You are an expert at summarizing long articles. Proceed to summarize the following text:

You are an expert at summarizing long articles. Proceed to summarize the following text: 
RELATED APPLICATIONS 
       [0001]    This application is a non-provisional of and claims priority to and the benefit of U.S. Provisional Patent Application No. 61/539,171, titled “Methods Of Evaluating Rock Properties While Drilling Using Downhole Acoustic Sensors And A Downhole Broadband Transmitting System,” filed on Sep. 26, 2011, and is related to U.S. patent application Ser. No. ______, filed on ______, titled “Apparatus and Program Product for Evaluating Rock Properties While Drilling Using Downhole Acoustic Sensors and a Downhole Broadband Transmitting System”; U.S. patent application Ser. No. ______, filed on ______, titled “Apparatus and Program Product for Evaluating Rock Properties While Drilling Using Downhole Acoustic Sensors and Telemetry System”; U.S. patent application Ser. No., ______, filed on ______, titled “Methods of Evaluating Rock Properties While Drilling Using Downhole Acoustic Sensors and Telemetry System”; U.S. patent application Ser. No., ______, filed on ______, titled “Apparatus for Evaluating Rock Properties While Drilling Using Drilling Rig-Mounted Acoustic Sensors”; and U.S. patent application Ser. No., ______, filed on ______, titled “Methods for Evaluating Rock Properties While Drilling Using Drilling Rig-Mounted Acoustic Sensors”; U.S. Provisional Patent Application No. 61/539,165, titled “Apparatus And Program Product For Evaluating Rock Properties While Drilling Using Downhole Acoustic Sensors And A Downhole Broadband Transmitting System,” filed on Sep. 26, 2011; U.S. Provisional Patent Application No. 61/539,201, titled “Apparatus For Evaluating Rock Properties While Drilling Using Drilling Rig-Mounted Acoustic Sensors,” filed on Sep. 26, 2011; U.S. Provisional Patent Application No. 61/539,213, titled “Methods For Evaluating Rock Properties While Drilling Using Drilling Rig-Mounted Acoustic Sensors,” filed on Sep. 26, 2011; U.S. Provisional Patent Application No. 61/539,242 titled “Apparatus And Program Product For Evaluating Rock Properties While Drilling Using Downhole Acoustic Sensors And Telemetry System,” filed on Sep. 26, 2011; and U.S. Provisional Patent Application No. 61/539,246 titled “Methods Of Evaluating Rock Properties While Drilling Using Downhole Acoustic Sensors And Telemetry System,” filed on Sep. 26, 2011, each incorporated herein by reference in its entirety. 
     
