EARLY DETECTION OF SCALE IN OIL PRODUCTION WELLS

Abstract

The disclosed technology is directed to methods and systems for detecting scale within a well production structure. The methods include generating a model for the well production structure. The method also includes executing a first test using the model to generate first scale data and executing a second test based on concurrently adjusting one or more of a plurality of model parameters using updated versions of synthetic or non-synthetic data to generate a plurality of second scale data. The method also includes executing a merging operation between scale data realizations comprised in the plurality of the second scale data to generate scaling signature data. The method further includes initiating generation of one or more of: a scale data visualization, or an intervention report, or an intervention signal for a control operation that mitigates against detected scale.

Description

BACKGROUND

A major problem associated with producing hydrocarbons (e.g., oil or gas) from wells at a resource site is scaling. In the context of the instant disclosure, scaling refers to mineral deposits (hereinafter referred to as scale) within well production structures including inner walls of fluid conveyor systems, gravel packs associated with a well, structural perforations associated with a well, or geological formations associated with a well. Scale deposits are common and can damage well production structures or systems thereby negatively impacting hydrocarbon extraction operations associated with production and/or injections wells at the resource site.

SUMMARY

According to an embodiment, a method for detecting scale in a well production structure comprises generating a model for the well production structure, the model including a plurality of model parameters that are adjusted based on one or more of: synthetic data which is not captured by a sensor deployed about the well production structure; and non-synthetic data which is captured by one or more sensors deployed about the well production structure. The method also comprises executing a first test using the model to generate first scale data, the first scale data indicating a normal mode of operation of the well production structure such that the normal mode of operation of the well production structure indicates one of: an absence of scale within the well production structure; or a presence of substantially minimal scale within the well production structure.

The method further comprises: executing a second test based on concurrently adjusting one or more of the plurality of model parameters using updated versions of the synthetic data or non-synthetic data to generate a plurality of second scale data; and executing, using the computer processor, a merging operation between scale data realizations comprised in the second scale data to generate scaling signature data, the merging operation including at least a union of the plurality of second scale data relative to the first scale data.

According to some embodiments, the method comprises initiating generation of one or more of: a visualization that indicates a superimposition of the scaling signature data over the first scale data; or an intervention report or an intervention signal for a control operation that mitigates against detected scale in one or more sections of the well production structure based on one or more of the first test or the second test.

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 visualization can comprise a 3-dimensional visualization that indicates fluid production signatures indicative of a presence or an absence of scale within a production tubing associated with the production structure.

Moreover, the scale can comprise an accumulation of one or more organic or inorganic materials within a production tubing comprised in the well production structure. In particular, the scale can impact the well production structure by: clogging the production tubing and thereby decreasing a rate of fluid production associated with the well production structure; or decreasing an inner diameter of the production tubing and thereby reducing the rate of fluid production associated with the well production structure.

According to one embodiment, the one or more sensors deployed about the well production structure can comprise at least one of: a multiphase flow sensor; a wellhead pressure sensor; a wellhead temperature sensor; a downhole pressure sensor; a downhole temperature sensor; a casing or tubing pressure sensor; a casing or tubing temperature sensor; and a wellhead flowrate sensor.

It is appreciated that the first the first test or the second test comprises a computing simulation that generates that first scale data or the second scale data, respectively. It is further appreciated that the first scale data is generated for a first section of the well production structure. In addition, the second scale data is generated for a second section of the well production structure according to some embodiments. Moreover, the first scale data and the second scale data may be merged to determine scale relationship data between the first scale data and the second scale data. The scale relationship data may be used to determine one of: a union relationship between the first scale data and the second scale data; an intersection between the first scale data and the second scale data; and a complement relationship between the first scale data and the second scale data.

In some embodiments, the model discussed above is a 2-phase model comprising: at least a pressure parameter associated with the well production structure; and at least a flow rate parameter associated with the well production structure.

In other embodiments, the model is a 3-phase model that includes: at least a flow rate parameter associated with the well production structure; at least a pressure parameter associated with the well production structure; and at least a temperature parameter associated with the well production structure.

BRIEF DESCRIPTION OF THE DRAWINGS

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.

FIG. 1 shows an exemplary well production structure having scale within its inner tubing/wall according to some embodiments.

FIG. 2 shows a cross-sectional view of a resource site according to some embodiments.

FIG. 3 shows a network system illustrating a communicative coupling of devices or systems associated with the resource site of FIG. 2 according to some embodiments.

FIG. 4A shows a cross-sectional view of a section of a well production structure being modeled according to some embodiments.

FIG. 4B illustrates a schematic diagram of an exemplary scale model associated with a well production structure according to some embodiments.

FIGS. 5A-5C provide an exemplary 2-dimension scale data realizations indicative of the presence of scale in one or more sections of the well production structure according to some embodiments.

FIG. 5D provides an exemplary 3-dimension scale data realization indicative of the presence of scale in one or more sections of the well production structure according to some embodiments.

