TECHNIQUES FOR AUTOMATICALLY GENERATING AND/OR PERFORMING A COILED TUBING TEST

Certain embodiments of the present disclosure include methods, systems, and apparatus for automatically generating a pull test schedule and/or executing one or more pull tests. In certain embodiments, a priori data may be used to automatically generate a pull test plan or schedule. In addition, in certain embodiments, real-time data may be used to automatically and/or manually adjust or fine-tune the pull test schedule. In addition, in certain embodiments, an application may automatically generate a pull test schedule and/or automate one or more pull tests. In addition, in certain embodiments, the application may automatically advise a coiled tubing (CT) operator regarding where and how to pull test in substantially real-time.

BACKGROUND

The present disclosure generally relates to techniques for automatically generating and/or performing a coiled tubing test.

Coiled tubing (CT) is used in the intervention of oil and gas wells. CT is often selected as an intervention method for a number of features, including its capacity to pump fluids, its rigidity for delivering extended reach in deviated wells, its pulling and pushing capacity, and its ability to intervene in live wells. However, stuck CT pipe is one of the costliest incidents that can occur in CT operations. To minimize and mitigate the risk of CT pipe becoming stuck, a CT operator may perform pull tests as CT pipe is run in hole (RIH). However, pull tests require an experienced CT operator to consider factors such as fatigue measurements and monitoring, a hole survey, a completion schematic, and changing and/or unforeseen circumstances to fine-tune the pull tests. Additionally, this process is relatively time-consuming, requires a CT operator's attention, and will vary widely from one CT operator to the next. Accordingly, improved methodologies of implementing pull tests may be beneficial to address such concerns.

SUMMARY

A summary of certain embodiments described herein is set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of these certain embodiments and that these aspects are not intended to limit the scope of this disclosure.

Certain embodiments of the present disclosure include methods, systems, and apparatus for automatically generating a pull test schedule and/or executing one or more pull tests. In certain embodiments, a priori data may be used to automatically generate a pull test plan or schedule. In addition, in certain embodiments, real-time data may be used to automatically and/or manually adjust or fine-tune the pull test schedule. In addition, in certain embodiments, an application may automatically generate a pull test schedule and/or automate one or more pull tests. In addition, in certain embodiments, the application may automatically advise a coiled tubing (CT) operator regarding where and how to pull test in substantially real-time.

Although certain embodiments described herein are described with reference to pull tests and stuck pipe prevention, it should be understood that such embodiments are not limited to such applications. In particular, the embodiments described herein may be used for other types of tests (e.g., pulling-like tests, such as sweeps).

DETAILED DESCRIPTION

As used herein, the terms “connect,” “connection,” “connected,” “in connection with,” and “connecting” are used to mean “in direct connection with” or “in connection with via one or more elements”; and the term “set” is used to mean “one element” or “more than one element.” Further, the terms “couple,” “coupling,” “coupled,” “coupled together,” and “coupled with” are used to mean “directly coupled together” or “coupled together via one or more elements.” As used herein, the terms “up” and “down,” “uphole” and “downhole”, “upper” and “lower,” “top” and “bottom,” and other like terms indicating relative positions to a given point or element are utilized to more clearly describe some elements. Commonly, these terms relate to a reference point as the surface from which drilling operations are initiated as being the top (e.g., uphole or upper) point and the total depth along the drilling axis being the lowest (e.g., downhole or lower) point, whether the well (e.g., wellbore, borehole) is vertical, horizontal or slanted relative to the surface. In addition, the term “interval” with respect to coiled tubing (CT) pipe is used to mean a particular axial portion along an axial length of the CT pipe. In addition, the term “a priori data” is used to mean data that is determined based on theoretical deduction rather than empirical measurement.

