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This patent application claims priority to U. Provisional Patent Application Ser. 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 nanorobots to move through a subsurface formation to identify various geophysical characteristics.
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. 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. 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: The scribble will not reveal the precise path that the fluids are likely to take.
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.
Post mortem and predictive mapping of erratically occurring high permeability pathways is a leading issue of concern to major petroleum companies. Permeability pathways are interwell phenomena. Unfortunately, it is interwell control that is very difficult to characterize. 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.
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.
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. 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. 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. 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.
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. The geostatistical distribution of attributes, including fractures detected on borehole image logs, at the wellbore, is the best we'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. 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. 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 nanometers wide. The pores can contain a variety of fluids including oil, water, or natural gas. 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.
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. 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 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 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. A nanoscale robot, with a dimension smaller than 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.
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 degrees Fahrenheit and being sized so that none of the length, width, or height of the robot body is greater than 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: 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 radiofrequency receivers.
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.
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.
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. 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.
In another embodiment, the propulsion device of each of the plurality of geophysical nanorobots can include one or more of the following: 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: In another embodiment, the sensor can of each of the plurality of geophysical nanorobots can sense one or more of the following: 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.
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.
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.
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.
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.
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. One embodiment of a technique to identify properties of a geophysical formation includes steps of: 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. In another embodiment of the technique, the plurality of geophysical robots include a nanorobot defined as having: 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. 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 nanometers, formed of a plurality of carbon nanotubes adapted to withstand temperatures exceeding 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: