Workflow for converting dual arrival events into curtain segments of formation slowness and logging of tool layer and shoulder layer slowness

Abstract

An automatic workflow for handling dual arrival events includes: (1) automatic time pickup that locates and characterizes dual longitudinal and transverse wave arrival events present in acoustic waveform measurements; and (2) a ray tracing inversion process that inverts these temporal picks and constructs a locally layered stratigraphic model of slowness along the well trajectory. The disclosed workflow embodiments provide the benefits of: (1) automatic time pickup that estimates variations in arrival events over measured depth and determines whether the shoulder layer is above or below the well trajectory; and (2) determining a ray tracing inversion of the ray path type of the dual arrival event. The disclosed workflow embodiments provide a log display of tool layer and shoulder layer longitudinal and transverse wave slowness, which can be used to make correct porosity, VP / VS and Poisson's ratio estimates, as well as other geomechanical answers.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to and the benefit of U.S. Provisional Application No. 63 / 368,177, filed on July 12, 2022, entitled “Workflow to Convert Dual Arrival Events into Curtain Plot Section of Formation Slowness and Logs of Tool Layerand Shoulder Bed Slowness,” the entire contents of which are incorporated herein by reference for all purposes. Technical Field

[0003] The present disclosure generally relates to methods of interpreting double arrival events occurring in sonic logging. Background Art

[0004] It is well known that double arrival events occur in sonic logs acquired in high-angle and horizontal wells. They present various interpretation challenges. In particular, double arrival events can complicate the process of determining compressional slowness and porosity in the formation where the sonic tool is located, especially when the sonic tool is located near multiple formations.

[0005] Dual P- and S-wave arrival events commonly occur in high-angle wells, where P-waves and S-waves not only propagate within the layer containing the tool detector (“tool-layer arrival waves”), but are also refracted (or reflected) along a nearby shoulder bed (“shoulder-bed arrival waves”). BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Certain embodiments, features, aspects and advantages of the present disclosure will be described below with reference to the accompanying drawings, wherein the same reference numerals represent the same elements. It should be understood that the accompanying drawings illustrate various embodiments described herein and are not intended to limit the scope of the various technologies described herein.

[0007] Figure 1 Three examples of projected slowness time coherence (STC) logs are depicted where double arrival event signatures are present in both compressional (DTCO) and shear (DTSH) logs.

[0008] Figure 2 A graphic representation of a double arrival event highlighting the shoulder and tool layer arrival waves is depicted.

[0009] Figures 3A to 3C A time picking method based on single-station slowness-time coherence is depicted.

[0010] Figure 4Depicted is a workflow diagram in accordance with one or more embodiments.

[0011] Figure 5 A formation model according to an embodiment of the present disclosure is depicted, the formation model having a tool layer, a shoulder layer, and a well trajectory having a 1° dip angle relative to the shoulder layer boundary.

[0012] Figures 6A to 6C Describes the use Figure 5 Co-offset of acoustic logging tool waveforms simulated by the formation model with marked tool layer and shoulder layer arrival waves described in ( Fig. 6A )、Common Azimuth( Figure 6B ) and common ring ( Figure 6C ) Gather view.

[0013] Figure 7 Depicts a formation model with tool layers, shoulder layers, and a well trajectory relative to the wells also in Figure 5 The shoulder layer boundary shown in has an inclination angle of 1°, where the tool probe is close to the shoulder layer boundary.

[0014] Figures 8A to 8C Describes the use Figure 7 Co-offset of the sonic scanner waveform simulated by the formation model with the marked tool layer and shoulder layer arrival waves described in ( Fig. 8A )、Common Azimuth( Figure 8B ) and common ring ( Figure 8C )view.

[0015] Fig. 9 Depicted is a graphical representation of the tau-P transform in the common offset gather (COG) domain, where linear events in the COG domain are mapped to peaks in the COG tau-P domain. The tau-P peak coordinates correspond to the slope and y-intercept of the line segment along which the arrival event is located.

[0016] Fig.10 Depicted is a graphic representation of the tau-P transform in the common shot or azimuth gather (CSG) domain, where linear events in the CSG domain are mapped to peaks in the CSG tau-P domain.

[0017] Fig.11 Depicted is an illustration of the tau-P transform in the co-ring gather (CRG) domain.

[0018] Fig. 12A shows a reflection arrival event visible in the COG view of an acoustic logging tool waveform, Fig. 12B The corresponding COG tau-P treatment is shown, where fluctuations in the waveform correspond to fluctuations in the tau-P peak signature, and Fig. 12C The same tau-P results are shown after performing coherent energy post-processing.

[0019] Fig.13A Shown from Fig. 12A The reflected CSG, COG, and CRG tau-P processed 3D slices of the field measurements shown in Fig. 13B The same 3D tau-P slice is shown after convolving the tau-P processing results in τ with a boxcar function of duration Δ=0.2 ms.

[0020] Figures 14A to 14F Describes the use Figure 5 and Figures 6A to 6C 2D slice of the 4D tau-P cube measured with the waveform described in . Figures 14A to 14C shows that the shoulder layer reaches the peak value, and Figures 14D to 14F The tool layer reaching the peak value is shown.

[0021] Figures 15A to 15F Depicted Figure 5 and Figures 6A to 6C Automatic time picking of modeled acoustic scanner measurements as described in .

[0022] Figures 16A to 16F Shown for Figure 7 and 2D slices of the 4D tau-P cube measured with the waveforms described in Figure 8, while Figures 16G to 16L The corresponding time pick-offs are shown superimposed on the waveform measurements.

[0023] Figures 17A to 17C Depicts example measurements of field data. Fig.17A Showing the shoulder and tool layer P and S arrival waves in a common offset gather (COG) view of a sonic scanner waveform. Fig. 17B A Slowness Time Coherence (STC) image log is shown, indicating the presence of both double P and S arrival waves. Fig. 17C A density image is shown indicating that a denser layer is above the well trajectory that passes through the measured depth zone.

[0024] Figures 18A to 18D Describes the use Fig.17A A 2D slice of the 4D tau-P cube measured with the waveform field shown in .

[0025] Figures 19A to 19D Depicted Fig.17A Results of automatic time picking of the sonic scanner waveform shown in .

[0026] Fig. 20Standard commercial P- and S-wave slowness logs (DTCO and DTSH) and slowness logs derived from automatic time picking (DTCOT, DTCOS, DTSHT, and DTSHS) are depicted. DTCOT and DTSHT correspond to tool layer P- and S-waves, while DTCOS and DTSHS correspond to shoulder layer P- and S-waves.

[0027] Figures 21A to 21D Illustration depicting four commonly observed double-arrival-wave raypath types: PPP refraction, PP reflection, SSS refraction, and SS reflection.

[0028] Fig.22A and 22B The labeled ray paths in PPP and PP ray tracing are depicted respectively.

[0029] Fig.23 A graphical representation of the ray tracing inversion parameters of interest is depicted.

[0030] Figures 24A to 24D Depicted are the input data for ray tracing inversion: travel times for forward modeling and slowness value and FIG. 21A to FIG. 21D The four ray path types shown in .

[0031] Fig.25 Depicted from FIG. 17A to FIG. 17C Example of field data from , where arrival events are marked using automated time picking.

[0032] Figures 26A to 26D Describes the use of Fig.25 Input data for ray tracing inversion obtained using automatic time picking shown.

[0033] Fig.27A and 27B Depicted is a diagram of ray paths and curtain segments derived from ray tracing inversion.

[0034] Fig.28 Depicts the corresponding Fig.21A Figure 2. Method of constructing curtain segment inversion results for a single PPP time pick of a synthetic example.

[0035] Fig.29A and 29B Depicted is the final curtain segment showing the Fig.21A Inversion results for the PPP synthetic example.

[0036] Fig. 30A and 30B Depicted by Fig.25 Final curtain segment resulting from ray tracing inversion of the field measurements shown in .

[0037] Fig.31A and 31B Depicted is the 3D geological modeling software (Petrel) along the well trajectory. Fig. 30A and 30B PPP and SSS curtain segments.

[0038] Fig.32 Further 3D results derived from the workflow are depicted. DETAILED DESCRIPTION

[0039] In the following description, many details are set forth to provide an understanding of some embodiments of the present disclosure. It should be understood that the following disclosure provides many different embodiments or examples for realizing the different features of various embodiments. Specific examples of components and arrangements are described below to simplify the present disclosure. Of course, these are merely examples and are not intended to be limiting. However, it will be understood by those of ordinary skill in the art that systems and / or methods may be practiced without these details, and that many variations or modifications from the described embodiments are possible. This description should not be considered to have a limiting meaning, but only for the purpose of describing the general principles of the embodiments. The scope of the described embodiments should be determined with reference to the issued claims.

[0040] As used herein, the terms "connect," "connection," "connected," "in connection with," and "connecting" are used to mean "directly connected with" or "connected with via one or more elements"; and the term "group" is used to mean "one element" or "more than one element." In addition, the terms "couple," "coupling," "coupled," "coupled together," and "coupled with" are used to mean "directly coupled together" or "coupled together via one or more elements." As used herein, the terms "upper" and "lower"; "upper" and "lower"; "top" and "bottom"; and other similar terms indicating relative positions to a given point or element are used to more clearly describe some elements. Generally, these terms refer to a reference point at the surface from which the drilling operation is started as the apex and the total depth as the lowest point, where the well (e.g., wellbore, borehole) is vertical, horizontal, or inclined relative to the surface.

[0041] As we all know, Figure 1As shown, double arrival events occur in sonic logs acquired in high-angle and horizontal wells. They present various interpretation challenges. In particular, double arrival events can complicate the process of determining compressional slowness and porosity in the formation where the sonic tool is located, especially when the sonic tool is located near multiple formations.

[0042] Dual P- and S-wave arrival events often occur in high-angle wells, where P-waves and S-waves not only propagate within the layer containing the tool detector (“tool layer arrival waves”), but are also refracted (or reflected) along nearby shoulder layers (“shoulder layer arrival waves”), e.g. Figure 2 shown. Figure 1 The dual arrival wave feature shown in the log corresponds to the apparent slowness of the arrival waves from these tool layers and the shoulder layer.

