Patent Description:
<CIT> describes a method of acoustic well logging comprising processing in the frequency domain a plurality of acoustic log signals representative of waveforms received at a plurality of acoustic well logging receivers to give rise to one or more flexural or Stoneley mode field dispersion curves, operating a neural net to generate one or more formation shear slowness values from either the flexural or Stoneley mode field dispersion curves and saving, transmitting, plotting, printing or processing one or more resulting signals that are indicative of shear slowness values. <CIT> describes a method for estimating sonic slowness comprising obtaining a plurality of sonic waveforms, obtaining slowness models of the subterranean formation, computing for each slowness model, a set of candidate travel times for a wave energy mode and a position of each of a plurality of receivers, computing a relevance indicator for each set of candidate travel times based on the recorded sonic waveforms; searching for a match between the sets of candidate travel times and the recorded sonic waveforms by searching a relevance indicator which is optimum and computing a sonic slowness estimate for the subterranean formation from a set of candidate travel times for which the relevance indicator is optimum.

The processing of sonic logging data has been an expert driven process. The results of the sonic logging analysis may vary significantly on the approach and skill of the expert conducting the analysis. Additionally, the capacity and availability of experts to perform the analysis limit the amount of sonic logging data that can be processed and may also introduce significant delay between the time that the acoustic data is recorded and the time that results are available. Improved approaches to acoustic data analysis are needed to increase accuracy and availability of sonic logging data analysis. Improved approaches are also needed to enable practical real time sonic slowness estimation for while drilling operations.

Methods and systems for determining sonic slowness are described.

The present invention resides in a method for determining sonic slowness as defined in claim <NUM>. Preferred embodiments are defined in claims <NUM> to <NUM>.

In another aspect, the invention resides in an apparatus as defined in claim <NUM>.

The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present disclosure. The disclosure may be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein.

Various features and advantageous details are explained more fully with reference to the embodiments that are illustrated in the accompanying drawings and detailed in the following description. It should be understood, however, that the detailed description and the specific examples are given by way of illustration and various substitutions, modifications or additions are considered to be within the scope of the invention as defined by the appended claims.

One technical problem addressed by the present embodiments is related to limitations in sonic logging analysis as an expert driven process. The present embodiments includes machine learning systems, such as deep learning, to process sonic logging data, for example, to determine sonic slowness. According to the invention, the present disclosure and the accompanying claims provide a technical solution to the technical problem of determining sonic slowness of sonic logging data. In some embodiments, the present embodiments may provide accurate results in a sufficiently short time period to allow for real-time logging while drilling decisions and processes to occur.

A subterranean formation may be a natural formation or artificial formation. It will be appreciated that an underground geological region may be below land or ocean without loss of generality. The underground geological region may include a subsurface formation in which a borehole is drilled. In addition to an area in close proximity to the borehole, the underground geological region may also include any area that affects or may affect the borehole or where the borehole may be drilled.

An exemplary embodiment is directed to slowness estimation for the formation around a borehole. A slowness estimate may be used to identify natural gas entry points in the borehole. A slowness estimate may also be used to estimate the porosity of a rock or of another material forming the borehole, to characterize the induced or natural anisotropies or orientations of the rock, to characterize the geomechanical properties of the rock in order for example to define a weight of fluid to be used while drilling the borehole. A slowness estimate may also be used to establish a time / depth relationship for the borehole, thus enabling a conversion of seismic data acquired for the borehole into depth data and to generate a cartography of the borehole properties.

<FIG> illustrates a wellsite system in which the examples disclosed herein can be employed. The wellsite can be onshore or offshore. In this example system, a borehole <NUM> is formed in subsurface formations by rotary drilling. However, the examples described herein can also use directional drilling, as will be described hereinafter.