In addition, as used herein, the terms “real time”, “real-time”, or “substantially real time” may be used interchangeably and are intended to described operations (e.g., computing operations) that are performed without any human-perceivable interruption between operations. For example, as used herein, data relating to the systems described herein may be collected, transmitted, and/or used in control computations in “substantially real time” such that data readings, data transfers, and/or data processing steps occur once every second, once every 0.1 second, once every 0.01 second, or even more frequent, during operations of the systems (e.g., while the systems are operating). In addition, as used herein, the terms “automatic”, “automatically”, and “automated” are intended to describe operations that are performed or caused to be performed, for example, by a processing/control system (i.e., solely by the processing/control system, without human intervention). In addition, as used herein, the term “approximately equal to” may be used to mean values that are relatively close to each other (e.g., within 5%, within 2%, within 1%, within 0.5%, or even closer, of each other).

As described above, coiled tubing (CT) is used in the intervention of oil and gas wells. CT is often selected as an intervention method for a number of features, including its capacity to pump fluids, its rigidity for delivering extended reach in deviated wells, its pulling and pushing capacity, and its ability to intervene in live wells. For example, CT pipe may be used to convey tools and fluids into a well. CT pipe is a continuous metal tubular with one outer diameter (OD) and one or more internal diameters (IDs) and is manufactured by welding long sections of pipe together. Typically, CT pipe can measure up to 30,000 feet or more in length and have outer diameters of 1.25 inches to 3 or more inches. CT pipe is sufficiently rigid such that it can be pushed into a well and sufficiently strong to withstand relatively high differential pressures in the flowpath of the CT pipe and external to the CT pipe. Additionally, CT pipe is sufficiently flexible that it can be spooled on a CT reel and fed through a gooseneck.

With the foregoing in mind,FIG.1illustrates a schematic diagram of an example CT system10. As illustrated, in certain embodiments, a CT string12may be run into a wellbore14that traverses a hydrocarbon-bearing formation16(i.e., reservoir). While certain elements of the CT system10are illustrated inFIG.1, other elements of the CT system10(e.g., blow-out preventers, wellhead “tree”, etc.) may be omitted for clarity of illustration. In certain embodiments, the CT system10includes an interconnection of pipes, including vertical and/or horizontal casings, CT pipe20, and so forth, that connect to a surface facility22at the surface24of the CT system10. In certain embodiments, the CT pipe20extends inside drill pipe, tubing, casing, or liner (collectively referred to as a tubular18) and terminates at a tubing head (not shown) at or near the surface24. In addition, in certain embodiments, the tubular18contacts the wellbore14and terminates at a casing head (not shown) at or near the surface24.

In certain embodiments, a bottom hole assembly (“BHA”)26may be run inside the tubular18by the CT pipe20. As illustrated inFIG.1, in certain embodiments, the BHA26may include a downhole motor28that operates to rotate a drill bit30(e.g., during drilling operations) or other downhole tools. In certain embodiments, the downhole motor28may be driven by hydraulic forces carried in fluid supplied from the surface24of the CT system10. In certain embodiments, the BHA26may be connected to the CT pipe20, which is used to run the BHA26to a desired location within the wellbore14. It is also contemplated that, in certain embodiments, the rotary motion of the drill bit30may be driven by rotation of the CT pipe20effectuated by a rotary table or other surface-located rotary actuator. In such embodiments, the downhole motor28may be omitted.

In certain embodiments, the CT pipe20may also be used to deliver fluid32to the drill bit30through an interior of the CT pipe20to aid in the drilling process and carry cuttings and possibly other fluid or solid components in return fluid34that flows up the annulus between the CT pipe20and the tubular18(or via a return flow path provided by the CT pipe20, in certain embodiments) for return to the surface facility22. It is also contemplated that the return fluid34may include remnant proppant (e.g., sand) or possibly rock fragments that result from a hydraulic fracturing application, and flow within the CT system10. Under certain conditions, fracturing fluid and possibly hydrocarbons (oil and/or gas), proppants and possibly rock fragments may flow from the fractured formation16through perforations in a newly opened interval and back to the surface24of the CT system10as part of the return fluid34. In certain embodiments, the BHA26may be supplemented behind the rotary drill by an isolation device such as, for example, an inflatable packer that may be activated to isolate the zone below or above it and enable local pressure tests. In addition, in certain embodiments, the BHA26may include a tractor or agitating system that is capable of improving reach and WOB of the BHA26during CT operations.

