Automatically generated kinematically consistent velocity models

Abstract

According to an embodiment, a method for computationally generating resolved data associated with a subsurface of a resource site includes: receiving seismic data from one or more sensors deployed at the resource site; generating a velocity model using the seismic data; formatting the velocity model into a data matrix; executing a minimization computing operation on the data matrix to decompose the velocity model into background velocity data and perturbation data and thereby generate a decomposed dataset; attenuating the perturbation data comprised in the decomposed dataset to generate resolved data; generating, based on the resolved data, a report indicating material properties comprised in the subsurface of the resource site; and executing an energy development operation associated with the resource site based on the report. The subsurface, according to one embodiment, comprises a region in the subsurface of the resource site through which the propagated seismic wavefield travels prior to being received.

Description

AUTOMATICALLY GENERATED KINEMATICALLY CONSISTENT VELOCITYMODELSINTRODUCTION

[0001] This disclosure is directed to generating resolved data using captured seismic data at a resource site.BACKGROUND

[0002] The kinematics of wave propagation modeled using approximations to wave equation solutions are generally accurate in the regime of wave number characteristics associated with a velocity model of a propagated wavefield. While such approximations may be comparable with the first Fresnel-zone of the propagated wavefield, ray-tracing methods applied to approximations in this context do not provide stable and accurate kinematic estimates of depth velocity models for wave-number data outside of the foregoing regime. Specifically, such approximations are not inherently optimized for depth migration techniques and reflection tomography.

[0003] Depth migration techniques and reflection tomography include operations that can be used to process a recorded seismic data. In particular, ray-based tomography and depth migration approaches seek to resolve difficulties introduced by: the low wave number constraints associated with velocity data comprised in a velocity model and describes desirable low wave number features associated with the velocity model; and undesirable high-wave number perturbation data associated with the velocity model that describe the scattering potential of the wavefield based on multipath or reflection data associated with the recorded seismic data. Depth migration processes rely on placing the reflection data at appropriate depths within the subsurface based on characterizations of the velocity data associated with the velocity model.

[0004] Prior techniques rely on generally executing smoothing operations on the entire velocity model to remove perturbation data in order to eliminate the high wave-number content associated with said perturbation data. However, smoothing the entire velocity model distorts or corrupts the desired correctly resolved background velocity data comprised in the velocity model thereby damaging the accuracy of the kinematics associated with the recorded seismicdata being analyzed. This in effect incorrectly places reflection data associated with the velocity model at incorrect depths within the subsurface. Such errors introduced by smoothing require corrections, generally through additional tomographic updates, which can adversely affect any progress made in resolving the desirable features present in the velocity model associated with the recorded seismic data. There is therefore a need to develop methodologies that can smooth, minimize, and / or eliminate perturbation data comprised in captured data without attenuating or distorting useful background velocity data comprised in a velocity model.SUMMARY

[0005] Disclosed are methods, systems, and computer programs that computationally generate resolved data associated with a subsurface of a resource site. According to an embodiment, a method for computationally generating resolved data associated with a subsurface of a resource site includes: receiving seismic data from one or more sensors deployed at the resource site, the seismic data including a recording of seismic data comprising a propagated seismic wavefield within a subsurface of the resource site that indicates one or more signal components of the seismic data; generating a velocity model using the seismic data, the velocity model characterizing a signal interaction between background velocity data and perturbation data associated with the seismic data; formatting the velocity model into a data matrix; executing a minimization computing operation on the data matrix to decompose the velocity model into the background velocity data and the perturbation data and thereby generate a decomposed dataset; attenuating the perturbation data comprised in the decomposed dataset to generate the resolved data based on one of: a basis function associated with the velocity model, or a constraining operator associated with the velocity model; generating, based on the resolved data, a report indicating material properties comprised in the subsurface of the resource site; and executing an energy development operation associated with the resource site based on the report. It is noted that the subsurface referenced above comprises a region in the subsurface of the resource site through which the propagated seismic wavefield travels prior to being received.