FIG. 6 shows an exemplary flowchart for generating and testing a scale model associated with a well production structure according to some embodiments.

FIGS. 7A-10 show plots between a wellhead temperature versus downhole pressure for regions of the fluid production structure with and without scale.

FIG. 11 shows the same information as indicated in FIG. 10 but with the wellhead temperature replaced with the oil flowrate for the fluid production structure under consideration.

FIG. 12 shows the same visualization as that of FIG. 11 with the plot of FIG. 12 being zoomed or rendered to correspond to 10 times the covariance matrix associated with FIG. 11.

FIG. 13 shows the same visualization as that of FIG. 12 with filtered production data history being superimposed on the plot of FIG. 12 together with the well test data.

DETAILED DESCRIPTION

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 invention. However, it will be apparent to one of ordinary skill in the art that the invention 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.

The disclosed systems and methods may be accomplished using interconnected devices and systems that obtain a plurality of data associated with various model parameters of interest at a resource site. The workflow/flowchart 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 exploring natural resources such as oil, gas, water well industries, and other mineral exploration operations. More specifically, the systems and methods presently disclosed may be applicable to exploring resources such as oil, natural gas, water, and Salar brines.

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.

The disclosed technology is directed to methods and systems for early detection of scale buildup associated with wells. According to one embodiment, multiphase simulators, are used to characterize one or more regions of one or more fluid production structures or well production structures individually on in aggregate and thereby indicate normal expected operation or normal expected operational regions (EOR) for said one or more well production structures. By modeling layers of scale within the walls (e.g., inner walls) associated with said well production structures (e.g., fluid conveyor systems, etc.), tests or simulations may be conducted to characterize the performance of said well production structures by varying various model parameters associated with said well production structures. According to some embodiments, the modeling of scale layers may be used to generate reports that indicate various sizes or thicknesses of scale within one or more sections of the well production structures.

In some implementations, an operating point (e.g., a current operating point) associated with the well production structures may be obtained from a plurality of sensor measurements captured using one or more sensors disposed about the well production structures. These sensor measurements may be compared to, or otherwise correlated with modeled layers of scale within the walls associated with the well production structures in order to identify one or more regions that are characteristically similar to the regions from which the one or more sensors are disposed. According to one embodiment, if the correlation indicates that there is presence of scale beyond a certain threshold of a given region associated with the well production structures, a notification is issued together with corresponding scale data indicating the thickness of the scale associated with said given region. In some cases, a thickness of the scale layer within said given region may be estimated or otherwise calculated based on a modified version of an annular flow model within said region.

FIG. 1 shows an exemplary well production structure (e.g., well tubing) 102 showing scale/scaling 104 within the inner walls of the well production structure or fluid conveyor system. It is appreciated that the above process for early detection of scale buildup within the fluid flow path of the well production structures such as fluid conveyor systems may be applied to detect not only scale but also detect wax in structures such as pipelines and/or pipeline networks.

Resource Site

FIG. 2 shows a cross-sectional view of a resource site 200. While the illustrated resource site 200 represents a subterranean formation, 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 model 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 geochemical information associated with 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. It is appreciated that the resource site of FIG. 2 can be a land-based/onshore hydrocarbon production site adjacently situated relative to an offshore hydrocarbon production site. Furthermore, production infrastructure (e.g., pipelines, wells, tubings, or other fluid conveyor systems) of both onshore and offshore hydrocarbon production sites can be exposed to scale. For example, offshore pipelines can be exposed to scale because the fluid therein may be cooled down by the ambient sea water resulting in scale buildup therein. Thus, the methods and principles discussed herein are not limited to detecting scale in just wells but can also be applied to detecting and mitigating against scale buildup in pipelines and other fluid conveyor systems based on synthetic and/or non-synthetic measurements associated with said pipelines or conveyor systems.

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, 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 geochemical characteristics of the various formations shown.

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 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.

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. 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 model 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 model parameters of operations as further discussed below.

Sensors may be positioned about the oil field 200 to collect data relating to various oil field 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 one embodiment, the data captured by the one or sensors may be used to characterize, or otherwise generate one or more model parameter values for a high-resolution result set used to, for example, generate a resource model. In other embodiments, test data or synthetic data may also be used in developing the resource model via one or more simulations such as those discussed in association with the flowcharts presented herein.

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 method include electromagnetic tools including imaging sensors such as FMI™ or QuantaGco™ (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 pulse-echo 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.).

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.

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.

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 model parameters of interest. Similar measurements may also be used to measure changes in formation aspects over time.

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.

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. In one embodiment, the data is stored in separate databases, or combined into a single database.

High-Level Networked System

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.

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 cloud-computing 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.

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 cloud-computing platform 310. In some embodiments, data collected by the one or more sensors/sensor interfaces 322a and 322b may be processed to generate one or more models (e.g., scale models and reservoir models) or one or more datasets used to generate a plurality of scale data and/or scale signature data as discussed elsewhere herein. These models and/or scale data and/or scale signature data 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.