    
     BACKGROUND OF THE INVENTION 
       [0002]    1. Field of the Invention 
         [0003]    This invention relates in general to hydrocarbon production, and more particularly, to identifying rock types and rock properties in order to improve or enhance drilling operations. 
         [0004]    2. Description of the Related Art 
         [0005]    Measuring rock properties during drilling in real time can provide the operator the ability to steer a drill bit in the direction of desired hydrocarbon concentrations. In current industrial practice and prior inventions, either resistivity or sonic logging while drilling (LWD) tools are employed to guide the drill bit during horizontal or lateral drilling. The center of these techniques is to calculate the locations of the boundary between the pay zone and the overlying rock (upper boundary), and the boundary between the pay zone and underlying rock at the sensors location. The drill bit is steered or maintained within the pay zone by keeping the drill string, at the sensors position, in the middle, or certain position between the upper and lower boundaries of the pay zone. The conventional borehole acoustic telemetry system, which transmits data at low rate (at about tens bit per second), is employed to transmit the measured data to surface. 
         [0006]    Since the sensors are located 30-50 feet behind the drill bit, theses conventional LWD steering tools only provide data used in steering the drill bit 30-50 feet behind the drill bit. As the result, it is only after the 30-50 feet that the operator finds out if the selected drilling path is or is not the desired one. Therefore, these tools are not true real-time tools. 
         [0007]    Some newer types of systems attempt to provide data at the drill bit, at-real-time, while still utilizing conventional borehole telemetry systems (having a relatively slow bit rate). Such systems, for example, are described as including a downhole processor configured to provide downhole on-site processing of acoustic data to interpret the lithologic properties of the rock encountered by the drill bit through comparison of the acoustic energy generated by the drill bit during drilling with predetermined bit characteristics generated by rotating the drill bit in contact with a known rock type. The lithologic properties interpreted via the comparison are then transmitted to the surface via the conventional borehole telemetry system. Although providing data in a reduced form requiring only a bit rate speed, as such systems do not provide raw data real-time which can be used for further analysis, it is nearly impossible to construct additional interpretation models or modify any interpretation models generated by the downhole processor. 
         [0008]    Some newer types of borehole data transmitting systems utilize a dedicated electronics unit and a segmented broadband cable protected by a reinforced steel cable positioned within the drill pipe to provide a much faster communication capability. Such systems have been employed into conventional LWD tools to enhance the resolution of the logged information. However the modified tools still measures rock properties at the similar location which is 30-50 feet behind the drill bit. 
         [0009]    Accordingly, recognized by the inventor is the need for apparatus, computer readable medium, program code, and methods of identifying rock properties in real-time during drilling, and more particularly, methods which include positioning acoustic sensors adjacent the drill bit to detect drill sounds during drilling operations, pushing raw acoustic sensor data to a surface computer over a broadband transmitting system, receiving the raw acoustic sensor data, and deriving the rock type and/or evaluating the properties of the rocks in real-time utilizing the raw acoustic sensor data. 
       SUMMARY OF THE INVENTION 
       [0010]    In view of the foregoing, various embodiments of the present invention advantageously provide apparatus, computer readable medium, program code, and methods of identifying rock types and rock properties of rock that is currently in contact with an operationally employed drilling bit, which can be used in real-time steering of the drilling bit during drilling. Various embodiments of the present invention provide methods which include positioning acoustic sensors adjacent the drill bit to detect drill sounds during drilling operations, pushing raw acoustic sensor data to a surface computer over a broadband transmitting system, receiving the raw acoustic sensor data, and deriving the rock type and/or evaluating the properties of the rocks in real-time utilizing the raw acoustic sensor data. 
         [0011]    According to various embodiments of the present invention, a surface computer/processor receives the raw acoustic sensor data. Utilizing the raw acoustic sensor data, the computer can advantageously function to derive a frequency distribution of the acoustic sensor data, derive acoustic characteristics from the raw acoustic data, and determine petrophysical properties of rock from the raw acoustic sensor data. The acoustic characteristics can advantageously further be used to identify the lithology type of the rock encountered by the drill bit, to determine the formation boundary, to determine an optimal location of the casing shoe, among other applications. According to various embodiments of the present invention, to determine petrophysical properties of the rock directly from the raw acoustic sensor data (generally after being converted into the frequency domain and filtered), a petrophysical properties evaluation algorithm can be derived from acoustic sensor data and correspondent petrophysical properties of formation samples. 
         [0012]    Various embodiments of a method of identifying rock properties of rock in real-time during operational drilling, to include identifying lithology type and other petrophysical properties, can include the deployment, installation, and/or positioning of both conventional components and additional/enhanced acoustic components. Some primary conventional components include a drill string containing a plurality of drill pipes each having an inner bore, a drill bit connected to the downhole end of the drill string, and a top drive system for rotating the drill string having both rotating and stationary portion. The additional/acoustic components can include a downhole sensor subassembly connected to and between the drill bit and the drill string, acoustic sensors (e.g. accelerometer, measurement microphone, contact microphone, hydrophone) attached to or contained within the downhole sensor subassembly adjacent the drill bit and positioned to detect drill sounds during drilling operations. The additional components can also include a broadband transmitting system operably extending through the inner bore of each of the plurality of drill pipes and operably coupled to the acoustic sensors through the downhole data transmitting interface position therewith, a surface data transmitting interface typically connected to a stationary portion of the top drive system, a surface data acquisition unit connected to the surface data transmitting interface, and a surface computer operably coupled to the downhole data transmitting interface through the data acquisition unit, the surface data transmitting interface, and the broadband transmitting system. 