[0043] This paper discloses improvements to automatic time picking constructed from single station slowness-time coherence (STC), where double arrival events are identified and then processed to map nearby shoulder layers. Figure 3A Shows that there is Figure 3B Common shot gather (CSG) view of a simulated waveform measurement of the event marked in . Figure 3B A slowness temporal coherence process of the waveform at 659.5 feet (ft) MD is shown. Figure 3C Shown is a common offset gather (COG) view with waveforms for events 1, 2, 4, and 8 hand marked at 659.5 ft MD.

[0044] Figure 3B Slowness-Time Coherence (STC) analysis of the waveform events labeled with index #1, 4, 8, and 2 in the left panel is shown. If event #1 is identified as a double arrival, the P-wave arrival from the shoulder layer has an apparent slowness of μs / ft and arrival time ms, then, assuming the shoulder and well path are parallel to each other, ray tracing can be used to calculate the relative distance to the shoulder to be approximately 1.3 ft. We can then use 3D Slowness Time Coherence (3D STC) on Event #1 to determine if the shoulder is above or below the well path. This single-station approach is most useful for mapping the shoulder and tool layers along the well path.

[0045] However, the single-station STC time picking method does not provide Figure 3A and 3B Automatic labeling of double arrival events in the shoulder layer. The single-station calculation above requires an expert to interpret event #1 as a P refraction in the shoulder layer. Correctly labeling double arrival events as P reflection, P refraction, S refraction, or S reflection is very important to determine what information the double arrival event can provide about the shoulder layer. In addition, single-station STC time picking does not capture the lateral continuity of the event. Figure 3CA co-offset (COG) or single sensor view of the waveform as a function of measured depth is depicted, and events are marked with indexes #1, 4, 8, and 2 at a measured depth of 660 ft on the display. In the disclosed embodiment of the disclosed method, automatic time picking is allowed to capture this lateral continuity of arrival events in order to quantify how the arrival times of double arrival events vary as a function of measured depth. This can be used to further constrain ray tracing inversion, particularly for determining the ray path type of double arrival events.

[0046] The 3D STC analysis performed as a subsequent and separate step after STC time picking strongly relies on The estimated value of,the arrival time of the selected event. Small errors or noise in can mean that the 3D STC azimuth information is contaminated by events other than double arrival events. Therefore, the disclosed method for automatic time picking captures the azimuth variation of the arrival events as a function of the nominal receiver azimuth, and therefore determines whether the shoulder layer is above or below the well trajectory.

[0047] Peak finding using a single station STC can be a very noisy process and can result in many false peaks that affect the interpretability of the resulting curtain segments. In addition, shifts in STC peak positions due to noise can also affect ray tracing inversion accuracy. The disclosed method for automatic time picking can stack double arrival events as a function of measured depth, source-receiver offset, and nominal receiver azimuth, which can increase the signal-to-noise ratio of time picking relative to background noise.

[0048] The disclosed method performs physical modeling of double arrival events to understand the details of how double arrival events present themselves to a 3D acoustic receiver array sensor, particularly as a function of measured depth, source-receiver offset, and nominal azimuth; develops automatic time picking that follows the physical modeling and captures the arrival time variations of double arrival events as a function of measured depth, source-receiver offset, and nominal azimuth; develops ray tracing inversion that can utilize the information generated by this new time picking to (a) automatically identify the ray path type of double arrival events; (b) automatically distinguish between shoulder layer arrival events and direct tool layer arrival waves; (c) generate tool layer and shoulder layer slowness logs to aid in formation evaluation; and (d) generate curtain plots of formation slowness along the well trajectory; and (e) generate true dip and azimuth logs that describe the local orientation of the shoulder layer boundary as a function of measured depth.

[0049] Figure 4An example workflow is depicted. Waveform data, preliminary sonic logs, well track surveys, and bedding / dip from BH images are workflow inputs, while logs of tool layer and shoulder layer slowness along the well track and curtain map slowness are workflow outputs. The other boxes are main workflow tasks. The workflow uses waveform measurements recorded using an acoustic logging tool with at least one source and receiver sensor array as the primary input. Figures 6A to 6C and Figures 8A to 8C The modeled waveforms processed using this workflow are depicted, and Figures 17 and Fig.25 Field data acquired using an acoustic logging tool is depicted. The workflow also takes as input preliminary compressional (DTCO) and shear (DTSM) acoustic logs, which are typically calculated on the acoustic logging tool during logging while drilling (LWD) operations or at the well site during wireline operations. These logs are used to help determine the range of values ​​for the time variable τ used for the tau-P transform.

[0050] The automated time-picking workflow outlined here generates τ-p peaks A collection of tau-P peaks that characterize the presence of double arrival events in the waveform measurement. Figures 14A to 14F and Figures 16A to 16L As shown in Figure 2, the acoustic logging tool used for modeling measures Figures 18A to 18D Equation (6) shows how to calculate the corresponding tau-P peak value of each double arrival event. To calculate a set of arrival times for each double arrival event (ie, time pickup). Figures 15A to 15F and Figures 16A to 16F These time picks measured by the modeled acoustic logging tool are shown, and Figures 19A to 19D Time picking of a field acoustic logging tool waveform is shown. We note that when considering measurements with an acoustic logging tool, it is very important to adjust the automatic time picking to handle tool rotation, which we will address below.

[0051] The time-picking classifier workflow described below classifies events according to their apparent slowness. tau-P peak Labeled as one of tool layer P-wave, tool layer S-wave, shoulder layer P-wave, or shoulder layer S-wave. This classification produces corresponding logging displays, such as Fig. 20 This classification also yields a new definition of the tool layer P-wave slowness. and tool layer shear wave slowness These estimates are used to formulate the forward model in equation (12), which is used by the ray tracing inversion.

[0052] The ray tracing inversion workflow described in detail below determines the ray path types of shoulder arrival events flagged by the classifier workflow and inverts the corresponding event travel times for a local layered model of formation slowness. This automatic ray path selection for shoulder events is demonstrated using the check marks for the modeled acoustic logging tool data in Table 1 (shown in the Appendix) and the field acoustic logging tool data in Table 3 (shown in the Appendix). Ray tracing inversion produces parameter estimates for the local layered formation model for the slowness of the selected ray path type. These parameter estimates are demonstrated for the modeled acoustic logging tool data in Table 2 (shown in the Appendix) and for the field acoustic logging tool data in Table 4 (shown in the Appendix).

[0053] The process described below for constructing a curtain segment of slowness along a well trajectory begins with a process for converting the local coordinates of the shoulder and sonic logging tool used by the ray tracing inversion to their coordinates along the well trajectory. Since the ray tracing inversion only plots the shoulder in 2D relative to the well trajectory, here we incorporate the time-picking available from the automatic The information obtained indicates whether the shoulder layer is above or below the well trajectory. If the acoustic receiver array does not have an azimuth receiver sensor, the estimated is not available, you can replace Fig. 17C density image or similar borehole image log. Fig.28 The results of this first process are shown in , where a curtain segment of slowness is constructed for a single double-arrival wave PPP refraction event. A subsequent spatial averaging process then merges these local curtain segments for each double-arrival event together into a complete curtain segment of slowness along the length of the well trajectory, as Fig.29A and 29B as well as Fig. 30A and 30B shown.

[0054] An important focus of our physical modeling approach is to understand the details of how shoulder and tool layer arrival events present themselves to receiver sensors mounted around the circumference of the acoustic receiver array as a function of measured depth, source-receiver offset, and nominal azimuth. A major result of this physical modeling work is the observation that shoulder and tool layer arrival events differ from each other in at least three ways:

[0055] The arrival time of the shoulder event varies with the distance to the shoulder, while the arrival time of the tool layer varies only with the slowness of the formation; e.g. Fig. 6A and 8A This distinction is most easily observed in the Common Offset (Single Sensor) (COG) view of the waveform measurements as a function of measured depth, highlighted in FIG.

[0056] The apparent slowness of the wave arriving from the shoulder layer is faster than that of the wave arriving from the tool layer; this difference is reflected in the following examples: Figure 6B and 8B This is most evident in the common shot or azimuth (CSG) view of the waveform as a function of source-receiver offset highlighted in .

[0057] The shoulder arrival waves exhibit significant sinusoidal time differences as a function of the nominal receiver azimuth in the common ring gather (CRG) domain, such as Figure 6C and 8C Highlighted in .

[0058] Our first example of physical modeling work is in Figure 5 , where we consider a two-layer model consisting of (a) a tool layer (with P- and S-wave slownesses of 76 μs / ft and 136 μs / ft, respectively, and a density of 2500 kg / m 3 ) and (b) the nearby shoulder layer (with corresponding slownesses of 61 and 110 μs / ft and density of 2450 kg / m 3 ). The wellbore trajectory has a 1° dip relative to the horizontal shoulder boundary. The ray paths from the source (approximately 192 feet square) to the shoulder boundary at 1000 feet vertical offset and then to each receiver sensor (approximately 178-182 feet square) show the P refraction. We performed similar ray tracing for the P-reflection, S-refraction, and S-reflection.

[0059] We used 3D finite-difference simulations to forward model the acoustic logging tool measurements in a 5 7 / 8” diameter borehole at each measurement station along the well trajectory in the measured depth region marked by the red ellipse. The sonic logging tool receiver array consists of 13 rings of 8 receiver sensors evenly distributed around the circumference of the tool sonde. Figures 6A to 6C (as well as Figures 8A to 8C ) show forward modeled waveform measurements in common offset (COG), common shot or azimuth (CSG), and common ring (CRG) gather views, respectively. Fig. 6A and 8A Shown is a waveform recorded by a single receiver sensor of a sonic logging tool receiver array as a function of measured depth along a well trajectory. Figure 6B and 8B Shown are waveforms recorded by 13 receiver sensors with fixed nominal azimuths along one side of an acoustic logging tool receiver array. Figure 6C and 8C Shown are waveforms recorded by eight receiver sensors around the circumference of an acoustic logging tool receiver array at a fixed offset from the acoustic logging tool source.

[0060] From checking from Figures 6A to 6C Several physical observations can be derived from the waveform. Figures 6A to 6C The waveforms shown in are generated by 3D finite difference simulations for the measured depth region at Figure 5 is circled. Fig. 6A It can be observed that the arrival time of the shoulder arrival wave varies depending on the distance from the tool probe to the shoulder. It can also be observed that because the sonic tool is 3 to 4 feet away from the shoulder, the time required for the shoulder arrival wave to propagate to the shoulder and back is long enough that the shoulder arrival wave arrives after the tool arrival wave, which propagates directly from the source to the receiver array within the tool. In addition, Figure 6B A careful inspection of the waveforms in shows that the tool layer arrival wave has a faster apparent slowness than the shoulder layer arrival wave. Finally, Figure 6C It is shown that the shoulder layer arrival wave does show a significant sinusoidal time difference compared to the tool layer arrival wave. This sinusoidal time difference occurs because the shoulder layer arrival wave first arrives at the receiver sensor facing the shoulder layer, and then arrives at the receiver sensor facing away from the shoulder layer later.