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.

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.

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).

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.

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.

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 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 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.

In some embodiments, a computing system is provided that includes at least one processor, at least one memory, and a signal processing engine stored in the at least one memory, such that the signal processing engine may cause the at least one processor to perform any method disclosed herein.

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.

Embodiments

The present disclosure is directed to methods and systems that automatically detect scale within well production structures (e.g., well production structure shown in FIG. 4A) and generate intervention strategies to mitigate against same. As indicated in the figure, the well production structure can include a production tubing 402, a casing 404 surrounding the tubing 402. According to one embodiment, the casing 404 can comprise an annulus embracing the production tubing 402. It is appreciated that the disclosed solution is directed to managing the formation of scale within the production tubing 402.

The following acronyms are used to characterize sensor operations associated with the disclosed system and methods and are provided provide as exemplary implementations of the disclosed technology:

• • WHF—multiphase flow meter(s)/sensor(s) (MPFM) at the wellhead measuring phase flowrates of fluid (gas, oil, or water). If the multiphase flow meters are not present at the wellhead, it can be assumed that the well in question runs through a test separator regularly.
  • WHP, WHT—wellhead pressure and temperature transmitter(s)/sensor(s) that can appear at multiple positions (e.g., three different positions) such as upstream, downstream of the production choke, and/or between master and wing valves.
  • DHP and DTH—downhole pressure and temperature sensor(s). Downhole pressure and temperatures can be captured at one or more (e.g., two) positions associated with the well production structures.
  • WHCP and WHCT—casing pressure and fluid temperature sensor(s) at the wellhead for measuring pressure and temperature data.
  • WHCF—wellhead flowrate sensor(s) for capturing gas injection rate data.

The WHP, WHT, DHP, WHCP, WHCF sensors/transmitters may be calibrated as needed based on simulation or test results and/or previous sensor measurements and/or real-time sensor measurements. According to some embodiments, the WHF sensor may be used to measure flowrate of hydrocarbons at the wellhead. Additionally, the robustness of modeling scale within well production structures may be improved based on using a plurality of measurements from the WHP, WHT, DHP, WHCP, WHCF, and thereby mitigate against false indications of scale within the well production structures.

Reservoir Parameters

According to one embodiment, a model (e.g., multiphase flow model) of a well production structure associated with a reservoir may be generated. The model may have model parameters such as a reservoir pressure parameter, a temperature parameter, a gas-oil ratio parameter, a water cut parameter, and a productivity index parameter. An inflow performance relationship (IPR) may be applied to close a boundary condition (e.g., reservoir boundary condition) in the model. The IPR may relate the model parameters to pressure (e.g., flowing tubing pressure) within the well production structure associated with the reservoir. According to one embodiment, the reservoir parameters comprise synthetic data, non-synthetic data such as actual data captured by sensors associated with the well production structure, or a combination thereof.

Modeling

The purpose of scale modeling is to correlate or otherwise determine fluid production relationships or determine fluid production signatures indicative of scaling using synthetic and/or non-synthetic data. As shown in FIG. 4B, the model may include a plurality of sensors or transmitters 406a . . . 406e, and/or other electronic systems that are calibrated to capture data associated with determining scaling within the well production structure. According to one embodiment, scale data may be generated in response to executing tests or simulations based on a scale model of a well production structure such that the scale data indicates one or more deviations associated with normal operation of the well production structure without scale relative to the well production structures having scale.

In some implementations, a pressure drop may be related to an apparent roughness in an annular fluid flow in the fluid production structure with presence of a liquid film at the wall (e.g., inner wall) of the fluid production structure. The apparent roughness may be caused by a wavy viscous sublayer which represents a turbulent drag. The thickness of the viscous sublayer may be related to the viscous length scale of the liquid film. According to one embodiment, the maximum film thickness may be proportional to the viscous length scale of the liquid film. This proportionality may depend on the Reynolds number for the liquid.

In the vertical parts of a well production structure (e.g., pipes, tubings, etc.) associated with, for example, a gas lifted well, it can be assumed that most of the fluid (e.g., liquid hydrocarbons) may be carried in the gas core as liquid droplets with a much higher velocity than the velocity of the liquid film at the wall. Based on this assumption, an estimate of the scale thickness may be generated for the well production structure. The scale thickness may indicate an apparent roughness needed to match the measured pressure drop between two pressure sensors in the vertical part of the wellbore.

