Patent Publication Number: US-2022236444-A1

Title: Gas saturation distribution monitoring in hydrocarbon reservoir

Description:
TECHNICAL FIELD 
     The present disclosure relates generally to hydrocarbon extraction. More specifically, but not by way of limitation, this disclosure relates to downhole gas saturation distribution monitoring in a hydrocarbon reservoir. 
     BACKGROUND 
     A hydrocarbon reservoir may include multiple wellbores drilled through a subterranean formation. The subterranean formation may include a rock matrix permeated by oil or gas that is to be extracted using the wellbores. Monitoring fluid propagation over time within the hydrocarbon reservoir may be beneficial for controlling hydrocarbon production at one or more of the wellbores drilled through the subterranean formation of the hydrocarbon reservoir. 
     Surface gravity surveys are often conducted for basin-scale density measurements in hydrocarbon exploration. The surface gravity measurements may provide information regarding potential locations of fluids within the hydrocarbon reservoirs. However, challenges exist with the use of surface gravity measurements. For example, the surface gravity measurements may provide coarse data resolution because the measurements are performed with only a vertical measurement component. Additionally, the surface gravity measurements may require a computationally expensive solution of an inverse problem to generate data useful to a wellbore operator. For example, a large matrix mathematical operation may be required to construct a three-dimensional earth property model that is representative of the hydrocarbon reservoir. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a cross-sectional view of an example of a well system according to some aspects. 
         FIG. 2  is an example of a representation of a hydrocarbon reservoir providing an indication of wellbore locations according to some aspects. 
         FIG. 3  is an example of a process for building a multicomponent gravity model over time according to some aspects. 
         FIG. 4  is an example of an overhead view of the hydrocarbon reservoir indicating a location of two wellbores according to some aspects. 
         FIG. 5  is an example of time-lapse multicomponent borehole gravity data at a gas injector identified in  FIG. 4  according to some aspects. 
         FIG. 6  is an example of time-lapse multicomponent borehole gravity data at a producer identified in  FIG. 4  according to some aspects. 
         FIG. 7  is an example of a section view of interpolated time-lapse borehole gravity models for the hydrocarbon reservoir of  FIG. 4  according to some aspects. 
         FIG. 8  is an example of a computing system for implementing certain embodiments of the present disclosure according to some aspects. 
     
    
    
     DETAILED DESCRIPTION 
     Certain aspects and features of the present disclosure relate to generating three-dimensional models representing fluid saturation in a hydrocarbon reservoir using multicomponent borehole gravity measurements from multiple wellbores located at the hydrocarbon reservoir. As used herein, the multicomponent borehole gravity measurements provide gravity data with multiple directional components (e.g., in an x-direction, a y-direction, and a z-direction). The multicomponent borehole gravity measurements may be performed by gravity sensors located along the length of tubing within each of the wellbores. In this manner, the gravity readings performed by the gravity sensors within the wellbores may include components in each of the x, y, and z coordinate directions. Gravity data associated with locations between the wellbores may be interpolated using an interpolation technique, such as simple kriging. Using the multicomponent borehole gravity measurements from the wellbores and the interpolated gravity data from the interpolation technique, a three-dimensional gravimetric model may be generated that represents reservoir gravity across a field of wellbores in the hydrocarbon reservoir. Because of a correlation between reservoir gravity and gas saturation, the three-dimensional gravimetric model may provide an indication of fluid saturation within the hydrocarbon reservoir. This process may be repeated over time to generate a time-lapse fluid saturation model that monitors fluid propagation in time and space in the hydrocarbon reservoir. 
     Some examples can offer techniques for efficiently generating three-dimensional fluid saturation models of a hydrocarbon reservoir. A system may involve avoiding inversion or geophysical analysis while generating three-dimensional models with high resolution. As discussed in detail below with respect to the figures, the time-lapse fluid saturation model may be used to monitor fluid propagation in time and space in the hydrocarbon reservoir such that an operator of a wellbore in the hydrocarbon reservoir can control hydrocarbon pumping or gas injection operations. 
     Illustrative examples are given to introduce the reader to the general subject matter discussed here and are not intended to limit the scope of the disclosed concepts. The following sections describe various additional features and examples with reference to the drawings in which like numerals indicate like elements, and directional descriptions are used to describe the illustrative aspects but, like the illustrative aspects, should not be used to limit the present disclosure. 
       FIG. 1  is a cross-sectional view of an example of a well system  100  that may employ one or more principles of the present disclosure. A wellbore  102  may be created by drilling into the formation  104 . In an example, the formation  104  may represent a portion of a hydrocarbon reservoir, as discussed herein. Upon completion of the wellbore  102 , the well system  100  may deploy production or injection tubing  106  into the wellbore  102 . The tubing  106  may be used for producing hydrocarbon fluids and other fluids from the wellbore  102  when the wellbore  102  is a production well, or the tubing  106  may be used for injecting an inert gas into the wellbore  102  in a gas injection well. 
