Abstract:
Systems and methods for evaluating a composition of a formation. A method includes obtaining a first set of well-logging data, via an NMR system, of a formation, and obtaining a second set of well-logging data, via a second well-logging system, of the formation. The method also includes determining from the first set and from the second set a model of the composition of the formation. This model of the composition of the formation may identify materials not directly identifiable by the first set of well-logging data alone or by the second set of well-logging data alone.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims the benefit of related U.S. Provisional Application Ser. No. 62/036,607, filed on Aug. 12, 2014, the disclosure of which is incorporated by reference herein in its entirety. 
     
    
     BACKGROUND 
       [0002]    This disclosure relates to methods for the estimation of hydrocarbon volumes in unconventional formations, such as shale formations. 
         [0003]    This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present techniques, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of any kind. 
         [0004]    Both water and hydrocarbons in earth formations produce detectable nuclear magnetic resonance (NMR) signals. It is desirable that the signals from water and hydrocarbons be separable so that hydrocarbon-bearing zones may be identified. However, it may not be easy to distinguish which signals are from water and which are from hydrocarbons. For example, a petrophysical challenge of shale reservoirs modeling is the estimation of producible hydrocarbon-filled porosity. The nanometer and micrometer sized pores in organic-rich shale reservoirs may contain bound water, kerogen, bitumen, and/or light hydrocarbon, among other things. While bulk density combined with spectroscopy measurements may resolve total porosity, and while resistivity or dielectric based models may provide total water-filled porosity, distinguishing, for example, kerogen from producible hydrocarbon remains a challenge. It is desirable to have improved methods that can enhance predictions of the presence of hydrocarbons in unconventional formations from NMR data. Furthermore, while two- and three-dimensional visualization has been developed to obtain primarily qualitative information, it is desirable to have quantitative interpretation techniques that can provide more accurate fluid-characterization results. 
       SUMMARY 
       [0005]    A summary of certain embodiments disclosed herein is set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of these certain embodiments and that these aspects are not intended to limit the scope of this disclosure. Indeed, this disclosure may encompass a variety of aspects that may not be explicitly set forth below. 
         [0006]    One or more embodiments of the disclosure relate to well-logging using nuclear magnetic resonance (NMR) systems. A method according to the disclosure includes obtaining a first set of well-logging data relating to a formation via a nuclear magnetic resonance device. The method further includes obtaining a second set of well-logging data relating to the formation via a first downhole measurement device other than the nuclear magnetic resonance tool. The method additionally includes determining a model of a composition of the formation using the first set of well-logging data and the second set of well-logging data, wherein the model of the composition of the formation identifies a plurality of materials not directly identifiable by the first set of well-logging data alone or by the second set of well-logging data alone. 
         [0007]    In another example, a system includes a processor. The processor is configured to receive a first set of well-logging data obtained by an NMR system of a formation. The processor is further configured to receive a second set of well-logging data obtained by a spectrographic system of the formation. The processor is additionally configured to determine a model of a composition of the formation using the first set of well-logging data and the second set of well-logging data, wherein the model of the composition of the formation identifies a plurality of materials not directly identifiable by the first set of well-logging data alone or by the second set of well-logging data alone. 
         [0008]    The system is more particularly configured to carry out one or more of the embodiments of the method as disclosed hereafter. 
         [0009]    Moreover, a non-transitory, tangible computer readable storage medium, comprising instructions is described. The instructions are configured to receive a first set of well-logging data obtained by an NMR system of a formation. The instructions are additionally configured to receive a second set of well-logging data obtained by a non-NMR system of the formation. The instructions are further configured to determine a model of a composition of the formation using the first set of well-logging data and the second set of well-logging data, wherein the model of the composition of the formation is identified by combining the first set of well-logging data with the second set of well-logging data. 
         [0010]    The instructions are configured to perform one or more of the embodiments of the method as disclosed in this application. 
         [0011]    Various refinements of the features noted above may be undertaken in relation to various aspects of the present disclosure. Further features may also be incorporated in these various aspects as well. These refinements and additional features may exist individually or in any combination. For instance, various features discussed below in relation to one or more of the illustrated embodiments may be incorporated into any of the above-described aspects of the present disclosure alone or in any combination. The brief summary presented above is intended to familiarize the reader with certain aspects and contexts of embodiments of the present disclosure without limitation to the claimed subject matter. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0012]    Various aspects of this disclosure may be better understood upon reading the following detailed description and upon reference to the drawings in which: 
           [0013]      FIG. 1  is a diagram of a downhole nuclear magnetic resonance (NMR) data acquisition system, in accordance with an embodiment; 
           [0014]      FIG. 2  is a more detailed diagram of the system of  FIG. 1 , in accordance with an embodiment; 
           [0015]      FIG. 3  is a block diagram of a pore fluid model that may be derived using the NMR data acquisition system of  FIGS. 1 and 2 , in accordance with an embodiment; 
           [0016]      FIG. 4  is a flowchart of a process suitable for deriving the model of  FIG. 3  and for estimating hydrocarbon volumes, in accordance with an embodiment; and 
           [0017]      FIG. 5  is a cross-section view of an embodiment of a Combinable Magnetic Resonance (CMR) device suitable for providing more accurate NMR measurements. 
       