According to one embodiment, a scale model (e.g., a 2-phase model) is provided and tested to determine a relationship between pressure drop and flow rates of fluid (e.g., gas and liquid). This model is 2-dimensional as it is based on fluid pressure data and fluid flow rate data. As shown in FIGS. 5A-5C, scale data may be generated in response to running tests or simulations using the model such that the scale data may be visualized on a graphical display device. According to some implementations, the scale data is generated for independently separate sections of the well production structure such that a merging operation is executed to determine, for example, a union relationship, or an intersection relationship, or a complement relationship among the scale data relative to scale data indicating normal operation. The merging operation may be used to generate a visualization. In some embodiments, the scale model is a 3-phase model that includes not only flow rate parameters and pressure parameters but also includes temperature parameters. The operations discussed above in association with the 2-phase model may be applied to the 3-phase model to generate a 3-dimensional (3D) visualization indicating fluid production signatures indicative of a presence or an absence of scaling. FIG. 5D provides an exemplary visualization of the scale data generated using the 3-phase model in the tests or simulations.

It is appreciated that the effect of a layer of scale within well production structures such as production tubing, and on parameters such as the wellhead temperature parameter may be measurable as an increase in the wellhead fluid temperature. The increase is due to the reduced diameter resulting in an increased flowrate within the well production structure. The increase in wellhead temperature may also increase, as the effect of scale in the well production structure will reduce the heat transfer from within the inner parts/walls of the well production structure (e.g., tubing to annulus heat transfer) in certain wells (e.g., gas lifted wells).

As previously noted, it is beneficial, according to some embodiments to include a plurality of sensors to optimize the scale model and thereby enhance or improve the scale data generated using the scale model and mitigate against false positives or false alarms indicating scale. Thus, including the temperature parameter as done in the 3-phase model, for example, may enhance scale detection within the well production structure (e.g., tubing). In some embodiments, the term optimize, optimal, and its variants (e.g., efficient, optimally, improve, enhance, etc.) may simply indicate improving, rather than the ultimate form of ‘perfection’ or the like. It is appreciated that the increase in a measured pressure drop in, for example, the tubing due to scale, may also indicate an increase in the measured wellhead casing pressure. Also, adding casing pressure as an indicator will improve robustness of the scale model. In some embodiments, an existing scale mode may be enhanced based on new synthetic or non-synthetic data.

Use of Regions

The following exemplary signatures/indicators for identification of early onset of scale within a well production structure may be included in the scale data generated after testing the scale model:

• • Wellhead pressure data
  • Downhole pressure data
  • Wellhead casing pressure data
  • Wellhead temperature data
  • Wellhead oil/liquid flowrate data

According to one embodiment, one or more regions of the well production structure may be characterized or otherwise modeled to indicate normal expected operation (no-scale) by varying flowrate and boundary conditions. The output of such modeling may be used to generate a first scale data for the well production structure that indicates an absence of scale or a presence of substantially little scale within the well production structure. In one embodiment, a multiphase flow simulator modeling tool like the OLGA modeling tool may be used to execute the modeling based on synthetic and/or non-synthetic data. Using the first scale data to generate a first visualization, a description of the region of normal operation (e.g., operation of the well production structure without scale) may be generated and may comprise an ellipsoid or some other multi-dimensional shape. Since there may be multiple scale signatures associated with multiple regions of the well production structure, the region may be multi-dimensional, such that each scale signature may have its own axis in the multi-dimensional space. The region(s) of normal (no or acceptable amount of scale) can initially be characterized using a multiphase flow simulator and the region can be updated with online measured data if needed. According to some embodiments, the model parameters are based on normalized measured non-synthetic data.

Furthermore, regions where scale appears with varying thickness within the well production structure can be characterized using a multiphase simulator like the OLGA modeling tool. Such a region may overlap with the normal operating region of the well production structure when the thickness of the scale is small. Different regions representing different scale thicknesses and sections of the well production structure where scale is deposited may be characterized based on the modeling. The union of all scale regions may provide an operation envelope where scale appears (see FIGS. 5A-5D). As the operation move closer or further into the scale envelope the thickness of the scale layer increases.

Measurements from sensors deployed about the well production structure can be used to indicate which sections of the well production structure (e.g., inner part of well tubing) has scale. As an example, scale may be present at the lower part of the wellbore tubing (below downhole pressure and gauge) closer to the reservoir influx, and may be detected using the techniques disclosed based on a reduction of wellhead flowrate but without an increase in downhole pressure and wellhead casing pressure. Scale in this section of the wellbore tubing can easily be mixed up with a reduced inflow performance due to declining reservoir pressure or reduced productivity index (due to scale or some other reasons). A full well test with closing in the well can be used to determine the reservoir pressure.

Flowchart

FIG. 6 shows an exemplary flowchart for detecting scale within a well production structure and executing a control or intervention operation against detected scale within said well production structure. It is appreciated that a data manager or signal processing engine stored in a memory device may cause a computer processor to execute the various processing stages of FIG. 6. It is further appreciated that the various processing stages of FIG. 6 may be implemented in a software application to, for example, model, outline, or provide insight on geological structures within a subsurface of a resource site.