     Multiple gravity sensors  110  may be positioned along a length of the tubing  106  at regular or irregular intervals. The gravity sensors  110  may be any sensors capable of taking gravimetric readings. For example, the gravity sensors  110  may be any sensor that is capable of measuring an acceleration effect of the Earth&#39;s gravity at a point in the wellbore  102 . In an example, the gravity sensors  110  may be accelerometers that include other sensors to remove linear accelerations from the gravimetric data. The gravity sensors  110  may be separated by between one foot and ten feet (i.e., 0.3 to three meters) along a length of the tubing  106 . In other embodiments, the gravity sensors may also be separated by greater than ten feet or by less than one foot. 
     The gravity sensors  110  may read gravity data in three-dimensions. Accordingly, the gravity data of the area surrounding each gravity sensor  110  may include an x-component, a y-component, and a z-component (collectively, a “multicomponent gravity reading”), where an x-direction  112 , a y-direction  114 , and a z-direction  116  associated with the components are orthogonal. In an example, communication devices associated with the gravity sensors  110  may transmit signals to a surface  118  of the wellbore  102  indicating the multicomponent gravity readings of the gravity sensors  110 . The signals transmitted to the surface may be acoustic signals, electromagnetic signals, mud-pulse telemetry signals, wireline signals, or any other types of signals capable of providing the multicomponent gravity readings to the surface  118  of the wellbore  102 . 
     The gravity sensors  110  may respond to variations in the density surrounding the gravity sensors  110 . Accordingly, when placed within the wellbore  102 , the gravity sensors  110  are able to detect variations in the density of the formation  104  surrounding the wellbore  102 . By monitoring the detected variations in the density of the formation surrounding the wellbore  102  over time, an operator is able to monitor fluid propagation in the area surrounding each of the gravity sensors  110 . 
     Still referring to  FIG. 1 , the downhole tools may be in communication with a computing device  140   a,  which is illustrated by way of example at the surface  118  in  FIG. 1 . In an additional embodiment, the computing device  140   a  may be located elsewhere, such as downhole, or the computing device may be a distributed computing system including multiple, spatially separated computing components (e.g.,  140   a,    140   b,  downhole, or any combination thereof). Other equipment of the well system  100  described herein may also be in communication with the computing device  140   a.  In some embodiments, one or more processors used to control a production or injection operation of the well system  100  may be in communication with or otherwise controlled by the computing device  140   a.    
     In  FIG. 1 , the computing device  140   a  is illustrated as being deployed in a work vehicle  142 . However, the computing device  140   a  that receives data from the gravity sensors  110  may be permanently installed surface equipment of the well system  100 . In other embodiments, the computing device  140   a  may be hand-held or remotely located from the well system  100 . In some examples, the computing device  140   a  may process at least a portion of the data received and transmit the processed or unprocessed data to an additional computing device  140   b  via a wired or wireless network  146 . The additional computing device  140   b  may be offsite, such as at a data-processing center. The additional computing device  140   b  may receive the data, process the data, execute computer program instructions to issue commands to control the operation of the well system  100 , and communicate those commands to computing device  140   a.    
     Further, the additional computing device  140   b  may receive additional gravity data from other gravity sensors positioned in additional wellbores within a same reservoir as the wellbore  102 . The additional computing device  140   b  may generate a three-dimensional fluid saturation model of the reservoir by interpolating gravity data about space between the wellbores  102  that is not read or otherwise detected by gravity sensors within the wellbores  102 . For example, the additional computing device  140   b  may apply a simple kriging interpolation method to the data from the gravity sensors to interpolate the additional gravity data. 
     When the three-dimensional fluid saturation model is generated by the additional computing device  140   b  at multiple time steps, a time-lapse, three-dimensional fluid saturation model may be generated by the additional computing device  140   b.  The time-lapse, three-dimensional fluid saturation model may indicate fluid propagation within the hydrocarbon reservoir in time and space such that an operator of the wellbore  102  in the hydrocarbon reservoir can control hydrocarbon pumping or gas injection operations. For example, if some of the gravity sensors  110  in a particular wellbore  102  show no change in fluid saturation in an adjacent productive reservoir region over the time, this region may be exposed to an additional well stimulation to ensure that the region starts or restarts hydrocarbon production. Also, if inferred 3D fluid saturation model derived from interpolated gravity measurements shows bypassed hydrocarbon regions in the reservoir, one or more additional wells may be drilled in these bypassed regions to extract trapped hydrocarbons that are not otherwise available for production from the reservoir. 
     The computing devices  140   a - b  may be positioned belowground, aboveground, onsite, in a vehicle, offsite, etc. The computing devices  140   a - b  may include a processor interfaced with other hardware via a bus. A memory, which may include any suitable tangible (and non-transitory) computer-readable medium, such as RAM, ROM, EEPROM, or the like, can embody program components that configure operation of the computing devices  140   a - b.  In some aspects, the computing devices  140   a - b  may include input/output interface components (e.g., a display, printer, keyboard, touch-sensitive surface, and mouse) and additional storage. 
     The computing devices  140   a - b  may include surface communication devices  144   a - b.  The surface communication devices  144   a - b  may represent one or more of any components that facilitate a network connection. In the example shown in  FIG. 1 , the surface communication devices  144   a - b  are wireless and may include wireless interfaces such as IEEE 802.11, Bluetooth, or radio interfaces for accessing cellular telephone networks (e.g., RF stage/antenna for accessing a CDMA, GSM, UMTS, or other mobile communications network). In some examples, the surface communication devices  144   a - b  may use acoustic waves, surface waves, vibrations, optical waves, or induction (e.g., magnetic induction) for engaging in wireless communications. In other examples, the surface communication devices  144   a - b  may be wired and can include interfaces such as Ethernet, USB, IEEE 1394, or a fiber optic interface. The computing devices  140   a - b  can receive wired or wireless communications from one another and perform one or more tasks based on the communications. 