[0061] A second example of our physical modeling work is Figure 7 As shown in the figure, we consider Figure 5 The same two-layer model and well path shown in Figure 1. Here, we perform finite difference modeling of the measured depth stations along the well path (circled) where the sonic tool is closer to the shoulder boundary. The ray paths from the source (about 338 feet square) to the shoulder boundary and then to each receiver sensor (about 323-330 feet square) show the P-refraction. We perform similar ray tracing for the P-reflection, S-refraction, and S-reflection.

[0062] By checking from Figures 8A to 8C Several further physical observations can be derived from the waveform of Fig. 8A It can be observed that the arrival time of the shoulder arrival waves varies depending on the distance from the tool probe to the shoulder. It can also be observed that because the sonic tool is 1 to 2 feet away from the shoulder, the time required for the longitudinal and shear shoulder arrival waves to travel to the shoulder and back is short enough, and the propagation along the shoulder is fast enough, so that the shoulder arrival waves arrive before the corresponding tool arrival waves. In addition, Figure 8B A careful examination of the mid-waveforms shows that the apparent slowness of the tool layer arrival wave is slower than the apparent slowness of the shoulder layer arrival wave. Figure 8C It is shown that the arrival of the longitudinal and shear waves in the shoulder layer exhibits a significant sinusoidal time difference compared to the arrival of the tool layer. This sinusoidal time difference occurs because the arrival of the shoulder layer arrives first at the receiver sensor facing the shoulder layer, and then later at the receiver sensor facing away from the shoulder layer. It should also be noted that the error! Reference source not found to Figure 8CThe curves shown in are the arrival time curves of the tool layer compressional and shear waves calculated based on the preliminary compressional wave (DTCO) and shear wave (DTSM) slowness logging.

[0063] The goal of automatic time picking is to detect and characterize tool and shoulder arrival waves present in waveform measurements recorded by an acoustic logging tool. Automatic time picking consists of (a) a series of tau-P transforms in the common offset (COG), common shot point (CSG), and common ring (CRG) gather domains that follow the patterns observed during our physical modeling; (b) post-processing that includes coherent energy and coherence estimation techniques that improve the clarity of the tau-P processing results; and (c) a peak finding method to locate local maxima from the results of steps (a) and (b) that correspond to tool and shoulder arrival events in the recorded wavefield measurements.

[0064] Tau-P processing methods in the common-offset (COG), common-shot or azimuth (CSG), and common-ring (CRG) gather domains are designed to separate and characterize tool and shoulder arrivals and are described herein. All three tau-P maps locate events in the recorded wavefield measurements and characterize arrival events in terms of: (1) their slope and arrival time in the COG domain; (2) their apparent slowness in the CSG domain; and (3) whether the event is from above or below the well trajectory based on its time difference in the CRG domain.

[0065] Error! Reference source not found provides an illustration of the COG tau-P processing, which maps linear events in the recorded wavefield domain to points in the tau-P domain. tau-P input waveform Typically recorded by a single receiver sensor of an acoustic receiver array at 15-60 continuously measured depth stations along the well trajectory Equation (1) describes the COG tau-P process, where is the slope of the arrival events in the COG view measured by the waveform, and τ is the depth interval measured The arrival time of the event at the midpoint of the tau-P domain The peaks in correspond to the slope and arrival time of the arrival event, and determine the line segment along which the arrival event is located. A subsequent event localization process is required to determine where the event is located along the line segment.

[0066]

[0067] Used for The default range for values ​​of is between -20 μs / ft and +20 μs / ft, and the default range for values ​​of τ is between ms and where DTCO and DTSM are preliminary P- and S-wave slowness log estimates, respectively, TR is the transmitter-receiver spacing of the acoustic logging tool, and the two fractions convert the two slowness estimates to their corresponding arrival times in milliseconds.

[0068] Fig.10 Provides an illustration of the CSG tau-P process that maps events in the common shot or azimuth gather domain to points in the tau-P domain. The tau-P peak coordinates correspond to the apparent slowness and arrival time of the event at the receiver sensor at the midpoint of the receiver array. tau-P input waveform The acoustic receiver array has a source-receiver offset along the side with a fixed nominal azimuth The receiver sensors are recorded at a single survey depth station. Equation (2) describes the CSG tau-P processing, where is the apparent slowness of the arrival event, τ is the receiver array The arrival time of the event at the midpoint of the peak in the tau-P domain Corresponds to the apparent slowness and arrival time of the arriving wave at the midpoint of the receiver array.

[0069]

[0070] Used for The default range for values ​​of is between 40 μs / ft and 125 μs / ft, while the default range for values ​​of τ is again between ms and where DTCO and DTSM are preliminary P- and S-wave slowness log estimates, respectively, TR is the transmitter-receiver spacing of the acoustic logging tool, and the two fractions convert the two slowness estimates to their corresponding arrival times.

[0071] Fig.11 An illustration of the CRG tau-P process is provided that maps events in the co-loop track set domain to points in the tau-P domain. The tau-P peak coordinates correspond to the magnitude of the event sinusoidal moveout as a function of the nominal receiver azimuth and event arrival time. Negative CRG tau values ​​correspond to when the shoulder is above the well trajectory, while positive CRG tau values ​​correspond to when the shoulder is below the well trajectory.

[0072] tau-P input waveform The receiver azimuth is , recorded by a single-loop receiving sensor at a measured depth station with a fixed source-receiver offset. In equation (3), is the receiver azimuth measured from the top of the tool detector, is the tool detector radius, so is the vertical offset of the kth receiver sensor relative to the center axis of the tool probe. CRG slowness estimate The magnitude of the sinusoidal moveout is thus represented as a function of the nominal azimuth angle around the circumference of the tool probe, and can take positive or negative values ​​depending on whether the shoulder arrival wave is from a shoulder below or above the well trajectory, respectively.

[0073]

[0074] Peaks in tau-P domains Corresponds to events at the center axis of the tool probe arrival time, and means that the arrival event corresponds to the shoulder layer above the well trajectory, while This means that the arrival event corresponds to the shoulder layer below the well trajectory. It will then be used in various log and waveform displays, as well as in the ray tracing inversion described below.

[0075] Used for The default range for values ​​of is between -110 μs / ft and +110 μs / ft, while the default range for values ​​of τ is again between ms and where DTCO and DTSM are preliminary P- and S-wave slowness log estimates, respectively, TR is the transmitter-receiver spacing of the acoustic logging tool, and the two fractions convert the two slowness estimates to their corresponding arrival times.

[0076] Although tau-P processing methods in the common-offset (COG), common-shot or azimuth (CSG), and common-ring (CRG) gather domains are designed to separate and characterize tool and shoulder arrivals, here we illustrate important post-processing methods that are useful for improving the tau-P peak Clarity is crucial.

[0077] When dealing with waveform measurements, fluctuations in waveform arrival events often result in fluctuations in the corresponding tau-P peaks, such as Fig. 12A and 12C As shown. We Fig. 12A Reflection arrival events (circled) sloping from lower left to upper right are observed around recording time 1.9 ms. Fig. 12B The corresponding COG tau-P processing results are shown, where the event slope indicated by the purple arrow and The corresponding peak features at φ show the same fluctuation characteristics. These fluctuations in the peak features occur because the corresponding COG tau-P processing from equation (1) performs line averaging of the waveform along the corresponding fluctuations of the reflected arrival events.

[0078] To address this problem for this example, we employ the coherent energy estimate shown in the first line of equation (4), which first calculates the COG slowness from The wave fields are superimposed as a function of the measured depth and the duration is estimated as The coherent energy in the time window of . This coherent energy technique is adapted from the corresponding slow time coherence method. The coherent energy processing results in Fig. 12C , where the fluctuations have been largely removed and the peak features corresponding to the reflected arrival waves highlighted with white arrows are more evident.

[0079]

[0080] The second line of equation (4) highlights the fact that the coherent energy estimate can be written as the COG tau-P transform from equation (1) convolved with a square pulse function of duration Δ This is a very helpful observation as it suggests that when combining the CSG, COG, and CRG tau-P processing from equations (1)-(3), we can use this convolution operation as post-processing, as described in the combined tau-P workflow described in the next section.

[0081] Similar post-processing methods can be used to improve the clarity of the tau-P peak, including by estimating the total energy Normalization To calculate the coherence estimate , as shown in Equation 5.

[0082]

[0083] Here is the number of measured depth stations in the common offset gather. Refer to the same COG tau-P operation as in equation (1), but applied to a squared waveform .

[0084] Fig.13A and 13B The effectiveness of this box-pulse convolution operation for improving the clarity of the combined CSG, COG, CRG tau-P processing as described in step 6 of the combined tau-P workflow described in the next section is illustrated. Fig.13A Shown for Fig. 12A The waveform shown in CSG-COG-CRG- We observed characteristic fluctuations in tau-P peaks, which made it difficult to locate and characterize arrival events. Fig. 13B It shows that the time variable 4D CSG-COG-CRG- The same 3D slice obtained after convolution with the square pulse function. Again we observe Significant improvement in clarity around the peaks, which is critical.

[0085] The entire automated time-picking workflow can be summarized as follows:

[0086] 1. Compute tau-P in the CSG domain for each MD position and each nominal azimuth according to Equation 2. Concatenate the results into a 4D cube. The cube is parameterized as tau-CSG × p-CSG × nominal receiver azimuth × MD.

[0087] 2. Permute the cube index to tau-CSG × nominal receiver azimuth × MD × p-CSG.

[0088] 3. Calculate CRG tau-P for each MD x p-CSG slice according to Equation 3. This results in tau-CRG x p-CRG x MD x p-CSG.

[0089] 4. Permute the cube index to tau-CRG × MD × p-CRG × p-CSG. Store the cube in step 8 for use in the event localization process.

[0090] 5. Calculate COG tau-P for each p-CRG x p-CSG slice according to Equation 1. This yields tau-COG x p-COG x p-CRG x p-CSG.

