Adaptive summation of das seismic recordings from multi-fiber cables

Abstract

The disclosed methods include recording pulse data indicating laser pulse(s) transmitted into or out of one or more fibers within a fiber cable to generate a first fiber record. The method determines whether at least one of the one or more fibers is spliced following which a trace in the first fiber record corresponding to a splicing point within the fiber cable is identified. The fiber record may be split into an inbound section and an outbound section such that an outbound trace numbering of the outbound section is mirrored to align with a trace numbering of the inbound section to generate mirrored data. Geometry data may be assigned to the mirrored data to generate a geometrically formatted fiber record. Signal components within the geometrically formatted fiber record may be reconstructed and used in a combination process to generate a report indicating captured measurements along the fiber cable.

Description

ADAPTIVE SUMMATION OF DAS SEISMIC RECORDINGS FROM MULTI-FIBERCABLESBACKGROUND

[0001] Distributed acoustic sensing (DAS) uses a pulse of laser / light sent by an interrogator device which transmits light into a fiber optic cable and records the backscattered energy and thereby acts as a seismic sensor. This recording of a seismic wavefield by the acquisition system comprising the interrogator device coupled to the fiber optic cable is often contaminated by random environmental noise and / or coherent environmental noise and / or noisy signals associated with anthropic activities in the proximity of the acquisition system. There is a need, therefore, to minimize said random and / or coherent, and / or anthropic-activity noise from the received or recorded seismic wavefield by the acquisition system to generate reports with high signal -to-noise ratios.SUMMARY

[0002] The disclosed technology, according to some embodiments relates to systems and methods for minimizing one or more noise types from received or recorded data associated with fiber optic sensors. According to some implementations of this disclosure, a fiber optic cable (also called a fiber cable) is engineered to have a plurality of fibers. Each of the plurality of fibers of the fiber optic cable can be communicatively coupled to a different laser such that each fiber of the fiber optic cable can be interrogated independently and / or simultaneously depending on the implementation. In some embodiments, a pair of fibers can be communicatively coupled by joining one end of a first fiber comprised in the pair of fibers with another end of a second fiber comprised in the pair of fibers and looped back to the interrogator. This process of joining two fibers, also referred to as splicing in a back-looping configuration, can effectively double the total recording length of laser being transmitted via the spliced fiberpair such that the same laser pulse can travel through the spliced fiber-pair in a forth and back manner.

[0003] According to one embodiment, the disclosed methods and systems facilitate simultaneously recording a seismic wavefield from multiple fibers inside the same fiber optic cable and / or from a fiber optic cable with fibers spliced in a back-looping configuration toattenuate noise in a seismic recording. The methods and systems disclosed facilitate adaptively combining or summing the seismic wavefield from multiple fibers inside the same fiber optic cable and / or from a fiber optic cable spliced in a back-looping configuration using a matching filter to improve the signal-to-noise ratio of the resulting seismic wavefield.BRIEF DESCRIPTION OF THE DRAWINGS

[0004] The disclosure is illustrated by way of example, and not by way of limitation in the figures of the accompanying drawings in which like reference numerals are used to refer to similar elements. It is emphasized that various features may not be drawn to scale and the dimensions of various features may be arbitrarily increased or reduced for clarity of discussion.

[0005] FIGS. 1A and IB respectively show an exemplary cross-section of a fiber cable with a plurality of fibers and an exemplary cross-section of a fiber cable with at least two pairs of fibers spliced in a back-loop configuration, according to some embodiments of this disclosure.

[0006] FIG. 2 shows a cross-sectional view of a resource site for which the process ofFIG. 1 may be executed.

[0007] FIG. 3 shows a networked system illustrating a communicative coupling of devices or systems associated with the resource site of FIG. 2.

[0008] FIGS. 4A and 4B show exemplary show exemplary flow charts for adaptively summing fiber records according to some implementations of this disclosure.

[0009] FIGS. 5A-5C respectively show an exemplary ideal / optimal reference trace or recording, a plurality of captured recordings at a resource site, and a resultant trace generated by applying the disclosed DAS techniques to the plurality of captured recordings.DETAILED DESCRIPTION

[0010] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the technology. However, it will be apparent to one of ordinary skill in the art that the disclosed systems and methods may be practiced without these specific details. In other instances, well- known methods, procedures, components, circuits and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0011] The disclosed systems and methods may be accomplished using interconnected devices and systems that obtain a plurality of data associated with various parameters of interest at a resource site. The workfl ows / flowcharts and / or systems described in this disclosure, according to the some embodiments, implicate a new processing approach (e.g., hardware, special purpose processors, and specially programmed general-purpose processors) because such analyses are too complex and cannot be done by a person in the time available or at all. Thus, the described systems and methods are directed to tangible implementations or solutions to specific technological problems in processing signals associated with developing natural resources such as oil, gas, water well industries, and other mineral exploration operations.

[0012] Attention is now directed to methods, techniques, infrastructure, and workflows for operations that may be carried out at a resource site. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein may be combined while the order of some operations may be changed. Some embodiments include an iterative refinement of one or more data associated with the resource site via feedback loops executed by one or more computing device processors and / or through other control devices / mechanisms that make determinations regarding whether a given action, template, or resource data, etc., is sufficiently accurate.

[0013] Distributed acoustic sensing (DAS) recording of, for example, a seismic wavefield from fibers within a fiber optic cable often get contaminated by environmental random and coherent noise or due to noise associated with anthropic activities (e.g., background noise associated with human activities, equipment operations, vehicular noise, unwanted wave modes from scattered energy, power generated noise, etc.) in the proximity of the acquisition system. DAS seismic wavefield recordings can be obtained simultaneously from multi-fiber cables as shown in FIG. 1A. In particular, FIG. 1A shows a plurality of fibers within a fiber optic cable (simply called cable elsewhere herein) such that multiple lasers can be connected or otherwise coupled to the plurality of fibers inside the cable thereby recording or simultaneously capturing data at the same time using the cable. According to some embodiments and as shown in FIG. IB, a single laser of the DAS interrogator system can be connected or otherwise coupled to a pair of fibers inside the same cable, or connected or otherwise coupled to one or more fibers within a single cable such that the pair of fibers and / or the one or more fibers within the single cable are spliced in a back-looping configuration. According to some embodiments, some ofthe fibers within the cable are connected to multiple lasers while two or more fibers within the same cable may be connected to the same laser as the case may require. Furthermore, the discretization of the seismic wavefield into seismic traces along each fiber recording depends on the chosen spatial sampling interval (5) during acquisition and it may be regularly spatially sampled along the fiber such that:1) All fiber recordings are configured to sample at the same spatial sampling interval (S);2) Each fiber recording is configured to sample at a different spatial sampling interval (Sn > S) ; and3) In case of back-looping configurations, two segments of the fiber have the same spatial sampling interval (S) .According to some implementations, local differences in the positioning of seismic traces, with an error given by the relationship, E < S may be expected between different fiber recordings. For example, each time one or more fibers within the fiber cable are interrogated, a relative position of realized traces along the fiber cable may slightly change based on a parameterization of the interrogation unit. In particular, this may be a result of the spatial sampling being a function of, for example, an averaging effect of a chosen gauge length. Hence the traces from different recordings may be sampled such that the wavefield and the noise within the recording may be in slightly different locations. According to one embodiment, coordinates may be assigned to each fiber recording and an interpolator used to reconstruct each fiber recording to the same physical set of coordinates along the cable with an adaptive matching filter being calculated, for example, in a least-squared sense for various reconstructed recordings in aggregate. Furthermore, the summation of a number of n fiber recordings may increase the signal -to-noise ratio by taking the square root of the number n in the stack. In particular, the signal-to-noise ratio after summing n fiber recordings increases by the square root of n . According to one embodiment, the signal-to-noise ratio after n fiber recordings have been captured may be given by , where z represents individual fibermeasurements / seismic recordings. These aspects are further discussed in conjunction with FIGS. 4A and 4B below.