     While  FIG. 1  depicts the well system  100  where the computing devices  140   a - b  receive data from the gravity sensors  110  for use in generating three-dimensional fluid saturation models, other systems may also be controlled using the computing devices  140   a - b.  For example, the computing devices  140   a - b  may receive performance data related to hydrocarbon production systems, wellbore casing and cementing systems, wellbore fracturing systems, wellbore maintenance programs, or any other wellbore technologies. The computing devices  140   a - b  may receive the performance data, execute computer program instructions to issue commands to control the operation of the wellbore technology, and apply those commands to equipment of the wellbore technology. In some aspects, the performance data may be considered “real-time” data as the performance data is collected and transmitted to the computing devices  140   a - b  as the wellbore equipment is operated. 
     Further, while  FIG. 1  depicts the wellbore  102  in a vertical orientation, the techniques described herein may also be used in horizontal wellbore systems. As used herein, the horizontal wellbore system may be a wellbore with a trajectory other than vertical (e.g., horizontal, inclined, etc.). In one or more examples, the gravity sensors  110  may be permanently installed within the wellbores  102  (e.g., within a casing of the wellbore  102 ), or the gravity sensors  110  may be removably installed within the wellbores  102  (e.g., along the tubing  106 , as illustrated). 
       FIG. 2  is an example of a representation of a hydrocarbon reservoir  200  providing an indication of locations  202  of the wellbores  102 . The hydrocarbon reservoir  200  may include multiple wellbores  102  spread over a surface  118  of the hydrocarbon reservoir  200 . While the wellbores  102  may be densely positioned along the surface  118  of the hydrocarbon reservoir  200 , the gravity sensors  110  positioned within the wellbores  102  may have a limited range for reading into the formation  104  of the hydrocarbon reservoir  200  surrounding the wellbores  102 . For example, the gravity sensors  110  may detect gravity data that is local (e.g., within 50 feet) to the position of the gravity sensors  110  within the wellbore  102 , while the locations  202  of the wellbores  102  may be several hundred feet or meters apart. This may leave a significant amount of gravity data of the hydrocarbon reservoir  200  that is not directly measured by the gravity sensors  110 . 
     In an example, the wellbores  102  positioned at the locations  202  may represent injection and production wells. For example, some of the wellbores  102  may be producing hydrocarbons from the hydrocarbon reservoir  200  while other wellbores  102  may be injecting inert gas (e.g., carbon dioxide) into the hydrocarbon reservoir  200  to maintain reservoir pressure and improve oil displacement in the formation  104  surrounding the injection wells. In one or more examples, the density difference between the hydrocarbons within the hydrocarbon reservoir  200  and the inert gas injected into the hydrocarbon reservoir  200  may provide a greater indication of fluid propagation in the time-lapse, three-dimensional fluid saturation model due to a large contrast in gravity measurements between the hydrocarbons and the inert gas. For example, the lighter inert gases are easily observable in the time-lapse, three-dimensional fluid saturation model as the inert gases replace the heavier hydrocarbons that are produced by the production wells. 
     Also included in  FIG. 2  is an overhead view  204  of the hydrocarbon reservoir  200 . The overhead view includes an indication of a fault  206  and a fault  208  within the hydrocarbon reservoir  200 . The faults  206  and  208  may be observable in the time-lapse, three-dimensional fluid saturation models. For example, the time-lapse, three-dimensional fluid saturation models may show a lack of fluid movement within the hydrocarbon reservoir  200  across locations of the faults  206  and  208 . Other impermeable layers of the formation  104 , or layers of the formation  104  with limited permeability, within the reservoir may be depicted in the time-lapse, three-dimensional fluid saturation models in a similar manner (e.g., by a lack of movement of fluid across locations of the impermeable layers or limited permeability layers). 
       FIG. 3  is an example of a process  300  for building a multicomponent gravity model over time (e.g., a time-lapse, three-dimensional fluid saturation model). One or more computing devices (e.g., the computing devices  140   a - b ) implement operations depicted in  FIG. 3  by executing suitable program code. For illustrative purposes, the process  300  is described with reference to certain examples depicted in the figures. Other implementations, however, are possible. 
     The processing depicted in  FIG. 3  may be implemented in software (e.g., code, instructions, program) executed by one or more processing units (e.g., processors, cores) of the respective systems, hardware, or combinations thereof. The software may be stored on a non-transitory storage medium (e.g., on a memory device). The process  300  presented in  FIG. 3  and described below is intended to be illustrative and non-limiting. Although  FIG. 3  depicts the various processing steps occurring in a particular sequence or order, this is not intended to be limiting. In certain alternative embodiments, the steps may be performed in some different order or some steps may also be performed in parallel. 