A drill string <NUM> may be suspended within the borehole <NUM> and has a bottom hole assembly <NUM> that includes a drill bit <NUM> at its lower end. The surface system may include a platform and derrick assembly <NUM> positioned over the borehole <NUM>. The assembly <NUM> may include a rotary table <NUM>, a kelly <NUM>, a hook <NUM> and a rotary swivel <NUM>. The drill string <NUM> may be rotated by the rotary table <NUM>. The rotary table <NUM> may engage the kelly <NUM> at the upper end of the drill string <NUM>. The drill string <NUM> may be suspended from the hook <NUM>, which is attached to a traveling block. The drill string <NUM> may be positioned through the kelly <NUM> and the rotary swivel <NUM>, which permits rotation of the drill string <NUM> relative to the hook <NUM>. A top drive system may be used to impart rotation to the drill string <NUM>. In this example, the surface system further includes drilling fluid or mud <NUM> stored in a pit <NUM> formed at the well site. A pump <NUM> delivers the drilling fluid <NUM> to the interior of the drill string <NUM> via a port in the swivel <NUM>, causing the drilling fluid <NUM> to flow downwardly through the drill string <NUM> as indicated by the directional arrow <NUM>. The drilling fluid <NUM> exits the drill string <NUM> via ports in the drill bit <NUM>, and then circulates upwardly through the annulus region between the outside of the drill string <NUM> and the wall of the borehole <NUM>, as indicated by the directional arrows <NUM>. In this manner, the drilling fluid <NUM> lubricates the drill bit <NUM> and carries formation cuttings up to the surface as it is returned to the pit <NUM> for recirculation.

The bottom hole assembly <NUM> of the example illustrated in <FIG> includes a logging-while-drilling (LWD) module <NUM>, a measuring-while-drilling (MWD) module <NUM>, a roto- steerable system and motor <NUM>, and the drill bit <NUM>.

The LWD module <NUM> may be housed in a special type of drill collar and may include one or more logging tools. In some examples, the bottom hole assembly <NUM> may include additional LWD and/or MWD modules. The LWD module <NUM> may include capabilities for measuring, processing, and storing information, as well as for communicating with the surface equipment. The LWD module <NUM> may include a sonic measuring device.

The MWD module <NUM> may also be housed in a drill collar and may include one or more devices for measuring characteristics of the drill string <NUM> and/or drill bit <NUM>. The MWD module <NUM> may include an apparatus for generating electrical power for at least portions of the bottom hole assembly <NUM>. The apparatus for generating electrical power may include a mud turbine generator powered by the flow of the drilling fluid. However, other power and/or battery systems may be employed. In this example, the MWD module <NUM> includes one or more of the following types of measuring devices: a weight-on-bit measuring device, a torque measuring device, a vibration measuring device, a shock measuring device, a stick slip measuring device, a direction measuring device and/or an inclination measuring device.

Although the components of <FIG> are shown and described as being implemented in a particular conveyance type, the examples disclosed herein are not limited to a particular conveyance type but, instead, may be implemented in connection with different conveyance types including, for example, coiled tubing, wireline wired drill pipe and/or any other conveyance types known in the industry.

<FIG> illustrates a sonic logging-while-drilling tool that can be used to implement the LWD tool <NUM> or may be a part of an LWD tool suite 120A. An offshore rig <NUM> having a sonic transmitting source or array <NUM> may be deployed near the surface of the water. In at least some embodiments, any other type of uphole or downhole source or transmitter may be provided to transmit sonic signals. In some examples, an uphole processor controls the firing of the transmitter <NUM>.

Uphole equipment may also include acoustic receivers and a recorder for capturing reference signals near the source of the signals (e.g., the transmitter <NUM>). The uphole equipment may also include telemetry equipment for receiving MWD signals from the downhole equipment. The telemetry equipment and the recorder may be coupled to a processor so that recordings may be synchronized using uphole and downhole clocks. A downhole LWD module <NUM> includes one or more acoustic receivers (e.g., <NUM> and <NUM>), which are coupled to a signal processor so that recordings may be made of signals detected by the receivers in synchronization with the firing of the signal source.