    
    
     DETAILED DESCRIPTION 
       [0018]    The disclosed subject matter describes one or more quantitative methods to interpret data modeling of an unconventional formation, such as a shale formation, by applying a joint interpretation of data from a variety of tools, such as nuclear magnetic resonance (NMR) tools, dielectric tools, resistivity tools, spectroscopy tools, and other formation modeling tools. The NMR tool may provide for NMR data, such as T1 and T2 data derived from NMR formation evaluation measurements. T1 data may include a spin-lattice relaxation time, for example, for a longitudinal (e.g., spin-lattice) recovery of a z component of nuclear spin magnetization due to NMR excitation. T2 data may include a spin-spin relaxation time, for example, for a transverse (e.g., spin-spin) relaxation of an XY component of nuclear spin magnetization due to the NMR excitation. In one embodiment, a process for inversion of estimation of hydrocarbon volumes in unconventional formations may apply a joint interpretation of NMR, dielectric, resistivity, spectroscopy and similar data, to derive a joint formation evaluation, for example, estimating a volume of certain fluids in the formation. For example, an NMR log may be used, to apply an NMR diffusion-based interpretation of the NMR log. However, the NMR diffusion-based interpretation alone may be undesirably complex due to overlapping oil and water signals in a T2 domain. The NMR diffusion-based interpretation alone may thus suffer from poor diffusion measurement resolution at short T2 intervals, as well as limited diffusion contrast between oil, water, and gas owing to their restricted diffusion in small pores (e.g., clay pores). 
         [0019]    A Total Organic Carbon (TOC) measured, for example, via spectroscopy logging tools, may be combined with a total NMR porosity derived from the NMR log and the combination may be used to quantify, for example, a kerogen volume fraction. The TOC and NMR combinatorial method may assume that a measured NMR signal is devoid of any signal from kerogen and/or bitumen, and that substantially all of a clay bound water signal is measured. The TOC derivation alone may have poor sensitivity to distinguish kerogen from bitumen or oil. For reservoirs containing heavy oil and kerogen, interpreting fluid volumes from NMR T2 measurements may become challenging without the disclosed technique because the heavy oil and bound water signals overlap in T2 dimension. In “unconventional formations” such as shale reservoirs, although the oil and water NMR signals overlap in T2 domain, test measurements appear to demonstrate sufficient contrast in a T1/T2 ratio. According to this disclosure, a T1/T2 contrast may be used to resolve a complex pore fluid model. The techniques described herein may allow evaluation logging systems used in standard formations, such as non-shale formations, to be applied instead to unconventional formations. The logging systems may evaluate density, neutron porosity, induced-neutron spectroscopy, NMR, deep and/or shallow resistivity, and/or dielectric permittivity in the unconventional formation. For example, a water measurement system, such as a dielectric system, resistivity-based system, or any system suitable for measuring volume of water may be used with the techniques described herein. Data from the logging systems may be combined with T1/T2 derivations, as described in more detail below, to produce a joint derivation (e.g., multi-dimensional model) of the unconventional formation. The joint derivation may more accurately estimate hydrocarbon volumes in the unconventional formation. Additionally, the T1/T2 derivation may include a short T2 derivation that may more accurately model formation volumes. 
         [0020]    Acquisition of NMR and other measurements according to one or more embodiments described herein may be accomplished using a variety of techniques. For example, the measurements may be performed in a laboratory or in the field using a sample removed from an earth formation. Additionally or alternatively, the NMR and other measurements may be performed in a logging operation using any suitable downhole tool (e.g., a wireline tool, a logging-while-drilling and/or measurement-while-drilling tool, and/or a formation tester).  