     At block  302 , the process  300  involves collecting multicomponent gravity data along a length of each of the wellbores  102 , which include the gravity sensors  110 , that are within the hydrocarbon reservoir  200 . As discussed above, gravity sensors  110  positioned along the tubing  106  within the wellbores  102  may provide multicomponent gravity readings of the formation  104  surrounding the wellbores  102  to the computing devices  140   a - b.  The gravity sensors  110  may be positioned at regular or irregular intervals within the wellbores  102 . In an example, the gravity sensors  110  may be positioned between 1 and 10 feet apart from one another. 
     At block  304 , the process  300  involves building a multicomponent gravity model in three-dimensions at an initial time step. For example, the multicomponent gravity data collected from the wellbores  102  is used with a spatial interpolation technique to generate a three-dimensional fluid saturation model of the hydrocarbon reservoir  200  at a specified time step. 
     The spatial interpolation technique may be a simple kriging interpolation applied to the multicomponent gravity data from the wellbores  102  to interpolate gravity data between the wellbores  102  of the hydrocarbon reservoir  200 . Other interpolation techniques may also be used such as an inverse distance method or deep neural network algorithms. Using the simple kriging interpolation technique, gravity measurements can be used to monitor gas propagation in the hydrocarbon reservoir  200  in time and space. Simple kriging may be expressed with the following equation: 
     
       
         
           
             
               
                 
                   
                     
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     where z*(r) is an estimated value at an unsampled location r, and m(r) is a prior mean value at the unsampled location r. z(r) is an existing data value, λ α  is a weight that is applied to an α-th data point (α=1, . . . , N). In an example, no trend data is applied to the equation. Accordingly, all prior mean values can be set as m(r)=m(r α )= m . 
     Using the simple kriging technique, weights may be calculated as functions of distance. For example, the weights, which may minimize a variance of an estimated value, may be written with the following equation: 
     
       
         
           
             
               
                 
                   
                     
                       
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     where C denotes a covariance. Applying the multicomponent gravity data to this simple kriging technique, the multicomponent gravity model (e.g., the three-dimensional fluid saturation model) is generated for a time step for the hydrocarbon reservoir  200 . 
     At block  306 , the process  300  involves inferring gas distribution in the hydrocarbon reservoir  200  from the multicomponent gravity model. As mentioned above, the density of the inert gas injected into the hydrocarbon reservoir  200  may be much less than a density of the hydrocarbons within the hydrocarbon reservoir  200 . Accordingly, the multicomponent gravity model may identify contrasts between the inert gas and the hydrocarbons within the hydrocarbon reservoir  200 . Using this contrast, the location of the inert gas may be inferred. 
     At block  308 , the process  300  involves repeating the process  300  at a subsequent time step to generate a time-lapse, multicomponent gravity model (e.g., the time-lapse, three-dimensional fluid saturation model). Further, block  308  may be repeated over an extended number of time steps to add robustness to the time-lapse, multicomponent gravity model. 
     In an example, the time-lapse, multicomponent gravity model may be used to monitor fluid propagation within the hydrocarbon reservoir  200  over time. For example, the time-lapse, multicomponent gravity model may show locations of the injected inert gas as it travels within the hydrocarbon reservoir  200 . By monitoring the fluid propagation, an operator of one or more wellbores  102  within the hydrocarbon reservoir  200  may control wellbore operations at the wellbores  102 . For example, the operator may increase gas injection in a wellbore  102  located in an area where fluid propagation is very active (e.g., where the gas injection may have a significant impact on hydrocarbon production). Alternatively, the operator may decrease gas injection in a wellbore  102  located in an area where fluid propagation is not very active (e.g., where the gas injection may have a minimal impact on hydrocarbon production). Other changes to the wellbore operations may also be performed in response to the indication of fluid propagation within the hydrocarbon reservoir  200 . 
       FIG. 4  is an example of an overhead view  400  of the hydrocarbon reservoir  200  indicating a location of two wellbores  102   a  and  102   b.  As illustrated, the wellbore  102   a  may be a gas injection wellbore, and the wellbore  102   b  may be a producer wellbore. Multicomponent gravity readings from multiple time steps from the wellbore  102   a  are depicted and described below with respect to  FIG. 5 . Further, multicomponent gravity readings from multiple time steps from the wellbore  102   b  are depicted and described below with respect to  FIG. 6 . While only two wellbores  102   a  and  102   b  are illustrated in  FIG. 4 , the hydrocarbon reservoir  200  may include dozens of additional wellbores  102  with the gravity sensors  110  positioned to take gravity readings of the hydrocarbon reservoir  200  from within the additional wellbores  102 . The additional wellbores  102  may provide additional data points to increase robustness of a three-dimensional fluid saturation model. 
       FIG. 5  is an example of time-lapse multicomponent borehole gravity readings  500  at the wellbore  102   a  (e.g., a gas injector) identified in the overhead view  400  of the hydrocarbon reservoir  200 . A graph  502  represents gravity readings with an x-component that are collected by the gravity sensors  110  within the wellbore  102   a  over 8 years. An abscissa  508  represents the gravity reading in microgals in the x-direction  112 , and an ordinate  510  represents a depth in feet within the wellbore  102   a.  A graph  504  represents gravity readings with a y-component that are collected by the gravity sensors  110  within the wellbore  102   a  over 8 years. An abscissa  512  represents the gravity reading in microgals in the y-direction  114 , and an ordinate  514  represents a depth in feet within the wellbore  102   a.  A graph  506  represents gravity readings with a z-component that are collected by the gravity sensors  110  within the wellbore  102   a  over 8 years. An abscissa  516  represents the gravity reading in microgals in the z-direction  116 , and an ordinate  518  represents a depth in feet within the wellbore  102   a.    