At block 602, the signal processing engine may be used to generate a model for the well production structure. The model may include a plurality of model parameters associated with the well production structure and/or associated with configurations associated with the well production structure and/or associated with a plurality of sensors disposed about the well production structure. In one embodiment, the plurality of model parameters may be adjusted based on one or more of: synthetic data which is not captured by a sensor deployed about the well production structure, and non-synthetic data which is captured by one or more sensors deployed about the well production structure.

At block 604, the signal processing engine may be operable to execute a first test using the model to generate first scale data. According to one embodiment, the first scale data characterizes a normal mode of operation of the well production structure such that the normal mode of operation of the well production structure indicates one of: an absence of scale within the well production structure, or a presence of substantially minimal scale within the well production structure.

At block 606, the signal processing engine is operable to execute a second test based on concurrently adjusting one or more of the plurality of model parameters using updated versions of the synthetic data or non-synthetic data to generate a plurality of second scale data. The updated version of the synthetic or non-synthetic data may be associated with the one or more sensors deployed about the well production structure.

At block 608, the signal processing engine may be used to execute a merging operation between scale data realizations (e.g., 2-dimensional and/or 3-dimensional fluid flow data indicating scaling as shown in FIGS. 5A-5D) comprised in the plurality of second scale data to generate scaling signature data. The merging operation may include at least a union of the plurality of the second scale data relative to the first scale data.

At block 610, the signal processing engine may be operable to initiate generation of one or more of: a visualization that indicates a superimposition of the scaling signature data over the first scale data; or an intervention report or an intervention signal for a control operation that mitigates against detected scale in one or more sections of the well production structure based on one or more of the first test or the second test.

These and other implementations may each optionally include one or more of the following features.

The visualization discussed in association with FIG. 6 can comprise a 3-dimensional visualization that indicates fluid production signatures indicative of a presence or an absence of scale within a production tubing associated with the production structure.

Moreover, the scale referenced in association with FIG. 6 can comprise an accumulation of one or more organic or inorganic materials within a production tubing comprised in the well production structure. In particular, the scale can impact the well production structure by: clogging the production tubing and thereby decreasing a rate of fluid production associated with the well production structure; or decreasing an inner diameter of the production tubing and thereby reducing the rate of fluid production associated with the well production structure.

According to one embodiment, the one or more sensors deployed about the well production structure discussed in association with FIG. 6 can comprise at least one of: a multiphase flow sensor; a wellhead pressure sensor; a wellhead temperature sensor; a downhole pressure sensor; a downhole temperature sensor; a casing or tubing pressure sensor; a casing or tubing temperature sensor; and a wellhead flowrate sensor.

It is appreciated that the first the first test or the second test comprises a computing simulation that generates that first scale data or the second scale data, respectively.

It is further appreciated that the first scale data is generated for a first section of the well production structure. In addition, the second scale data is generated for a second section of the well production structure according to some embodiments. Moreover, the first scale data and the second scale data may be merged to determine scale relationship data between the first scale data and the second scale data. The scale relationship data may be used to determine one of: a union relationship between the first scale data and the second scale data; an intersection between the first scale data and the second scale data; and a complement relationship between the first scale data and the second scale data.

In some embodiments, the model discussed in association with FIG. 6 is a 2-phase model comprising: at least a pressure parameter associated with the well production structure; and at least a flow rate parameter associated with the well production structure.

In other embodiments, the model is a 3-phase model that includes: at least a flow rate parameter associated with the well production structure; at least a pressure parameter associated with the well production structure; and at least a temperature parameter associated with the well production structure.

It is appreciated that the intervention report may be used to resolve scale instances within the well production structure such that a targeted approach to mitigate against said scale instances can be adopted. In some embodiments, the control operation may be based on a fluid control signal that automatically adjusts valves based on the intervention report and automatically actuates pumps that discharge scale dissolution fluids to mitigate against identified scale at specific sections of the well production structure.

According to some embodiments, a computer program for detecting scale in a well production structure is disclosed. The computer program comprises instructions, that when executed by a computer processor of a computing device, causes the computing device to generate a model for the well production structure such that the model includes a plurality of model parameters that are adjusted based on one or more of: synthetic data which is not captured by a sensor deployed about the well production structure, non-synthetic data which is captured by one or more sensors deployed about the well production structure, or a combination thereof. In addition, the computer program can be executed by the computer processor to cause the computing device to execute a first test using the model to generate first scale data. The first scale data, according to one embodiment, indicates a normal mode of operation of the well production structure such that the normal mode of operation of the well production structure indicates one of: an absence of scale within the well production structure, or a presence of substantially minimal scale within the well production structure. The computer program can also be executed by the computer processor to cause the computing device to execute a second test based on concurrently adjusting one or more of the plurality of model parameters using updated versions of the synthetic or non-synthetic data to generate a plurality of second scale data. Furthermore, the computer program can be executed by the computer processor to cause the computing device to execute a merging operation between scale data realizations comprised in the second scale data to generate scaling signature data. According to one embodiment, the merging operation includes at least a union of the plurality of the second scale data relative to the first scale data. The computer program can be further executed by the computer processor to cause the computing device to initiate generation of one or more of: a visualization that indicates a superimposition of the scale signature data over the first scale data, or an intervention report or an intervention signal for a control operation that mitigates against detected scale in one or more sections of the well production structure based on one or more of the first test or the second test.