         [0013]    Various embodiments of the method can also include both computer employable steps (operations), as described later with respect to the operations performed by various featured apparatus/program code, and various non-computer implemented steps which provide substitutable replacements for the featured computer implemented steps, in conjunction with additional non-computer implemented steps as described below and/or as featured in the appended claims. Examples of various embodiments of the method are described below. 
         [0014]    According to an embodiment of a method of analyzing properties of rock in a formation in real-time during drilling, the method can include the steps of sending sampling commands to the data acquisition unit and receiving raw acoustic sensor data from a surface data interface in communication with a communication medium further in communication with a downhole data interface operably coupled to a plurality of acoustic sensors. The method can also include various processing steps which include deriving a frequency distribution of the raw acoustic sensor data, deriving a plurality of acoustic characteristics including mean frequency and normalized deviation of frequency from the raw acoustic sensor data utilizing, for example, an acoustics characteristics evaluation algorithm, and/or deriving petrophysical properties from the raw acoustic sensor data utilizing, for example, a petrophysical properties evaluation algorithm employable to predict one or more petrophysical properties of rock undergoing drilling. 
         [0015]    According to an embodiment of the method, the step of deriving a frequency distribution of the acoustic data from the raw acoustic sensor data includes transforming the raw acoustic sensor data into the frequency domain (e.g., employing a Fast Fourier Transform (FFT)), and filtering the transformed data. 
         [0016]    According to an embodiment of the method, the step of deriving the plurality of acoustic characteristics from the raw acoustic sensor data can include providing the acoustic characteristics evaluation algorithm and comparing the mean frequency, the normalized deviation of frequency, the mean amplitude, the normalized deviation of amplitude, and apparent power for the rock undergoing drilling with the mean frequency, normalized deviation of frequency, mean amplitude, normalized deviation of amplitude, and apparent power for a plurality of rock samples having different known lithologies according to a first configuration, or comparing only part of the acoustic characteristics, such as the mean frequency and the normalized deviation of frequency of the rock undergoing drilling with the same type of acoustic characteristics of a plurality of rock samples having different known lithologies according to another configuration. The method can also include identifying lithology type of the rock undergoing drilling, determining a location of a formation boundary encountered during drilling, and/or identifying an ideal location for casing shoe positioning, among others. 
         [0017]    According to an exemplary implementation, the mean frequency and normalized deviation of frequency are examined together to determine an amount of correlation of the acoustic characteristics associated with the rock undergoing drilling and the acoustic characteristics associated with the rock samples. Also or alternatively, the mean frequency and the mean amplitude can be examined together and/or with normalized deviation of frequency and/or normalized deviation of amplitude and apparent power, or a combination thereof. The step of comparing can beneficially be performed substantially continuously during drill bit steering in order to provide enhanced steering ability. 
         [0018]    According to an embodiment of the method, the step of deriving petrophysical properties from the raw sensor data can include deriving a bit-specific petrophysical properties evaluation algorithm for use in evaluating the received signals. The derivation of the algorithm can include collecting petrophysical properties data describing one or more petrophysical properties of rock for a plurality of formation samples and correspondent acoustic data for a preselected type of drill bit and processing the collected acoustic data to produce filtered FFT data. The algorithm derivation can also include determining one or more relationships between features of the filtered FFT data and correspondent one or more petrophysical properties of rock describing petrophysical properties of a plurality of formation samples, e.g., utilizing mathematical modeling techniques such as, multiple regression analysis, artificial neural network modeling, etc. The algorithm derivation can also include coding the determined relationships into computer program code defining the bit-specific petrophysical properties evaluation algorithm. The derived algorithm can then be used in predicting one or more petrophysical properties of the rock undergoing drilling real-time responsive to filtered data associated with raw acoustic sensor data produced in response to the drilling. 
         [0019]    According to another embodiment of the method, the step of deriving petrophysical properties from the raw sensor data can also or alternatively include deriving a bit-independent petrophysical properties evaluation algorithm. The derivation of the algorithm can include collecting petrophysical properties data describing one or more petrophysical properties of rock for a plurality of formation samples and correspondent acoustic data for a plurality of different types of drill bits, processing the collected acoustic data to produce filtered FFT data, and determining bit-type independent features of the filtered FFT data. The algorithm derivation can also include determining one or more relationships between the bit-type independent features of the filtered FFT data and correspondent one or more petrophysical properties of the rock, e.g., using mathematical modeling techniques, such as artificial neural network modeling, etc., to provide a bit-independent evaluation methodology. The algorithm derivation can also include coding the determined relationships into computer program code defining the bit-independent petrophysical evaluation properties algorithm. Correspondingly, the method can include employing the derived petrophysical properties evaluation algorithm to predict one or more petrophysical properties of the rock undergoing drilling real-time responsive to filtered data associated with raw acoustic sensor data produced in response to the drilling, as described, for example, with respect to the prior described bit-specific evaluation methodology. 
         [0020]    According to various embodiments of the present invention, apparatus for analyzing properties of rock in a formation in real-time during drilling are also provided. An example of an embodiment of such an apparatus can include a drill string containing a plurality of drill pipes each having an inner bore, a drill bit connected to the downhole end of the drill string, and a top drive system for rotating the drill string having both rotating and stationary portion. The apparatus can also include a downhole sensor subassembly connected to a rotating portion of the system, such as, for example, to and between the drill bit and the drill string, acoustic sensors (e.g. accelerometer, measurement microphone, contact microphone, hydrophone) attached to or contained within the downhole sensor subassembly adjacent the drill bit and positioned to detect drill sounds during drilling operations. The apparatus can further include a broadband transmitting system operably extending through the inner bore of each of the plurality of drill pipes and operably coupled to the acoustic sensors through the downhole data transmitting interface position therewith, a surface data transmitting interface typically connected to a stationary portion of the top drive system, a data acquisition unit in communication with the surface data transmitting interface, and a surface computer operably coupled to the downhole data transmitting interface through surface acquisition unit, the surface data transmitting interface, and the broadband transmitting system. 