[0006] In another embodiment, a system and a computer program can include or execute the method described above. These and other implementations may each optionally include one or more of the following features. The velocity model, according to someimplementations, is based on: processing the seismic data using a Born approximation process. In addition, the velocity model indicates a dataset comprised in the seismic data that corresponds to a scattering potential perturbation velocity associated with the propagated seismic wavefield. The perturbation velocity, for example, can be associated with multipath data comprising one or more reflections of the seismic wavefield as the seismic wavefield travels through the subsurface.

[0007] Moreover, the background velocity data may comprise a smooth background velocity component of the propagated seismic wavefield which is preserved during executing the minimization computing operation while the perturbation data comprises a high resolution perturbation data including one or more multipath components of the propagated seismic wavefield that are attenuated in response to the minimization computing operation.

[0008] In one embodiment, the basis function may comprise a radial basis function while the constraining operator is a smoothing operator according to one embodiment.

[0009] It is appreciated that the basis function or the constraining operator limits one or more values of the background velocity data to a smooth, low wave number domain (e.g., low frequency signal) comprising, for example, a wave having a low frequency (e g , waves with frequency of 10 Hz - 50 Hz, or 10 Hz - 60 Hz, or 10 Hz - 70 Hz).

[0010] In other embodiments, the basis function associated with the velocity model is a bandlimited impulse basis function.

[0011] In some implementations, the bandlimited impulse basis function includes one of a sine wavelet, a Ricker wavelet, a curvelet, a seislet, or a chirplet as the case may require.

[0012] It is appreciated that the one or more sensors discussed in association with the disclosed techniques can comprise one or more of hydrophonic sensors, geophonic sensors, broadband sensors, or a distributed acoustic (DAS) sensors. Furthermore, the resource site can comprise one or more of: an onshore resource site or an offshore resource site.

[0013] In addition, the material properties discussed in association with the report generated using the disclosed approach, according to one embodiment, comprises one or more of: geological boundary data associated with the subsurface of the resource site; rock property data associated with the subsurface of the resource site; fluid flow condition data associated with the subsurface of the resource site; air gap data associated with the subsurface of the resource site; subsurface discontinuity data associated with the subsurface of the resource site;subsurface layering data associated with the subsurface of the resource site; hydrocarbon data associated with the subsurface of the resource site; and mineral deposit data associated with the subsurface of the resource site.

[0014] Moreover, the report can comprise one or more of image data indicating a multidimensional image of the subsurface of the resource site including a 2-dimensional image or a 3-dimensional image; and / or textual data indicating a quantification of at least one material property comprised in the material properties.

[0015] It is appreciated that the energy development operations, according to some embodiments, comprise initiating, based on the material properties comprised in the report, one or more of: adjusting a drill bit spin rate at the resource site; regulating one or more flow control devices at the resource site; or optimizing gas storage operations in the subsurface of the resource site. In some cases, the energy development operations comprise evaluations that are based on the report for determining whether the resource site (e.g., a surface and / or a subsurface) of the resource site is suitable for implementing an energy project (e.g., installing of wind mills, installing of drill rigs, extracting crude or water from the subsurface, etc.).

[0016] It is appreciated that decomposing the velocity model into the background velocity data and the perturbation data may be based on reflectivity data comprised in the captured seismic data according to some embodiments. In addition, diagonal data comprised in the data matrix referenced in association with the methods may include weighted data elements based on the reflectivity data comprised in the seismic data.BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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.

[0018] FIG. 1 shows a high-level workflow for generating resolved data according to some embodiments of this disclosure.

[0019] FIG. 2 shows a cross-sectional view of a resource site for which the process of FIG. 1 may be executed.

[0020] FIG. 3 shows a networked system illustrating a communicative coupling of devices or systems associated with the resource site of FIG. 2.

[0021] FIG. 4 provides an exemplary workflow for methods, systems, and computer programs that computationally generate resolved data associated with a subsurface of the resource site of FIGS. 2 and 3.DETAILED DESCRIPTION

[0022] 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 disclosed subject-matter. However, it will be apparent to one of ordinary skill in the art that the solutions disclosed 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.

[0023] 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 described in this disclosure, according to 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 developing natural resources such as oil, gas, water well industries, and other mineral exploration operations.