[0091] 6. Following the pattern of Equation 4, plot the 4D cube as a function of tau-COG vs. duration Convolution with a 1D square impulse function.

[0092] To help the time picker search for high energy coherent events in the waveform measurement, an automatic time picking procedure is performed as follows.

[0093] 7. Use a local median filter on the 4D tau-P cube to reduce noise and sharpen peaks.

[0094] 8. Find and sort local maxima in a 4D cube.

[0095] 9. For the selected local maximum, employ the event location procedure of the projected waveform from the tau-CRG x MD x p-CRG x p-CSG cube to find the start and end MD of the arrival event in the common offset gather domain.

[0096] The output of the automatic time picking process includes: (a) a list of peak coordinates and (b) the corresponding arrival event travel time ,in denote the source-receiver offset and nominal receiver azimuth of the acoustic receiver array, respectively, and i denotes the depth station at which the wavefield was measured. These travel times It can be calculated from the tau-P peak coordinates according to Equation 6.

[0097]

[0098] We now consider some additional important details to consider when performing automatic time picking as the tool rotates. When performing the general tau-P processing step, the input waveform Defined in the time domain grid On and in the spatial grid On the spatial grid, the grid has The value on .

[0099] Input waveform tau-P transform Will have in the grid In addition, a common practice for defining the output τ grid is to define the same slowness as the input waveform The same time sampling interval of , and according to equation (7) and .

[0100]

[0101] When performing CSG or COG tau-P processing as in steps 1 and 5 listed above, the spatial grid x defining the input waveform will always be fixed such that and The value of and hence the tau-P output grid will also be fixed. For CSG tau-P processing, the spatial grid is given by the source-receiver offsets of the acoustic logging tool. For COG tau-P processing, the spatial grid will be given by the distance between the measured depth stations, which is typically fixed at 0.5 ft.

[0102] However, when CRG tau-P processing is performed as in step 3 of the automatic time-picking workflow listed above, the spatial grid consists of the spatial locations of the receiver sensors around the circumference of the tool detector. This will depend on the relative tool orientation. Therefore, for the CRG tau-P transform defined in Eq. The output τ grid will also depend on the tool orientation. This tool orientation dependency will greatly complicate the subsequent COG tau-P processing in step 5. To address this issue, we adopt the CRG tau-P transformation as in Equation 8 The fixed output time grid of .

[0103]

[0104] This modification is helpful when recording waveforms using a wireline acoustic logging tool, where the tool can often be rotated. When using a logging while drilling (LWD) tool (where the tool rotates every few seconds) ), this modification is crucial.

[0105] The automatic time picking process has been tested on several modeling and field datasets. Figures 14A to 14F Two-dimensional (2D) slices from a 4D tau-P array are shown, which were obtained using a Figure 5 and Figures 6A to 6C The three panels on the left of the figure show the peaks of the shoulder layer arrival wave. The COG-CRG slowness, COG-CSG slowness, and time-CSG slowness slices are shown in the figure, while the three panels on the right side show the peaks corresponding to the tool layer arrival events. Each panel shows two peak coordinates in the 2D slice, and the caption of each panel gives the other two peak coordinate values. We observed that tau-P processing clearly separated the peaks corresponding to the shoulder and tool layer arrival waves. We also observed that the post-processing described in step 6 of the automated workflow resulted in a high level of clarity in the 4D array for identifying tool layer and shoulder layer arrival events.

[0106] Figures 15A to 15F The calculated values ​​according to Equation 6 are shown and overlaid on the Figures 14A to 14F The tau-P coordinates identified in Automatic time picking on waveform measurements . Figures 14A to 14CThe time picking of the shoulder arrival waves in the common offset (COG), common shot point (CSG), and common ring (CRG) gather domains is shown, while Figures 14D to 14F The time picking of the tool layer arrival wave is shown. The time window shown as the black waveform overlay is calculated as and , where Δ is the window length of the boxcar function used in step 6 of the automatic time-picking workflow. We observe that the time-picked waveform superposition is very informative for identifying various arrival events.

[0107] Figures 16A to 16F 2D slices from a 4D tau-P array are shown for a Figure 7 and Figures 8A to 8C The waveform of the shoulder layer and tool layer arrival wave described in Figures 16G to 16L The corresponding time-picked event overlay is shown. FIG. 16A to FIG. 16C shows the peak of the wave reaching through the shoulder layer P COG-CRG slowness, COG-CSG slowness and time-CSG slowness slices, while FIG. 16D to FIG. 16F The corresponding 2D slices for the shoulder layer S arrival wave are shown. As shown in Figure 14, each panel shows the coordinates of two peaks in the 2D slices, and the title of each panel gives the other two peak coordinate values. We again observe that the tau-P processing results produced by the automated workflow clearly separate the peaks corresponding to the two shoulder layer refractions. For the sake of brevity, we have omitted the slice set corresponding to the tool layer arrival wave, although the automated processing separates and identifies the tool layer arrival wave in a very similar and effective manner.

[0108] Figures 16G to 16L The calculation according to Equation 6 and superimposed on the corresponding Figures 16A to 16F The tau-P coordinates identified in Automatic time picking on waveform measurements . Figures 16G to 16I The time picking of the shoulder P arrival waves in the common offset (COG), common shot point (CSG), and common ring (CRG) gather domains is shown, while Figures 16J to 16L The time picking of the arrival wave of the shoulder layer S is shown. The time window shown as the black waveform superposition is calculated as and Likewise, time-picked waveform overlays are very useful for identifying various arrival events.

[0109] Figures 17A to 17C An example of field data is presented where automatic time picking was used to identify and characterize tool and shoulder arrival waves along a highly deviated well in a limestone layer located between two faster dolomite layers. Fig.17AA common offset (COG) gather view of the sonic scanner waveform is shown with the tool and shoulder arrivals labeled. The tool arrivals appear as mostly horizontal waveform features in the COG waveform view, while the shoulder P and S arrivals have arcuate features received before the corresponding tool arrivals. We note the preliminary arrival time estimates of the tool P- and S-wave arrival times calculated from the P- (DTCO) and S-wave (DTSM) slowness logs, respectively. Fig. 17B A Slowness Time Coherence (STC) image log is shown where both double arrival wave P and S features are present. Fig. 17C is an azimuthal density image log, which indicates the The denser layer (dolomite) in relative azimuth is above the well trajectory.

[0110] Figures 18A to 18D Shown from the Fig.17A The waveform shown in Figure 2 produces a 2D slice of a 4D tau-P array, while Figures 19A to 19D The corresponding time-picked event overlay is shown. Figures 18A to 18D The four 2D slices shown in are all COG-CSG slices for arrival events #1, 4, 7, and 11, which are Figures 19A to 19D These same four events are identified in the fourth measured depth interval near 9180 ft MD. Fig.25 As with the modeled data example, we observe that the tau-P processing clearly separates the peaks corresponding to the shoulder and tool layer arrival waves, as these arrival events have different values ​​of CSG apparent slowness. We also observe that the post-processing described in step 6 of the automated workflow results in a high level of clarity in the 4D tau-P array for identifying tool and shoulder layer arrival events.

[0111] Figures 19A to 19D Shown by the Fig.17A The waveform overlay produced by the automatic time-picking workflow of the acoustic scanner waveform measurement shown in . The shoulder layer S and P arrival waves are Fig.19A and 19C As shown in the figure, the tool layer S and P arrival waves are Fig.19B and 19D The arrival event includes an estimate of the apparent slowness and an upward or downward arrow indicating whether the shoulder is above or below the well trajectory. The arrival event is calculated using and The boxes also include the CSG slowness of the indicated events ( ) and an upward or downward arrow indicating, for example, whether the shoulder is above or below the well trajectory. If the CRG slowness is negative ( ), the arrow points up if the CRG slowness is positive ( The shoulder layer P and S arrival wave marks indicate that the shoulder layer arrival waves are faster than their corresponding tool layer arrival waves, and the shoulder layer is above the well trajectory - the observation result is consistent with Fig. 17C These waveform annotations are very useful in identifying various double arrival events present in waveform measurements.

[0112] Correctly identifying tool-layer and shoulder-layer P- and S-wave slownesses is critical to providing accurate formation evaluation answers derived using sonic measurements. This is especially true when the well trajectory traverses multiple reservoir zones, where porosity estimates reservoir layers and drives decisions about completion design. Here, we describe a process for (a) separating tool-layer CSG apparent slowness estimates and shoulder-layer P- and S-wave slownesses generated by automated time picking, and (b) creating a well log display that is useful for addressing this formation evaluation challenge.

[0113] Figures 19A to 19D The notes in provide CSG slowness estimates for P- and S-wave slownesses in the tool and shoulder layers. From these CSG slowness estimates, we can create four corresponding slowness logs (DTCOT, DTCOS, DTSMT, DTSMS), where the first four letters indicate whether this is a compressional (DTCO) or shear slowness (DTSM) log, and where the last letter indicates whether this compressional or shear slowness estimate is associated with the tool layer (T) or the shoulder layer (S). This can be done manually or using the automatic classifier process described below.

[0114] Fig. 20 The top panel shows tool and shoulder slowness logs derived from automatic time picking (DTCOT, DTCOS, DTSMT, DTSMS) using the autoclassifier workflow listed below, as well as standard commercial P-wave (DTCO) and S-wave (DTSH) logs in the same measured depth interval. Tool layer P and S slowness values ​​are marked with grey squares in the left panel as are CRG azimuth estimates. The slowness estimate for the tool layer is not significant. Fig. 20The bottom panel of shows the same log in a commercial log interpretation software (TechLog). We observe that the commercial compressional wave log (DTCO) is picking up the shoulder layer compressional wave slowness, while the commercial shear wave slowness log (DTSH) is picking up the tool layer slowness. Therefore, the VP / VS ratio and Poisson's ratio derived through this measured depth interval using the commercial slowness log will be inaccurate, as well as leading to incorrectly derived estimates of tool layer porosity and rock strength. Using the tool layer and shoulder layer slownesses derived using automatic time picking will lead to correct formation assessment answers for the tool layer. In addition to this, automatic time picking also provides the opportunity to estimate the VP / VS and Poisson's ratio of the shoulder layer, thereby providing important new formation assessment estimates of porosity and rock strength in the shoulder layer.

[0115] Our workflow for classifying the automatic time picking results into four categories (tool layer P-wave, tool layer S-wave, shoulder layer P-wave, and shoulder layer S-wave) proceeds as follows:

[0116] (1) Select four constants to configure this autoclassifier workflow.