         [0021]    According to an embodiment of the apparatus, the computer includes a processor, memory in communication with the processor, and petrophysical properties analyzing program, which can adapt the computer to perform various operations. The operations can include, for example, sending sampling commands to the data acquisition unit, receiving raw acoustic data from the downhole data transmitting interface, processing the received raw acoustic sensor data—deriving a frequency distribution of the acoustic data from the raw acoustic data, employing an acoustics characteristics evaluation algorithm to thereby derive acoustic characteristics from the raw acoustic sensor data (e.g., via analysis of the processed acoustics data), and employing a petrophysical properties evaluation algorithm to thereby derive petrophysical properties of rock undergoing drilling, real-time, from the acoustics data. 
         [0022]    According to an embodiment of the apparatus, the acoustic characteristics evaluation algorithm evaluates filtered Fast Fourier Transform data for acoustic characteristics. The acoustic characteristics can include mean frequency, normalized deviation of frequency, mean amplitude, normalized deviation of amplitude, and apparent power. These characteristics can be predetermined for rock samples having a known lithology type and/or petrophysical properties, and thus, can be used to identify lithology type and other properties by comparing such characteristics of the acoustic data received during drilling to that determined for the rock samples. According to another embodiment of the apparatus, the computer uses the derived acoustic characteristics to determine formation boundaries based on real-time detection of changes in the lithology type of the rock being drilled and/or petrophysical properties thereof. 
         [0023]    According to an exemplary configuration, the petrophysical properties analyzing program or separate program code functions derive a “bit specific” or “bit independent” petrophysical properties evaluation algorithm. Similarly, the derived bit specific or bit independent petrophysical properties evaluation algorithm evaluates filtered Fast Fourier Transform data for petrophysical properties. This petrophysical property data can advantageously be applied by other applications to include real-time lithology type identification, formation boundary determination, casing shoe position fine-tuning, etc. 
         [0024]    According to an embodiment of the present invention, the petrophysical properties analyzing program can be provided either as part of the apparatus or as a standalone deliverable. As such, the petrophysical properties analyzing program can include a set of instructions, stored or otherwise embodied on a non-transitory computer readable medium, that when executed by a computer, cause the computer to perform various operations. These operations can include the operation of receiving raw acoustic sensor data from a surface data interface in communication with a communication medium that is further in communication with a downhole data interface operably coupled to a plurality of acoustic sensors. The operations can also include the processing operations of deriving a frequency distribution of the raw acoustic sensor data, deriving a plurality of acoustic characteristics including mean frequency and normalized deviation of frequency from the raw acoustic sensor data, and/or deriving petrophysical properties from the raw acoustic sensor data utilizing a derived petrophysical properties evaluation algorithm employable to predict one or more petrophysical properties of rock undergoing drilling. 
         [0025]    According to an embodiment of the petrophysical properties analyzing program, the operation of deriving a frequency distribution of the acoustic data from the raw acoustic sensor data includes transforming the raw acoustic sensor data into the frequency domain (e.g., employing a Fast Fourier Transform), and filtering the transformed data. 
         [0026]    According to an embodiment of the petrophysical properties analyzing program, the operation of deriving the plurality of acoustic characteristics from the raw acoustic sensor data can include comparing the mean frequency, the normalized deviation of frequency, the mean amplitude, the normalized deviation of amplitude, and apparent power for the rock undergoing drilling with the mean frequency, normalized deviation of frequency, mean amplitude, normalized deviation of amplitude, and apparent power for a plurality of rock samples having different known lithologies according to a first configuration, or comparing only part of the acoustic characteristics, such as the mean frequency and the normalized deviation of frequency of the rock undergoing drilling with the same type of acoustic characteristics of a plurality of rock samples having different known lithologies according to another configuration. The operations can also include identifying lithology type of the rock undergoing drilling, determining a location of a formation boundary encountered during drilling, and/or identifying an ideal location for casing shoe positioning, among others. 
         [0027]    According to an exemplary implementation, the mean frequency and normalized deviation of frequency are examined together to determine an amount of correlation of the acoustic characteristics associated with the rock undergoing drilling and the acoustic characteristics associated with the rock samples. Also or alternatively, the mean frequency and the mean amplitude can be examined together and/or with the normalized deviation of frequency or apparent power, or a combination thereof. The operation of comparing can beneficially be performed substantially continuously during drill bit steering in order to provide enhanced steering ability. 
         [0028]    According to an embodiment of the petrophysical properties analyzing program employing a bit-specific evaluation methodology, the operation of deriving petrophysical properties from the raw acoustic sensor data can include deriving a bit-specific petrophysical properties evaluation algorithm. The derivation of the algorithm can include collecting petrophysical properties data describing one or more petrophysical properties of rock for a plurality of formation samples and correspondent acoustic data for a preselected type of drill bit, processing the collected acoustic data to produce filtered FFT data, and determining one or more relationships between features of the filtered FFT data and correspondent one or more petrophysical properties of rock describing petrophysical properties of the plurality of formation samples. This can be accomplished, for example, by utilizing mathematical modeling techniques such as, multiple regression analysis, such as artificial neural network modeling, etc. The derivation of the algorithm can also include coding the determined relationships into computer program code defining the petrophysical properties evaluation algorithm. The operations can correspondingly include employing the derived petrophysical properties evaluation algorithm to predict one or more petrophysical properties of the rock undergoing drilling real-time responsive to filtered data associated with raw acoustic sensor data produced in response to the drilling. 