     Each of the lines  520  in the graphs  502 ,  504 , and  506  represent gravity data collected at different time steps. As illustrated, the time step for data collection is 1 year. Larger or smaller time steps may also be used. For example, the time step for data collection may be 1 month, 1 week, or 1 day. Collecting the multicomponent gravity readings at multiple time steps enables generation of the time-lapse, three-dimensional fluid saturation model such that a user is able to observe fluid propagation within the hydrocarbon reservoir  200  over time. 
     In an example, a steady change in gravity at each depth over time may represent a porous area of the hydrocarbon reservoir  200  where fluid propagation within the reservoir may occur. In particular, the graphs  502  and  504  generally indicate that fluids are steadily moving through the reservoir in the x and y-directions  112  and  114 . In the graph  506 , the gravity readings at different time steps cross each other at various depths within the wellbore  102   a.  This may indicate the presence of a large, impermeable layer in the z-direction  116 . 
       FIG. 6  is an example of time-lapse multicomponent borehole gravity readings  600  at the wellbore  102   b  (e.g., a producer) identified in the overhead view  400  of the hydrocarbon reservoir  200 . A graph  602  represents gravity readings with an x-component that are collected by the gravity sensors  110  within the wellbore  102   b  over 8 years. An abscissa  608  represents the gravity reading in microgals in the x-direction  112 , and an ordinate  610  represents a depth in feet within the wellbore  102   b.  A graph  604  represents gravity readings with a y-component that are collected by the gravity sensors  110  within the wellbore  102   b  over 8 years. An abscissa  612  represents the gravity reading in microgals in the y-direction  114 , and an ordinate  614  represents a depth in feet within the wellbore  102   b.  A graph  606  represents gravity readings with a z-component that are collected by the gravity sensors  110  within the wellbore  102   b  over 8 years. An abscissa  616  represents the gravity reading in microgals in the z-direction  116 , and an ordinate  618  represents a depth in feet within the wellbore  102   b.    
     Each of the lines  620  in the graphs  602 ,  604 , and  606  represent gravity data collected at different time steps. As illustrated, the time step for data collection is 1 year. Larger or smaller time steps may also be used. Collecting the multicomponent gravity readings at multiple time steps enables generation of the time-lapse, three-dimensional fluid saturation model such that a user is able to observe fluid propagation within the hydrocarbon reservoir  200  over time. 
     In an example, a steady change in gravity at each depth over time may represent a porous area of the hydrocarbon reservoir  200  where fluid propagation within the reservoir may occur. In particular, the graphs  602  and  604  generally indicate that fluids are steadily moving through the reservoir in the x-direction  112  and the y-direction  114 . The oil replacement rate by the gas flow may slow down at later time steps resulting in a smaller changes over time. In the graph  606 , minimal changes to the gravity readings at different time steps (e.g., especially later time steps) at various depths within the wellbore  102   a  may indicate the presence of a number of large, impermeable layers in the z-direction  116 . 
       FIG. 7  is an example of a section view  700  of an interpolated time-lapse, multicomponent borehole gravity model for the hydrocarbon reservoir  200 . As discussed above, the multicomponent gravity data collected for each of the wellbores  102  within the hydrocarbon reservoir  200  may be interpolated (e.g., using a simple kriging technique or other suitable interpolation technique) to estimate gravity data of portions of the hydrocarbon reservoir  200  not directly measured by the gravity sensors  110 . As illustrated, vertical sections of an x-component  702 , a y-component  704 , and a z-component  706  of the interpolated time-lapse, multicomponent borehole gravity model (i.e., the time-lapse, multicomponent fluid saturation model) are depicted along a line  708  of the hydrocarbon reservoir  200 . 
     Further, the x-component  702   a  represents the multicomponent borehole gravity model at a 2-year time step in the x-direction  112 , the x-component  702   b  represents the multicomponent borehole gravity model at a 4-year time step in the x-direction  112 , and the x-component  702   c  represents the multicomponent borehole gravity model at an 8-year time step in the x-direction  112 . Similarly, the y-component  704   a  represents the multicomponent borehole gravity model at a 2-year time step in the y-direction  114 , the y-component  704   b  represents the multicomponent borehole gravity model at a 4-year time step in the y-direction  114 , and the y-component  704   c  represents the multicomponent borehole gravity model at an 8-year time step in the y-direction  114 . Additionally, the z-component  706   a  represents the multicomponent borehole gravity model at a 2-year time step in the z-direction  116 , the z-component  706   b  represents the multicomponent borehole gravity model at a 4-year time step in the z-direction  116 , and the z-component  706   c  represents the multicomponent borehole gravity model at an 8-year time step in the z-direction  116 . 