[0117] a. Select the threshold P slowness value, , which is larger (i.e., slower) than all tool layer and shoulder layer P slowness values ​​derived using automatic time picking. This threshold is intended to separate tool layer and shoulder layer P slowness from slower tool layer and shoulder layer S slowness. For the field data example shown here, we have μs / ft.

[0118] b. Select the tool layer P slowness event window size, , which describes the range of tool layer PC Slowness values. This window size is intended to help capture the automated time-picked slowness values ​​that belong to the tool layer P arrival wave. All shoulder layer PC Slowness values ​​will be less than the tool layer PC Slowness value. For the field data example shown here, we use μs / ft.

[0119] c. Select the threshold S slowness value, , which is larger (i.e., slower) than all tool layer and shoulder layer slowness values ​​derived using automatic time picking. This threshold is intended to separate tool layer and shoulder layer slownesses from the slower Stoneley slowness values. For the field data example shown here, we use μs / ft.

[0120] d. Select the tool layer S slowness event window size, , which describes the range of tool layer P slowness values. This window size is intended to help capture the automatic time-picking slowness values ​​that belong to the tool layer S arrival wave. All shoulder layer S CSG slowness values ​​will be less than the tool layer S CSG slowness value. For the field data example shown here, we use μs / ft.

[0121] (2) For each measured depth interval where automatic time picking is run, we derive the tool layer and shoulder layer P slowness values ​​as follows:

[0122] a. For CSG slowness value Sort in ascending order

[0123] b. Find the final CSG slowness value that is less than This CSG value is called .

[0124] c. Yes and Group all CSG slowness values ​​between . These are the tool layer P slowness values. We index and label them.

[0125] d. will be less than (faster than) All CSG slowness values ​​are grouped. These are the shoulder layer P slowness values. We index and label them.

[0126] e. Remove all tool and shoulder P values ​​from the sorted list of CSG slowness values.

[0127] (3) Then, to derive the tool and shoulder slowness values ​​for the same measured depth interval as in step (2), we proceed as follows:

[0128] a. Find the final CSG slowness value that is less than This CSG value is called .

[0129] b. Yes and Group all CSG slowness values ​​between . These are the tool layer S slowness values. We index and label them.

[0130] c. will be less than (faster than) All CSG slowness values ​​are grouped. These are the shoulder layer S slowness values. We index and label them.

[0131] The output of this automatic classification workflow is to convert the CSG slowness values ​​generated by automatic time picking into Labeled as tool layer P, shoulder layer P, tool layer S and shoulder layer S arrival events. Fig. 20 In the well logging display, they are marked as DTCOT, DTCOS, DTSMT, and DTSMS respectively.

[0132] The goal of the ray tracing inversion workflow is to invert the time picks associated with each shoulder arrival event along the well trajectory where dual arrival events exist, for a local layered model of formation slowness. Here we discuss an important aspect of this inversion problem - the fact that there are multiple types of commonly observed shoulder arrival waves. The term "ray path type" is used to describe these various types of shoulder arrival waves. Error! Reference source not found shows some of the most common ray path types.

[0133] Ray path type refers to the type of path along which refracted or reflected energy may propagate from a source through a stratified formation to a receiver array. Figures 21A to 21D Four commonly observed double-arrival-wave ray-path types are shown: PPP refraction, PP reflection, SSS refraction, and SS reflection. Fig.21A and 21C It is shown how the PPP and SSS refracted waves first propagate from the source to the shoulder layer boundary, then refract along the shoulder layer, and then finally refract back towards the receiver array. Fig.21B and 21D Shows how PP and SS reflections originate from the source location, propagate to the shoulder boundary, and then reflect back to the receiver array. Each type of double arrival event provides different information about the local slowness profile along the well trajectory, so distinguishing the ray path type of the shoulder arrival wave is critical to making a correct interpretation. The separate letters P and S refer to the propagation mode during each leg of the ray path from the source to the receiver array. P means that the wavefront propagates as a longitudinal wave along that particular leg of its ray path, and S means that the wavefront propagates as a shear wave along that particular leg of its ray path. Figures 21A to 21D All of these ray paths shown in the other figures are rays traced according to Snell's law.

[0134] PPP and SSS refraction occur in the strata containing the tool detector (the “tool stratum”) more slowly than in the shoulder strata ( ), and the wavefront propagating from the source to the receiver array is refracted and propagates along the boundary within the shoulder layer. The angle of this refraction at the shoulder layer boundary is given by Snell's law and is the critical refraction angle shown in Equation 9.

[0135]

[0136] Figures 21A to 21D The different types of shoulder arrival waves shown in provide different information about the local formation slowness along the well trajectory, so distinguishing the ray path types of shoulder arrival waves is critical to making a correct interpretation. In particular, the refracted wavefront propagates within the shoulder layer and therefore contains information about the shoulder layer velocity, while the reflected wavefront does not propagate within the shoulder layer and therefore does not provide information about the shoulder layer velocity.

[0137] Fig.22Aand 22B The travel time calculation between the source and receiver array sensors for both refracted and reflected ray path types as provided in equations (10) and (11) is shown. Fig.22A The source S and the jth receiver sensor are labeled The PPP ray path between the two refraction points along the shoulder layer boundary and Equation (10) shows the corresponding travel time calculation, which includes three terms, one for each branch of the ray path. Fig. 22B The source S and the jth receiver sensor are labeled The PP ray path between the two includes reflection points along the shoulder layer boundary. Equation (11) shows the corresponding travel time calculation, which includes two terms, one for each of the two branches of the ray path. It can be observed from equations (10) and (11) that the travel time of the reflected wavefield does not depend on the shoulder layer velocity , so distinguishing whether the shoulder layer arrival wave is reflected or refracted is crucial for making a correct interpretation.

[0138] Note that we have added a superscript "i" to all terms in equations (10) and (11) indicating the depth station of the measurement along the well trajectory. This corresponds to the index i used in equations 1-6, since we will perform these travel time calculations at each measurement station along the time pick. Note that equations (10) and (11) have been written in a general manner without explicitly specifying the velocity and is the P speed or the S speed. When using equation (10) to calculate the travel time of PPP refraction, and is the P velocity, and when the travel time of SSS refraction is calculated using equation (10), and is the S velocity. Equation (11) can be used to calculate the travel time for both PP and SS reflections in a similar manner.

[0139]

[0140] The time-picked ray tracing inversion produced by automatic time picking consists of two parts - a model selection problem and a parameter inversion problem. The model selection problem involves identifying the ray path type of the shoulder arrival wave. The model selection problem determines the appropriate model parameterization for the shoulder arrival event. The parameter inversion involves inverting the shoulder arrival wave travel time corresponding to the model parameters from Eq. 6 .

[0141] The model parameters of interest for ray tracing inversion include: (a) shoulder layer P slowness ; (b) Slowness of shoulder layer ; (c) Distance to the shoulder layer ; (d) Relative inclination of shoulder layer The layered earth model parameterized by the ray tracing inversion has horizontal layers and arbitrary deviations of the well trajectories, e.g. Fig.23 Distance to shoulder layer is constrained to be positive, . Measure the relative inclination of the shoulder layer from the vertical direction , so that This means that the sonic logging tool and the formation are parallel to each other. In addition, the tool layer p slowness is derived using the above automatic classification workflow or via other slowness logs. and tool layer S slowness will be considered as input parameters and will not be inverted via ray tracing inversion.

[0142] Based on this layered earth model parameterization, we can calculate the travel times between the source and various receiver sensors for four different ray path types according to Equations 10 and 11. In this way, we can express the function as , which transforms the model parameters The corresponding forward modeling travel time is related, as shown in Equation 12.

[0143]

[0144] Parameters listed after the model parameter m are hyperparameters, and will be treated as input parameters, and are not inverted here.

[0145] We can further detail Equation 12 in Equations 13a-d, which explicitly show that for different ray path types, the travel time Depends on which model parameters.

[0146]

[0147] We note that the forward model Only the travel time is modeled as a function of the source-receiver offset j and the measured depth position index i, rather than as a function of the nominal receiver azimuth k. The ray tracing inversion is intended only to plot the shoulder position in 2D relative to the well trajectory. In fact, the distance to the boundary parameter is always positive ,therefore Fig.23The shoulder boundary used in the ray tracing inversion will always be above the well trajectory. We will plot the shoulder boundary in 3D along the well trajectory and construct the corresponding curtain segment display of the final inversion results in the next section.

[0148] Here are the receiver sensors used for ray tracing inversion Heyuan The local coordinates of the location are helpful. The ray tracing inversion places the boundary of the shoulder layer along the x-axis and the receiver array below the origin ft.

[0149]

[0150] Here is the average source-receiver offset, and TR is the source-receiver spacing. Again, the parameterization does not model the azimuth receiver sensors, but only the source-receiver offsets. The ray tracing inversion is only intended to map the shoulder layer in 2D relative to the well trajectory.

[0151] Here we demonstrate a ray tracing inversion to determine the ray path type for a shoulder arrival event (i.e., solving the model selection problem) and also solve the corresponding parameter inversion to determine the local layered model for the slowness. We first show this for a forward modeled shoulder arrival event, where we know the ray path type and the correct inversion parameter values.

[0152] We begin by forward modeling the raypaths along the Figures 21A to 21D Travel times for the 15 measured depth stations for the wellbore trajectory shown and the corresponding peak CSG and COG slowness values ,Prepare Figures 24A to 24D The input data shown is for ray tracing inversion. These travel times It is for 15 measured depth stations All 13 source-receiver offsets Calculated. Here ,as well as ft.

[0153] Fig.24A and 24B These travel times are shown . Figures 24A to 24D Notes 1, 2, 3, and 4 used in correspond to ray path types PPP, SSS, PP, and SS, respectively. Fig.24A The depth station of the 8th (i=8) measurement is shown Forward modeling of travel times for 13 transmitter-receiver offsets at ,and Fig. 24B The 7th transmitter-receiver offsets for all 15 measured depth stations are shown The travel time of forward modeling .

[0154] For the four ray path types, the travel times corresponding to the forward modeling of and Slowness value in Fig.24C and 24D According to Equation 15, we estimate and Slowness value.

[0155]

[0156] Further observation Fig.24C and 24D The details we observed and How the peaks are separated from each other and also characterize the Fig.24A and 24B The travel time of the forward modeling shown in This separation allows the ray tracing inversion to distinguish the correct ray path type for each of these four arrival events.