As such, in certain embodiments, the CT system10may include a downhole well tool36that is moved along the wellbore14via the CT pipe20. In certain embodiments, the downhole well tool36may include a variety of drilling/cutting tools coupled with the CT pipe20. In the illustrated embodiment, the downhole well tool36includes the drill bit30, which may be powered by the downhole motor28(e.g., a positive displacement motor (PDM), or other hydraulic motor) of the BHA26. In certain embodiments, the wellbore14may be an openhole wellbore or a cased wellbore defined by the tubular18. In addition, in certain embodiments, the wellbore14may be vertical or horizontal or inclined. It should be noted the downhole well tool36may be part of various types of BHAs26coupled to the CT pipe20.

As also illustrated inFIG.1, in certain embodiments, the CT system10may include a downhole sensor package38having multiple downhole sensors40. In certain embodiments, the sensor package38may be mounted along the CT string12, although certain downhole sensors40may be positioned at other downhole locations in other embodiments. In addition, in certain embodiments, downhole sensors40disposed on the CT pipe20may be configured to detect downhole flow rates, downhole temperatures, and downhole pressures, and so forth, in the wellbore14. In addition, in certain embodiments, downhole sensors40disposed on the tubular18may be configured to detect downhole temperatures, downhole pressures, axial load (or “weight”) and torque applied on the bit, casing collar locators (CCLs), resistivity, and so forth, in the wellbore14.

In certain embodiments, data from the downhole sensors40may be relayed uphole to a processing/control system42(e.g., a computer-based processing system) disposed at the surface24and/or other suitable location of the CT system10. In certain embodiments, the data may be relayed uphole in substantially real time (e.g., relayed while it is detected by the downhole sensors40during operation of the downhole well tool36) via a wired or wireless telemetric control line44, and this real-time data may be referred to as edge data. In certain embodiments, the telemetric control line44may be in the form of an electrical line, fiber-optic line, or other suitable control line for transmitting data signals. In certain embodiments, the telemetric control line44may be routed along an interior of the CT pipe20, within a wall of the CT pipe20, or along an exterior of the CT pipe20. In addition, as described in greater detail herein, additional data (e.g., surface data) may be supplied by surface sensors46and/or stored in a memory location48. By way of example, historical data and other useful data may be stored in the memory location48such as a cloud storage50.

As illustrated, in certain embodiments, the CT pipe20may be deployed from a CT reel55of a CT unit52and delivered downhole via an injector head54. In certain embodiments, the injector head54may be controlled to slack off or pick up the CT pipe20so as to control the tubing string weight and, thus, the weight-on-bit (WOB) acting on the drill bit30(or the downhole well tool36). In certain embodiments, the downhole well tool36may be moved along the wellbore14via the CT pipe20under control of the injector head54so as to apply a desired tubing weight and, thus, to achieve a desired rate of penetration (ROP) as the drill bit30is operated. Depending on the specifics of a given application, various types of data may be collected downhole, and transmitted to the processing/control system42in substantially real time to facilitate improved operation of the downhole well tool36. For example, as described in greater detail herein, the data may be used to fully or partially automate downhole operations, to optimize the downhole operations, and/or to provide more accurate predictions regarding components or aspects of the downhole operations.

In certain embodiments, fluid32may be delivered downhole under pressure from a pump unit56. In certain embodiments, the fluid32may be delivered by the pump unit56through the downhole motor28to power the downhole motor28and, thus, the drill bit30. In certain embodiments, the return fluid34is returned uphole, and this flow back of the return fluid34is controlled by suitable flowback equipment58. In certain embodiments, the flowback equipment58may include chokes and other components/equipment used to control flow back of the return fluid34in a variety of applications, including well treatment applications.

As described in greater detail herein, the CT unit52, the injector head54, the pump unit56, and the flowback equipment58may include advanced surface sensors46, actuators, and local controllers, such as PLCs, which may cooperate together to provide sensor data to receive control signals from, and generate local control signals based on communications with, respectively, the processing/control system42. In certain embodiments, as described in greater detail herein, the surface sensors46may include flow rate, pressure, and fluid rheology sensors46, among other types of sensors. In addition, as described in greater detail herein, the actuators may include actuators for pump and choke control of the pump unit56and the flowback equipment58, respectively, among other types of actuators.