     Observing the x-component  702 , the y-component  704 , and the z-component  706  over time may provide a user with the ability to infer a range of gas penetration (e.g., from the injector wells within the hydrocarbon reservoir  200 ) or other fluid saturation within the hydrocarbon reservoir  200 . The time-lapse, multicomponent borehole gravity model may enable direct observation of a vertical variation in a gravity response by the hydrocarbon reservoir  200 , as in areas  710 ,  712 , and  714  of the x-component  702   c,  the y-component  704   c,  and the z-component  706   c,  respectively. Further, the time-lapse, multicomponent borehole gravity model may provide an indication of structural barriers within the hydrocarbon reservoir  200  (e.g., impermeable formations, faults, etc.). 
     In general, a decrease in the gravity values may indicate that heavier reservoir fluid, such as oil, is displaced by injected lighter fluid, such as an inert gas. Using the time-lapse, multicomponent borehole gravity model, a user can observe fluid propagation within the hydrocarbon reservoir  200 . Having three components of the gravity data may enhance understanding of the inert gas propagation in time as highlighted by circles  710 ,  712  and  714 . All three components indicate presence of the impermeable layer in the middle of the formation, which may be seen the best from the interpolated x-component gravity  702   c.  Further, operators of the wellbores  102  may adjust wellbore operations based on the observed propagation. For example, production or injection may increase or decrease at the wellbores  102  based on the observed fluid propagation. In one or more examples, the computing devices  140   a - b  may automatically adjust operations associated with hydrocarbon production or gas injection at the wellbores  102 . For example, the computing devices  140   a - b  may control production pumps to increase or decrease production pumping rates based on the fluid flow within the hydrocarbon reservoir  200 . Similarly, the computing devices  140   a - b  may control injection pumps to increase or decrease injection pumping rates based on the fluid flow within the hydrocarbon reservoir  200 . 
     Any suitable computing system or group of computing systems can be used for performing the operations described herein. For example,  FIG. 8  depicts an example of a computing system  800 . The computing system  800  implements a communication module  802  and a gravity data interpretation module  804  stored on a memory device  806 . In an embodiment, the communication module  802  is implemented by the computing system  800  to provide communication between devices. For example, the computing system  800  may communicate with other computing systems  800  and the computing system  800  may communicate with downhole devices, such as the gravity sensors described herein. Further, the gravity data interpretation module  804  is implemented by the computing system  800  to perform the interpretation techniques described herein on multicomponent gravity readings received from the gravity sensors  110 . In an embodiment, a computing system  800  having devices similar to those depicted in  FIG. 8  (e.g., a processor, a memory, etc.) combines the one or more operations and data stores that may be operated as separate subsystems. 
     The depicted example of the computing system  800  includes a processor  808  communicatively coupled to one or more memory devices  806 . The processor  808  executes computer-executable program code stored in a memory device  806 , accesses information stored in the memory device  806 , or both. Examples of the processor  808  include a microprocessor, an application-specific integrated circuit (“ASIC”), a field-programmable gate array (“FPGA”), or any other suitable processing device. The processor  808  can include any number of processing devices, including a single processing device. 
     The memory device  806  includes any suitable non-transitory computer-readable medium for storing program code (e.g., the communications module  802  and the gravity data interpretation module  804 ), program data (e.g., data associated with the communications module  802  and the gravity data interpretation module  804 ), or both. A computer-readable medium can include any electronic, optical, magnetic, or other storage device capable of providing a processor with computer-readable instructions or other program code. Non-limiting examples of a computer-readable medium include a magnetic disk, a memory chip, a ROM, a RAM, an ASIC, optical storage, magnetic tape or other magnetic storage, or any other medium from which a processing device can read instructions. The instructions may include processor-specific instructions generated by a compiler or an interpreter from code written in any suitable computer-programming language, including, for example, C, C++, C#, Visual Basic, Java, Python, Perl, JavaScript, ActionScript, Fortran, or any other programming language. In various examples, the memory device  806  can be volatile memory, non-volatile memory, or a combination thereof. 
     The computing system  800  executes program code that configures the processor  808  to perform one or more of the operations described herein. Examples of the program code include, in various embodiments, the communications module  802 , the gravity data interpretation module  804 , or any other suitable systems or subsystems that perform one or more operations described herein. The program code may be resident in the memory device  806  or any suitable computer-readable medium and may be executed by the processor  808  or any other suitable processor. 
     The processor  808  is an integrated circuit device that can execute the program code. The program code can be used for executing an operating system, an application system or subsystem (e.g., the communications module  802  and the gravity data interpretation module  804 ), wellbore operation tools (e.g., injection pumps, production pumps, etc.), or a combination thereof. When executed by the processor  808 , the instructions cause the processor  808  to perform operations of the program code. When being executed by the processor  808 , the instructions are stored in a system memory, possibly along with data being operated on by the instructions. The system memory can be a volatile memory storage type, such as a Random Access Memory (RAM) type. The system memory is sometimes referred to as Dynamic RAM (DRAM) though need not be implemented using a DRAM-based technology. Additionally, the system memory can be implemented using non-volatile memory types, such as flash memory. 