[0024] 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.

[0025] This disclosure provides computational methods, techniques, and systems for integrating tomography and full waveform inversion (FWI) workflows through a unified velocity model representation. According to one embodiment, the velocity model comprises a model that characterizes or is associated with trajectory data associated with a propagated seismic wavefield, velocity data (e g., smooth background velocity data) associated with the propagated seismic wavefield, reflection / multipath / perturbation data associated with the propagated seismic wavefield, and other signal interactions properties associated with the propagated seismic wavefield. The velocity model may be decomposed into resolved components to facilitate a minimization operation that attenuate undesirable components of the velocity model. Starting with the velocity model with associated reflection (e.g., perturbation) data, the velocity model may be decomposed using a Bom approximation process associated with tomography while preserving the desirable features of the velocity model.

[0026] In particular, the velocity model may be decomposed into a smooth background velocity component that is desired and an undesirable perturbation component. In one embodiment, the approach for decomposing the velocity model is based on combining a high resolution full waveform inversion process with a ray -based tomography and imaging technique to separate the background velocity component from the perturbation component. In particular, the proposed approach addresses issues associated with smoothing an entire velocity model by automatically constructing a smooth background model that preserves the best features comprised in a recorded seismic data as well as attenuating the high wave number content of perturbation data associated with the recorded seismic data. In one embodiment, the velocity model V associated with the seismic data may be decomposed into a background velocity data Csand a high resolution perturbation data 6CSV = CS+ 8CS(1).

[0027] Moreover, it is appreciated that reflector or multipath positions described by the trajectory or migration of the background velocity data Csmay be characterized using bandlimited derivatives of the perturbation data 8CS

[0028] According to one embodiment, a minimization operation may be executed on the velocity model to attenuate the perturbation data comprised in the velocity model. The minimization operation may be executed using a minimization operation given by:min | D m - (1 / V) | (2). meRn

[0029] In one embodiment, the diagonal weighting parameter £> / shown in equation (2) may be derived from an image reflectivity data associated with the recorded seismic data in combination with one or more basis functions or smoothing operators (e.g., smoothing basis functions) Gs. The one or more basis functions may comprise a radial basis function according to one embodiment. The one or more basis functions or smoothing operators may constrain the solution of Gsm to a smooth, low wave number domain which indicates an inverse of the velocity data Cscomprised in the velocity model V. In particular, the solution of Gsm can be regarded, according to one embodiment, as the smooth component of the slowness of the velocity model given by 1 / 7, and therefore Cs— l / (Gsm) indicating the smooth background component of the velocity model. The diagonal weighting parameter Dtencodes a multiplicative relationship between the slowness of the velocity model 1 / V and the perturbation data given by:(1 / V) = Gsm(l + a) (3).

[0030] It is appreciated that a represents a high-wave number perturbation term of equation 3 while the bandlimited derivate of a corresponds to the reflector or multipath positions of the propagated seismic data. In order to make the disclosed techniques more robust with additional improvements to the signal-to-noise ratio after executing the disclosed approach, the minimization operation can be augmented to simultaneously provide optimal approximations to the high-wave number perturbation term a relative to the smooth background velocity data. Such an augmented minimization operation may be executed using the an operation given by:

[0031] It is appreciated that the term H in equation (4) can represent a collection of bandlimited impulse basis functions such as a sine wavelet, a Ricker wavelet, a curvelet, a seislet, or a chirplet. This augmented minimization effectively constrains the bandlimitedderivatives of the perturbations within well-defined bounds thereby maintaining the integrity of the background velocity data Cscomprised in the velocity model.