[0014] Resource Site

[0015] FIG. 2 shows a cross-sectional view of a resource site 200 for which the process of FIG. 1 may be executed. While the illustrated resource site 200 represents a subterraneanformation, the resource site, according to some embodiments, may comprise wind farms, geological or pipeline structures, various energy development equipment, locations associated with water bodies such as oceans, seas, lakes, ponds, wetlands, rivers, etc. According to one embodiment, various measurement tools capable of sensing one or more parameters such as seismic two-way travel time, density, resistivity, production rate, etc., of a subterranean formation and / or geological formations or structures may be provided at the resource site. As an example, wireline tools may be used to obtain measurement information related to geological attributes (e.g., geological attributes of a wellbore and / or reservoir) including geophysical and / or chemical information. For example, the chemical information may include chemical information associated with the subsurface and / or chemical information associated with the surface / above ground areas of the resource site 200. In some embodiments, various sensors may be located at various locations around the resource site 200 to monitor and collect data for executing the process of FIG. 1. In other embodiments, the techniques disclosed herein may be applied to surface seismic monitoring applications, surface gravity applications, surface electromagnetic applications, surface ground heave applications, and surface measurement of induced seismicity applications. According to some implementations, the disclosed techniques may be applied to remote sensing applications (e.g., satellite-based measurements), subsea applications associated with permanent sensors, temporary sensor applications, applications associated with remotely operated vehicles or equipment in the subsurface.

[0016] Part, or all, of the resource site 200 may be on land, on water, or below water. In addition, while a resource site 200 is depicted, the technology described herein may be used with any combination of one or more resource sites (e.g., multiple oil fields or multiple wellsites, one or more saline aquifers, one or more depleted oil / gas fields, etc.), one or more processing facilities, etc. As can be seen in FIG. 2, the resource site 200 may have data acquisition tools 202a, 202b, 202c, and 202d positioned at various locations within the resource site 200. The subterranean structure 204 may have a plurality of geological formations 206a-206d. As shown, this structure may have several formations or layers, including a shale layer 206a, a carbonate layer 206b, a shale layer 206c, and a sand layer 206d. A fault 207 may extend through the shale layer 206a and the carbonate layer 206b. The data acquisition tools, for example, may be adapted to take measurements and detect geophysical and / or chemical characteristics of the various formations shown.

[0017] While a specific subterranean formation with specific geological structures is depicted, it is appreciated that the oil field 200 may contain a variety of geological structures and / or formations, sometimes having extreme complexity. In some locations of a given geological structure, for example below a water line (e.g., aquifer) relative to the given geological structure, fluid may occupy pore spaces of the formations. Each of the measurement devices may be used to measure properties of the formations and / or other geological features. While each data acquisition tool is shown as being in specific locations in FIG. 2, it is appreciated that one or more types of measurement may be taken at one or more locations across one or more sources of the resource site 200 or other locations for comparison and / or analysis. The data collected from various sources at the resource site 200 may be processed and / or evaluated and / or used as training data, and or used to generate high resolution result sets for characterizing a resource at the resource site, and / or used for generating resource models, etc. In one embodiment, the data collected by one or more sensors at the resource site may include data associated with the number of wells of a first reservoir or second reservoir at the resource site, data associated with the number of grid cells of the first or second reservoir, data associated with the average permeability of the first or second reservoir, data associated with the production duration history (e.g., number of years of production) of the first reservoir or second, etc.

[0018] According to one embodiment, a surface acquisition tool (e.g., Data acquisition tool 202a or a surface seismic acquisition too) illustrated as a measurement truck, may comprise devices or sensors that take measurements of the subsurface through, for example, sound vibrations such as, but not limited to, seismic measurements using for example, fiber optic sensors. Drilling tool 202b may include a downhole sensor adapted to perform logging while drilling (LWD) data collection. Wireline tool 202c may include a downhole sensor deployed in a wellbore or borehole. Production tool 202d may be deployed from a production unit or Christmas tree into a completed wellbore. Examples of parameters that may be measured include weight on bit, torque on bit, subterranean pressures (e.g., underground fluid pressure), temperatures, flow rates, compositions, rotary speed, particle count, voltages, currents, and / or other parameters of operations as further discussed below. It is appreciated that the surface acquisition tool / sensor may include a seismic acquisition tool having a fiber optic cable according to some embodiments. The fiber optic cable in such cases may comprises a cabledeployed horizontally and / or vertically on the surface of the resource site and may be connected or otherwise coupled to an interrogator box that records seismic signals from active and / or passive sources. In one embodiment, one or more noise types may be comprised in the seismic signals traveling through the fiber optic cable.

[0019] Sensors (e.g., surface acquisition sensors) may be positioned about the resource site to collect data relating to various sensing operations, such as sensors deployed by the data acquisition tools 202. The sensors may include any type of sensor such as a metrology sensor (e.g., temperature, humidity), an automation enabling sensor, an operational sensor (e.g., pressure sensor, H2S sensor, thermometer, depth, tension), evaluation sensors, or a fiber optic sensor that can be used for acquiring data associated with a subterranean formation, an equipment, a wellbore, formation fluid / gas, wellbore fluid, gas / oil / water comprised in the formation / wellbore fluid, or any other suitable sensor. For example, the sensors may include accelerometers, flow rate sensors, pressure transducers, electromagnetic sensors, acoustic sensors, temperature sensors, chemical agent detection sensors, nuclear sensor, and / or any additional suitable sensors. In one embodiment, the data captured by the one or sensors may be used to characterize, or otherwise generate one or more parameter values for a high resolution result set with a substantially high signal-to-noise ratio.