FIG. 1  illustrates a schematic of an embodiment of an NMR logging system. In  FIG. 1 , an NMR logging tool  30  that may investigate earth formations  31  traversed by a borehole  32  is shown. The NMR logging device  30  is suspended in the borehole  32  on a cable  33  (e.g., an armored cable), the length of which may substantially determine the relative axial depth of the device  30 . The cable length may be controlled by a winching device such as a drum and winch mechanism  8 . Surface equipment  7  may be of any suitable type and may include a processor subsystem (e.g., a processor, memory, and/or storage) that communicates with downhole equipment including the NMR logging tool  30 . The techniques of this disclosure may be carried out by the processor subsystem at the surface and/or by a processor subsystem associated with the NMR logging device  30  downhole. 
         [0021]    The NMR logging tool  30  may be any suitable nuclear magnetic resonance logging device; it may be one for use in wireline logging applications, or one that can be used in logging-while-drilling (LWD) or measurement-while-drilling (MWD) applications. Additionally or alternatively, the NMR logging device  30  may be included in any formation tester tool, such as tools available under the trade name of MDT™ by Schlumberger Limited, of Houston, Tex. The NMR logging device  30  may include a permanent magnet or magnet array that produces a static magnetic field in the formation, and a radio frequency (RF) antenna system to produce pulses of magnetic field in the formations and to receive resulting spin echoes from the formations. The techniques for producing a static magnetic field may include a permanent magnet or magnet array, and the RF antenna system for producing pulses of magnetic field and receiving spin echoes from the formations may include one or more RF antennas. 
         [0022]      FIG. 2  illustrates a schematic of some of the components of one type of NMR logging device  30 , such as a general representation of closely spaced cylindrical thin shells,  38 - 1 ,  38 - 2  . . .  38 -N, which may be frequency-selected in a multi-frequency logging operation. One such device is disclosed in U.S. Pat. No. 4,710,713. In  FIG. 2 , another magnet or magnet array  39  is shown. Magnet array  39  may be used to pre-polarize the earth formation ahead of the investigation region as the logging device  30  is raised in the borehole in the direction of arrow Z. Examples of such devices are disclosed in U.S. Pat. Nos. 5,055,788 and 3,597,681. It is to be noted that NMR data, such as logging data, may be captured from any suitable number of NMR systems, including Combinable Magnetic Resonance (CMR) systems (e.g. as described in  FIG. 5 ), Magnetic Resonance Imager Log (MRIL) systems, Magnetic Resonance scanners, and the like. The tool  30  may thus provide data representative of T1 and T2, useful in estimating volumetric measurements of the formation. 
         [0023]      FIG. 3  depicts an embodiment of a joint derivation or multi-dimensional (e.g., multi-row) model  50  that may be derived, for example, by a sequential combination of density, neutron porosity, induced-neutron spectroscopy, NMR, deep and shallow resistivity, and/or dielectric permittivity data. The data may be derived via NMR tools such as those shown in  FIGS. 1 and 2  above, and  FIG. 5  below, dielectric logging tools, spectroscopy logging tools, or a combination thereof. In one example, the measurements used to provide for the joint derivations  50  include bulk density, rock or ρ matrix (RHGE), magnetic resonance porosity (MRP), T1, T2, water-filled porosity output (PWXO), total organic carbon (TOC), and neutron porosity (NPHI). The techniques described herein enable the derivation of one or more volumes of interest in a top row  51 . In certain embodiments, columns  64 ,  66 ,  67 ,  68 ,  70 ,  72 , and/or  74  of the top row  51  may be derived by using one or more measurements found in rows  52 ,  54 ,  56 ,  58 ,  60 , and/or  62 . Column  64  corresponds to kerogen, column  66  corresponds to bitumen, column  67  corresponds to heavy oil (HO), column  68  corresponds to oil, column  70  corresponds to clay-bound water (CBW), column  72  corresponds to free water, and column  74  corresponds to gas. 
         [0024]    In one embodiment, the computations of rows  52 ,  54 ,  56 ,  60 , and/or  62  may be combined with T1/T2 (e.g., row  58 ) in order to derive more accurate columns  64 ,  66 ,  67 ,  68 ,  70 ,  72 , and/or  74 . In one example, the equations below may then be used to build the joint derivations  50 . 
         [0000]    
       