         [0029]    According to an embodiment of the petrophysical properties analyzing program employing a bit-independent evaluation methodology, the petrophysical properties evaluation algorithm derivation can also or alternatively include collecting petrophysical properties data describing one or more petrophysical properties of rock for a plurality of formation samples and correspondent acoustic data for a plurality of different types of drill bits, processing the collected acoustic data to produce filtered FFT data, determining bit-type independent features of the filtered FFT data, and determining one or more relationships between the bit-type independent features of the filtered FFT data and correspondent one or more petrophysical properties of the rock to provide a bit-independent evaluation methodology. The algorithm derivation can also include coding the determined relationships into computer program code defining a bit-independent petrophysical properties evaluation algorithm. The operations can correspondingly include employing the derived petrophysical properties evaluation algorithm to predict one or more petrophysical properties of the rock undergoing drilling real-time responsive to filtered data associated with raw acoustic sensor data produced in response to the drilling, as described, for example, with respect to the prior described bit-specific evaluation methodology. 
         [0030]    Various embodiments of the present invention advantageously supply a new approach for a much better drilling steering. Various embodiments of the present invention provide apparatus and methods that supply detailed information about the rock that is currently in contact with the drilling bit, which can be used in real-time steering the drilling bit. That is, various embodiments of the present invention advantageously provide an employable methodology of retrieving a sufficient level of information so that the driller always knows the rock he is drilling, so that the drilling bit can be steered to follow the desire path more accurately than conventionally achievable. In comparison with conventional drilling steering tools, the real-time data provided by various embodiments of the present invention advantageously allow the driller to drill smoother lateral or horizontal wells with better contact with the production zone, to detect formation boundaries in real-time, and to detect the fractured zones in real-time, and to perform further analysis on raw sensor data, if necessary. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0031]    So that the manner in which the features and advantages of the invention, as well as others which will become apparent, may be understood in more detail, a more particular description of the invention briefly summarized above may be had by reference to the embodiments thereof which are illustrated in the appended drawings, which form a part of this specification. It is to be noted, however, that the drawings illustrate only various embodiments of the invention and are therefore not to be considered limiting of the invention&#39;s scope as it may include other effective embodiments as well. 
           [0032]      FIGS. 1A-1B  is a partial perspective view and partial schematic diagram of a general architecture of an apparatus for identifying rock properties in real-time during drilling according to an embodiment of the present invention; 
           [0033]      FIG. 2  is a schematic diagram showing a data processing procedure performed by a computer program according to an embodiment of the present invention; 
           [0034]      FIG. 3  is a schematic diagram illustrating major components of a data preprocess module according to an embodiment of the present invention; 
           [0035]      FIGS. 4A-4B  are graphs illustrating examples of a frequency distribution of two types of carbonate according to an embodiment of the present invention; 
           [0036]      FIG. 5  is a graph illustrating a three dimensional depiction of the frequency distribution in correlation with various lithography types according to an embodiment of the present invention; 
           [0037]      FIG. 6  is a graph illustrating a comparison of mean frequency and normalized deviation of frequency correlated with a plurality of lithology types according to an embodiment of the present invention; 
           [0038]      FIG. 7  is a schematic flow diagram illustrating steps for forming a petrophysical properties evaluation algorithm for a particular type of drill bit according to an embodiment of the present invention; and 
           [0039]      FIG. 8  is a schematic flow diagram illustrating steps for forming a drill bit independent petrophysical properties evaluation algorithm according to an embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0040]    The present invention will now be described more fully hereinafter with reference to the accompanying drawings, which illustrate embodiments of the invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the illustrated embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Like numbers refer to like elements throughout. Prime notation, if used, indicates similar elements in alternative embodiments. 
         [0041]    When drilling into different lithologies or the same lithology with different properties (e.g., porosity, water saturation, permeability, etc.) the generated acoustic sounds emanating from the drill bit when drilling into rock, are distinctly different. The sounds, termed as drilling acoustic signals hereafter, transmit upward along the drill string. According to various embodiments of the present invention, a sensor subassembly containing acoustic sensors is positioned above the drill bit and connected to the above drill string. The drilling acoustic signals transmit from the drill bit to the sensor subassembly and are picked up by the acoustic sensors. The drilling acoustic signals received by the sensors are transmitted (generally after amplification) to surface by a borehole transmitting system which can include various components such as, for example, a downhole data interface, a broadband conductor, a surface data interface, etc. On the surface, the received acoustic signals are transformed by a data processing module into the frequency domain using, for example, a Fast Fourier Transformation (FFT) to generate FFT data (primarily the frequency and amplitude data). Some acoustic characteristics are derived directly from the FFT data. The frequency distribution and acoustic characteristics, for example, can be used immediately in some applications, such as lithology type identification and formation boundary determination. The FFT data can be further analyzed using a calibrated mathematical model, for the lithology type and petrophysical properties, which have wider applications than the direct results (frequency distribution and acoustic characteristics). 
         [0042]    Where conventional measurement-while-drilling tools are typically located 30 to 50 feet behind the drill bit, beneficially, a major advantage of approaches employed by various embodiments of the present invention is that such approaches can derive information about lithologies from a position located at the cutting surface of the drill bit to provide such information to the operator steering the drill bit, in real time. This advantage makes aspects of various embodiments of the present invention ideal in the application of horizontal and lateral well drill steering, locating the relative position for setting the casing shoe, detecting fractured zones, and interpreting rock lithologies and petrophysical properties in real time. 