[0020] Evaluation sensors may be featured in downhole tools such as tools 202b-202d and may include for instance a fiber optic, electromagnetic, acoustic, nuclear, surface seismic sensors, and optic sensors. Examples of tools including evaluation sensors that can be used in the framework of the current method include electromagnetic tools including imaging sensors such as FMI™ or QuantaGeo™ (mark of Schlumberger); induction sensors such as Rt Scanner™ (mark of Schlumberger), multifrequency dielectric dispersion sensor such as Dielectric Scanner™ (mark of Schlumberger); acoustic tools including sonic sensors, such as Sonic Scanner™ (mark of Schlumberger) or ultrasonic sensors, such as pulse-echo sensor as in UBI™ or PowerEcho™ (marks of Schlumberger) or flexural sensors PowerFlex™ (mark of Schlumberger); nuclear sensors such as Litho Scanner™ (mark of Schlumberger) or nuclear magnetic resonance sensors; fluid sampling tools including fluid analysis sensors such as InSitu Fluid Analyzer ™ (mark of Schlumberger); and distributed sensors including fiber optic sensors discussed in conjunction with FIGS. 4A and 4B. Such evaluation sensors may be used in particular for evaluating the formation in which the well is formed (i.e., determiningpetrophysical or geological properties of the formation), for verifying the integrity of the well (such as casing or cement properties) and / or analyzing the produced fluid (flow, type of fluid, etc.).

[0021] As shown, data acquisition tools 202a-202d may generate data plots or measurements 208a-208d, respectively. These data plots are depicted within the resource site 200 to demonstrate that data generated by some of the operations executed at the resource site 200.

[0022] Data plots 208a-208c are examples of static data plots that may be generated by data acquisition tools 202a-202c, respectively. However, it is herein contemplated that data plots 208a-208c may also be data plots that may be generated and updated in real time. These measurements may be analyzed to better define properties of the formation(s) and / or determine the accuracy of the measurements and / or check for and compensate for measurement errors. The plots of each of the respective measurements may be aligned and / or scaled for comparison and verification purposes. In some embodiments, base data associated with the plots may be incorporated into site planning, modeling a test at the resource site 200. The respective measurements that can be taken may be any of the above.

[0023] Other data may also be collected, such as historical data of the resource site 200 and / or sites similar to the resource site 200, user inputs, information (e.g., economic information) associated with the resource site 200 and / or sites similar to the resource site 200, and / or other measurement data and other parameters of interest. Similar measurements may also be used to measure changes in formation aspects over time.

[0024] Computer facilities such as those discussed in association with FIG. 3 may be positioned at various locations about the resource site 200 (e.g., a surface unit) and / or at remote locations. A surface unit (e.g., one or more terminals 320) may be used to communicate with the onsite tools and / or offsite operations, as well as with other surface or downhole sensors. The surface unit may be capable of sending commands to the oil field equipment / systems, and receiving data therefrom. The surface unit may also collect data generated during production operations and can produce output data, which may be stored or transmitted for further processing.

[0025] The data collected by sensors may be used alone or in combination with other data. The data may be collected in one or more databases and / or transmitted on or offsite. Thedata may be historical data, real time data, or combinations thereof. The real time data may be used in real time, or stored for later use. The data may also be combined with historical data or other inputs for further analysis or for modeling purposes to optimize production processes at the oil field 200. In one embodiment, the data is stored in separate databases, or combined into a single database.

[0026] High-Level Networked System

[0027] FIG. 3 shows a high-level networked system diagram illustrating a communicative coupling of devices or systems associated with the resource site 200. The system shown in the figure may include a set of processors 302a, 302b, and 302c for executing one or more processes discussed herein. The set of processors 302 may be electrically coupled to one or more servers (e.g., computing systems) including memory 306a, 306b, and 306c that may store for example, program data, databases, and other forms of data. Each server of the one or more servers may also include one or more communication devices 308a, 308b, and 308c. The set of servers may provide a cloud-computing platform 310. In one embodiment, the set of servers includes different computing devices that are situated in different locations and may be scalable based on the needs and workflows associated with the oil field 200. The communication devices of each server may enable the servers to communicate with each other through a local or global network such as an Internet network. In some embodiments, the servers may be arranged as a town 312, which may provide a private or local cloud service for users. A town may be advantageous in remote locations with poor connectivity. Additionally, a town may be beneficial in scenarios with large networks where security may be of concern. A town in such large network embodiments can facilitate implementation of a private network within such large networks. The town may interface with other towns or a larger cloud network, which may also communicate over public communication links. Note that cloud-computing platform 310 may include a private network and / or portions of public networks. In some cases, a cloud-computing platform 310 may include remote storage and / or other application processing capabilities

[0028] The system of FIG. 3 may also include one or more user terminals 314a and314b each including at least a processor to execute programs, a memory (e.g., 316a and 316b) for storing data, a communication device and one or more user interfaces and devices that enable the user to receive, view, and transmit information. In one embodiment, the userterminals 314a and 314b is a computing system having interfaces and devices including keyboards, touchscreens, display screens, speakers, microphones, a mouse, styluses, etc. The user terminals 314 may be communicatively coupled to the one or more servers of the cloudcomputing platform 310. The user terminals 314 may be client terminals or expert terminals, enabling collaboration between clients and experts through the system of FIG. 3.

[0029] The system of FIG. 3 may also include at least one or more oil fields 200 having, for example, a set of terminals 320, each including at least a processor, a memory, and a communication device for communicating with other devices communicatively coupled to the cloud-computing platform 310. The resource site 200 may also have one or more sensors (e.g., one or more sensors described in association with FIG. 2) or sensor interfaces 322a and 322b communicatively coupled to the set of terminals 320 and / or directly coupled to the cloudcomputing platform 310. In some embodiments, data collected by the one or more sensors / sensor interfaces 322a and 322b may be processed to generate a one or more resource models (e.g., reservoir models) or one or more resolved data sets used to generate the resource model which may be displayed on a user interface associated with the set of terminals 320, and / or displayed on user interfaces associated with the set of servers of the cloud computing platform 310, and / or displayed on user interfaces of the user terminals 314. Furthermore, various equipment / devices discussed in association with the resource site 200 may also be communicatively coupled to the set of terminals 320 and or communicatively coupled directly to the cloud-computing platform 310. The equipment and sensors may also include one or more communication device(s) that may communicate with the set of terminals 320 to receive orders / instructions locally and / or remotely from the resource site 200 and also send statuses / updates to other terminals such as the user terminals 314.