[0157] Table 1 (shown in the Appendix) provides Figures 24A to 24D Summary of the results of ray tracing inversion for the input data described in . Table row indices #1-#4 correspond to the rows used to label Figures 24A to 24D The entries in the table columns labeled Tau_ms, pCOG_usFt, and pCSG_usFt correspond to the peak travel times estimated from the forward modeling using Equation 15. The last four columns contain the travel times when the forward modeling is inverted using the Levenberg–Marquardt inversion algorithm using the four equations 13a-d. . The mismatch errors are color-coded according to their magnitude. The diagonal check mark indicates that the ray tracing inversion with the smallest mismatch corresponds to the ray path type of the arrival event. In this way, we demonstrate the ability of the ray tracing inversion to distinguish the correct ray path type for double arrival events.

[0158] Table 2 (shown in the Appendix) provides Figures 21A to 21DA more detailed summary of the ray tracing inversion results for each of the four arrival events described in and listed in the rows of Table 1. Each panel shows the event index in the second column of the first row. The second column of each table also repeats the corresponding values. The last four columns of the first row give the data misfit when the travel times are inverted using Equations 13a-d and the Levenberg-Marquardt inversion algorithm. To help improve readability and highlight the correspondence with the entries in Table 1, the last four columns are color coded according to the magnitude of the inversion misfit, and we mark the event ray path type with the smallest data misfit with a circled check mark indicating the ray tracing inversion ray path type selection. The last four rows of this selected column provide the corresponding inversion parameter values ​​that are correctly consistent with the input formation model. Note that the NaN value shown for some of the inversion model parameter values ​​means that the model parameter was not inverted.

[0159] Here we demonstrate a ray tracing inversion to determine the ray path type of the shoulder arrival event (i.e., the model selection problem), and solve the corresponding parameter inversion to determine the local layered model of slowness for in situ acoustic measurements. Figures 17A to 17C The live data example shown in Figure 1 generates four automatic time picks that perform these tasks. Fig.25 Highlighted in FIG. 5 are these time picks (#1, #4, #7 and #11) from the measured depth interval between 9165 ft and 9205 ft MD.

[0160] We begin by preparing the input data for ray tracing inversion. The time picks generated by automatic time pickering using Eq. Characterize the time difference of events as source-receiver offset , measured depth and the nominal receiver azimuth However, the input to our ray tracing inversion workflow only requires the input of the travel time , because the ray tracing inversion only determines a 2D mapping of the shoulder relative to the well trajectory. In the next section, we will use the peak common loop gather slowness values To determine whether the shoulder layer is above or below the wellbore trajectory and place the shoulder layer in 3D along the wellbore trajectory.

[0161] To prepare the input data for the ray tracing inversion, we average the arrival times as a function of the nominal receiver azimuth as in Equation 16, where the number of nominal receiver azimuths is here The resulting time picks for arrival events #1, #4, #7, and #11 like Figures 26A to 26Dshown, and will be used for ray tracing inversion.

[0162]

[0163] Fig.26A The time pick values ​​from Equation 16 are shown for 13 transmitter-receiver offsets j at depth station i=33 measured along the center of the time pick. ,and Fig.26B The time pick values ​​from Equation 16 are shown for the 7th source-receiver offset (j=7) at the depth station for all 66 measurements along the time pick. . Fig.26C and 26D Shown for Fig.25 The highlighted time pick in the figure is a peak directly generated by automatic time picking. and value.

[0164] Table 3 (shown in the Appendix) provides Figures 26A to 26D Summary of the results of ray tracing inversion of the input data described in . The entries in the table columns labeled Tau_ms, pCOG_usFt, pCRG_usFt, and pCSG_usFt correspond to the peaks produced by automatic time picking. and The last four columns contain the travel times when the Levenberg-Marquardt inversion algorithm is used using the four equations 13a-d. Data mismatch when performing an inversion. Mismatch errors are color-coded according to their magnitude. A diagonal check mark indicates that the ray-tracing inversion with the smallest mismatch corresponds to the type of ray path that reached the event.

[0165] from Fig.25 From visual inspection of the #1, #4, #7, and #11 arrival events in , we know that #1 and #11 are tool layer S and tool layer P arrival waves, respectively, because of their lateral continuity as a function of measured depth. In addition, #4 and #7 are shoulder layer S and shoulder layer P arrival events, respectively, because they arrive before the corresponding tool layer arrival waves and because of their faster CSG slowness values, as shown in Fig.26C In this way, we demonstrate that the ray tracing inversion distinguishes the correct ray path type for the double arrival events for this field data example.

[0166] Table 4 (shown in the Appendix) provides a more detailed summary of the ray tracing inversion results for the shoulder layer arrival events #4 and #7 listed in the rows of Table 3 . FIG. 26A to FIG. 26D The event index in the second column of the first row is shown. The second column of each table also repeats the corresponding event index listed in the corresponding row of Table 3. The last four columns of the first row give the data misfit when the travel times are inverted using Equations 13a-d and the Levenberg-Marquardt inversion algorithm. To aid readability and highlight the correspondence with the entries in Table 3, the last four columns are color coded according to the magnitude of the inversion misfit, and we mark the event ray path type with the smallest data misfit with a circled check mark indicating the ray tracing inversion ray path type selection.

[0167] Although Fig. 20 The well log display of shoulder and tool layer slowness shown in is very useful for formation evaluation purposes, but a curtain segment showing the formation layering above and below the well trajectory and the estimated tool layer and shoulder layer slowness can also assist in well placement. Here, we first construct this curtain segment based on the ray tracing inversion results of the forward modeling time picking discussed earlier. The main steps are (1) computing the local coordinates of the ray paths used by the ray tracing inversion; and (2) computing the reflection / rotation transform that maps the local coordinates of the source and receiver locations to the coordinates of the source and receiver locations along the well trajectory (this step uses the azimuth information from the automatic time picking). ); (3) applying the reflection / rotation transform to the local ray paths determined by ray tracing inversion to obtain a map of the shoulder layer along the wellbore trajectory; and (4) combining the mapping from step (3) for all measured depth positions along the wellbore trajectory to construct a curtain map display.

[0168] The detailed ray tracing inversion results shown in Table 2 provide the distance to the shoulder boundary and relative inclination We can use the parameterization in Equation 14 to calculate the receiver sensor Heyuan The local coordinates of . Fig.27A Source Shown as an approximately 14-foot square, place the receiver Shown as a square of approximately -3 feet to +3 feet, with the shoulder layer boundary location shown as a horizontal line offset vertically by 0 feet. Fig.27B Receiver locations along the wellbore trajectory showing the depth station measured at a measured depth of 518 feet and source location .

[0169] There is a reflection / rotation transformation L, followed by a translation T, which inverts the ray tracing to the source and receiver location The local coordinates of the wellbore are mapped to the receiver positions along the wellbore trajectory. Heyuan The local coordinate system used in the ray tracing inversion places the shoulder boundary above the wellbore trajectory. If the automatic time picking described previously has , indicating that the shoulder is below the wellbore trajectory, we begin by replacing the local x-coordinates of the source and receiver with their negative values, thus placing the shoulder on the opposite side of the wellbore trajectory. We then calculate the rotation angle between the local coordinates of the source-receiver axis and the source-receiver axis along the wellbore trajectory , as shown in Equation 17, to produce the rotation matrix L.

[0170]

[0171] Translation Matrix is then specified by the first line of Equation 18, while the second line of Equation 18 describes the complete mapping between the local coordinates used by the ray tracing inversion and the coordinates along the well trajectory.

[0172]

[0173] Here, we emphasize that automatic time picking is very useful in the 3D domain when the acoustic receiver array does not have an orientation sensor. In the run, and without telling whether the shoulder layer is above or below the wellbore trajectory, we can use azimuthal information from other wellbore measurements, e.g. Fig. 17C The density image shown here provides appropriate value of .

[0174] Fig.28 The process used to construct a curtain segment display of ray tracing inversion results for a single PPP time pick is shown. The main idea is to highlight areas along the well trajectory where the refracted wavefield is traveling at the speed (slowness) predicted by the ray tracing inversion. The four corners of the quadrilateral labeled Region 2 are calculated using the first and last ray paths generated for the first and last measured depth positions of the time pick. The boundaries of Regions 1 and 3 are determined using the top and bottom edges of the Region 2 quadrilateral and extend vertically to the top and bottom of the curtain segment display. Regions 1 and 2 are estimated by the tool layer slowness Determined, region 3 is estimated by the shoulder slowness or The actual formation slowness of this synthetic example is shown as Fig.28 The right edge of the column.

[0175] Fig.29A and 29B Shown along Fig.21A The final curtain segment inversion results for all PPP time picks calculated for the synthetic example well trajectory shown. By calculating such Fig.28 The spatial average of the individual curtain segments shown in Fig.29AThe curtain segment shown in . Fig.29B The curtain segments shown in FIG. 1 are obtained after modulating, for example, the color saturation. Fig.29A Produced to convey an indication of the detection depth, which is approximately 1 spatial wavelength (about 2 feet) of refracted sound waves beyond the mapped interface.

[0176] Fig. 30A and 30B Shown by Fig.25 Final curtain segment resulting from ray tracing inversion of field measurements shown. Fig. 30A Shown using Fig.25 The curtain segments produced by the PPP refraction events numbered #11, 5, 11, 7, 6, and 3 from left to right in the figure are as follows: Fig. 30B Shown are curtain segments produced using SSS refraction with events #6, 3, 5, 4, 9, 6, and 13 highlighted from left to right. Fig. 30A and 30B The line segments in Fig. 30A The true vertical depth is between about 6228 feet and 6232 feet, and from Fig. 30B The true vertical depth between approximately 6230 feet and 6233 feet in ) represents the estimated location of the shoulder layer along the well trajectory determined by the ray tracing inversion used to construct the curtain segment.

[0177] The tools used in line tracking inversion are the estimation of layer P-wave slowness (DTCOT) and S-wave slowness (DTSMT). Fig. 20 As shown in Table 3, the arrival events used to generate the curtain segments are selected by ray tracing inversion and classified into PPP and SSS events, where the Fig.25 The highlighted middle measured depth interval event #7 is classified as PPP, while event #4 is classified as SSS. We observe that because the CRG slowness value For all selected events, Fig. 30A and 30B The shoulder layer shown in the curtain segment of is consistently shown above the well trajectory. Close inspection of the two curtain segments shows that the shoulder layer is closer to the well trajectory in the SSS inversion result than in the PPP inversion result, so ray tracing inversion that considers constraints on both PPP and SSS arrival events may help reconcile these differences.