In certain embodiments, surface sensors46of the CT unit52may be configured to detect positions of the CT pipe20, weights of the CT pipe20, and so forth. In addition, in certain embodiments, surface sensors46of the injector head54may be configured to detect wellhead pressure, and so forth. In addition, in certain embodiments, surface sensors46of the pump unit56may be configured to detect pump pressures, pump flow rates, and so forth. In addition, in certain embodiments, surface sensors46of the flowback equipment58may be configured to detect fluids production rates, solids production rates, and so forth.

FIG.2illustrates a well control system60that may include the processing/control system42to control the CT system10described herein. In certain embodiments, the processing/control system42may include one or more analysis modules62(e.g., a program of computer-executable instructions and associated data) that may be configured to perform various functions of the embodiments described herein. In certain embodiments, to perform these various functions, the one or more analysis modules62may execute on one or more processors64of the processing/control system42, which may be connected to one or more storage media66of the processing/control system42. Indeed, in certain embodiments, the one or more analysis modules62may be stored in the one or more storage media66.

In certain embodiments, the computer-executable instructions of the one or more analysis modules62, when executed by the one or more processors64, may cause the one or more processors64to generate one or more models (e.g., including the FM described in greater detail herein). Such models may be used by the processing/control system42to predict values of operational parameters that may or may not be measured (e.g., using gauges, sensors) during well operations.

In certain embodiments, the one or more processors64may include a microprocessor, a microcontroller, a processor module or subsystem, a programmable integrated circuit, a programmable gate array, a digital signal processor (DSP), or another control or computing device. In certain embodiments, the one or more processors64may include machine learning and/or artificial intelligence (AI) based processors. In certain embodiments, the one or more storage media66may be implemented as one or more non-transitory computer-readable or machine-readable storage media. In certain embodiments, the one or more storage media66may 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); or other types of storage devices. Note that the computer-executable instructions and associated data of the analysis module(s)62may be provided on one computer-readable or machine-readable storage medium of the storage media66, or alternatively, may be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes. Such computer-readable or machine-readable storage medium or media are considered to be part of an article (or article of manufacture), which may refer to any manufactured single component or multiple components. In certain embodiments, the one or more storage media66may be located either in the machine running the machine-readable instructions, or may be located at a remote site from which machine-readable instructions may be downloaded over a network for execution.

In certain embodiments, the processor(s)64may be connected to a network interface68of the processing/control system42to allow the processing/control system42to communicate with the multiple downhole sensors40and surface sensors46described herein, as well as communicate with the actuators70and/or PLCs72of the surface equipment74(e.g., the CT unit52, the injector head54, the pump unit56, the flowback equipment58, and so forth) and of the downhole equipment76(e.g., the BHA26, the downhole motor28, the drill bit30, the downhole well tool36, and so forth) for the purpose of controlling operation of the CT system10, as described in greater detail herein. In certain embodiments, the network interface68may also facilitate the processing/control system42to communicate data to the cloud storage50(or other wired and/or wireless communication network) to, for example, archive the data or to enable external computing systems78to access the data and/or to remotely interact with the processing/control system42.

It should be appreciated that the well control system60illustrated inFIG.2is only one example of a well control system, and that the well control system60may have more or fewer components than shown, may combine additional components not depicted in the embodiment ofFIG.2, and/or the well control system60may have a different configuration or arrangement of the components depicted inFIG.2. In addition, the various components illustrated inFIG.2may 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. Furthermore, the operations of the well control system60as described herein may be implemented by running one or more functional modules in an information processing apparatus such as application specific chips, such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), programmable logic devices (PLDs), systems on a chip (SOCs), or other appropriate devices. These modules, combinations of these modules, and/or their combination with hardware are all included within the scope of the embodiments described herein.