         
           
             
               
                 
                   
                     DPHI 
                     = 
                     
                       
                         RHGE 
                         - 
                         
                           ρ 
                            
                           
                               
                           
                            
                           b 
                         
                       
                       
                         RHGE 
                         - 
                         1 
                       
                     
                   
                   , 
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
         [0000]    where DPHI is density porosity. 
         [0000]      ρ b=Vker*ρker+Vho*ρho+Vcbw*ρcbw+Vfw*ρfw+Vg*μg+V oil*ρoil  (2)
 
         [0000]        MRP=Vho+Vcbw+Vfw+V oil+ Vg*HI   (3)
 
         [0000]        PWXO=Vcbw+Vfw   (4)
 
         [0000]        TOC=Vker*ρker*DWCker+Vho*ρho*DWCho+V oil*ρoil* DWC oil+ Vg*μg*DWCg   (5)
 
         [0000]        T 1 T 2 ho=Vho   (6)
 
         [0000]        T 1 T 2 cbw=Vcbw   (7)
 
         [0000]        NPHI=Vker+Vho+Vcbw+Vfw+V oil+ Vg*HI   (8)
 
         [0025]    Any suitable processor subsystem (e.g., at the surface or in the downhole tool) may build the joint derivation  50  by solving the above set of equations according to the following parameters: 
         [0026]    ρker &amp; DWCker (density of kerogen and dry weight fraction of carbon in Kerogen): The properties of Kerogen are dependent on its maturity. Depending on maturity, the density of Kerogen may vary from 1.1 to 1.4 g/cc, whereas the dry weight fraction of carbon in kerogen may vary from less than 0.8 (oil Kerogen) to 1 (graphite). 
         [0027]    ρho &amp; ρoil (density of heavy oil and density of oil): This is based on composition and may be measured. Local knowledge of oil properties from a previously measured sample in the reservoir may serve as a good input. 
         [0028]    ρcbw (density of clay bound water): A value of 1.0 g/cc may be an accurate approximation, though any other suitable value may be used. 
         [0029]    Pfw (density of free water): This value depends on formation water salinity, which may be estimated from the dielectric measurement. 
         [0030]    μg &amp; HI (density and hydrogen index of gas): These two parameters may be estimated as a function of temperature and pressure. 
         [0031]    DWCoil &amp; DWCho (dry weight fraction of carbon in oil and heavy oil): Local knowledge of the oil composition may be used to establish the DWC parameter for oil. 
         [0032]    DWCg (dry weight fraction of carbon in gas): This parameter may be assumed as weight fraction of carbon in methane. The parameter may be multiplied with density of gas (a small number), and thus hence the impact of any error would be small. 
         [0033]    In other examples, the joint derivation  50  may be derived similar to solving a system of equations with N unknowns, where columns  64 ,  66 ,  67 ,  68 ,  70 ,  72 , and/or  74  of row  51  are representative of the N unknowns. As shown, the rows under row  51  may be more particularly suited to derive one or more of the columns  64 ,  66 ,  67 ,  68 ,  70 ,  72 , and  74 . For example, T1/T2 in row  54  may be more suited for derivations of heavy oil (column  67 ) and clay-bound water (column  70 ). The rows under row  51  (e.g.,  52 ,  54 ,  56 ,  58 ,  60 , and/or  62 ) are representative of equations that may solve for one or more of the N unknowns. As more equations are solved, more of the N unknowns may be solved or may be solved with increased accuracy. Using derivations from all of the rows  52 ,  54 ,  56 ,  58 ,  60 , and  62  may then result in all solving for all of the columns  64 ,  66 ,  67 ,  68 ,  70 ,  72 , and  74  of row  51 . 