     In some embodiments, one or more memory devices  806  store the program data that includes one or more datasets and models described herein. Examples of these datasets include gravity sensor data, three-dimensional model data, etc. In some embodiments, one or more of data sets, models, and functions are stored in the same memory device (e.g., one of the memory devices  806 ). In additional or alternative embodiments, one or more of the programs, data sets, models, and functions described herein are stored in different memory devices  806  accessible via a data network. One or more buses  810  are also included in the computing system  800 . The buses  810  communicatively couple one or more components of a respective one of the computing system  800 . 
     In some embodiments, the computing system  800  also includes a network interface device  812 . The network interface device  812  includes any device or group of devices suitable for establishing a wired or wireless data connection to one or more data networks. Non-limiting examples of the network interface device  812  include an Ethernet network adapter, a modem, and/or the like. The computing system  800  is able to communicate with one or more other computing devices via a data network using the network interface device  812 . 
     The computing system  800  may also include a number of external or internal devices, a presentation device  814 , or other input or output devices. For example, the computing system  800  is shown with one or more input/output (“I/O”) interfaces  816 . An I/O interface  816  can receive input from input devices or provide output to output devices. Non-limiting examples of input devices include a touchscreen, a mouse, a keyboard, a microphone, a separate mobile computing device, etc. A presentation device  814  can include any device or group of devices suitable for providing visual, auditory, or other suitable sensory output. Non-limiting examples of the presentation device  814  include a touchscreen, a monitor, a speaker, a separate mobile computing device, etc. 
     Although devices that provide input or output to the computing system  800  through the I/O interface  816  may be local to the computing device that executes the program code, other implementations are possible. For instance, in some embodiments, one or more of input devices and output devices (e.g., including the presentation device  814 ) can include a remote client-computing device that communicates with the computing system  800  via the network interface device  812  using one or more data networks. 
     Numerous specific details are set forth herein to provide a thorough understanding of the claimed subject matter. However, those skilled in the art will understand that the claimed subject matter may be practiced without these specific details. In other instances, methods, apparatuses, or systems that would be known by one of ordinary skill have not been described in detail so as not to obscure claimed subject matter. 
     Unless specifically stated otherwise, it is appreciated that throughout this specification discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining,” and “identifying” or the like refer to actions or processes of a computing device, such as one or more computers or a similar electronic computing device or devices, that manipulate or transform data represented as physical electronic or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the computing platform. 
     The system or systems discussed herein are not limited to any particular hardware architecture or configuration. A computing device can include any suitable arrangement of components that provide a result conditioned on one or more inputs. Suitable computing devices include multi-purpose microprocessor-based computer systems accessing stored software that programs or configures the computing system from a general purpose computing apparatus to a specialized computing apparatus implementing one or more embodiments of the present subject matter. Any suitable programming, scripting, or other type of language or combinations of languages may be used to implement the teachings contained herein in software to be used in programming or configuring a computing device. 
     Embodiments of the methods disclosed herein may be performed in the operation of such computing devices. The order of the blocks presented in the examples above can be varied—for example, blocks can be re-ordered, combined, or broken into sub-blocks. Certain blocks or processes can be performed in parallel. 
     The use of “based on” is meant to be open and inclusive, in that a process, step, calculation, or other action “based on” one or more recited conditions or values may, in practice, be based on additional conditions or values beyond those recited. Headings, lists, and numbering included herein are for ease of explanation only and are not meant to be limiting. 
     While the present subject matter has been described in detail with respect to specific embodiments thereof, it will be appreciated that those skilled in the art, upon attaining an understanding of the foregoing, may readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, it should be understood that the present disclosure has been presented for purposes of example rather than limitation, and does not preclude the inclusion of such modifications, variations, and/or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art. 
     In some aspects, systems, devices, and methods for generating time-lapse, three-dimensional fluid saturation models are provided according to one or more of the following examples: 
     As used below, any reference to a series of examples is to be understood as a reference to each of those examples disjunctively (e.g., “Examples 1-4” is to be understood as “Examples 1, 2, 3, or 4”). 
     Example 1 is a method comprising: collecting, from a first set of gravity sensors, a first set of multicomponent borehole gravity data at a first time step along a first length of a first wellbore; collecting, from a second set of gravity sensors, a second set of multicomponent borehole gravity data at the first time step along a second length of a second wellbore; interpolating a third set of multicomponent borehole gravity data at the first time step in an area between the first wellbore and the second wellbore using the first set of multicomponent borehole gravity data and the second set of multicomponent borehole gravity data; determining a first fluid saturation in a reservoir containing the first wellbore and the second wellbore using the first set, the second set, and the third set of multicomponent borehole gravity data; determining a fluid saturation change in the reservoir between the first time step and a second time step by comparing the first fluid saturation in the reservoir with a second fluid saturation in the reservoir for the second time step; and controlling wellbore production operations or wellbore injection operations at the first wellbore based on the fluid saturation change. 
     Example 2 is the method of example 1, wherein interpolating the third set of multicomponent borehole gravity data comprises applying a simple kriging interpolation to the first set of multicomponent borehole gravity data and the second set of multicomponent borehole gravity data. 