[0032] According to some embodiments, a data manager or a signal processing engine stored in a memory device can cause a computer processor to execute the various processing stages associated with the disclosed methods. Turning to FIG. 1 which exemplifies a high- level flowchart of the methods, the data manager at block 102, according to one embodiment, may generate a velocity model using seismic data captured at a resource site. For example, generating the velocity model may comprise: extracting one or more data elements comprised in seismic data captured by one or more sensors at the resource site; and organizing the extracted data elements into a data structure defining or indicating kinematic data associated with seismic data. The seismic data may include a recording of seismic data comprising a propagated seismic wavefield within a subsurface of the resource site such that the propagated seismic wavefield indicates one or more signal components or subsurface data encoded within the received seismic data due to the propagated wavefield traveling through the subsurface. At block 104, the data manager may format the velocity model into a data matrix. In particular, formatting the velocity model into a data matrix may comprise arranging one or more data elements comprised in the velocity model into dimensional structures that enable executing one or more analysis computing operations on the velocity model. For example, formatting the velocity model into a data matrix may comprise arranging, organizing, aligning, or designating within one or more data arrays, one or more data elements comprised in, or associated with the velocity model to facilitate the execution of one or more computing operations including a minimization computing operation on the velocity model. In addition, the data manager may generate, at block 106, a decomposed dataset using the data matrix. The decomposed dataset may indicate a split, a breakage, a separation, or a division of the one or more data elements comprised in the data matrix into components that can be further independently analyzed to affect some of the data elements comprised in the data matrix without affecting other data elements comprised in the data matrix. The data manager may attenuate, at block 108, perturbation data comprised in the decomposed dataset to generate resolved data. Attenuating the perturbation data may comprise reducing, eliminating, erasing, or deleting one or more data elements associated with the data matrix that correspond to the perturbation data comprised in the data matrix. At block 110, the data manager may generate based on the resolved data, a report indicating materialproperties comprised in the subsurface of the resource site. The report for example, may comprise image data (e.g., an image of one or more geological structures) associated with the subsurface of the resource site. The report may also comprise textual data that provides quantitative and / or qualitative identifiers that characterize one or more sections of the subsurface of the resource site. In one embodiment, the report may be generated on a graphical interface with which a user can interact. According to some implementations, the data manager may execute, at block 112, an energy development operation associated with the resource site based on the report. For example, the energy development operations may include initiating, based on the material properties comprised in the report, one or more of: adjusting a drill bit spin rate at the resource site; regulating one or more flow control devices at the resource site; or optimizing gas storage operations in the subsurface of the resource site, etc. In some cases, the energy development operations comprise evaluations that are based on the report for determining whether the resource site (e.g., a surface and / or a subsurface) of the resource site is suitable for implementing an energy project (e.g., installing of wind mills, installing of drill rigs, extracting crude or water from the subsurface, etc.).Resource Site

[0033] 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 subterranean fonnation, the resource site, according to some embodiments, may be below 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 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 FIGS. 1 and 4. In other embodiments, the techniques disclosed herein may be applied to surface seismicmonitoring applications. According to some implementations, the disclosed techniques may be applied to remote sensing applications, subsea applications associated with permanent sensors, temporary sensor applications, applications associated with remotely operated vehicles.

[0034] 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.

[0035] 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 theproduction duration history (e.g., number of years of production) of the first reservoir or second, etc.

[0036] Data acquisition tool 202a is illustrated as a measurement truck, which may comprise devices or sensors that take measurements of the subsurface through sound vibrations such as, but not limited to, seismic measurements. Drilling tool 202b may include a downhole sensor adapted to perform logging while drilling (LWD) data collection. The 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.

[0037] Sensors may be positioned about the resource site to collect data relating to various energy development operations, such as sensors deployed by the data acquisition tools 202. The sensor 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, that can be used for acquiring data regarding the formation, 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 some cases, the one or more sensors comprise hydrophonic sensors, geophonic sensors, broadband sensors, or distributed acoustic (DAS) sensors. Furthermore, 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 used to, for example, label or configure a machine learning (ML) engine, a resource model, or a velocity model, as the case may require. In other embodiments, test data or synthetic data may also be used in developing the ML engine or resource model via one or more parameterization / labeling operations such as those discussed in association with the workflows presented herein.

