobot of a given size is present at that location, then the cross-section of the cavity at that location is at least as big as the nanorobot. The surface locations at that location, thus can be approximated round the location of the nanorobot. Surface locations near the contact points are interpolated from the contact and cavity data at  354 . The machine, thus, generates an interpolated map, responsive to the interior surface location data, by projecting surfaces between representations of the at least one points on each of the plurality of interior surfaces of the geophysical formation, the interpolated map identifying a physical shape and a location of a plurality of surfaces in the geophysical formation. If the data density is not sufficient to develop a map, the computer requests and waits for data at  332 . If it is sufficient, a 3D map is developed at  358 . The 3D map from contact data and/or the 3D map from non-contact data is used to identify the edges of the reservoir at  360 . The locations of fluids identified by the nanorobots are added to the reservoir map at  362 . The computer then determines whether dark regions exist on the map. A dark region is an area where no data is available from nanorobots—because nanorobots have not yet explored the cavities, the receivers are not able to receive information from the nanorobots, or because the region is solid and not accessible to nanorobots. If no dark regions exist, the computer continues to receive data from the nanorobots to monitor the extent of fluid movement, such as the extent of water drive or floodwater movement. If dark regions exist and the computer determines nanorobots should be able to provide data, the computer can instruct nanorobots to move toward the dark region at step  368  and then wait for data at  332 . 
         [0135]    The Fluid Mapper  308  set of instructions can plot the locations of fluids on the map and identify and monitor various fluid properties. In one embodiment, shown in  FIG. 25 , the computer first receives 3D map information at step  372 . The instructions then cause the computer to create a fluid map by plotting the type and location of fluids onto the interpolated map of the geophysical formation so that physical representations of fluids within the geophysical formation are displayed on the computer  374 . From the fluid locations and the map of the cavities in the formation, the computer can then identify the edges of the reservoir at  376 . The computer can then use fluid data, such as the pressure of the fluid at various locations, and assign pressures to fluid regions on the map at  378 . As water or other drive fluids move through the formation, the computer can monitor the extent of the water or fluid movement at step  380 . Finally, the fluid mapper instructions can cause the computer to model the current and future flows of fluids through the formation based on the fluid data and the cavity data, at step  382 . 
         [0136]    The Pathway Mapper  306  set of instructions locates pathways through the formation  538 . The computer program interprets data from swarms of nanorobots moving through pathways within the formation. The data includes the nanorobot&#39;s movement, trajectory, and velocity. The computer program also considers nanorobot sensor readings, such as ultrasonic sensors and contact with rock surfaces. The data is combined to create detailed maps of pathways and pores within the formation. The Pathway Mapper  306  set of instructions also identifies high permeability pathways within the formation. When water is injected for secondary recovery, the pressurized water tends to flow through large pathways. The water may take a circuitous route through several high permeability pathways from the injection point to the hydrocarbon extraction point (the production well). These pathways frequently bypass substantial amounts of hydrocarbons. The computer program is able to integrate pathway data from multiple nanorobots to form a model of the pathways. In some embodiments, the model can identify locations for production wells and injection wells to achieve maximum extraction of hydrocarbons. The computer can use data from the nanorobots to map “thieve zones” or high-permeability “super-K” zones within the reservoir. This data can be used to enhance conformance control and water shut off operations. The nanorobot can also identify the locations of pathways through the formation. Some pathways are larger than others. Water-drive water may pass through the larger pathways while bypassing hydrocarbon deposits. The pathway mapper instructions can cause the control computer to identify such large pathways based on data from the nanorobots. The computer can also use the data to create a pore network model (“PNM”) that depicts the entire pore network of the formation. 