[0030] The system of FIG. 3 may also include one or more client servers 324 including a processor, memory and communication device. For communication purposes, the client servers 324 may be communicatively coupled to the cloud-computing platform 310, and / or to the user terminals 314a and 314b, and / or to the set of terminals 320 at the resource site 200 and / or to sensors at the oil field, and / or to other equipment at the resource site 200.

[0031] A processor, as discussed with reference to the system of FIG. 3, may include a microprocessor, a graphical processing unit (GPU), a microcontroller, a processor module orsubsystem, a programmable integrated circuit, a programmable gate array, or another control or computing device.

[0032] The memory / storage media discussed above in association with FIG. 3 can be implemented as one or more computer-readable or machine-readable storage media that are non-transitory. In some embodiments, storage media may be distributed within and / or across multiple internal and / or external enclosures of a computing system and / or additional computing systems. Storage media may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories; magnetic disks such as fixed, floppy and removable disks; other magnetic media including tape; optical media such as compact disks (CDs) or digital video disks (DVDs), BluRays or any other type of optical media; or other types of storage devices. “Non-transitory” computer readable medium refers to the medium itself (i.e., tangible, not a signal) and not data storage persistency (e.g., RAM vs. ROM).

[0033] Note that instructions can be provided on one computer-readable or machine- readable storage medium, or alternatively, can be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes and / or non-transitory storage means. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). The storage medium or media can be located either in a computer system running the machine- readable instructions, or located at a remote site from which machine-readable instructions can be downloaded over a network for execution.

[0034] It is appreciated that the described system of FIG. 3 is an example that may have more or fewer components than shown, may combine additional components, and / or may have a different configuration or arrangement of the components. The various components shown may be implemented in hardware, software, or a combination of both, hardware and software, including one or more signal processing and / or application specific integrated circuits.

[0035] Further, the steps in the flowcharts described below may be implemented by running one or more functional modules in an information processing apparatus such as general-purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, GPUsor other appropriate devices associated with the system of FIG. 3. For example, the flowchart of FIG. 1 as well as the flowcharts below may be executed using a signal processing engine stored in memory 306a, 306b, or 306c such that the signal processing engine includes instructions that are executed by the one or more processors such as processors 302a, 302b, or 302c as the case may be. The various modules of FIG. 3, combinations of these modules, and / or their combination with general hardware are included within the scope of protection of the disclosure. While one or more computing processors (e g., processors 302a, 302b, or 302c) may be described as executing steps associated with one or more of the flowcharts described in this disclosure, the one or more computing device processors may be associated with the cloudbased computing platform 310 and may be located at one location or distributed across multiple locations. In one embodiment, the one or more computing device processors may also be associated with other systems of FIG. 3 other than the cloud-computing platform 310.

[0036] In some embodiments, a computing system is provided that includes at least one processor, at least one memory, and one or more programs stored in the at least one memory, such that the programs comprise instructions, which when executed by the at least one processor, are configured to perform any method disclosed herein.

[0037] In some embodiments, a computer readable storage medium is provided, which has stored therein one or more programs, the one or more programs including instructions, which when executed by a processor, cause the processor to perform any method disclosed herein. In some embodiments, a computing system is provided that includes at least one processor, at least one memory, and one or more programs stored in the at least one memory for performing any method disclosed herein. In some embodiments, an information processing apparatus for use in a computing system is provided for performing any method disclosed herein.

[0038] Workflows

[0039] FIG. 4A shows an exemplary workflow for configuring a fiber cable and applying adaptive summation fiber recordings therefrom in order to increase the signal -to-noise ratio of fiber recordings. At block 402, one or more distributed acoustic sensing (DAS) interrogators may be communicatively coupled to one or more fibers in a fiber optic cable. According to some embodiments, a DAS interrogator comprises a system having one or more lasers, one or more memory devices storing for example, a signal processing engine which is configured to continually record or capture real-time measurements, using for example, the oneor more lasers, along the entire length of a fiber optic cable. As further discussed below (e.g., at block 404), the signal processing engine may record, using a computer processor associated with one or more interrogators communicatively coupled to one or more fibers within the fiber optic cable, pulse data indicating one or more laser pulses transmitted into or out of the one or more fibers within the fiber cable to generate a fiber record (e.g., a first fiber record). The pulse data may be associated with at least one of: a subterranean formation (e.g. subterranean formation data of the resource site), a predictive maintenance operation (e.g., predictive maintenance data at the resource site) associated with one or more equipment, vibration activity (e.g., predictive maintenance data) associated with one or more structures, or flow conditions (e.g., flow condition data) along a pipeline. It is appreciated that the one or more optical fibers within the fiber optic cable serve as sensing elements which enable the DAS interrogator s) to capture or record various measurements along the fiber optic cable. In some embodiments, two or more interrogators are coupled to two or more fibers within the fiber cable such that one or more of the fibers are coupled to each other in a back-looping configuration whereas other fibers within the fiber cable are not coupled in the back-looping configuration.

[0040] At block 404, the DAS interrogator is used to record captured data along the fiber optic cable by the one or more fibers therein. In particular, the recording of each of the one or more fibers by the one or more DAS interrogator s) may be executed using a specific gauge length associated with the fiber optic cable that is equivalent to the gauge length for all fibers comprised in the one or more fibers of the fiber optic cable. According to some implementations, the gauge length is an acquisition parameter that may be selected and used to dynamically configure hardware of, for example, one or more interrogator systems to which the fiber cable comprising a plurality of fibers is coupled. In some cases, the gauge length may be digitally applied to a raw optical data recording from one or more fiber cables associated with the one or more interrogator systems. It is appreciated that the disclosed adaptive summation process is applicable to scenarios where simultaneous optical data recordings from different fibers (e g., associated with one or more fiber cables) with different gauge lengths, simultaneous optical recordings from different fibers (e.g., associated with one or more fiber cables) with the same gauge length, simultaneous optical recordings from the same fibers (e.g., associated with one or more fiber cables) with different gauge lengths, and simultaneous optical recordings from the same fibers (e.g., associated with one or more fiber cables) with the same gauge length.It is appreciated that the gauge length of the fiber optic cable, according to some embodiments, comprises a length of a section of the fiber optic cable used to evaluate wavefield changes in terms of length or phase associated with the wavefield. In some cases, the gauge length represents distance data associated with the fiber cable used to measure the string response or fiber response of one or more fibers comprised in the fiber cable. In some embodiments, the gauge length may be determined as a function of the one or more fibers comprised in the fiber optic cable. In yet other embodiments, an aggregate or combined gauge length or averaged gauge lengths of one or more fibers is applied in instances where the fiber optic cable has a plurality of fibers. Block 405 is a decision block that determines whether there are one or more fiber fibers within the fiber optic cable that are spliced at the end of the fiber optic cable. In particular, the signal processing engine may determine, using a computer processor, whether at least one of the one or more fibers within the fiber cable is spliced. If this is not the case (e.g., instances where there is no splicing) then the workflow proceeds to node A which is further discussed in association with FIG. 4B. In such cases, at least two fiber records, for example, from two parallel fiber records may be generated for additional processing using, for example, two DAS interrogators communicatively coupled to two or more fibers within the fiber cable.