         [0034]    A more simplified approach to build the joint derivation  50  may be used in another example. Rows  52 ,  54 ,  56 ,  58 ,  60 , and/or  62  may be derived. The rows  52 ,  54 ,  56 ,  58 ,  60 , and/or  62  may then be used to derive the compositions or volumes  64 ,  66 ,  68 ,  70 ,  72 , and/or  74  of interest. For example, the volumes of  64 ,  66 ,  68 ,  70 ,  72 , and/or  74  may be viewed as columnar results of combining the rows beneath a top row. The combination may include averaging, weighted averaging, distribution via statistical techniques (e.g., Gaussian distribution, non-Gaussian distribution), via data fusion techniques, and the like. By applying the combination of data (e.g., density, neutron porosity, induced-neutron spectroscopy, NMR, deep and shallow resistivity, and dielectric permittivity data) and the derivations described with respect to joint derivation  50 , a more efficient and accurate estimation of unconventional formation volumes may be provided. 
         [0035]    Turning now to  FIG. 4 , the figure is a flow chart of an embodiment of a process  100  suitable for more accurately deriving unconventional formation volumes via the joint derivation or model  50  of  FIG. 3 . The process  100  may be executed via a hardware processor included in a computing device (e.g., a processor subsystem at the surface, in the downhole tool  30 , a computer, a server, a workstation, a laptop, a smartphone, a tablet, and so forth) and implemented as non-transitory executable instructions stored in an article of manufacture that includes a computer-readable medium, such as a hard drive, flash drive, secure digital (SD) card, and so on. Additionally or alternatively, the hardware processor may be included in the NMR system described with respect to  FIGS. 1 ,  2 , and  5 . 
         [0036]    In the depicted embodiment, the process  100  may first log a variety of measurements (block  102 ). As mentioned earlier, the measurements may include density, neutron porosity, induced-neutron spectroscopy, NMR, deep and shallow resistivity, and dielectric permittivity measurements. The measurements may be derived using any suitable logging tools, such as the NMR system described above with respect to  FIGS. 1 ,  2 , and  5 , dielectric logging tools, and/or spectroscopic logging tools. The measurements may be obtained in a single well-logging operation or may be obtained from a number of different well-logging operations that may take place at different times. Indeed, the techniques described herein may combine historical log data to derive improved measurements. 
         [0037]    The process  100  may then produce the joint derivations or model  50  (block  104 ). As mentioned earlier, one or more of the rows  52 ,  54 ,  56 ,  58 ,  60 , and  62 , including the T1/T2 (row  58 ) may be used to derive one or more formation volume estimates, e.g., one or more columns  64 ,  66 ,  67 ,  68 ,  70 ,  72 , and  74  or row  51 . For example, the process  100  may apply equations 1-8 as described above with respect to  FIG. 3  to derive one or more rows  52 ,  54 ,  56 ,  58 ,  60 , and  62 , which may be useful in deriving one or more columns  64 ,  66 ,  67 ,  68 ,  70 ,  72 , and  74  of row  51 . The process may then derive desired formation volumes (block  106 ), for example, as the volumes or columns  64 ,  66 ,  68 ,  70 ,  72 , and/or  74  that are shown in row  51 . The columns  52 ,  54 ,  56 ,  58 ,  60 , and  62  of row  51  may be derived by combining the derivations of rows  52 ,  54 ,  56 ,  58 ,  60 , and/or  62 . In this manner, the process  100  may more efficiently and accurately estimate a variety of volumes in an unconventional formation. Additionally, as described in more detail below with respect to  FIG. 5 , an enhanced T1/T2 (e.g., row  58 ) having, for example, a “short” T2 may be used to derive more accurate T1/T2 measurements. 