In operation, the transmitter <NUM> transmits signals and/or waves that are received by one or more of the receivers <NUM>, <NUM>. The received signals are recorded and/or logged to generate associated waveform data. The waveform data may be processed by processors <NUM> and/or <NUM> to determine slowness values as disclosed herein.

One exemplary objective of sonic logging is to estimate the slowness of propagating acoustic waves in the borehole formation. A sonic tool may include one or several transmitters and one or several receivers. For a certain tool position in the wellbore, a transmitter is fired. The released energy travels through different mediums (e.g. borehole fluid, borehole formation rock. Part of this energy is captured by the receivers available in the sonic tool. When the transmitter is fired, the receivers start recording for a time duration. In this record, there may be ambient noise. Along the ambient noise, at a later time, the captured transmitter-released energy is recorded. This record may be referred to as a sonic waveform. An example of a sonic waveform is depicted in <FIG>. For a given shot, several waveforms may be recorded associated with a plurality of receivers. An example of a shot gather associated with a sonic tool having thirteen receivers is depicted in <FIG>. The set of waveforms recorded for a shot may be referred to as a shot gather. The analysis of these waveforms on a shot basis, helps identify the different onset arrivals (e.g. compressional waves, shear waves, Stoneley waves, leaky-P waves). The slowness of these waves can be determined for the rock available at the considered depth.

The analysis of the recorded waveforms may provide the travel times of the acquired energy modes (e.g. compressional waves, shear waves, Stoneley waves, leaky-P waves) along the associated slowness values. The sonic slowness can be determined from arrival time picking (such as by a first motion detection algorithm or a fast arrival time and slowness estimation) or directly by algorithms such as a slowness time coherence (STC) algorithm, a dispersive STC algorithm, dipole inversion, and quadrupole inversion.

The previously referred to algorithms may be applied to wireline and LWD data, particularly STC. Sonic waveforms have been acquired in thousands of wells and processed to generate compressional, shear and/or Stoneley slowness. The processing of such data has required the intervention of expert users.

A workflow, which may be implemented in real time, will now be discussed. In one example, the workflow determines the slowness from the recorded borehole sonic waveforms. In another example, the workflow determines travel-times from waveforms. The obtained travel-times can then be used to derive high-depth-resolution slowness logs. The workflow preferably includes machine learning techniques.

A collection of sonic waveforms and associated processing results are gathered and subdivided into shot gathers. The waveforms may undergo a preparation step such as noise reduction or information enhancement following which the content of interest becomes easier to process and/or to interpret. A convolutional neural network may be trained with both sonic waveforms and associated results (e.g. slowness). A relationship may be established between the input sonic waveforms and the sought result. Following this process, a trained neural network may be created. The neural network may directly derive a slowness result (or travel-times) from the input waveforms. The generated neural network can be used for prediction purposes. When a set of waveforms is input to the network, the associated result (e.g. slowness, travel-times) may be computed directly.

This trained model may be used with data acquired by both wireline and Logging While Drilling (LWD) sonic tools. The workflow can be used in real-time embedded and postprocessing applications. The generated results of the workflow may provide slowness determinations. Additionally, the output may be used to validate the results of other processing techniques (e.g. Slowness Time Coherence).

According to the invention, the method includes a machine learning technique such as a convolutional neural network (CNN). A collection of input and output data may be used to train a CNN model.

The input data for model training includes one or more of recorded data, synthetic data or combinations thereof, for example as described below.

Recorded data: sonic waveforms acquired by sonic tools in a well or logging environment. The waveforms may be pre-processed (e.g. noise attenuation). Outliers may be removed prior to incorporation in the database or the outlier may be removed.

Synthetic data: synthetic sonic waveforms. Noise or transforms may be added to increase the quantity of data in the database.