[0038] Evaluation sensors may be featured in downhole tools such as tools 202b-202d and may include for instance electromagnetic, acoustic, nuclear, and optic sensors. Examples of tools including evaluation sensors that can be used in the framework of the current methodinclude electromagnetic tools including imaging sensors such as FMI™ or QuantaGeo™ (mark of Schlumberger, Houston, TX); induction sensors such as Rt Scanner™ (mark of Schlumberger, Houston, TX), multifrequency dielectric dispersion sensor such as Dielectric Scanner™ (mark of Schlumberger, Houston, TX); acoustic tools including sonic sensors, such as Sonic Scanner™ (mark of Schlumberger, Houston, TX) or ultrasonic sensors, such as pulseecho sensor as in UBI™ or PowerEcho™ (marks of Schlumberger, Houston, TX) or flexural sensors PowerFlex™ (mark of Schlumberger, Houston, TX); nuclear sensors such as Litho Scanner™ (mark of Schlumberger, Houston, TX) or nuclear magnetic resonance sensors; fluid sampling tools including fluid analysis sensors such as InSitu Fluid Analyzer ™ (mark of Schlumberger, Houston, TX); distributed sensors including fiber optic. Such evaluation sensors may be used in particular for evaluating the formation in which the well is formed (i.e., determining petrophysical 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.).

[0039] 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.

[0040] 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.

[0041] 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 othermeasurement data and other parameters of interest. Similar measurements may also be used to measure changes in formation aspects over time.

[0042] 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.

[0043] 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. The data 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. Tn one embodiment, the data is stored in separate databases, or combined into a single database.High-Level Networked System

[0044] FIG. 3 shows a high-level networked system diagram illustrating a communicative coupling of devices or systems associated with the resource site 200 as described in FIG. 2. 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 cloudcomputing 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 anInternet 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.

[0045] The system of FIG. 3 may also include one or more user terminals 314a and 314b 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 user terminals 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.

[0046] The system of FIG. 3 may also include at least one or more resource sites 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 becommunicatively 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.

[0047] 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.

[0048] 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 or subsystem, a programmable integrated circuit, a programmable gate array, or another control or computing device.

[0049] 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)

[0050] 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 storagemedium 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.

[0051] 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.

[0052] 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, GPUs or 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 or a data manager (e.g., computing module) stored in memory 306a, 306b, or 306c such that the signal processing engine or data manager 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 cloud-based 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.

[0053] 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.

[0054] 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.Flowchart

[0055] FIG. 4 provides an exemplary workflow for methods, systems, and computer programs that computationally generate resolved data associated with a subsurface of a resource site. It is appreciated that a data manager or a data processing engine or a signal processing engine stored in a memory device may cause a computer processor to execute the various processing stages of FIG. 4.

[0056] It is appreciated that the various processing stages of FIG. 4 may be implemented in a software application to, e.g., model, outline, or provide insight on geological structures within a subsurface of the resource site. In particular, the processes disclosed beneficially enhance the signal -to-noise ratio of processed seismic data by: isolating the useful background velocity data comprised in received seismic data from the perturbation data indicating noise within the captured seismic data; and selectively attenuating or selectively reducing the amount of perturbation data comprised in the captured seismic data. This in effect ensures that only part (e.g. undesirable perturbation data) of the captured seismic data is operated on without affecting the useful part (e.g. background velocity data) of the captured seismic data.

[0057] In some embodiments, the various steps outlined in FIG. 4 may be implemented in a computing framework such as a high-performance computing framework. For example, the computing framework may be based on a Blade computing framework or a Blade architecture that comprises one or more server modules or multiple computing devices optimized, in aggregate, to perform complex simulation operations or testing operations based on the disclosed velocity model. Furthermore, the high-performance computing framework may be customized to comply with regularization strategies associated with a plurality of applications comprised in the high-performance computing framework. In addition, the high- performance computing framework may be enhanced with matrix solver engines / modules,and / or signal processing routines that can process or otherwise execute computing operations on data associated with band limited derivatives, or other matrix operations associated with the velocity model.

[0058] Turning to block 402 of FIG. 4, the data manager mentioned above may receive seismic data from one or more sensors deployed at the resource site. The seismic data may include a recording of seismic data comprising a propagated seismic wavefield within a subsurface of the resource site that indicates one or more signal components of the seismic data. In particular, the seismic data may comprise a seismic wavefield propagated or transmitted by one or more sensors deployed at the resource site such that the propagated wavefield travels through a region (e.g., a subsurface region of the resource site) and interacts with materials (e.g., geological materials) comprised in the subsurface before being received by one or more sensors also deployed at the resource site.