         [0043]      FIGS. 1A-1B  schematically show the setup of an exemplary apparatus for identifying rock properties in real-time during drilling  100 . Acoustic sensors  102  are connected to a downhole data “transmitting” interface  103 . According to the exemplary configuration, both are contained in a sensor subassembly  104 , which is positioned above a drill bit  101  and connected to a drill string  117 . In operation, the drilling acoustic signals are generated when the drill bit  101  bites rocks at the bottom of a borehole  118  during the drilling process. 
         [0044]    Different acoustic sensors  102  may be used, e.g. accelerometer, measurement microphone, contact microphone, and hydrophone. According to the exemplary configuration, at least one, but more typically each acoustic sensor  102  either has a built-in amplifier or is connected directly to an amplifier (not shown). The drilling acoustic signals picked up by the acoustic sensors  102  are amplified first by the amplifier before transmitted to the downhole data interface  103 . 
         [0045]    From the downhole data interface  103 , acoustic signals are transmitted to a surface data “transmitting” interface  106  through a borehole broadband data transmitting system  105 . Currently, one commercially available broadband data transmitting system, NOV™ IntelliServ®, can transmit data at the rate of 1000,000 bit/s. A study indicated that with two acoustic sensors  102  at normal working sampling rate of 5 seconds per sample, the required data transmitting rate was about 41,000 bits/s. Therefore, the NOV™ IntelliServ® borehole broadband data transmitting system is an example of a broadband communication media capable of transmitting acoustic signals data for at least four acoustic sensors  102  to surface directly from a downhole data interface  103 . 
         [0046]    According to the exemplary configuration, the surface data interface  106  is located at the stationary part of the top drive  107 . From the surface data interface  106 , the acoustic signals are further transmitted to a data acquisition unit  110  through an electronic cable  108 , which is protected inside a service loop  109 . The data acquisition unit  110  is connected to a computer  124  through an electronic cable  126 . The data acquisition unit  110  samples the acoustic signal in analog format and then converts the analog acoustic signals into digit data in  FIG. 2 . 
         [0047]    Referring to  FIGS. 1 and 2 , the digitized data  111  is read by a computer program  112  (e.g., a petrophysical properties analyzing program), installed in memory  122  accessible to processor  123  of computer  124 . The computer program  112  analyzes the digitized data  111  to derive a frequency distribution  113 , acoustic characteristics  114 , and petrophysical properties  115  of the rock undergoing drilling. The respective results, e.g., frequency distribution  113 , acoustic characteristics  114 , and petrophysical properties  115 , can be used in various applications  116  to include lithology identification, drill bit steering, formation boundary identification, among others. Such data along with rock sample data, rock modeling data, etc. can be stored in database  125  stored in either internal memory  122  or an external memory accessible to processor  123 . 
         [0048]    Note, the computer  124  can be in the form of a personal computer or in the form of a server or server farm serving multiple user interfaces or other configurations known to those skilled in the art. Note, the computer program  112  can be in the form of microcode, programs, routines, and symbolic languages that provide a specific set or sets of ordered operations that control the functioning of the hardware and direct its operation, as known and understood by those skilled in the art. Note also, the computer program  112 , according to an embodiment of the present invention, need not reside in its entirety in volatile memory, but can be selectively loaded, as necessary, according to various methodologies as known and understood by those skilled in the art. Still further, at least portions of the computer program  112  can be stored in memory of the sensor subassembly  104  when so configured. 
         [0049]    Referring to  FIG. 3 , according to the exemplary configuration, the digitized data  111  needs to be preprocessed before any use. According to the exemplary configuration, this is accomplished by a subroutine program referred to as data preprocess module  200 . As illustrated in the figure, the digitized data is transformed into Fast Fourier Transform (FFT) data  202  by a FFT  201 . The FFT data  202  is then filtered by a filter  203  to remove some low/high frequency and/or low amplitude data points, generated from other sources, i.e. not from the bit cutting into the rocks. The filtered FFT data  301  is then used in the various part of data process. Note. the filtered FFT data  301  is relabeled as  403  in  FIGS. 7 . and  503  in  FIG. 8 . Note also, the digitized data  111  is relabeled as  402  in  FIGS. 7 , and  502  in  FIG. 8 . 
         [0050]    Major components and functions of the computer program  112  according to an exemplary configuration are detailed in  FIG. 2 . According to the exemplary configuration, there are four modules (components) in the computer program  112 : a data preprocess module  200 , a data sampling module  210 , an acoustic characteristics evaluation algorithm  302 , and a petrophysical properties evaluation algorithm  303 . The sampling module  210  sends sampling commands  127 , such as sampling rate, to the data acquisition unit  110  for data sampling control. The main part of the filtered FFT data  301  is a frequency distribution  113 , which is the frequency and amplitude information of a sampled acoustic signal. Two examples of such signal are shown in  FIGS. 4A and 4B .  FIG. 4A  illustrates the frequency distribution for a limestone and  FIG. 4B  illustrates the frequency distribution for limestone dolomite. A review of the frequency distribution of the two different types of carbonates illustrates how the frequency distribution can be used directly to distinguish lithologies. 
         [0051]    According to the exemplary configuration, the frequency distribution  113  can be used directly in some applications, such as lithology type identification, formation boundaries determination, etc., represented by example at  116 . The frequency distribution  113  can be plotted into time-frequency spectrum which can be used directly in some applications, such as lithology type identification, formation boundaries determination, etc., represented by example at  116 . 
         [0052]    An example of such signal displaying diagram is shown in  FIG. 5 , which illustrates results of a laboratory experiment showing different lithologies have different frequency spectrums and lithology boundaries can be determined using the diagram. In  FIG. 5 , the color represents amplitude, with color normally displayed as red being highest (the intermixed color mostly concentrated just below the 4000 Hz range in this example) and the color normally displayed as blue being the lowest (the more washed out color in this example). 
         [0053]    According to the exemplary configuration, an acoustic characteristics evaluation algorithm  302  evaluates the filtered FFT data  301  for select acoustic characteristics, such as, for example, mean frequency, normalized deviation of frequency, mean amplitude, normalized deviation of amplitude, and apparent power. These acoustic characteristics for an acoustic signal sample are defined as follows: 
         [0000]    
       