[0041] If the fiber cable has one or more spliced fibers, additional processing is required as outlined in blocks 406, 408, and 410 of FIG. 4A. In such cases, the total length of the fiber record may be multiplied (e.g., doubled, tripled, quadrupled, etc.) as the case may require due to the splicing. In some cases, multiplying the total length of the fiber record may be a function of n number of fibers connected or otherwise spliced in a series configuration. At block 406, a trace in a received measurement or fiber record may be identified such that the trace corresponds to a splicing point associated with at least one spliced fiber comprised in the fiber optic cable. In one embodiment, the splicing point may be indicated (e.g., visibly indicated) in the fiber record since it comprises at least a mirror image of one or more sections within the fiber record. It is appreciated that the trace, according to some embodiments represents one or more realizations of optical data or a captured record from the fiber optic cable. The one or more realizations may indicate seismic wiggles indicative of phase changes of light / laser pulses within the fiber optic cable as a function of space and time. It is further appreciated that the signal processing engine may identify (e.g., at block 406), using a computer processor, the trace in the first fiber record corresponding to the splicing point within the fiber cable. In such cases,the trace may indicate a mapping of location data along the fiber cable to specific locations around the fiber cable as the one or more laser pulses travel through the one or more fibers within the fiber cable.

[0042] In one embodiment, two portions of the fiber record may be split into an inbound section and an outbound section as noted at block 408. In particular, the fiber record may, according to some embodiments, be split by the signal processing engine into a plurality of different sections between (e.g., a plurality of inbound and outbound sections), or associated with a plurality of splicing points corresponding to the plurality of different sections, according to some embodiments. For example, the fiber record may be split into a plurality of different sections including an inbound section and an outbound section, such that the plurality of different sections are between at least two splicing points within the fiber cable. A trace numbering of the outbound section may be mirrored to align with the trace numbering of the inbound section at block 410. Specifically, the signal processing engine may mirror, using the computer processor, an outbound trace numbering of the outbound section to align with a trace numbering of the inbound section to generate mirrored data. For example, at a splicing point within the fiber record, an image of the subsurface may be mirrored (e g., by inverting the direction of light propagation within one or more fibers within the fiber cable), such that the trace numbering may progressively keep increasing. To have the trace numbering consistent with the subsurface (e.g., in a multidimensional coordinate system (x-y coordinate system orx- y-z coordinate system)), an appropriate trace number may be re-assigned to the fiber or seismic record. According to some embodiments, a new "reference numbering system" may be assigned to the fiber or seismic record that is based on the disclosed adaptive summation technique such that two or more "mirrored" traces may have slightly different multidimensional coordinates (e.g., x-y coordinates). Doing this may provide at least two data points for the same coordinates along the fiber cable thereby providing more data for each location along the fiber cable to improve the signal to noise ratio of data for each of said locations. This effectively creates, for example, two fiber records for the additional processing outlined in FIG. 4B. It is appreciated that the mirroring process may comprise a process of organizing (e.g., sorting, arranging, filtering) one or more traces associated with the one or more fiber records to match or correspond to specific locations within the subsurface such that each instance between the splicing point is mapped to an exact location within the subsurface. It is appreciated that themirroring operation discussed above may involve, according to some embodiments, mirroring a trace numbering associated with each section (e.g. each section comprised in a plurality of different sections between, or associated with a plurality of splicing points of the fiber cable) that is oriented in an opposite direction for the alignment of a plurality of inbound and outbound sections comprised in the fiber optic cable to generate the mirrored data.