         [0038]    In one example, the short T2 may include T2 having between 0.1 and 3 milliseconds. The enhanced T1/T2 derivation incorporating the short T2 may thus be able to more accurately measure a volume, for example, when compared to using longer T2&#39;s.  FIG. 5  is a top cross-sectional view of an embodiment of a Combinable Magnetic Resonance (CMR) tool  120  shown disposed inside of a bore wall  122  that may be used to derive the enhanced T1/T2 measurements. The CMR tool  120  may include memory suitable for storing executable instructions or computer code, which may be executed in one or more processors of the CMR tool  120 . An example CMR tool  120  is available under the trade name of CMR-Plus™ by Schlumberger Limited, of Houston, Tex. The CMR tool  120  may use a pulse acquisition sequence referred to as an Enhanced Precision Mode (EPM). In EPM, one long wait time pulse sequence may be followed by one or more short wait time pulse sequences. EPM may improve the precision of the data associated with fast relaxing components, such as water disposed in pores, small pore heavy crude oils, and the like. In this mode EPM it may be possible to derive a more precise T1 and/or T2 distribution, improving the precision of bound-fluid volume and porosity observations (e.g., row  58 , columns  67 ,  70 ). 
         [0039]    The CMR tool  120  may include two permanent magnets  124 , and a RF antenna  126 , suitable for NMR measurements. In particular, the antenna may more accurately measure an area of interest  128  via the aforementioned EPM pulse acquisition sequence. Accordingly, the T1/T2 ratio may more accurately derive volumes for heavy oil (column  67 , row  58 ), and/or clay-bound water (CBW) (column  70 , row  58 ). Applying the enhanced T1/T2 in combination with one or more of the rows  52 ,  54 ,  56 ,  60 , and  62  may thus provide for more accurate measurements of columns  64 ,  66 ,  67 ,  68 ,  70 ,  72 , and  74  of row  51 . Indeed, by combining T1/T2 with additional measurements, the techniques described herein may more accurately and efficiently derive volumetric information for a variety of formations, including shales. 
         [0040]    Although only a few example embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the example embodiments without materially departing from “Systems and Methods for Estimation of Hydrocarbon Volumes in Unconventional Formations.” Features shown in individual embodiments referred to above may be used together in combinations other than those which have been shown and described specifically. Accordingly, all such modifications are intended to be included within the scope of this disclosure. In the claims, means-plus-function clauses are intended to cover the structures described herein as performing the recited function and not only structural equivalents, but also equivalent structures. Thus, although a nail and a screw may not be structural equivalents in that a nail employs a cylindrical surface to secure wooden parts together, whereas a screw employs a helical surface, in the environment of fastening wooden parts, a nail and a screw may be equivalent structures. It is the express intention of the applicant not to invoke 35 U.S.C. §112, paragraph 6 for any limitations of the any of the claims herein, except for those in which the claim expressly uses the words ‘means for’ together with an associated function.