         
           
             
               
                 
                   
                     μ 
                     f 
                   
                   = 
                   
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         n 
                       
                        
                       
                         
                           A 
                           i 
                         
                         · 
                         
                           f 
                           i 
                         
                       
                     
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         n 
                       
                        
                       
                         A 
                         i 
                       
                     
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
             
               
                 
                   
                     σ 
                     
                       f 
                        
                       _ 
                        
                       N 
                     
                   
                   = 
                   
                     
                       1 
                       
                         μ 
                         f 
                       
                     
                      
                     
                       
                         
                           ∑ 
                           
                             i 
                             = 
                             1 
                           
                           n 
                         
                          
                         
                           
                             
                               A 
                               i 
                             
                             
                               
                                 ∑ 
                                 
                                   i 
                                   = 
                                   1 
                                 
                                 n 
                               
                                
                               
                                 A 
                                 i 
                               
                             
                           
                            
                           
                             
                               ( 
                               
                                 
                                   f 
                                   i 
                                 
                                 - 
                                 
                                   μ 
                                   f 
                                 
                               
                               ) 
                             
                             2 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
             
               
                 
                   
                     μ 
                     A 
                   
                   = 
                   
                     
                       1 
                       n 
                     
                      
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         n 
                       
                        
                       
                         A 
                         i 
                       
                     
                   
                 
               
               
                 
                   ( 
                   3 
                   ) 
                 
               
             
             
               
                 
                   
                     σ 
                     
                       A 
                        
                       _ 
                        
                       N 
                     
                   
                   = 
                   
                     
                       1 
                       
                         μ 
                         A 
                       
                     
                      
                     
                       
                         
                           1 
                           n 
                         
                          
                         
                           
                             ∑ 
                             
                               i 
                               = 
                               1 
                             
                             n 
                           
                            
                           
                             
                               ( 
                               
                                 
                                   A 
                                   i 
                                 
                                 - 
                                 
                                   μ 
                                   f 
                                 
                               
                               ) 
                             
                             2 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   4 
                   ) 
                 
               
             
             
               
                 
                   
                     P 
                     a 
                   
                   = 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         1 
                       
                       n 
                     
                      
                     
                       
                         A 
                         i 
                         2 
                       
                        
                       
                         f 
                         i 
                         2 
                       
                     
                   
                 
               
               
                 
                   ( 
                   5 
                   ) 
                 
               
             
           
         
       
     
         [0000]    wherein:
       μ f —mean frequency, Hz,   σ f     —     N —normalized deviation of frequency, Hz,   μ A —mean amplitude, the unit depending on the type of acoustic sensor used in the measurement,   σ A     —     N —normalized deviation of amplitude, the unit depending on the type of acoustic sensor used in the measurement,   P a —apparent power, the unit depending on the type of acoustic sensor used in the measurement,   f i —frequency of the i th  point of the acoustic signal sample, Hz,   A i —amplitude of the i th  point of the acoustic signal sample, the unit depending on the type of acoustic sensor used in the measurement, and   n—number of data points of the acoustic signal sample.       
 