[0043] Turning to FIG. 4B, each fiber record (e.g., mirrored data corresponding to each fiber segment) may be assigned geometry data at block 412, for example, such that a coordinate system (2-dimensional or 3 -dimensional coordinate system) may be used to assign coordinates to one or more traces associated with each fiber record. Specifically, the signal processing engine may assign, using a computer processor, geometry data to the mirrored data to generate a geometrically formatted fiber record. This technique may enable the mapping of location data along the fiber optic cable to various locations (e.g., subterranean locations) associated with, for example, the resource site, or along various locations associated with sensing data around a medium through which the fiber cable is passed. Based on the coordinate assignment, useful data or the signal component of the fiber record may be reconstructed, at block 414, at the exact same coordinate locations for each fiber recording Tn particular, the signal components within the geometrically formatted fiber record may be reconstructed by the signal processing engine, for example, using the coordinate locations in the inbound section of the fiber record that correspond to coordinate locations in the outbound section of the fiber record. For example, the reconstruction of the signal component may require the specification of one or more desired output coordinates that are applied by a signal processing engine comprised in the interrogator; these, according to some embodiments, can be the same for each simultaneous fiber recording. The high spatial and / or temporal sampling rate applied to the fiber recording can guarantee that the reconstruction preserves the fidelity of data within the fiber recording. It is appreciated that many interpolators can be applied to the fiber record / recording during the sampling phase. It is further appreciated that different methods may be used to arrive at different degrees of accuracy to assign coordinates to each trace recorded from each fiber. In particular, data reconstruction for one or more fiber records at the exact same set of coordinate locations (e.g., at a spatial sampling interval equal to (5)) for all the recorded segments and / or collocated fiber records may be implemented using data interpolation in the frequency domain along an axis (e.g., fiber axis) comprised in the coordinate system. One or more filters or a machine learningengine may be generated at block 416 based on the reconstructed data. Specifically, the signal processing engine may be used to generate the one or more filters (e.g., a matching filter) or a machine learning engine based on the reconstructed signal components within the geometrically formatted fiber record. According to one embodiment, the one or more filters generated may comprise a least squares matching filter between cascaded pairs of fiber recordings. For example, the one or more filters (e.g., matching filter(s)) may be selected by determining, using data associated with pairs of fiber record segments of fiber records in back-looping configurations or using pairs of simultaneous fiber recordings as the case may require. In such instances, a matching filter that enables an optimal match, for example, in a least square sense, may be constructed. Other filter types such as carbon filters, Bayesian filters, Machine learning algorithm -based filters, etc., may be employed in some embodiments. According to some implementations, the least square matching filter, for example, may vary in a windowed space and / or a time domain so that the filter is changed periodically as the characteristics of the time series (e.g., data point of one or more fiber records in time) change. This process may be reiterated or otherwise repeated to facilitate the matching of all acquired collocated fiber records and / or fiber data acquired in back-looping configurations. Tn some embodiments, a machine learning tool or process may be applied to align one or more traces along corresponding segments (e.g., fiber record segments) in conjunction with the one or more filters, or separate from the one or more filters to enhance the matching of all acquired collocated fiber records and / or fiber data acquired in back-looping configurations. It is appreciated that one or more fibers spliced in a back-looping configuration may enable the same measurements of data associated with similar or dissimilar locations along the fiber cable to be captured multiple times. This provides additional data or more data to confirm information associated with various positions along the fiber cable or various locations within the subterranean formation being observed using the fiber optic cable and thereby increase the sensitivity of the fiber sensor to which the DAS interrogator is coupled. Furthermore, the one or more filters or machine learning engine may be applied, at block 418, to the first fiber record or a second fiber record to generate a plurality of matched fiber records. In some embodiments, applying the matching filter or the machine learning engine to the first fiber record or the second fiber record to generate the plurality of matched fiber records comprises executing one or more of: seismic data matching operations, using the matching filter or the machine learning engine, on the firstfiber record or the second fiber record; noise attenuation operations on the first fiber record and the second fiber record; differential attribute signal processing or similarity attribute signal processing operations of the first fiber record relative to the second fiber record. At block 420, the signal processing engine may combine, using a computer processor, the plurality of matched fiber records to generate a report indicating captured measurements along the fiber cable. In one embodiment, the captured measurements may have a noise component that is substantially small relative to a noise component in the first fiber record or the second fiber record. In particular, matched seismic data comprised in one or more fiber records and / or from collocated fiber records and / or from fiber records associated with fibers in a back-looping configuration may be combined (e.g., summed) together to generate a single seismic data at known assigned coordinates. This combination or summation may be adaptively executed in real-time to generate reports associated with subsurface of the resource site. In some embodiments, the combination or summation process may be embedded in a computing device (e.g., an edge computing device) comprised in or associated with a signal processing engine (e.g. an interrogator software) of the interrogator system in the case of a single-lase or multi- lase interrogator system. Tn some implementations, a signal processing engine associated with an acquisition system (e.g., field acquisition system) having multiple interrogators may be used to execute the combination or summation process. A benefit of either approaches is the advantage of generating output data based on the summation or combination process using one or more smaller datasets that are optimized based on the disclosed techniques. In particular, the adaptive summation or combination process is based on dynamically windowing sets or subsets of data comprised in one or more fiber records and subsequently combining or summing the windowed sets or subsets of the data comprised in the one or more fiber records. The window or windowing function may be expanded or contracted based on. This combination of matched seismic data has an improved or optimal signal-to-noise ratio for measurements captured using a fiber cable with one or more fibers. It should be noted that the aggregations of combination technique described above is applicable to fiber data processing associated predictive maintenance applications relating to vibration data, wind farm vibration sensing, micro-seismic applications, structural monitoring of vibrations in concrete and / or mining structures, pipeline data management, etc.

[0044] FIGS. 5A-5C respectively show an exemplary ideal / optimal reference trace or recording (FIG. 5A) with no noise, a plurality of captured seismic recordings (e.g., pulse data captured at a resource site) at a resource site (FIG. 5B) with a lot of noise (e.g., one or more noise types), and a resultant trace / recording (FIG. 5C) generated by applying the DAS techniques disclosed to the plurality of captured recordings and which has substantially minimized noise. It can be seen from these figures that the noise associated with the four recordings of FIG. 5B is substantially reduced after adaptively summing the four fiber recordings using the disclosed techniques to generate the report including the adaptively summed trace of FIG. 5C which is substantially similar to the ideal trace of FIG. 5A. It is appreciated that for the illustrated examples shown in FIGS. 5A-5C, the signal-to-noise ratio is substantially improved (e.g., by a factor A / 4) in the resultant trace comprised in the report of FIG. 5C relative to the signal-to-noise ratio of the traces in FIG. 5B.

[0045] Turning back to FIGS. 4A and 4B, it is appreciated that one or more of the various processing stages of FIGS. 4A and 4B may be executed with a signal processing engine associated with the DAS interrogator and which is stored in a memory device. The signal processing engine according to some embodiments may comprise instructions that are executed by a computer processor.

[0046] These and other implementations may each optionally include one or more of the following features. Determining that the fiber cable is spliced may comprise determining that the fiber cable is spliced in a back-looping configuration. In addition, the one or more interrogators may comprise a distributed acoustic sensing (DAS) interrogator. In one embodiment, assigning the geometry data may comprise using a coordinate system to assign coordinates to the trace in the fiber record. Moreover, the coordinate system discussed above may comprise one of a 2-dimensional coordinate system or a 3-dimensional coordinate system. Assigning the geometry data, according to some embodiments, enables mapping of location data along the fiber optic cable to subterranean locations associated with a resource site. The matching filter may comprise one of a least squares filter, a carbon filter, a Bayesian filter, or a filter associated with machine learning. Furthermore, recording the pulse data may be executed using a gauge length of the fiber optic cable this is equivalent to a gauge length for the one or more fibers comprised in the fiber cable. It is appreciated that the report generated based on the disclosed adaptive summation process may comprise multi-dimensional visualizations ofseismic activity data within a subterranean formation of a resource site associated with energy development or energy development operations (e.g., hydrocarbon exploratory operations, selection of optimal location for wind turbine installation, selection of robust materials for road and infrastructure construction based on geological data derived from the report). This report may characterize geological formations by reconstructing seismic wavefield data or signals received using the one or more interrogator systems as discussed above. According to some embodiments, the seismic activity data may indicate phase changes associated with the laser pulses within the fiber optic cable as a function of space or time. Additionally, the noise component (e.g., one or more noise types) may comprise one of: a coherent noise, a noncoherent noise, noise associated with anthropic activities around the fiber cable, or a combination of the coherent noise, the non-coherent noise, or the noise associated with the anthropic activities around the fiber cable.

[0047] According to some embodiments of this disclosure, a method or computer program associated with a system for minimizing one or more noise types from fiber records is disclosed. The method or computer program associated with the system, according to one implementation comprises recording pulse data at a resource site, using a computer processor associated with one or more interrogators communicatively coupled to one or more fibers within a fiber cable. The pulse data may indicate one or more laser pulses transmitted into or out of the one or more fibers within the fiber cable to generate a first fiber record. The method also includes assigning, using the computer processor, geometry data to mirrored data associated with the pulse data to generate a geometrically formatted fiber record following which signal components within the geometrically formatted fiber record is reconstructed using coordinate locations in an inbound section of the first fiber record that correspond to coordinate locations in an outbound section of the first fiber record. The method further comprises generating, using the computer processor, a matching filter based on the reconstructed signal components within the geometrically formatted fiber record. In some cases, the method includes applying, using the computer processor, the matching filter to the first fiber record or a second fiber record to generate a plurality of matched fiber records. The plurality of matched fiber records may be combined, according to some implementations, to generate a report indicating captured measurements along the fiber cable. In particular, the captured measurements along the fiber cable may be identified, based at least in part on combining theplurality of matched fiber records to generate a report that indicates the captured measurements. The captured measurements may have a noise component that is substantially small relative to a noise component in the first fiber record or the second fiber record.