Combination of the above: recorded waveforms in a shot gather with known slowness values can be time-shifted in order to obtain a new shot gather with a pre-defined synthetic slowness value. In the following <NUM> microsecond/foot = <NUM>,<NUM> microsecond/meter. For a given recorded shot gather with known slowness value, a large collection of pseudo-synthetic shot gathers can be generated to provide a range of possible slowness values. The moveout/real slowness (e.g., <NUM>/ft) may be corrected by respectively time-shifting the waveforms of the shot gather to provide a new moveout associated with a synthetic slowness value (e.g., <NUM>/ft). This process may be repeated to cover a range of slowness values (e.g., from <NUM>/ft to <NUM>/ft for compressional slowness) and provide a large collection of pseudo-synthetic waveforms (e.g., recorded waveforms with associated synthetic slowness value).

Time shifting of waveforms may including padding the waveforms. Padding may include extrapolating, e.g., adding time samples to each waveform signal to provide a common start time and end time. The waveform padding may include applying a linear prediction filter, padding the waveforms with a constant value, and linear extrapolation to the mean value.

<FIG> shows an example of a recorded shot gather. The recorded shot gather is plotted in the dotted lines. In this example, the compressional slowness is <NUM>/ft. The compressional slowness is provided by the slope of the diagonal dotted line. A time shifted waveform is plotted in the dashed lines to provide a pseudo-synthetic shot gather. In the time shifted waveform, each receiver is time shifted except for the near receiver to create a waveform with a compressional slowness of <NUM>/ft. The compressional slowness is provided by the slope of the diagonal dashed line. This process may also be applied to generate gathers focused on P-waves, S-waves, and Stoneley waves.

Referring to <FIG>, at step <NUM>, data is input to the model. Preferably, the type of data input is the type of data that the database was trained on. Where the database was trained on a data set of a different type, a transform may be applied to the input data to transform it to the type of data for which the database was trained. For example, a trained full array monopole CNN model may be used to process the monopole waveform data acquired by a sonic tool made of an array of receivers in full array mode.

The input waveforms may be represented as a collection of shot gathers. A shot gather may include a set of waveforms recorded by a number of receivers. If the number of waveforms exceeds the number of receivers for which the database was trained, the number of receivers may be reduced, automatically or by the user, in the data preparation step.

The input data may be prepared in a data preparation step <NUM>. The data preparation step <NUM> may include noise reduction through the application of filters such as a classic bandpass filter, a linear prediction filter, an adaptive filter, an adaptive block thresholding filter, median filter, or any other filter that may enhance the quality of the data of interest.

The data preparation step <NUM> may also include identifying and removing outliers in the input data.

With reference to <FIG>, the waveforms recorded in a shot gather may be represented in a "wiggle" plot. The shot gather may also be represented as a greyscale image such as that shown in <FIG>. The x-axis of the image corresponds to the acquisition time, and the y-axis corresponds to the receiver index. The darkness of a pixel corresponds to the waveform's amplitude at a time for a receiver. An exemplary objective of the sonic data processing is to estimate the slowness of a wave such as a compressional wave, a shear wave, or a Stoneley waves in a shot gather.

The intercepted waves of interest may be headwaves. For a given tool position, the processing may consider set of receivers illuminating a piece of rock as having a constant slowness. The processing may look for coherent events in a shot gather under a linear moveout constraint. The processed receivers may represent a sub-array of the receivers available in the tool. When the sonic tool is not positioned at a boundary between geological layers, these headwaves may be observed as shown in <FIG>. The compressional, shear or Stoneley arrivals can be represented by a linear moveout. A straight line may capture the arrivals of these waves. The slope of such a line is used to compute the slowness of the considered type of waves.

If the tool is positioned at a geological boundary, then preferably a line is determined for every geological layer illuminated by the receivers' aperture. For example, in the presence of two layers, a total of two lines are preferable to capture the arrivals of the waves of interest (e.g. P-waves). In one example, the number of receivers used in the processing may be reduced to reduce the likelihood that the tool is positioned at a geological boundary. With a smaller receiver aperture, the moveout of the waves of interest may appear linear.