         [0062]    The mean frequency and the normalized deviation of frequency characterize the frequency distribution, while the mean amplitude and the normalized deviation of amplitude characterize the loudness level of the drilling sound. Apparent power represents the power of the acoustic signals. In the evaluation, these characteristics can be calculated within the whole range or a partial range of the frequency of the acoustic samples. The range is selected to achieve the maximum difference of these characteristics among different lithologies. 
         [0063]    The derived acoustic characteristics  114  can be used directly for certain applications, such as lithology type identification, formation boundary determination represented by example at  116 ′.  FIG. 6  illustrates results of a laboratory experiment showing that the mean frequency and normalized deviation of frequency correlated well with different lithology types. 
         [0064]    According to an exemplary embodiment of the present invention, the mean frequency, the normalized deviation of frequency, the mean amplitude, the normalized deviation of amplitude, and/or the apparent power of the rock undergoing drilling can be compared with a corresponding mean frequency, normalized deviation of frequency, mean amplitude, normalized deviation of amplitude and/or apparent power of a plurality of rock samples having different known lithologies, to thereby determine an amount of correlation of the acoustic characteristics associated with the rock undergoing drilling and the acoustic characteristics associated with the rock samples. Responsively, the lithology type of the rock undergoing drilling can be determined. 
         [0065]      FIGS. 7 and 8  illustrate examples of the construction of two types of petrophysical properties evaluation algorithms  303 : one designed for a particular type of drill bit shown at  303 A and the other designed to be drill bit type independent shown at  303 B. Unlike the FFT  201  and the acoustic characteristics evaluation algorithm  302 , which are based on known mathematical equations, the petrophysical properties evaluation algorithm  303  is based on mathematical models, which are to be built utilizing acoustic data and petrophysical properties according to an exemplary configuration. 
         [0066]      FIG. 7  illustrates the procedure for constructing a “Petrophysical Properties Evaluation Algorithm” for a particular type of drill bit. According to the exemplary configuration, datasets of petrophysical properties  401  and correspondent digitized acoustic data  402  for a particular drill bit are collected. The digitized acoustic data  402  is preprocessed by the data preprocess module  200  (referred to in  FIG. 2 ) to produce the filtered FFT data  403 . The relationships  405  between filtered FFT data  403  and petrophysical properties  401  are constructed (step  404 ) using suitable mathematical modeling techniques, such as, multiple regression analysis, artificial neural networks modeling. Once relationships  405  between the filtered FFT data  403  and petrophysical properties  401  are constructed, the relationships are coded (step  406 ) to produce a computer program, module, subroutine, object, or other type of instructions to define the “petrophysical properties evaluation algorithm”  303 A. The algorithm  303 A is then available to be used in the computer program  112  to predict the petrophysical properties from drilling acoustic signals for the particular drill bit type. 
         [0067]      FIG. 8  illustrates the procedure for constructing a drill bit type independent “Petrophysical Properties Evaluation Algorithm”  303 B. The datasets of petrophysical properties  501  and the correspondent acoustic data  502  measured from different types of drill bit are collected. The acoustic data  502  is preprocessed by the data preprocess module  200  (e.g., the module referred to  FIGS. 2 and 3 ) to produce the filtered FFT data  503 . Bit type independent features  505  of the filtered FFT data  503  are then determined by comparing the filtered FFT data of different types of drill bit and the correspondent petrophysical properties  501  (step  504 ). Features which have weakest correlation with the drill bit types and strong correlation with the petrophysical properties are the bit-type independent ones. The relationships  507  between the petrophysical properties  501  and the bit type independent features  505  are constructed (step  506 ) using suitable mathematical modeling techniques, such as, for example, multiple regression analysis, artificial neural networks modeling, among others. The constructed relationships  507  are then coded (step  508 ) into a computer program, module, subroutine, object, or other type of instructions to define the “petrophysical properties evaluation algorithm”  303 B. The algorithm  303 B is then available to be used in the computer program  112  to predict the petrophysical properties from drilling acoustic signals. 
         [0068]    Application of the Results from the Processed Acoustic Signal. 
         [0069]    One direct result is the frequency distribution  113  ( FIG. 2 ), which may be used directly in lithology type identification, formation boundary determination.  FIGS. 4A and 4B , for example, show the frequency distribution of two different types of carbonates. The figures illustrate that the frequency distribution can be used in the lithology type identification from matching a detective frequency distribution with a frequency distribution of a rock of known lithography type. 
         [0070]      FIG. 6  demonstrates the feasibility of using acoustic characteristics  114  ( FIG. 2 ) to derive lithology information. In  FIG. 6 , mean frequency and normalized deviation were calculated from FFT data of the drilling sounds of a sample corer drilling into cores of different lithologies. The figure demonstrates how the lithology types can be distinguished by the combination of the two characteristics: mean frequency and the normalized deviation of frequency. If mean amplitude and the normalized deviation of the amplitude are also used, an even better result may be achieved. The figure also inherently demonstrates that formation boundaries can be determined from acoustic characteristics.  FIGS. 7 and 8  demonstrate the feasibility of building a petrophysical properties evaluation algorithm  303  ( FIG. 2 ) which can be used to evaluate processed forms of the sound generated by operationally engaging the drilling bit with the rock being drilled. 
         [0071]    Various embodiments of the present invention provide several advantages. For example, various embodiments of the present invention beneficially provide a means to identify lithology type and physical properties, truly in real-time. This advantage makes various embodiments of the present invention ideal in the applications of (1) horizontal and lateral well drill steering and (2) locating the relative position for setting the casing shoe at a much higher precision. Various embodiments can also be used to (3) detect fractured zones; and (4) interpret rock lithologies and petrophysical properties. Various embodiments of the present invention beneficially supply more information for evaluating petrophysical properties of the rocks, such as porosity, strength, and presence of hydrocarbons, through the utilization of data obtained through the analysis of acoustic signals to evaluate these petrophysical properties. Such data can beneficially be beyond that which can be conventionally supplied. 
         [0072]    In the drawings and specification, there have been disclosed a typical preferred embodiment of the invention, and although specific terms are employed, the terms are used in a descriptive sense only and not for purposes of limitation. The invention has been described in considerable detail with specific reference to these illustrated embodiments. It will be apparent, however, that various modifications and changes can be made within the spirit and scope of the invention as described in the foregoing specification.

Summary:
Methods of identifying rock properties in real-time during drilling, are provided. An example of such a method includes connecting a downhole sensor subassembly between a drill bit and a drill string, operably coupling acoustic sensors to a downhole data interface, and operably coupling a surface computer to the downhole data interface. The method can also include receiving raw acoustic sensor data generated real-time as a result of rotational contact of the drill bit with rock during drilling, transforming the raw data into the frequency domain, filtering the transformed data, and deriving a plurality of acoustic characteristics from the filtered data. This can be performed by a petrophysical properties analyzing program stored in memory of the computer. The method can also include deriving petrophysical properties from the filtered data utilizing a petrophysical properties evaluation algorithm employable to predict one or more petrophysical properties of rock undergoing drilling.