[0048] These and other implementations may each optionally and / or additionally include one or more of the following features. The pulse data may indicate or be associated with at least one of: subterranean data associated with the resource site, predictive maintenance operation data associated with one or more equipment in proximity of the resource site; vibration activity data associated with one or more structures at the resource site; or flow condition data along a pipeline at the resource site. Furthermore, prior to generating the geometrically formatted data, the method comprises executing, by the computer processor: determining whether at least one of the one or more fibers within the fiber cable is spliced; and in response to determining that the at least one of the one or more fibers within the fiber cable is spliced, executing one or more of: identifying, using the computer processor, a trace in the first fiber record corresponding to a splicing point within the fiber cable, the trace indicating a mapping of location data along the fiber cable to specific locations around the fiber cable as the one or more laser pulses travel through the one or more fibers within the fiber cable; splitting, using the computer processor, the first fiber record into the inbound section and the outbound section; and mirroring, using the computer processor, an outbound trace numbering of the outbound section to align with a trace numbering of the inbound section to generate the mirrored data. According to one embodiment, the noise component comprises the one or more noise types discussed in association with one or more workflows in this disclosure.

[0049] While any discussion of or citation to related art in this disclosure may or may not include some prior art references, Applicant neither concedes nor acquiesces to the position that any given reference is prior art or analogous prior art.

[0050] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to use the invention and various embodiments with various modifications as are suited to the particular use contemplated. It is appreciated that theterm optimize / optimal and its variants (e.g., efficient or optimally) may simply indicate improving, rather than the ultimate form of 'perfection' or the like.

[0051] It will also be understood that, although the terms first, second, etc., may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the invention. The first object or step, and the second object or step, are both objects or steps, respectively, but they are not to be considered the same object or step.

[0052] The terminology used in the description herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used in the description of the invention and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and / or” as used herein refers to and encompasses any possible combination of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0053] As used herein, the term “if’ may be construed to mean “when” or “upon” or“in response to determining” or “in response to detecting,” depending on the context.

[0054] Those with skill in the art will appreciate that while some terms in this disclosure may refer to absolutes, e.g., all source receiver traces, each of a plurality of objects, etc., the methods and techniques disclosed herein may also be performed on fewer than all of a given thing, e.g., performed on one or more components and / or performed on one or more source receiver traces. Accordingly, in instances in the disclosure where an absolute is used, the disclosure may also be interpreted to be referring to a subset.

Claims

What is claimed is:

1. A method for minimizing one or more noise types from fiber records, the method comprising: recording, using a computer processor associated with one or more interrogators communicatively coupled to one or more fibers within a fiber cable, pulse data indicating one or more laser pulses transmitted into or out of the one or more fibers within the fiber cable to generate a first fiber record, the pulse data being associated with at least one of: a subterranean formation, a predictive maintenance operation associated with one or more equipment, vibration activity associated with one or more structures, or flow conditions along a pipeline; determining, using the computer processor, whether at least one of the one or more fibers within the fiber cable is spliced; in response to determining that the at least one of the one or more fibers within the fiber cable is spliced, executing one or more of: identifying, using the computer processor, a trace in the first fiber record corresponding to a splicing point within the fiber cable, the trace indicating a mapping of location data along the fiber cable to specific locations around the fiber cable as the one or more laser pulses travel through the one or more fibers within the fiber cable, splitting, using the computer processor, the first fiber record into an inbound section and an outbound section, mirroring, using the computer processor, an outbound trace numbering of the outbound section to align with a trace numbering of the inbound section to generate mirrored data; assigning, using the computer processor, geometry data to the mirrored data to generate a geometrically formatted fiber record; reconstructing, using the computer processor, signal components within the geometrically formatted fiber record using coordinate locations in the inbound section of the first fiber record that correspond to coordinate locations in the outbound section of the first fiber record; generating, using the computer processor, a matching filter or a machine learning engine based on the reconstructed signal components within the geometrically formatted fiber record; applying, using the computer processor, the matching filter or the machine learning engine to the first fiber record or a second fiber record to generate a plurality of matched fiber records; and combining, using the computer processor, the plurality of matched fiber records to generate a report indicating captured measurements along the fiber cable, the captured measurements having a noise component that is substantially small relative to a noise component in the first fiber record or the second fiber record.

2. The method of claim 1, wherein determining that the fiber cable is spliced comprises determining that the fiber cable is spliced in a back-looping configuration.

3. The method of claim 1, wherein the one or more interrogators comprises a distributed acoustic sensing (DAS) interrogator.

4. The method of claim 1, wherein assigning the geometry data comprises using a coordinate system to assign coordinates to the trace in the first fiber record.

5. The method of claim 4, wherein the coordinate system comprises one of a 2-dimensional coordinate system or a 3-dimensional coordinate system.

6. The method of claim 4, wherein assigning the geometry data enables mapping of location data along the fiber cable to subterranean locations associated with a resource site.

7. The method of claim 1, wherein the matching filter comprises one of a least squares filter, a carbon filter, a Bayesian filter, or a filter associated with machine learning.

8. The method of claim 1, wherein recording the pulse data is executed using a gauge length of the fiber cable this is equivalent to a gauge length for the one or more fibers comprised in the fiber cable.

9. The method of claim 1, wherein the report comprise multi-dimensional visualizations of seismic activity data within a subterranean formation of a resource site associated with energy development.

10. The method of claim 9, wherein the seismic activity data indicate phase changes associated with the laser pulses within the fiber cable as a function of space or time.

11. The method of claim 1, wherein the noise component comprises one of: a coherent noise, a non-coherent noise, noise associated with anthropic activities around the fiber cable, or a combination of the coherent noise, the non-coherent noise, or the noise associated with the anthropic activities around the fiber cable.

12. The method of claim 1, wherein splitting the first fiber record into an inbound section and an outbound section comprises splitting the fiber record into a plurality of different sections including the inbound section and the outbound section, such that the plurality of different sections are between at least two splicing points within the fiber cable.