[0059] At block 404, the data manager may generate a velocity model using the seismic data. The velocity model may characterize a signal interaction between background velocity data associated with the seismic data and perturbation data associated with the seismic data.

[0060] Furthermore, the data manager may format, at block 406, the velocity model into a data matrix. Formatting the velocity model into a data matrix may comprise arranging one or more data elements comprised in the velocity model into dimensional structures that enable executing one or more analysis computing operations on the velocity model. For example, fonnatting the velocity model into a data matrix may comprise arranging, organizing, aligning, or designating within one or more data arrays, one or more data elements comprised in, or associated with the velocity model to facilitate the execution of one or more computing operations including a minimization computing operation on the velocity model.

[0061] In one embodiment, the data manager executes, at block 408, a minimization computing operation on the data matrix to decompose the velocity model into the background velocity data and the perturbation data and thereby generate a decomposed dataset.

[0062] At block 410, the data manager may attenuate the perturbation data comprised in the decomposed dataset to generate the resolved data based on one of: a basis function associated with the velocity model, or a constraining operator associated with the velocity model.

[0063] The data manager may further generate at block 412, based on the resolved data, a report indicating material properties comprised in the subsurface of the resource site. It is noted that this subsurface comprises a region in the subsurface of the resource site through which the propagated seismic wavefield travels.

[0064] In addition, the data manager may execute, at block 414, an energy development operation associated with the resource site based on the report.

[0065] These and other implementations may each optionally include one or more of the following features. The velocity model, according to some implementations, is based on processing the seismic data using a Bom approximation process. In addition, the velocity model indicates a dataset comprised in the seismic data that corresponds to a scattering potential perturbation velocity associated with the propagated seismic wavefield.

[0066] Moreover, the background velocity data may comprise a smooth background velocity component of the propagated seismic wavefield which is preserved during executing the minimization computing operation while the perturbation data comprises a high resolution perturbation data including one or more multipath components of the propagated seismic wavefield that are attenuated in response to the minimization computing operation.

[0067] In addition, the basis function may comprise a radial basis function while the constraining operator is a smoothing operator according to one embodiment.

[0068] It is appreciated that the basis function or the constraining operator limits one or more values of the background velocity data to a smooth, low wave number domain (e.g., low frequency signal) comprising, for example, a wave having a low frequency (e g., waves with frequency of 10 Hz - 50 Hz, or 10 Hz - 60 Hz, or 10 Hz - 70 Hz).

[0069] In other embodiments, the basis function associated with the velocity model is a bandlimited impulse basis function.

[0070] The bandlimited impulse basis function in some cases can comprises one of a sine wavelet, a Ricker wavelet, a curve! et, a seislet, or a chirplet as the case may require.

[0071] It is appreciated that the one or more sensors discussed in association withFIG. 4 can comprise one or more of hydrophonic sensors, geophonic sensors, broadband sensors, or a distributed acoustic (DAS) sensors. Furthermore, the resource site can comprise one or more of: an onshore resource site or an offshore resource site.

[0072] In addition, the material properties discussed in association with the report at block 412 of FIG. 4, according to one embodiment, comprises one or more of: geological boundary data associated with the subsurface of the resource site; rock property data associated with the subsurface of the resource site; fluid flow condition data associated with the subsurface of the resource site; air gap data associated with the subsurface of the resource site; subsurface discontinuity data associated with the subsurface of the resource site; subsurface layering data associated with the subsurface of the resource site; hydrocarbon data associated with the subsurface of the resource site; and mineral deposit data associated with the subsurface of the resource site.

[0073] Moreover, the report can comprise one or more of: image data indicating a multidimensional image of the subsurface of the resource site including a 2-dimensional image or a 3-dimensional image; and / or textual data indicating a quantification of at least one material property comprised in the material properties.