         [0137]    In one embodiment, the Pathway Mapper  306  causes the computer to perform the following instructions, as shown in  FIG. 26 . The computer receives 3D map data at step  384 . The data can include the locations of cavities, or pores, within the formation, and the locations of the surfaces that define the cavities. The computer analyzes the cavity data to identify continuous series of cavities  386 . These are cavities that are in communication with one another, such that fluid can flow through all of the series. The computer then determines whether, for any continuous series of cavities, any cross sectional area is smaller than a predetermined amount  388 . A small cross sectional area serves as a choke point that restricts fluid flow. If a small cross sectional area exists, the series of cavities is identified as a pathway at step  390 . In one embodiment, the program instructs the computer to identify a plurality of cavities in communication with one another, wherein each of the cavities has a cross-sectional area larger than a predetermined value. The cross-sectional area is the area located between outer surfaces, or walls, of the cavity, transverse to the path of a geophysical nanorobot traveling through the cavity. The plurality of cavities in communication with each other and all having a cross-sectional area larger than a predetermined value is defined as a pathway. 
         [0138]    In one embodiment, geophysical nanorobots having a substantially spherical shape are used to identify the sizes of pathways. The nanorobots can have a plurality of different size diameters. A unique radio frequency identification tag can be used for each nanorobot or for each different size diameter. The program can instruct the computer to identify a location within the formation accessible to a first set of the plurality of geophysical nanorobots having one of the different sized diameters, but not accessible to a second set of the geophysical nanorobots having another one of the different sized diameters. 
         [0139]    If a continuous series of pathways does not have any cross sectional area less than a predetermined amount (X), then the continuous cavities are marked as a high speed pathway in  400 . In other words, if a plurality of cavities are in communication with each other, and each cavity has a cross-sectional area greater than a predetermined amount, the computer defines it as a pathway. The computer then identifies continuous high speed pathways between a water injection location and the extraction wellbore in  394 . A high speed pathway linking the injection and extraction points could cause water drive fluid to bypass pockets of hydrocarbons. After mapping pathways and high speed pathways, the computer determines whether all pathways are mapped at  398 . If so, the pore network model can be developed at  396 . If not, the computer receives more data at  384  to further develop the models. The computer can also receive velocity data from the nanorobots at  402 . In some embodiments, nanorobots can determine their own velocity. In other embodiments, the control computer can determine nanorobot velocity from wellbore receiver positional data (triangulation) received over a period of time. If the velocity of a nanorobot is greater than a predetermined amount (Y) for a specified length of time, the computer can use that data to conclude the nanorobot is traveling along a high speed pathway and, thus, map the pathway as such at  392 . Data regarding the size and location of pathways can be used, for example, to determine the locations of future drive or extraction wellbores. 
         [0140]    The Hydrocarbon Locator  310  set of instructions uses data from the nanorobots  114  to locate deposits of hydrocarbons  110 . The map can indicate the types of fluid present in the various regions of the geophysical formation in which the nanorobots are located. In an exemplary embodiment, the computer program three-dimensionally plots a location point from each nanorobot, along with the rock formation type and fluid type reported by the nanorobot for that location. This can be performed by the computer executing instructions from the Formation Mapper program. By using data from hundreds or thousands of nanorobots, the computer is able to interpolate a complete map of the geological formation and its contents, including a three dimensional perimeter of each hydrocarbon formation  536 . One of the mapping functions is to determine the location of bypassed hydrocarbon deposits  540 . One skilled in the art will appreciate the importance of determining the location of such deposits. 