     Example 3 is the method of examples 1-2, further comprising: collecting a fourth set of multicomponent borehole gravity data at the second time step along the first length of the first wellbore; collecting a fifth set of multicomponent borehole gravity data at the second time step along the second length of the second wellbore; interpolating a sixth set of multicomponent borehole gravity data at the second time step in the area between the first wellbore and the second wellbore using the fourth set of multicomponent borehole gravity data and the fifth set of multicomponent borehole gravity data; and determining the second fluid saturation at the second time step in the reservoir containing the first wellbore and the second wellbore using the fourth, fifth, and sixth sets of multicomponent borehole gravity data. 
     Example 4 is the method of examples 1-3, wherein determining the first fluid saturation in the reservoir comprises generating a multicomponent, three-dimensional fluid saturation model. 
     Example 5 is the method of examples 1-4, further comprising: generating a time-lapse, three-dimensional fluid saturation model indicating the fluid saturation change in the reservoir over time. 
     Example 6 is the method of examples 1-5, wherein the first and second sets of multicomponent borehole gravity data comprise x-components, y-components, and z-components. 
     Example 7 is the method of examples 1-6, wherein the second time step is at least one month after the first time step. 
     Example 8 is the method of examples 1-7, wherein the first wellbore comprises a gas injection wellbore, and the second wellbore comprises a production wellbore. 
     Example 9 is the method of examples 1-8, further comprising: identifying structural barriers in the reservoir using the fluid saturation change between the first time step and the second time step. 
     Example 10 is a well system comprising: a set of gravity sensors positionable within a first wellbore; a first communication device positionable to transmit a first set of multicomponent borehole gravity data from the set of gravity sensors to a surface of the first wellbore; and a computing device in communication with the first communication device, the computing device comprising: a processor; and a non-transitory computer-readable medium that includes instructions that are executable by the processor to perform operations comprising: receiving, from the first communication device, the first set of multicomponent borehole gravity data at a first time step along a first length of the first wellbore; receiving, from a second communication device of a second wellbore, a second set of multicomponent borehole gravity data at the first time step along a second length of the second wellbore; interpolating a third set of multicomponent borehole gravity data at the first time step in an area between the first wellbore and the second wellbore using the first set of multicomponent borehole gravity data and the second set of multicomponent borehole gravity data; determining a first fluid saturation in a reservoir containing the first wellbore and the second wellbore based on the first set, the second set, and the third set; and determining a fluid saturation change in the reservoir between the first time step and a second time step by comparing the first fluid saturation in the reservoir with a second fluid saturation in the reservoir for the second time step. 
     Example 11 is the well system of example 10, wherein the set of gravity sensors are positionable at regular intervals along the first length of the first wellbore. 
     Example 12 is the well system of examples 10-11, wherein the first wellbore comprises a gas injection wellbore, and the second wellbore comprises a production wellbore. 
     Example 13 is the well system of examples 10-12, wherein the set of gravity sensors are removably positionable along a length of tubing within the first wellbore. 
     Example 14 is the well system of examples 10-13, wherein the second wellbore is at least 100 feet from the first wellbore. 
     Example 15 is the well system of examples 10-14, wherein the first wellbore comprises a horizontal wellbore. 
     Example 16 is a non-transitory computer-readable medium that includes instructions that are executed by a processing device to perform operations, the operations comprising: collecting, from a first set of gravity sensors, a first set of multicomponent borehole gravity data at a first time step along a first length of a first wellbore; collecting, from a second set of gravity sensors, a second set of multicomponent borehole gravity data at the first time step along a second length of a second wellbore; interpolating a third set of multicomponent borehole gravity data at the first time step in an area between the first wellbore and the second wellbore using a simple kriging interpolation of the first set of multicomponent borehole gravity data and the second set of multicomponent borehole gravity data; determining a first fluid saturation in a reservoir containing the first wellbore and the second wellbore based on the first set, the second set, and the third set; and determining a fluid saturation change in the reservoir between the first time step and a second time step by comparing the first fluid saturation in the reservoir with a second fluid saturation in the reservoir determined for second time step. 
     Example 17 is the non-transitory computer-readable medium of example 16, the operations further comprising: collecting a fourth set of multicomponent borehole gravity data at the second time step along the first length of the first wellbore; collecting a fifth set of multicomponent borehole gravity data at the second time step along the second length of the second wellbore; interpolating a sixth set of multicomponent borehole gravity data at the second time step in the area between the first wellbore and the second wellbore using the fourth set of multicomponent borehole gravity data and the fifth set of multicomponent borehole gravity data; and determining the second fluid saturation at the second time step in the reservoir containing the first wellbore and the second wellbore using the fourth, fifth, and sixth sets of multicomponent borehole gravity data. 
     Example 18 is the non-transitory computer-readable medium of examples 16-17, wherein the operation of determining the first fluid saturation comprises generating a multicomponent, three-dimensional fluid saturation model. 
     Example 19 is the non-transitory computer-readable medium of examples 16-18, wherein the operation of determining the fluid saturation change comprises generating a time-lapse fluid saturation model. 
     Example 20 is the non-transitory computer-readable medium of examples 16-19, the operations further comprising: identifying structural barriers in the reservoir using the fluid saturation change between the first time step and the second time step. 
     The foregoing description of certain examples, including illustrated examples, has been presented only for the purpose of illustration and description and is not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Numerous modifications, adaptations, and uses thereof will be apparent to those skilled in the art without departing from the scope of the disclosure.