[0074] It is appreciated that the energy development operations, according to some embodiments, comprise initiating, based on the material properties comprised in the report, one or more of: adjusting a drill bit spin rate at the resource site; regulating one or more flow control devices (e.g., controlling the size of an aperture of a flow control valve) at the resource site; or optimizing gas storage operations in the subsurface of the resource site. In some cases, the energy development operations comprise evaluations that are based on the report for determining whether the resource site (e.g., a surface and / or a subsurface) of the resource site is suitable for implementing an energy project (e.g., installing of windmills, installing of drill rigs, extracting crude or water from the subsurface, etc.).

[0075] It is appreciated that decomposing the velocity model into the background velocity data and the perturbation data may be based on reflectivity data comprised in the captured seismic data according to some embodiments. In addition, diagonal data comprised in the data matrix referenced at block 406 of FIG. 4 may include weighted data elements based on the reflectivity data comprised in the seismic data.

[0076] 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.

[0077] 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 disclosed approach 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 this disclosure and its practical applications, to thereby enable others skilled in the art to use the techniques disclosed and various embodiments with various modifications as are suited to the particular use contemplated. It is appreciated that the term optimize / optimal and its variants (e.g., efficient or optimally) may simply indicate improving, rather than the ultimate form of 'perfection' or the like.

[0078] 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 this disclosure. 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.

[0079] 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 disclosed subject-matter 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.

[0080] 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.

[0081] 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 perfonned on fewer than all of a giventhing, 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

CLAIMSWhat is claimed is:

1. A method for computationally generating resolved data associated with a subsurface of a resource site, the method comprising: receiving, using a computer processor, seismic data from one or more sensors deployed at the resource site, the seismic data including a recording of seismic data comprising a propagated seismic wavefield within a subsurface of the resource site that indicates one or more signal components of the seismic data; generating, using the computer processor, a velocity model using the seismic data, the velocity model characterizing a signal interaction between background velocity data and perturbation data associated with the seismic data; formatting, using the computer processor, the velocity model into a data matrix; executing, using the computer processor, a minimization computing operation on the data matrix to decompose the velocity model into the background velocity data and the perturbation data and thereby generate a decomposed dataset; attenuating, using the computer processor, the perturbation data comprised in the decomposed dataset to generate the resolved data based on one of: a basis function associated with the velocity model, or a constraining operator associated with the velocity model; generating, by the computer processor, based on the resolved data, a report indicating material properties comprised in the subsurface of the resource site; and executing, using the computer processor, an energy development operation associated with the resource site based on the report.

2. The method of claim 1, wherein: the velocity model is based on processing the seismic data using a Bom approximation process; and the velocity model indicates a dataset comprised in the seismic data that corresponds to a scattering potential perturbation velocity associated with the propagated seismic wavefield.

3. The method of claim 1, wherein:the background velocity data comprises a smooth background velocity component of the propagated seismic wavefield which is preserved during executing the minimization computing operation; and the perturbation data comprises a high resolution perturbation data including one or more multipath components of the propagated seismic wavefield that are attenuated in response to the minimization computing operation.

4. The method of claim 1, wherein: the basis function comprises a radial basis function; and the constraining operator is a smoothing operator.

5. The method of claim 4, wherein one of the basis function or the constraining operator limits one or more values of the background velocity data to a smooth, low wave number domain.

6. The method of claim 1 , wherein the basis function associated with the velocity model is a bandlimited impulse basis function.

7. The method of claim 6, wherein the bandlimited basis function comprises one of a sine wavelet, a Ricker wavelet, a curvelet, a seislet, or a chirplet.

8. The method of claim 1, wherein: the one or more sensors comprise one or more of: hydrophonic sensors, geophonic sensors, broadband sensors, or a distributed acoustic (DAS) sensors; and the resource site comprises one or more of: an onshore resource site or an offshore resource site.

9. The method of claim 1, wherein the material properties comprise one or more of: geological boundary data associated with the subsurface of the resource site; rock property data associated with the subsurface of the resource site; fluid flow condition data associated with the subsurface of the resource site;air gap data associated with the subsurface of the resource site; subsurface discontinuity data associated with the subsurface of the resource site; subsurface layering data associated with the subsurface of the resource site; hydrocarbon data associated with the subsurface of the resource site; and mineral deposit data associated with the subsurface of the resource site.