13. A system for minimizing one or more noise types from fiber records, the system comprising: a computer processor associated with one or more interrogators communicatively coupled to one or more fibers within a fiber cable, and memory storing a data processing engine that comprises instructions that are executable by the computer processor to: record pulse data indicating one or more laser pulses transmitted into or out of the one or more fibers within the fiber cable to generate a first fiber record, the pulse data being associated with at least one of: a subterranean formation, a predictive maintenance operation associated with one or more equipment, vibration activity associated with one or more structures, or flow conditions along a pipeline; determine whether at least one of the one or more fibers within the fiber cable is spliced, in response to determining that the at least one of the one or more fibers within the fiber cable is spliced, executing one or more of: identify a trace in the first fiber record corresponding to a splicing point within the fiber cable, the trace indicating a mapping of location data along the fiber cable to specific locations around the fiber cable as the one or more laser pulses travel through the one or more fibers within the fiber cable, split the first fiber record into an inbound section and an outbound section, mirror an outbound trace numbering of the outbound section to align with a trace numbering of the inbound section to generate mirrored data; assign geometry data to the mirrored data to generate a geometrically formatted fiber record; reconstruct signal components within the geometrically formatted fiber record using coordinate locations in the inbound section of the first fiber record that correspond to coordinate locations in the outbound section of the first fiber record; generate a matching filter or a machine learning engine based on the reconstructed signal components within the geometrically formatted fiber record; apply the matching filter or the machine learning engine to the first fiber record or a second fiber record to generate a plurality of matched fiber records; and combine the plurality of matched fiber records to generate a report indicating captured measurements along the fiber cable, the captured measurements having a noise component that is substantially small relative to a noise component in the first fiber record or the second fiber record.

14. The system of claim 13, wherein determining that the fiber cable is spliced comprises determining that the fiber cable is spliced in a back-looping configuration.

15. The system of claim 13, wherein the one or more interrogators comprises a Distributed acoustic sensing (DAS) interrogator.

16. The system of claim 13, wherein assigning the geometry data comprises using a coordinate system to assign coordinates to the trace in the first fiber record.

17. The system of claim 16, wherein assigning the geometry data enables mapping of location data along the fiber cable to subterranean locations associated with resource site.

18. The system of claim 13, wherein recording the pulse data is executed using a gauge length of the fiber cable this is equivalent to a gauge length for the one or more fibers comprised in the fiber cable.

19. The system of claim 13, wherein the report comprise multi-dimensional visualizations of seismic activity data within a subterranean formation of a resource site associated with energy development.

20. A computer program for minimizing one or more noise types from fiber records, that when executed by a computer processor of a computing device, causes the computing device to: record pulse data indicating one or more laser pulses transmitted into or out of one or more fibers within a fiber cable to generate a first fiber record, the pulse data being associated with at least one of: a subterranean formation, a predictive maintenance operation associated with one or more equipment, vibration activity associated with one or more structures, or flow conditions along a pipeline; determine whether at least one of the one or more fibers within the fiber cable is spliced, in response to determining that the at least one of the one or more fibers within the fiber cable is spliced, executing one or more of: identify a trace in the first fiber record corresponding to a splicing point within the fiber cable, the trace indicating a mapping of location data along the fiber cable to specific locations around the fiber cable as the one or more laser pulses travel through the one or more fibers within the fiber cable, split the first fiber record into an inbound section and an outbound section, mirror an outbound trace numbering of the outbound section to align with a trace numbering of the inbound section to generate mirrored data;assign geometry data to the mirrored data to generate a geometrically formatted fiber record; reconstruct signal components within the geometrically formatted fiber record using coordinate locations in the inbound section of the first fiber record that correspond to coordinate locations in the outbound section of the first fiber record; generate a matching filter or a machine learning engine based on the reconstructed signal components within the geometrically formatted fiber record; apply the matching filter or the machine learning engine to the first fiber record or a second fiber record to generate a plurality of matched fiber records; and combine the plurality of matched fiber records to generate a report indicating captured measurements along the fiber cable, the captured measurements having a noise component that is substantially small relative to a noise component in the first fiber record or the second fiber record.

21. The computer program of claim 20, wherein the report comprise multi-dimensional visualizations of seismic activity data within a subterranean formation of a resource site associated with energy development.

22. A method for minimizing one or more noise types from fiber records, the method comprising: recording, using a computer processor associated with one or more interrogators communicatively coupled to one or more fibers within a fiber cable, pulse data at a resource site that indicate one or more laser pulses transmitted into or out of the one or more fibers within the fiber cable to generate a first fiber record; assigning, using the computer processor, geometry data to mirrored data associated with the pulse data to generate a geometrically formatted fiber record; reconstructing, using the computer processor, signal components within the geometrically formatted fiber record using coordinate locations in an inbound section of the first fiber record that correspond to coordinate locations in an outbound section of the first fiber record; generating, using the computer processor, a matching filter or a machine learning engine based on the reconstructed signal components within the geometrically formatted fiber record; and applying, using the computer processor, the matching filter or the machine learning engine to the first fiber record or a second fiber record to generate a plurality of matched fiber records.

23. The method of claim 22, wherein the pulse data indicates or is associated with at least one of: subterranean data associated with the resource site;predictive maintenance operation data associated with one or more equipment in proximity of the resource site; vibration activity data associated with one or more structures at the resource site; or flow condition data along a pipeline at the resource site.

24. The method of claim 22, wherein prior to generating the geometrically formatted data, executing, by the computer processor: determining whether at least one of the one or more fibers within the fiber cable is spliced; and in response to determining that the at least one of the one or more fibers within the fiber cable is spliced, executing one or more of: identifying, using the computer processor, a trace in the first fiber record corresponding to a splicing point within the fiber cable, the trace indicating a mapping of location data along the fiber cable to specific locations around the fiber cable as the one or more laser pulses travel through the one or more fibers within the fiber cable, splitting, using the computer processor, the first fiber record into the inbound section and the outbound section, and mirroring, using the computer processor, an outbound trace numbering of the outbound section to align with a trace numbering of the inbound section to generate the mirrored data.

25. The method of claim 22, further comprising identifying, using the computer processor, captured measurements along the fiber cable based at least in part on combining the plurality of matched fiber records to generate a report indicating the captured measurements.

26. The method of claim 25, wherein: the captured measurements have a noise component that is substantially small relative to a noise component in the first fiber record or the second fiber record; and the noise component comprises the one or more noise types.

27. The method of claim 22, wherein applying the matching filter or the machine learning engine to the first fiber record or the second fiber record to generate the plurality of matched fiber records comprises executing one or more of: seismic data matching operations, using the matching filter or the machine learning engine, on the first fiber record or the second fiber record; noise attenuation operations on the first fiber record and the second fiber record; and differential attribute signal processing or similarity attribute signal processing operations of the first fiber record relative to the second fiber record.

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