As described in greater detail herein, the embodiments described herein include methods, systems, and apparatus for automatically generating a pull test schedule and/or executing one or more pull tests. In certain embodiments, a priori data may be used to automatically generate a pull test plan or schedule. In addition, in certain embodiments, real-time data may be used to automatically and/or manually adjust or fine-tune the pull test schedule. In addition, in certain embodiments, an application may automatically generate a pull test schedule and/or automate one or more pull tests. In addition, in certain embodiments, the application may automatically advise a CT operator regarding where and how to pull test in substantially real time.

FIG.3illustrates an exemplary CT system10. In particular,FIG.3illustrates how CT pipe20may be spooled (e.g., stored) on a CT reel55and be run in hole (RIH) by an injector head54, which has chains capable of pushing and pulling the CT pipe20. In certain embodiments, a gooseneck80may align the CT pipe20with the chains of the injector head54.

CT Pipe Fatigue Based on Plastic Deformation

When the CT pipe20is RIH or pulled out of the hole (POOH), the CT pipe20may experience plastic deformation as the CT pipe20bends and/or straightens through the core of the CT reel55and gooseneck80because the radii of the core of the CT reel55and the gooseneck80are lower than the limit for plastic deformation. Such plastic deformation induces fatigue on the CT pipe20. Certain models may estimate fatigue at each section of the CT pipe20based on how many times the CT pipe20has been cycled (e.g., how many times the CT pipe20has been RIH and/or POOH) and other factors, such as pressures and corrosion of pumped fluids. When a section of CT pipe20is sufficiently fatigued, the CT pipe20is expected to fail (e.g., result in a parted pipe) at the section. In some pipe fatigue models, a section of CT pipe20is only allowed to be used until the section reaches 100% fatigue, at which point the section of CT pipe20may be cut off if the section is near an endpoint or the entire CT pipe20reaches an end-of-life (EOL) status and is retired.

FIG.4is a graph82of a pipe fatigue measurement and monitoring model of an exemplary CT pipe20that has experienced 32% fatigue from cycling. As illustrated inFIG.4, the periodic spikes84relate to weak points in the CT pipe20(e.g., weld points at which welds exist in the CT pipe20), which are considered weaker points and, thus, are originally derated compared to other portions of the CT pipe20. The weak points (e.g., relating to the spikes84) are usually where fatigue is most advanced and/or where fatigue accelerates the fastest.

CT Pipe Mechanical Defects and Pipe Integrity Measurements

The CT pipe20may have other weak points besides the welds and sections with fatigue due to cycling. For example,FIGS.5A-5Cshow pictures of various examples of mechanical defects that could occur to CT pipe20.FIG.5Ais a picture of a pinhole86in CT pipe20,FIG.5Bis a picture of corrosion88of CT pipe20, andFIG.5Cis a picture of gouging90in CT pipe20. Such defects can occur for a multitude of reasons. For instance, gouging90may occur when sediments or foreign objects become trapped between CT pipe wraps on a CT reel55, the CT pipe20, and chains of the injector head54, and/or CT pipe20and the downhole environment. Corrosion88may occur as a result of corrosive fluids inside or outside of the CT pipe20, including pumped fluids or naturally-occurring H2S. Corrosion88and erosion can reduce the wall thickness of the CT pipe20or embrittle it. Reduced wall thickness and embrittlement can lead to pinholes86in the CT pipe20or parted pipe.

Pipe integrity measurement devices can measure physical attributes of the CT pipe20to provide insight regarding mechanical defects and deterioration. Examples of such measurements include magnetic flex leakage (MFL), acoustic, and/or contact to determine pipe diameter, ovality, wall thickness, pinholes, notches, and corrosion. Based on the measurements of the physical attributes of the CT pipe20, mechanical defects can be identified in real time when a CT operation is executed and the development of such defects can be tracked over the length of the pipe and/or over time.FIGS.6A-6Cillustrate various graphs of exemplary measurements that can detect mechanical defects. In particular,FIG.6Aillustrates a graph92that indicates magnetic flux leakage over (MFL) along CT pipe20,FIG.6Billustrates a graph94that indicates MLF evolution over time, andFIG.6Cillustrates a graph96that indicates wall thickness of CT pipe20over time.