[0178] Although Fig. 20 The slowness log and Fig.29A and 29B as well as Fig. 30A and 30B The curtain segments shown in Figure 2 can be used for formation evaluation and well placement, but here we highlight these workflow results in 3D, as shown in Figure 2. Fig.31A and 31B as well as Fig.32 to demonstrate their additional explanatory value.

[0179] Fig.31A Shown are Fig. 30A and 30B , but now they appear in 3D along the well trajectory. To complete this rendering, we compute the source and receiver locations along the well trajectory in 3D and then apply a local linear transformation much like the 2D rendering shown in Equations 17 and 18. Fig. 30A and 30B The layer boundary positions marked by line segments in are mapped to Fig.31A and 31B Furthermore, we can calculate the true inclination and azimuth of those 3D disks and form Fig.32 3 and 4. These true dip and azimuth logs are useful in understanding the formation structure and can be compared with true dip and azimuth logs derived from formation image logs such as fullbore formation microimager logs or ultrasonic formation imager logs.

[0180] The embodiments presented herein provide a method of automatic acoustic tool waveform time picking, and a computer readable tangible medium having computer instructions stored thereon to cause a processor to perform the method of automatic acoustic tool waveform time picking as described and illustrated herein.

[0181] For example, in certain embodiments, a method for automatic time picking of arrival events of dual-arrival waveforms recorded by an acoustic logging tool at at least one measured depth position having at least one source and one receiver sensor may include: for each measured depth MD position and for each nominal receiver azimuth, calculating a tau-P transform in a common shot gather CSG domain; concatenating the results into a 4D cube; permuting the cube index to tau-CSG × nominal receiver azimuth × MD × p-CSG; calculating a tau-P transform in a common ring gather CRG domain for each MD × p-CSG slice; permuting the cube index to tau-CRG × MD × p-CRG × p-CSG, and wherein the cube is stored for event localization; calculating a tau-P transform in a common offset gather COG domain for each p-CRG × p-CSG slice, providing a 4D cube tau-COG × p-COG × p-CRG × p-CSG; convolve the 4D cube as a function of tau-COG with a 1D square pulse function of duration Δ = 0.2 milliseconds; use a local median filter on the 4D tau-P cube to reduce noise and sharpen peaks; find and sort local maxima in the 4D cube; select the local maximum and use event location from the projected waveform of the tau-CRG × MD × p-CRG × p-CSG cube to find the start and end measured depths of the arrival event in the common-offset track set domain; use the local maximum and the start and end measured depths of the event to generate a set of arrival times for dual arrival events in the common-offset, common-shot, and common-loop track set domains; and create a waveform display overlay using the arrival times to mark the arrival events in the waveform measurement.

[0182] Additionally, in certain embodiments, a method for generating a well log of tool layer and shoulder layer slowness using common shot gather slowness estimates generated by automatic time picking of dual arrival events may include: selecting a threshold P slowness value that is greater (i.e., slower) than the tool layer and shoulder layer P slownesses; selecting a tool layer p-slowness event window size that describes the range of tool layer p-CSG slowness values ​​derived from the automatic time picking; determining all p-CSG slowness values ​​that are less than the maximum P slowness value; grouping all of these p-CSG slowness values ​​within the P-slowness event window size of the maximum P slowness value, and averaging these to form the tool layer P slowness; and grouping and averaging the remaining p-CSG slowness values ​​to form the shoulder layer apparent P slowness. ; remove p-CSG slowness values ​​less than the maximum P slowness value; select a threshold shear wave S slowness value, which is greater than (i.e., slower than) the tool layer and shoulder layer S slowness and less than the Stoneley slowness; select a tool layer shear wave S slowness event window size that describes the range of tool layer p-CSG slowness values ​​derived from the automatic time picking; determine all p-CSG slowness values ​​less than the maximum shear wave S slowness value; group all p-CSG slowness values ​​within the shear wave S slowness event window size of the maximum S slowness value and average these values ​​to form the tool layer S slowness; group the remaining p-CSG slowness values ​​and average them to form the shoulder layer apparent shear wave S slowness.

[0183] Additionally, in certain embodiments, a method of inverting travel times resulting from automated time picking of double arrival events to determine event ray path types and local model parameters describing a shoulder layer may include parameterizing a layered model of an acoustic tool having at least a tool layer and a shoulder layer and having at least one source and receiver array (e.g., local model parameters include at least one of tool layer slowness, shoulder layer slowness, distance to the shoulder layer, and inclination of the shoulder layer); preparing forward modeling of predicted double arrival wave travel times that maps the layered model to different ray path types (e.g., ray path types including at least one of p-reflection, p-refraction, s-reflection, and s-refraction); using an inversion workflow to determine model parameters that best fit the travel times resulting from automated time picking for each of the different ray path types; selecting the ray path type having the best fit to the travel times as the ray path type for the double arrival event; and selecting local model parameters corresponding to the selected ray path type as the local model parameters for the shoulder layer.

[0184] Additionally, in certain embodiments, a method of forming a curtain segment of formation slowness along a well trajectory using local parameters of a shoulder layer generated by ray tracing inversion of double arrival event travel times may include: performing a linear mapping in 2D or 3D from source and receiver locations used by ray tracing inversion layer model parameterization to actual source and receiver locations along the well trajectory; applying the linear mapping to the estimated shoulder layer boundary location of the ray tracing inversion to obtain the location of the shoulder layer boundary in 2D or 3D along the well trajectory. The linear mapping includes a common loop gather slowness value generated by automatic time picking of double arrival events or an azimuthal direction to the shoulder layer generated by azimuthal information derived from borehole image logging; forming a layered slowness model in 2D or 3D along the well trajectory using the location of the shoulder layer boundary in 2D or 3D along the well trajectory, tool layer slowness, and shoulder layer slowness; and estimating the orientation of the 3D shoulder layer boundary to form a log of the true dip and azimuth of the shoulder layer.

[0185] The embodiments described herein may be implemented by an acoustic logging data processing system, which may include, inter alia, a memory medium on which processor executable instructions are stored, and one or more processors configured to execute the processor executable instructions stored in the memory medium, such that the processor executable instructions, when executed by the one or more processors, cause the acoustic logging data processing system to perform the data processing and analysis techniques described herein. In general, the embodiments described herein are particularly capable of providing logging displays of longitudinal and shear wave slownesses of tool and shoulder layers. In addition, however, in certain embodiments, the data processing and analysis techniques described herein may be used by an acoustic logging data processing system to send control commands to an acoustic logging tool for detecting data associated with a double arrival wave waveform event, as described in more detail herein.

[0186] In some embodiments, one or more processors of the acoustic logging data processing system may include a microprocessor, a microcontroller, a processor module or subsystem, a programmable integrated circuit, a programmable gate array, a digital signal processor (DSP), or another control or computing device. In some embodiments, one or more processors of the acoustic logging data processing system may include a processor based on machine learning and / or artificial intelligence (AI). In some embodiments, the storage medium of the acoustic logging data processing system may be implemented as one or more non-transitory computer-readable or machine-readable storage media. In some embodiments, the storage medium of the acoustic logging data processing system may include one or more different forms of memory, including semiconductor memory devices, such as dynamic or static random access memory (DRAM or SRAM), erasable and programmable read-only memory (EPROM), electrically erasable and programmable read-only memory (EEPROM), and flash memory; magnetic disks, such as fixed disks, floppy disks, and removable disks; other magnetic media, including magnetic tapes; optical media, such as compact disks (CDs) or digital video disks (DVDs); or other types of storage devices. Note that processor executable instructions and associated data may be provided on one computer-readable or machine-readable storage medium of the acoustic logging data processing system's memory medium, or alternatively, may be provided on multiple computer-readable or machine-readable storage media distributed in a large system that may have multiple nodes. Such one or more computer-readable or machine-readable storage media are considered part of an article (or article of manufacture), which may refer to any manufactured single component or multiple components. In some embodiments, the storage medium of the acoustic logging data processing system may be located in the machine running the machine-readable instructions, or may be located at a remote site from which the machine-readable instructions may be downloaded over a network for execution.

[0187] Language of degree used herein, such as the terms "approximately," "about," "typically," and "substantially" used herein, refers to a value, amount, or feature that is close to a stated value, amount, or feature that still performs a desired function or achieves a desired result. For example, the terms "approximately," "about," "typically," and "substantially" may refer to an amount that is within less than 10%, less than 5%, less than 1%, less than 0.1%, and / or less than 0.01% of the stated amount. As another example, in certain embodiments, the terms "approximately parallel" and "substantially parallel" or "approximately perpendicular" and "substantially perpendicular" refer to a value, amount, or feature that deviates from exact parallelism or perpendicularity by less than or equal to 15 degrees, 10 degrees, 5 degrees, 3 degrees, 1 degree, or 0.1 degrees, respectively.

[0188] Although some embodiments of the present disclosure have been described in detail above, it will be readily appreciated by those skilled in the art that many modifications are possible without substantially departing from the teachings of the present disclosure. Therefore, such modifications are intended to be included within the scope of the present disclosure as defined in the claims. It is also contemplated that various combinations or sub-combinations of the specific features and aspects of the described embodiments may be made and still fall within the scope of the present disclosure. It should be understood that the various features and aspects of the disclosed embodiments may be combined or substituted with each other to form a variation pattern of embodiments of the present disclosure. Therefore, it is intended that the scope disclosed herein should not be limited by the specific embodiments described above.

[0189] appendix

[0190]

[0191] Table 1: Summary of the results of the model selection procedure for the four arrival events depicted in the panels of Figure 16. The row indices #1-#4 in the first column correspond to the indices #1-4 used to label the forward modeled data shown in Figure 19. The last four columns contain the data mismatches when these forward modeled travel times are inverted using the four equations 13a-d, and are color-coded according to the magnitude of the mismatch. The circled check mark indicates the ability of the ray tracing inversion to distinguish the correct ray path type for the double arrival event.