10. The method of claim 1, wherein the report comprises one or more of: image data indicating a multidimensional image of the subsurface of the resource site including a 2-dimensional image or a 3 -dimensional image; and textual data indicating a quantification of at least one material property comprised in the material properties.

11. The method of claim 1, wherein the energy development operations comprise initiating, based on the material properties comprised in the report, one or more of: adjusting a drill bit spin rate at the resource site; regulating one or more flow control devices at the resource site; or optimizing gas storage operations in the subsurface of the resource site.

12. A system for computationally generating resolved data associated with a subsurface of a resource site, the system comprising: a computer processor, and memory storing a data processing engine that comprises instructions which are executable by the computer processor to: receive seismic data from one or more sensors deployed at the resource site, the seismic data including a recording of seismic data comprising a propagated seismic wavefield within a subsurface of the resource site that indicates one or more signal components of the seismic data; generate a velocity model using the seismic data, the velocity model characterizing a signal interaction between background velocity data and perturbation data associated with the seismic data; format the velocity model into a data matrix;execute a minimization computing operation on the data matrix to decompose the velocity model into the background velocity data and the perturbation data and thereby generate a decomposed dataset; attenuate the perturbation data comprised in the in the decomposed dataset to generate the resolved data based on one of: a basis function associated with the velocity model, or a constraining operator associated with the velocity model; generate, based on the resolved data, a report indicating material properties comprised in the subsurface of the resource site; and execute an energy development operation associated with the resource site based on the report.

13. The system of claim 12, wherein the basis function comprises a radial basis function; and the constraining operator is a smoothing operator.

14. The system of claim 12, wherein the basis function associated with the velocity model is a bandlimited impulse basis function.

15. The system of claim 12, wherein the material properties comprise one or more of: geological boundary data associated with the subsurface of the resource site; rock property data associated with the subsurface of the resource site; fluid flow condition data associated with the subsurface of the resource site, air gap data associated with the subsurface of the resource site; subsurface discontinuity data associated with the subsurface of the resource site; subsurface layering data associated with the subsurface of the resource site; hydrocarbon data associated with the subsurface of the resource site; and mineral deposit data associated with the subsurface of the resource site.

16. The system of claim 12, wherein the energy development operations comprise initiating, based on the material properties comprised in the report, one or more of:adjusting a drill bit spin rate at the resource site; regulating one or more flow control devices at the resource site; or optimizing gas storage operations in the subsurface of the resource site.

17. A computer program for computationally generating resolved data associated with a subsurface of a resource site, the computer program comprising a non-transitory computer- readable medium comprising code configured to: receive seismic data from one or more sensors deployed at the resource site, the seismic data including a recording of seismic data comprising a propagated seismic wavefield within a subsurface of the resource site that indicates one or more signal components of the seismic data; generate a velocity model using the seismic data, the velocity model characterizing a signal interaction between background velocity data and perturbation data associated with the seismic data; format the velocity model into a data matrix; execute a minimization computing operation on the data matrix to decompose the velocity model into the background velocity data and the perturbation data and thereby generate a decomposed dataset; attenuate the perturbation data comprised in the decomposed dataset to generate the resolved data based on one of: a basis function associated with the velocity model, or a constraining operator associated with the velocity model; generate, based on the resolved data, a report indicating material properties comprised in the subsurface of the resource site; and execute an energy development operation associated with the resource site based on the report.

18. The computer program of claim 17, wherein to decompose the velocity model into the background velocity data and the perturbation data is based on reflectivity data comprised in the seismic data.

19. The computer program of claim 18, wherein diagonal data comprised in the data matrix includes weighted data elements based on the reflectivity data comprised in the seismic data.

20. The computer program of claim computer program of claim 17, wherein the energy development operations comprise initiating, based on the material properties comprised in the report, one or more of: adjusting a drill bit spin rate at the resource site; regulating one or more flow control devices at the resource site; or optimizing gas storage operations in the subsurface of the resource site.

Images