The identification of the headwaves in such greyscale images (e.g., <FIG> and <FIG> can be difficult and require significant input by expert operators, for example, to edit or relabel data to accurately determine slowness.

In one example, machine learning such as deep neural networks, are employed to improve the accuracy of the image recognition. According to the invention, a convolutional neural network (CNN) is used.

An objective of the CNN is to identify the lines that are capturing the arrivals of the waves of interest (e.g., P-waves). Referring back to <FIG>, following the preparation of the data at step <NUM>, the shot gather waveform may be provided to a deep neural network such as a CNN at step <NUM>. The CNN is trained with the input data discussed above that has known slowness or slowness determined by an expert for the purpose of training the CNN database.

In an example, a data transformation step <NUM> is performed after the data preparation step <NUM> prior to providing the data to the neural network at step <NUM>. An example of the data transformation is to apply an operator to the waveforms to generate new signals. An example operator includes a deconvolution. Preferably, the newly generated signals provide content that is easier to process by the neural network.

In one particular example, a Short Time Average/ Long Time Average (STA/LTA) operator is applied to the input waveforms. The use of an STA/LTA image may improve the network processing reliability by reducing the uncertainty associated with moveout identification when dealing with images of sonic waveforms. <FIG> is an example of applying the STA/LTA approach to generate a CNN input image from a shot gather of monopole waveforms. An exemplary equation for the STA/LTA operator is:
<MAT>
where g represents the Hilbert envelope of the considered waveform, <NUM> < sw ≤ lw, and ε is a small constant number. The transformed data may then be provided to the neural network at step <NUM>.

For waveforms with stronger dispersive content such as the dipole waveforms, the transform may include a phase shift in the frequency domain for pairs of waveforms. In a shot gather, the phase shift may be computed between a receiver's waveform and a near receiver's one. <FIG> shows an exemplary result of a determination of the phase shift for dipole waveforms recorded by a wireline sonic tool with thirteen inline receivers. In this case, twelve curves were generated in the frequency domain. In <FIG>, the phase-shift curves have not been unwrapped though it will be appreciated that the curves may also be unwrapped. The transformed curves may also be displayed as a greyscale image such as that shown in <FIG>. The process then proceeds to the neural network at step <NUM>.

Referring to the neural network processing step <NUM>, in an example, a CNN network is used to process the (transformed) shot waveform input data to output slowness or travel-time data. <FIG> illustrates an exemplary schematic diagram for a CNN and <FIG> illustrates exemplary configuration parameters for the CNN of <FIG>. It will also be appreciated that the neural network processing step <NUM> may operate with raw data that has not been transformed such as in the data transformation step <NUM>.

As discussed above, a set of waveforms and associated slowness is used to train the multi-layer CNN, for example such as that of <FIG>.

In an example with input data with monopole waveforms and associated compressional slowness logs, the input waveforms may be processed for noise reduction. Then, the STA/LTA operator may be applied to the input data.

With reference to <FIG>, waveforms of a shot gather with monopole waveforms are input at step <NUM>. In some embodiments, outliers are detected and removed from the input data at step <NUM>. In some embodiments, data quality improvement (e.g., noise removal) is performed at step <NUM>. In some embodiments, the data is transformed such as by an STA/LTA operator at step <NUM>. At step <NUM>, the CNN is applied to the (transformed) input data for slowness determination. At step <NUM>, the determined slowness is output.

With reference to <FIG>, waveforms of a shot gather with monopole waveforms are input at step <NUM>. In some embodiments, outliers are detected and removed from the input data at step <NUM>. In some embodiments, data quality improvement (e.g., noise removal) is performed at step <NUM>. In some embodiments, the data is transformed such as by an STA/LTA operator at step <NUM>. At step <NUM>, the CNN is applied to the (transformed) input data for travel time determination. At step <NUM>, the determined travel times are output. At step <NUM>, the determined travel times are converted to high depth resolution slowness and output.