In some embodiments, a system for detecting scale in a well production structure is disclosed. The system, according to one embodiment, comprises a computer processor and memory storing a signal processing engine that includes instructions that are executable by the computer processor to generate a model for the well production structure such that the model includes a plurality of model parameters that are adjusted based on one or more of: synthetic data which is not captured by a sensor deployed about the well production structure, non-synthetic data which is captured by one or more sensors deployed about the well production structure, or a combination thereof. In addition, the instructions are executable by the computer processor to execute a first test using the model to generate first scale data. The first scale data, according to one embodiment, indicates a normal mode of operation of the well production structure such that the normal mode of operation of the well production structure indicates one of: an absence of scale within the well production structure, or a presence of substantially minimal scale within the well production structure. The instructions are further executable by the computer processor to execute a second test based on concurrently adjusting one or more of the plurality of model parameters using updated versions of the synthetic or non-synthetic data to generate a plurality of second scale data. Furthermore, instructions are further executable by the computer processor to execute a merging operation between scale data realizations comprised in the second scale data to generate scaling signature data. According to one embodiment, the merging operation includes at least a union of the plurality of the second scale data relative to the first scale data. The instructions are also executable by the computer processor to initiate generation of one or more of: a visualization that indicates a superimposition of the scale signature data over the first scale data, or an intervention report or an intervention signal for a control operation that mitigates against detected scale in one or more sections of the well production structure based on one or more of the first test or the second test.

Exemplary Implementation

According to one embodiment, a well was analyzed using the disclosed techniques to determine the presence or absence of scale associated with a fluid production structure or system coupled to the well for a given period. Regions of the fluid production structure without scale are indicated with a first set of markings while regions with various thicknesses of scale within the fluid production structure are marked with a second set of markings.

Results from analyzing the well using the disclosed approach show that regions of the fluid production structure with or without scale (e.g., a two-variable scenario) can be determined using various well measurements such that regions without scale move away from the regions with scale.

Considering the two-variable scenario at a time with the associated impact on gas-lift wells, it is appreciated that while a gas-lift rate for such wells may vary, the reservoir depletion for such wells causes the reservoir pressure and productivity index to drop. At the wellhead of such wells, the pressure downstream the wellhead production choke can vary due to interactions with other wells connected to the same flowline comprised in the fluid production structure. Furthermore, the gas-lift injection rate and the pressure down-stream the wellhead production choke for such wells can be measured to facilitate implementation of the disclosed process. Thus, the dimensionality (e.g., number of variables) of the parameters for the analyzed well can be extended with additional measurements.

According to one embodiment, visualizing results from the disclosed process can be based on two-dimensional and/or 3-dimensional images that characterize the presence or absence of scale within the well production structure. For example, FIGS. 7A-10 show plots between the wellhead temperature versus downhole pressure for regions of the fluid production structure with and without scale. In particular, the variable combinations for these figures comprise downhole pressure gauges on the x-axis and the wellhead temperature measurements on the y-axis. It is appreciated that the various rendered regions correspond to covariance matrix data associated with the pressure and temperature measurements about the fluid production structure under consideration.

According to one embodiment, the well under consideration comprises a friction dominated well that starts to deplete over time and moves from the upper right downwards to the left as indicated by the first arrow 802 as indicated in FIG. 8. According to one embodiment, a well that is subject to the onset of scale and increasing deposition will move in the direction of the second arrow 804 going from the upper middle of the graph towards the lower right of FIG. 8. This comes out from the plotted centers of the ellipsoidal shapes shown in the figure. It is appreciated that the depletion and scale formation directions are different as shown in the figure.

According to one embodiment, an increase in the gas lift rate in a non-optimal operation point of the fluid production structure may lower the downhole pressure and at the same time increase the fluid (e.g., oil or gas) production rate. In such cases, the wellhead temperature may increase which may result in taking the operation from a point upwards to the left as indicated by the third arrow 806. On the other hand, an increase in the gas injection rate when operation is beyond or above the optimal point of the downhole pressure gauge may increase thereby causing the fluid (e.g., oil) production rate to decrease slightly as indicated by direction of the fourth arrow 808.