         [0141]    In one embodiment, shown in  FIG. 27 , the Hydrocarbon Locator  310  set of instructions causes the computer to receive 3D formation map data created in response to the Formation Mapper  304  set of instructions  406 . The computer then receives fluid data from the nanorobots  408 . The fluid data includes the types of fluid and, thus, whether or not the fluids are hydrocarbons. The type and location of each fluid is recorded at  410  and plotted on the 3D map at  412 . From this data, the computer can interpolate areas to indicate the presence of and type of fluid in each cavity at  414 . The computer then identifies regions where a plurality of cavities are in communication with each other at  416 . By determining the fluids in each cavity  414  and the cavities in communication  416 , the computer can identify cavities in communication having a homogenous fluid type  418 . The computer, thus, identifies a three-dimensional region filled with a homogenous fluid to define a fluid pocket within the geophysical formation. If the cavities with a homogenous fluid type, together, have less than a predetermined volume of fluid  420 , the fluids are plotted on the map at  422 . If the cavities with homogenous fluids have greater than a predetermined amount of the fluid, the computer determines whether the fluid is in communication with the wellbore at  424 . If so, the fluid is mapped as a fluid pocket at  426 . If not, the fluid is mapped as a bypassed fluid pocket at  428 . 
         [0142]    The Gas Plume Mapper  312  set of instructions uses data from the nanorobots  114  to map the locations and movements of gas plumes within the formation. In an application wherein the nanorobots enter a porous geological formation used to store a gas, such as carbon dioxide or natural gas, the control computer can be used to create maps and models depicting the travel of the gas plume within the rock formation. The data from the nanorobots sensors can be used to monitor how much of the injected gas goes into solution with in situ fluids and whether and how it affects the chemical and physical properties of the fluids. Furthermore, the data can show how a rock mineral reacts with injected gas. The plume and pressure data can be interpreted to show whether gas is leaking out of the storage facility to the surface or to an adjacent rock formation. 
         [0143]    In one embodiment, shown in  FIG. 28 , the Gas Plume Mapper  312 , when executed, causes the computer to perform the following instructions. The computer receives Formation Data from the Formation Mapper  304 . The computer also receives data from the nanorobots in step  434 . This data includes fluid data that may not have been included in the Formation Mapper data. The properties of the fluids, including type, location, and pressure, are recorded by the computer in  436 . The coordinates of each fluid are plotted on the 3D map  438 . The computer then interpolates the presence and type of fluid in each cavity  440 . The computer determines whether the identified fluid is a gas of interest  442 . If not, the computer receives additional data from the nanorobots until it receives information regarding a gas of interest. If a fluid is a gas of interest, the locations of the cavities having the gas of interest are identified on the 3D map  444 . The computer receives fluid property data regarding other fluids that may be in the same cavities that contain the gas of interest  446 . From this data, the computer determines whether the gas of interest is dissolved in the other fluid  448 . If so, the computer can determine the percent of gas present in the other fluid  450 . The computer can also determine whether properties of the other fluid are known, such as in a database  452 . If so, the known properties of the other fluid can be evaluated to determine whether a reaction between the fluids is likely and the nature of any reaction  454 . After mapping the locations of cavities having gas of interest at step  444 , the computer can receive additional geophysical property data from the nanorobots  456 . The additional data can include, for example, data regarding the type of rock. This data, plus database data, can be evaluated by the computer to determine whether the rock material is likely to react with the gas, and evaluate the type of any potential reaction  458 . Also after mapping cavities having the gas of interest  444 , the computer can receive pressure data, such as the pressure of the gas at various locations, from the nanorobots  460 . With the pressure data and the 3D map, the computer can determine the volume of gas present in the cavities at  462 . With the map showing location and pressure, the computer can determine the extent of the gas plume  464 . Finally, the dissolution data, rock reaction data, and gas plume data can be combined by the computer to predict the movement of the gas plume  466 . 
         [0144]    The Potable Water Locator  314  set of instructions uses fluid and mineral data collected by the nanorobots  114  and transmitted to the nanorobot control computer to find underground water sources and determine whether the water is potable water. 