In an example with input data with dipole waveforms and associated shear slowness logs, the input dipole waveforms may be used to compute a phase shift between the respective receiver's waveform and the near offset receiver's waveform in the shot gather.

With reference to <FIG>, waveforms of a shot gather with dipole waveforms are input at step <NUM>. In some embodiments, outliers are detected and removed from the input data at step <NUM>. In some embodiments, data quality improvement (e.g., noise removal) is performed at step <NUM>. In some embodiments, the data is transformed such as by a pairwise phase shift operator at step <NUM>. At step <NUM>, the CNN is applied to the (transformed) input data for slowness determination. At step <NUM>, the determined slowness is output.

With reference to <FIG>, in the training process of the CNN, a set of reference input <NUM> (e.g., STA/LTA image for a given well at a given depth and a given bit size) and associated outputs <NUM> (e.g., compressional slowness) are provided. By providing a large collection of such training input/output data, the CNN model will update its numeric parameters/weights at <NUM>. In this process, the model computes outputs to be matched with the provided reference ones. The model updates to reduce the error between its outputs and the provided reference output data.

The CNN may be trained for more than one output, e.g., provide input waveforms along the compressional slowness value(s), the shear slowness value(s), and the Stoneley slowness value(s) for the input image. The neural network training phase provides with a trained CNN model that can be exported and implemented in embedded software applications.

<FIG> shows the results of the training of a CNN model on a well. The input monopole waveforms were subject to noise reduction, then to the STA/LTA operator. In this example, the processing was done in a full array with all waveforms in the shot gather considered. It will be appreciated that less than a full array may also be used. The right track shows a comparison between the desired compressional slowness log (dashed line) and the CNN training output compressional slowness log (dotted line). The left track shows the gamma ray log <NUM>, the bit size (dashed line <NUM>) and the hole diameter (<NUM>). The middle track <NUM> shows the waveforms recorded by the first receiver over a depth interval. It will be appreciated that the CNN may also be trained with input waveforms and associated travel-times for compressional waves, shear waves, Stoneley waves and other data.

<FIG> shows the results of the training of a CNN model on a well. The input dipole waveforms were subject to transformation by a phase shift operator. In this example, the processing was done in a set of five consecutive receivers. It will be appreciated that a full array may also be used. The right track shows a comparison between the desired compressional slowness log (dashed line) and the CNN training output compressional slowness log (dotted line). The left track shows the gamma ray log <NUM>, the bit size (dashed line <NUM>) and the hole diameter (<NUM>). The middle track <NUM> shows the waveforms recorded by the first receiver over a depth interval. The multi-shot technique was used to generate both shear slowness logs.

While in the above discussion, the input waveforms have been subdivided into shot gathers to help generate the input images for the CNN, it will be appreciated that other ways of grouping the input data can also be used. For example, waveforms recorded by one receiver can be used to generate a CNN input image. Travel-times, and/or slowness values may also be used to train the neural network. In another example, the input data may include 3D images of the waveforms recorded by a sonic tool for a depth interval. A 3D image may include a collection of waveforms recorded by different receivers in a sonic tool. For respective tool position in a well, a 2D image is recorded (e.g., shot gather). When moving the sonic tool in the wellbore to cover a depth interval, a set of 2D images is generated yielding a 3D image. Along the 3D image, travel-times and/or slowness values may be used train the neural network.

In various embodiments, the inputs provided to the artificial intelligence may include 2D or 3D images generated by transforms such as deconvolution, STA/LTA, and phase shift operators. The input may also include 2D images generated by STC processing (for example as shown in <FIG> that depicts a 2D coherence image) or alternative processing techniques such as the quadrupole inversion (for example as shown in <FIG>).

It will be appreciated that combinations of the described input may also be used. In some embodiments with several types of inputs such as a combination of different input images (e.g. STC single depth image, STA/LTA Image, Dispersion Analysis image), a CNN network may be used for each type of input. The outputs of the CNN networks may be combined to generate a final output.