FIG. 9 shows movement in the visualization of FIG. 7A as recorded in well test points associated with the well under consideration. Note that during the trajectory displayed in this plot, the gas lift rate changes from 120 thousand Sm³/d to 254 thousand Sm³/d with a mean value of 179 thousand Sm³/d. In the illustrated case, the downstream choke pressure may vary between 15 barg (e.g., 15 gauge pressure) and 18.3 barg (e.g., 18.3 gauge pressure) with a mean value of 16.7 barg (e.g., 16.7 gauge pressure). Furthermore, the reservoir pressure for the illustrated implementation varies between 148 bara (e.g., 148 absolute pressure) and 189 bara (e.g., 189 absolute pressure) with a mean pressure of 160.4 bara (160.4 absolute pressure). In addition, the productivity index was determined to vary between 18.9 Sm³/d/bar and 46 Sm³/d/bar with a mean value of 32.65 Sm³/d/bar. It is appreciated that the regions at issue were generated as variations around the aforementioned mean values. Moreover, fluid movement data is indicated at the upper right corner of the figure from Date 25 to Date 24. Turning to Date 23, the gas lift rate (e.g., fluid movement data) is 168 Sm³/d and then it stays at that rate up to Date 19.

From Date 19 to Date 12, the gas-lift rate is kept almost constant at 144 thousand Sm³/d. Within this period, the movement in the map indicates some scale build-up associated with the fluid production structure.

In a third period from date 28 to the end with data series associated with Date 1 the gas injection increases and the operation point moves to the opposite side of a no scale region of the fluid production structure. However, the gas injection rate is about 40% higher than the mean of 179 thousand Sm³/d. Note the low value of the wellhead temperature at date 1. For the tested well, a test value of 152 bara of reservoir pressure and 18.9 Sm³/d/bar for productivity index were applied. These values correspond to the values given in the well test point associated with Date 2.

FIG. 10 shows the same data indicated in FIG. 9 with the size of the regions corresponding to 10 times the covariance matrix associated with FIG. 9. When testing for scale in a current operating point in the disclosed setup, testing the inside versus the outside of a given region of the fluid production structure can be considered. But more likely, the distances from a current operating point to centers for all regions may be computed following which subset regions closest to the current operating point may be considered for deriving the level of scaling from information in those regions (e.g., the scale thickness tagged to those regions).

FIG. 11 shows the same information as indicated in FIG. 10 but with the wellhead temperature replaced with the oil flowrate for the fluid production structure under consideration. Here, it is easier to see that the earlier points associated with a prior time frame do not have the same scale issues relative to the plots with the wellhead temperature referenced in association with FIG. 10. Several of the later points in the second half of the prior time frame and a current timeframe indicate issues with scale.

FIG. 12 shows the same visualization as that of FIG. 11 with the plot of FIG. 12 being zoomed or rendered to correspond to 10 times the covariance matrix associated with FIG. 11. As can be seen in this figure, there is a tendency to form scale about the second half of the prior timeframe and the current timeframe except for a few points like Date 7, Date 15, Date 6, and Date 2.

FIG. 13 shows the same visualization as that of FIG. 12 with filtered production data history being superimposed on the plot of FIG. 12 together with the well test data. Again, it can be seen that there are several points in the lower left part of the figure indicating that scale is present in the well under consideration.

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.

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 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.

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.

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.

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

1. A method for detecting scale in a well production structure, the method comprising: generating, using a computer processor, a model for the well production structure, the model including a plurality of model parameters that are adjusted based on one or more of: synthetic data which is not captured by a sensor deployed about the well production structure, andnon-synthetic data which is captured by one or more sensors deployed about the well production structure;executing, using the computer processor, a first test using the model to generate first scale data, the first scale data indicating a normal mode of operation of the well production structure such that the normal mode of operation of the well production structure indicates one of: an absence of scale within the well production structure, ora presence of substantially minimal scale within the well production structure;executing, using the computer processor, a second test based on concurrently adjusting one or more of the plurality of model parameters using updated versions of the synthetic data or non-synthetic data to generate a plurality of second scale data; andexecuting, using the computer processor, a merging operation between scale data realizations comprised in the second scale data to generate scaling signature data, the merging operation including at least a union of the plurality of second scale data relative to the first scale data.

2. The method of claim 1, further comprising initiating, using the computer processor, generation of one or more of: a visualization that indicates a superimposition of the scaling signature data over the first scale data, oran intervention report or an intervention signal for a control operation that mitigates against detected scale in one or more sections of the well production structure based on one or more of the first test or the second test.

3. The method of claim 2, wherein the visualization comprises a 3-dimensional visualization that indicates fluid production signatures indicative of a presence or an absence of scale within a production tubing associated with the production structure.

4. The method of claim 1, wherein the scale comprises an accumulation of one or more organic or inorganic materials within a production tubing comprised in the well production structure, the scale impacting the well production structure by: clogging the production tubing and thereby decreasing a rate of fluid production associated with the well production structure; ordecreasing an inner diameter of the production tubing and thereby reducing the rate of fluid production associated with the well production structure.