[0192]

[0193] Table 2: Details of the ray tracing inversion results for each of the four arrival events listed in the row of Table 1. Each panel shows the event index in the second column of the first row. The second column also repeats the index listed in the corresponding row of Table 1. The last four columns of the first row of each table give the data misfit when the traveltimes are inverted using Eqs. 13a-d, and the columns are color coded according to the magnitude of the inversion misfit. The last four rows of each table provide the inverse parameter values.

[0194]

[0195] Table 3: Depth intervals from the measurements described in the panels labeled Fig. 20 Summary of the results of the model selection process for the four arrival events #1, #4, #7, and #11 of the field example shown in .

[0196]

[0197] Table 4: Details of the results of the model selection procedure for event #4 and event #7 shown in Table 3. Each panel shows the event index in the second column of the first row. The second column also repeats the event index listed in the corresponding row of Table 1. The last four columns of the first row of each table give the data misfit when the traveltimes are inverted using Eqs. 13a-d, and the columns are color coded according to the magnitude of the inversion misfit. The last four rows of each table provide the inverse parameter values.

Claims

1. An acoustic logging data processing system, comprising: a storage medium having processor-executable instructions stored thereon, wherein the processor-executable instructions include instructions for performing automatic acoustic tool waveform time picking of double-arrival waveform events recorded by an acoustic logging tool at one or more measured depth locations using one or more source and one or more receiver sensors of the acoustic logging tool; and one or more processors configured to execute processor-executable instructions stored in the storage medium, wherein the processor-executable instructions, when executed by the one or more processors, cause the acoustic logging data processing system to: computing a first tau-P transform in a common shot gather (CSG) domain for each of the one or more measured depth positions and for each nominal receiver azimuth of the one or more receiver sensors; Concatenate the results of the first tau-P transform into a four-dimensional (4D) cube; Permute the cube index of the 4D cube to tau-CSG × nominal receiver azimuth × MD × p-CSG; calculating a second tau-P transform in a common ring gather (CRG) domain for each MD × p-CSG slice of the 4D cube; Permute the cube index of the 4D cube to tau-CRG × MD × p-CRG × p-CSG; computing a third tau-P transform in the common offset gather (COG) domain for each p-CRG × p-CSG slice of the 4D cube to provide a 4D cube with an index of tau-COG × p-COG × p-CRG × p-CSG; Convolving the 4D cube as a function of tau-COG to provide a 4D tau-P cube; Event localization was used on the projected waveform from the 4D cube with an index of tau-CRG × MD × p-CRG × p-CSG to find the depth positions of the start and end measurements of the arrival events in the COG domain; A set of arrival times for double arrival events in the COG, CSG, and CRG domains was generated using the local maxima in the 4D tau-P cube and the measured depth positions of the start and end of the arrival events; and A waveform display overlay is created using the set of arrival times to mark double arrival events in a waveform measurement recorded by the acoustic logging tool.

2. The acoustic logging data processing system of claim 1, wherein the processor-executable instructions, when executed by one or more processors, cause the acoustic logging data processing system to store the 4D cube for use in the event location.

3. The acoustic logging data processing system of claim 1 , wherein the processor executable instructions, when executed by one or more processors, cause the acoustic logging data processing system to convolve a 4D cube as a function of tau-COG with a one-dimensional (1D) box-pulse function. 4 . The acoustic logging data processing system according to claim 3 , wherein the 1D box pulse function has a duration of Δ=0.2 milliseconds.

5. The acoustic logging data processing system of claim 1 , wherein the processor executable instructions, when executed by one or more processors, cause the acoustic logging data processing system to use a local median filter on the 4D tau-P cube to reduce noise and sharpen peaks before finding and sorting local maxima in the 4D tau-P cube.

6. The acoustic logging data processing system of claim 1 , wherein the processor-executable instructions, when executed by one or more processors, cause the acoustic logging data processing system, prior to using the event location on the projected waveform: Find and sort local maxima in a 4D tau-P cube; and Select a local maximum from the local maxima.

7. The acoustic logging data processing system of claim 1, wherein the processor executable instructions, when executed by one or more processors, cause the acoustic logging data processing system to provide a logging display of tool layer and shoulder layer P- and S-wave slownesses.

8. An acoustic logging data processing system, comprising: a memory medium storing processor-executable instructions thereon, wherein the processor-executable instructions include instructions for using local model parameters of the shoulder layer using ray tracing inversion of double-arrival waveform event travel times recorded by an acoustic logging tool at one or more measured depth locations using one or more source and one or more receiver sensors of the acoustic logging tool to form a curtain segment of formation slowness along a well trajectory; and one or more processors configured to execute processor-executable instructions stored in the storage medium, wherein the processor-executable instructions, when executed by the one or more processors, cause the acoustic logging data processing system to: generating a linear map from data associated with the positions of one or more source and one or more receiver sensors of the acoustic logging tool; applying a linear mapping to the estimated shoulder boundary position determined by the ray tracing inversion to obtain the position of the shoulder boundary along the well trajectory; forming a layered slowness model along the well trajectory using the location of the shoulder layer boundary along the well trajectory, the tool layer slowness, and the shoulder layer slowness; and The orientation of the shoulder layer boundary is estimated based at least in part on the layered slowness model to form a log of the true dip and azimuth of the shoulder layer.

9. An acoustic logging data processing system according to claim 8, wherein the processor executable instructions, when executed by one or more processors, cause the acoustic logging data processing system to use data related to the positions of one or more sources and one or more receiver sensors of the acoustic logging tool for ray tracing inversion layered model parameterization of the actual positions of one or more sources and one or more receiver sensors of the acoustic logging tool along the wellbore trajectory in two-dimensional (2D) space or three-dimensional (3D) space.

10. The acoustic logging data processing system of claim 8, wherein the processor executable instructions, when executed by one or more processors, cause the acoustic logging data processing system to apply the linear mapping to the estimated shoulder boundary position determined by the ray tracing inversion to obtain the position of the shoulder boundary in a two-dimensional (2D) space or a three-dimensional (3D) space along the well trajectory.

11. The acoustic logging data processing system of claim 8, wherein the linear mapping includes azimuthal directions to the shoulder layer produced by common ring gather (CRG) slowness values ​​produced by automatic acoustic tool waveform time picking of double arrival waveform events.

12. The acoustic logging data processing system of claim 8, wherein the linear mapping includes azimuthal information derived from borehole image logging.

13. The acoustic logging data processing system of claim 8, wherein the processor executable instructions, when executed by one or more processors, cause the acoustic logging data processing system to use the position of the shoulder layer boundary along the well trajectory, the tool layer slowness, and the shoulder layer slowness to form the layered slowness model along the well trajectory in two-dimensional (2D) space or three-dimensional (3D) space.

14. The acoustic logging data processing system of claim 8, wherein the processor executable instructions, when executed by one or more processors, cause the acoustic logging data processing system to provide a logging display of tool layer and shoulder layer P- and S-wave slownesses.

15. An acoustic logging data processing system, comprising: a storage medium storing processor-executable instructions thereon, wherein the processor-executable instructions include instructions for inverting travel times resulting from automatic acoustic tool waveform time picking of double arrival waveform events recorded by an acoustic logging tool at one or more measured depth locations using one or more source and one or more receiver sensors of the acoustic logging tool to determine event ray path types and local model parameters defining a shoulder layer; and one or more processors configured to execute processor-executable instructions stored in the storage medium, wherein the processor-executable instructions, when executed by the one or more processors, cause the acoustic logging data processing system to: parameterizing a layered model using local model parameters, the local model parameters including at least a tool layer and a shoulder layer and characteristics of the acoustic logging tool; preparing a forward model that maps the layered model to predicted double-arrival wave travel times for different raypath types; determining, using an inversion workflow, model parameters of the forward model that most closely approximate double-arrival wave travel times produced by automatic acoustic tool waveform time picking for each of the different ray path types; Selecting the ray path type that most closely approximates the travel times of the double-arriving waves; and The local model parameters corresponding to the selected ray path type are selected as the local model parameters of the shoulder layer. 16 . The acoustic logging data processing system of claim 15 , wherein the local model parameters include at least one of a tool layer slowness, a shoulder layer slowness, a distance to the shoulder layer, and a dip angle of the shoulder layer.

17. The acoustic logging data processing system of claim 15, wherein the ray path type comprises at least one of p-reflection, p-refraction, s-reflection, and s-refraction.

18. The acoustic logging data processing system of claim 15, wherein the processor executable instructions, when executed by one or more processors, cause the acoustic logging data processing system to provide a logging display of tool layer and shoulder layer P- and S-wave slownesses.

19. An acoustic logging data processing system, comprising: a storage medium storing processor-executable instructions thereon, wherein the processor-executable instructions include instructions for generating a log of tool layer and shoulder layer slowness using common shot gather (CSG) slowness estimates generated by automatic acoustic tool waveform time picking of double arrival waveform events recorded by an acoustic logging tool at one or more measured depth locations using one or more source and one or more receiver sensors of the acoustic logging tool; and one or more processors configured to execute processor-executable instructions stored in the storage medium, wherein the processor-executable instructions, when executed by the one or more processors, cause the acoustic logging data processing system to: Select a threshold P slowness value greater than the P slowness of the tool layer and the shoulder layer; selecting a tool layer p slowness event window size that defines a range of tool layer p-CSG slowness values ​​derived from automatic time picking; Determine all p-CSG slowness values ​​less than the maximum P slowness value; grouping all p-CSG slowness values ​​within the tool layer P slowness event window size of the maximum P slowness value and averaging these p-CSG slowness values ​​to form the tool layer P slowness; The remaining p-CSG slowness values ​​are grouped and averaged to form the shoulder layer apparent P slowness.

20. The acoustic logging data processing system of claim 19, wherein the processor-executable instructions, when executed by one or more processors, cause the acoustic logging data processing system to: removing p-CSG slowness values ​​less than the maximum P slowness value; Select a threshold shear wave S slowness value that is greater than the S slowness of the tool layer and shoulder layer and less than the Stoneley slowness; selecting a tool layer shear wave S slowness event window size, the tool layer shear wave S slowness event window size describing a range of tool layer p-CSG slowness values ​​derived from the automatic time picking; Determine all p-CSG slowness values ​​that are less than the maximum S-wave S slowness value; Grouping all p-CSG slowness values ​​within the tool layer shear wave S slowness event window size of the maximum S slowness value, and averaging these p-CSG slowness values ​​to form the tool layer S slowness; and The remaining p-CSG slowness values ​​were grouped and averaged to form the shoulder apparent shear wave S slowness.

Images