An example of a dispersion analysis is shown in <FIG>. This image is an example of data that may be used to train the neural network. The image includes data content for a sonic tool at a depth (e.g., <NUM>) In this example, the transformed monopole waveforms are represented by the circles <NUM>. The monopole compressional slowness may be determined based on the data of circles <NUM> (the transformed monopole waveform information). The wireline dipole waveforms are represented by the squares <NUM> and diamonds <NUM>. The shear slowness and/r the mud slowness may be determined based on the data of the squares <NUM> and the diamonds <NUM> (the wireline dipole waveform information). The data of the cyan circles may also be used in the determination of the Stoneley slowness and/or the mud slowness may also be determined. Various aspects of the dispersion data may be used for training. For example, one slowness value (e.g. dipole or quadrupole shear slowness) may be selected or several or all slowness values (e.g., compressional slowness along the dipole/quadrupole shear slowness, along the Stoneley slowness, and the mud slowness) may be selected.

Referring back to <FIG>, output from the neural network is provided at step <NUM>. The output may be in the form of an interface to other software for further processing of the output. The output may also include displaying the neural network output. <FIG> is an exemplary format for displaying the output. In <FIG>, output of a compressional slowness determination provided by a CNN trained model on monopole waveforms is shown. The trained model was applied to the monopole waveforms of a well that was not used in the training phase. The determined compressional slowness is displayed in the last track (dashed lines). For the sake of having a reference result, in the same track in purple, the result of the compressional slowness processing provided by an expert user when running the STC algorithm is shown in a dotted line. The left track shows the gamma ray log, the bit size, and the hole diameter. The second track shows the waveforms recorded by the first receiver over a depth interval. The third track shows STC intermediate processing results. The displayed results were obtained for the full array processing approach and the CNN predicted compressional slowness logs.

It will be appreciated that the present disclosure is not limited to the determination of sonic slowness. The described approach may also be used to determine any attribute in a sonic waveform without limitation. For example, the processing sonic waveforms may be used to determine attributes including travel-times of the mode of interest (e.g. P-waves, S-waves), fastshear azimuth, stiffness tensor parameters, and so forth.

<FIG> depicts an example geological system <NUM> in accordance with some embodiments. The system <NUM> can be an individual system 1101A or an arrangement of distributed systems. The system 1101A includes one or more geosciences analysis modules <NUM> that are configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, geosciences analysis module <NUM> executes independently, or in coordination with, one or more processors <NUM>, which is (or are) connected to one or more storage media 1106A. The processor(s) <NUM> is (or are) also connected to a network interface <NUM> to allow the system 1101A to communicate over a data network <NUM> with one or more additional systems and/or systems, such as 1101B, 1101C, and/or 1101D (note that systems 1101B, 1101C and/or 1101D may or may not share the same architecture as system 1101A, and may be located in different physical locations, e.g. systems 1101A and 1101B may be on a ship underway on the ocean or at a wellsite, while in communication with one or more systems such as 1101C and/or 1101D that are located in one or more data centers on shore, other ships, and/or located in varying countries on different continents). Note that data network <NUM> may be a private network, it may use portions of public networks, it may include remote storage and/or applications processing capabilities (e.g., cloud computing).

The storage media <NUM> can be implemented as one or more computer-readable or machine-readable storage media. Note that while in the exemplary embodiment of <FIG> storage media <NUM> is depicted as within computer system 1101A, in some embodiments, storage media <NUM> may be distributed within and/or across multiple internal and/or external enclosures of computing system 1101A and/or additional computing systems. Storage media <NUM> may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories; magnetic disks such as fixed, floppy and removable disks; other magnetic media including tape; optical media such as compact disks (CDs) or digital video disks (DVDs), BluRays or any other type of optical media; or other types of storage devices. Note that the instructions discussed above can be provided on one computer-readable or machine-readable storage medium, or alternatively, can be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes and/or non-transitory storage means. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). The storage medium or media can be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions can be downloaded over a network for execution.