5. The method of claim 1, wherein the one or more sensors deployed about the well production structure comprise at least one of: a multiphase flow sensor;a wellhead pressure sensor;a wellhead temperature sensor;a downhole pressure sensor;a downhole temperature sensor;a casing or tubing pressure sensor;a casing or tubing temperature sensor; anda wellhead flowrate sensor.

6. The method of claim 1, wherein the first test or the second test comprises a computing simulation that generates that first scale data or the second scale data, respectively.

7. The method of claim 1, wherein: the first scale data is generated for a first section of the well production structure; andthe second scale data is generated for a second section of the well production structure.

8. The method of claim 6, wherein the first scale data and the second scale data are merged to determine scale relationship data between the first scale data and the second scale data.

9. The method of claim 8, wherein the scale relationship data is used to determine one of: a union relationship between the first scale data and the second scale data;an intersection between the first scale data and the second scale data; anda complement relationship between the first scale data and the second scale data.

10. The method of claim 1 wherein the model is a 2-phase model comprising: at least a pressure parameter associated with the well production structure; andat least a flow rate parameter associated with the well production structure.

11. The method of claim 1, wherein the model is a 3-phase model that includes: at least a flow rate parameter associated with the well production structure;at least a pressure parameter associated with the well production structure; andat least a temperature parameter associated with the well production structure.

12. A computer program for detecting scale in a well production structure, the computer program comprising instructions, that when executed by a computer processor of a computing device, causes the computing device to: generate a model for the well production structure, the model including a plurality of model parameters that are adjusted based on one or more of: synthetic data which is not captured by a sensor deployed about the well production structure, andnon-synthetic data which is captured by one or more sensors deployed about the well production structure;execute a first test using the model to generate first scale data, the first scale data indicating a normal mode of operation of the well production structure such that the normal mode of operation of the well production structure indicates one of: an absence of scale within the well production structure, ora presence of substantially minimal scale within the well production structure;execute a second test based on concurrently adjusting one or more of the plurality of model parameters using updated versions of the synthetic data or non-synthetic data to generate a plurality of second scale data;execute a merging operation between scale data realizations comprised in the second scale data to generate scaling signature data, the merging operation including at least a union of the plurality of second scale data relative to the first scale data; andinitiate generation of one or more of: a visualization that indicates a superimposition of the scaling signature data over the first scale data, oran intervention report or an intervention signal for a control operation that mitigates against detected scale in one or more sections of the well production structure based on one or more of the first test or the second test.

13. The computer program of claim 12, wherein the first test or the second test comprises a computing simulation that generates that first scale data or the second scale data, respectively.

14. The computer program of claim 12, wherein the model is one of a 2-phase model or a 3-phase model.

15. A system for or detecting scale in a well production structure, the system comprising: a computer processor, andmemory storing a signal processing engine that comprises instructions that are executable by the computer processor to: generate a model for the well production structure, the model including a plurality of model parameters that are adjusted based on one or more of: synthetic data which is not captured by a sensor deployed about the well production structure, andnon-synthetic data which is captured by one or more sensors deployed about the well production structure;execute a first test using the model to generate first scale data, the first scale data indicating a normal mode of operation of the well production structure such that the normal mode of operation of the well production structure indicates one of: an absence of scale within the well production structure, ora presence of substantially minimal scale within the well production structure;execute a second test based on concurrently adjusting one or more of the plurality of model parameters using updated versions of the synthetic data or non-synthetic data to generate a plurality of second scale data;execute a merging operation between scale data realizations comprised in the second scale data to generate scaling signature data, the merging operation including at least a union of the plurality of second scale data relative to the first scale data;initiate generation of one or more of: a visualization that indicates a superimposition of the scaling signature data over the first scale data, oran intervention report or an intervention signal for a control operation that mitigates against detected scale in one or more sections of the well production structure based on one or more of the first test or the second test.

16. The system of claim 15, wherein the scale comprises an accumulation of one or more organic or inorganic materials within a production tubing comprised in the well production structure, the scale impacting the well production structure by: clogging the production tubing and thereby decreasing a rate of fluid production associated with the well production structure; ordecreasing an inner diameter of the production tubing and thereby reducing the rate of fluid production associated with the well production structure.

17. The system of claim 15, wherein the one or more sensors deployed about the well production structure comprise at least one of: a multiphase flow sensor;a wellhead pressure sensor;a wellhead temperature sensor;a downhole pressure sensor;a downhole temperature sensor;a casing or tubing pressure sensor;a casing or tubing temperature sensor; anda wellhead flowrate sensor.

18. The system of claim 15, wherein the first test or the second test comprises a computing simulation that generates that first scale data or the second scale data, respectively.

19. The system of claim 15, wherein the model is a 2-phase model comprising: at least a pressure parameter associated with the well production structure; andat least a flow rate parameter associated with the well production structure.

20. The system of claim 15, wherein the model is a 3-phase model that includes: at least a flow rate parameter associated with the well production structure;at least a pressure parameter associated with the well production structure; andat least a temperature parameter associated with the well production structure.