         [0145]    In one embodiment, shown in  FIG. 29 , the Potable Water Locator  314 , when executed, causes the computer to perform the following instructions. The computer receives 3D formation map information from the Formation Mapper set of instructions  470 . The computer also receives data from the nanorobots regarding fluids in contact with the nanorobots  472 . If the fluid is not water, the computer waits until it receives additional data  474 . If the fluid is water, the computer determines from the data whether the water is potable  476 . This data can be determined, for example, from fluid properties including resistance, pH, and bacteria analysis. If the water is not potable, it is plotted as a non-potable water source on the map  478 . If the water is potable, it is identified as a potable water source  480 . The computer then identifies a plurality of cavities having potable water that are in communication with each other  482 . Each plurality of cavities in communication with each other and having potable water is mapped as a continuous formation of potable water  484 . The computer then determines whether the volume of the potable water formation is greater than a predetermined value  486 . If not, the formation is mapped as a location of potable water. If so, the formation is mapped as a source of potable water  490 . 
         [0146]    In one embodiment, shown in  FIG. 30 , the Surface Approximator  491  set of instructions, when executed, causes the computer to perform the following functions. In step  492 , the computer receives the locations of each of the nanorobots over a period of time. The locations can be determined by, for example, triangulating, responsive to a signal reflected by each of the plurality of geophysical robots from one or more transmitters associated with one or more wellbores to one or more receivers associated with one or more wellbores. By receiving multiple locations, over time, the computer can plot the path traveled by each nanorobot within the formation  493 . From the paths traveled by each nanorobot, the computer can generate an interpolated map  494 . The interpolated map can approximate the locations of surfaces be determining, from the traveled paths, where the nanorobots are not able to travel and concluding that the nanorobots cannot travel through a surface. From the pathways, over time, the computer can estimate the velocity of each of the nanorobots using a time/distance calculation. From this velocity, and knowing that the nanorobots are traveling within the fluid flow of the geophysical formation, the computer can estimate with velocity of the fluid passing through each of the plurality of traveled paths  495 . If the nanorobot has a fluid sensor, the computer can receive the fluid data indicating the type at each of a plurality of locations within the formation, and then plot the type of fluid on the interpolated map to display a physical representation of fluids within the formation  496 . 
         [0147]    While the invention has been shown or described in only some of its forms, it should be apparent to those skilled in the art that it is not so limited, but is susceptible to various changes without departing from the scope of the invention. 
         [0148]    Furthermore, recitation of the term about and approximately with respect to a range of values should be interpreted to include both the upper and lower end of the recited range. As used herein, the terms first, second, third and the like should be interpreted to uniquely identify elements and do not imply or restrict to any particular sequencing of elements or steps. 
         [0149]    Although the present invention has been described in detail, it should be understood that various changes, substitutions, and alterations can be made hereupon without departing from the principle and scope of the invention. Accordingly, the scope of the present invention should be determined by the following claims and their appropriate legal equivalents. 
         [0150]    The singular forms “a”, “an” and “the” include plural referents, unless the context clearly dictates otherwise. 
         [0151]    Optional or optionally indicates that the subsequently described event or circumstances may or may not occur. The description includes instances where the event or circumstance occurs and instances where it does not occur. 
         [0152]    Ranges may be expressed herein as from about one particular value, and/or to about another particular value. When such a range is expressed, it is to be understood that another embodiment is from the one particular value and/or to the other particular value, along with all combinations within said range. 
         [0153]    In the drawings and specification, there have been disclosed a typical preferred embodiment of the invention, and although specific terms are employed, the terms are used in a descriptive sense only and not for purposes of limitation. The invention has been described in considerable detail with specific reference to these various illustrated embodiments. It will be apparent, however, that various modifications and changes can be made within the spirit and scope of the invention as described in the foregoing specification and as defined in the appended claims. 
         [0154]    This patent application is a divisional of U.S. Non-Provisional patent application Ser. No. 12/722,357, titled “System, Method, and Nanorobot to Explore Subterranean Geophysical Formations” and filed on Mar. 11, 2010, which claims the benefit of and priority to U.S. Provisional Patent Application No. 61/159,943, titled “System, Method, and Nanorobot to Explore Subterranean Geophysical Formations” and filed on Mar. 13, 2009, the contents both of which are incorporated herein by reference in their entireties.

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