It should be appreciated that system 1101A is only one example and that system 1101A may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of <FIG>, and/or system 1101A may have a different configuration or arrangement of the components depicted in <FIG>. The various components shown in <FIG> may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application specific integrated circuits.

It should also be appreciated that system <NUM> may include user input/output peripherals such as keyboards, mice, touch screens, displays, etc. The system <NUM> may include desktop workstations, laptops, tablet computers, smartphones, server computers, etc..

These modules, combinations of these modules, and/or their combination with hardware are all included within the scope of the disclosure.

Data acquisition system <NUM> may include systems, sensors, user interface terminals, and the like, which are configured to receive data corresponding to records collected at an oil services facility, such as an exploration unit, oil drilling rig, oil or gas production system, etc. Acquired data may include acoustic data such ass the sonic logging data discussed above as well as other sensor data, employee log data, computer generated data, and the like.

With reference to <FIG>, a multi-client system <NUM> may include a centralized services system <NUM>, which may be implemented on a cloud services system, for example. In such an embodiment, the centralized services system <NUM> may include one or more cloud data storage systems <NUM> and one or more compute nodes <NUM>. If such an embodiment, the system <NUM> may include multiple client networks, including a first client network <NUM>, a second client network <NUM>, and a third client network <NUM>. Each client network <NUM>-<NUM> may communicate with the centralized services system <NUM> via a system communication network <NUM>, which may be the Internet or a dedicated WAN connection.

In such embodiments, each of the client networks <NUM>-<NUM> may include components described in <FIG>, such as the computer systems 1101A-D and the data acquisition system <NUM>, etc. Such devices may be further connected via an internal network <NUM>. In an embodiment, the first client network <NUM> may be operated by a first customer of a data analysis system provider. In another embodiment, the second client network <NUM> and the third client network <NUM> may both be operated by a second customer, but at separate geographic locations. One of ordinary skill will recognize a variety of client/customer relationships that may be established.

In such an embodiment, each of the client networks <NUM>-<NUM> may communicate with the centralized services system <NUM> for data storage and implementation of certain centralized data processing and analysis processes. Beneficially, the centralized services system <NUM> may be configured for large scale data storage and data processing.

The present embodiments have been described with particular benefit for geological systems and services. The individual aspects and ordered combinations provide a unique and improved solution to provide accurate determination of sonic slowness, in some cases automatically, that may, in some embodiments, facilitate practical real time decision processes based on the determined sonic slowness. While these benefits have been highlighted for geological systems and services, it will be appreciated that additional fields, which may benefit from the present embodiments, include archeology, marine biology, and the like. Although the embodiments described herein may be useful in any of these many geological fields, the present embodiments are described primarily with reference to oil services.

It will also be appreciated that the described methods cannot be performed mentally. For example, the neural network has image processing capabilities that cannot be achieved by a person on any reasonable time scale. Moreover, machine learning techniques are performed, for example, by specially programmed machines.

Claim 1:
A computer implemented method for determining sonic slowness, comprising:
accessing (<NUM>) sonic logging data recorded in a borehole, where the sonic logging data includes a sonic waveform associated with a plurality of shot gathers;
applying (<NUM>) a transformation operator to the sonic logging data to provide a transformed sonic image, the transformation operator including at least one of a short time average long time average (STA/LTA) operator, a phase shift operator, and a deconvolution operator;
performing (<NUM>) a machine learning process using the transformed sonic image to determine a sonic slowness associated with the sonic logging data, wherein the machine learning process includes a convolutional neural network CNN that has been trained with input data having a known slowness or a slowness determined by an expert, the input data comprising one of recorded sonic waveforms, synthetic sonic waveforms or a combination of recorded and synthetic sonic waveforms and wherein the transformation operation is selected to convert the sonic logging data to the type of data used to train the CNN; and
providing (<NUM>) the sonic slowness as an output.