Pull Tests to Minimize Risk of Stuck CT Pipe

Stuck CT pipe20is one of the costliest incidents that can occur in CT operations. To minimize and mitigate the risk of CT pipe20becoming stuck, a CT operator may perform pull tests as the CT pipe20is RIH to a target depth (Dtd).FIG.7illustrates a pull test schedule98indicating a series of pull tests performed periodically during a CT operation when CT pipe20is RIH. Generally, the first pull test will take place after an initial minimum depth (Di) has been reached and the pull test will cover a prescribed interval (Dpt). After finishing the pull test, the operation will continue the RIH operation and perform more pull tests at prescribed intervals (Dint).

Following a pull test schedule98(e.g., as illustrated inFIG.7), there will be a total number of pull tests:

Given an average speed during RIH (vRIH), and POOH (vPOOH), and the time that it takes to reset the system between RIH and POOH transitions (Treset), the time spent performing pull tests may be given by:

Table 1 provides typical values for an example pull test schedule98.

It will be appreciated that, in addition to defining pull test intervals at which pull tests are initiated, pull test schedules may also define optimized pull test lengths (e.g., how long each particular pull test is performed) that may be optimized, for example, based on real-time data including the geometry of the assets at surface, pipe integrity measurements, fatigue life, and elongation models, to name but a few examples. The optimization of pull test lengths enables access to several benefits: eliminating time wasted on excessive pull test lengths, introducing longer pull test lengths to minimize risk of stuck CT pipe20, and detecting stuck CT pipe20situations. Related to the last point (i.e., stuck CT pipe20), the “pull test length” optimization also enables access to early identification of stuck CT pipe20(e.g., faster than is currently possible) and enables automation of the detection of stuck CT pipe20(i.e., automated stuck CT pipe20avoidance and automated stuck CT pipe20detection).

Time spent pull testing is productive if it helps minimize and mitigate the risk of stuck CT pipe20. However, prescribed periodic pull test schedules (e.g., as illustrated inFIG.7) do not necessarily distinguish or address intervals where there is a higher risk of CT pipe20becoming stuck. Therefore, some pull tests may be deemed less productive than others.

Additionally, pull tests also introduce fatigue to CT pipe20. For example, a pull test may introduce approximately 1% fatigue to a section of CT pipe20. In the example illustrated in Table 1, the string will have incurred an approximately 1% increase in fatigue in 15 sections of the CT pipe20. These sections will, thus, incur accelerated fatigue after each run and could reach 100% fatigue, at which point the CT pipe20would have to be retired.

Considerations for Pull Tests

Depending on a CT operator's level of experience, a CT operator may consider the following elements to fine-tune a prescribed pull test schedule: (1) pipe fatigue profile; (2) a completion schematic; (3) a hole survey; and (4) changing and/or unforeseen downhole conditions. A CT operator may monitor a real-time updating pipe fatigue profile or model (e.g., as illustrated inFIG.4) to avoid pull testing across sections that have higher fatigue. Such sections of higher fatigue can occur due to overcycling and/or due to the position of weak points along the CT pipe20.

A completion schematic provides a CT operator with elements of a completion, including sizes and depths of tubing, casing, jewelry (e.g., packers, sleeves, no-go profiles, and hardware that are typically attached to production tubing), restrictions, and fish (e.g., anything left in a wellbore14, including junk and tools that were dropped or left in the hole and never recovered).FIG.8illustrates a simplified completion schematic100that depicts jewelry, such as sliding sleeves102, a no-go profile104, and a mule shoe106where the tubing ends. As illustrated inFIG.8, the simplified completion schematic100also includes production tubing108, tubular18, and an open hole110at the bottom. Such information may be useful for fine-tuning pull tests to ensure that the CT pipe20does not become stuck. In other cases, the CT operator may choose to avoid pull testing across jewelry that is deemed more susceptible to damage.

In addition, a hole survey provides a CT operation with geometry of a wellbore14.FIG.9is an exemplary hole survey112for a deviated well with a kickoff point (KOP)114and two valleys116in the horizontal section. This information is important for at least two different reasons. First, intervals in which there is a relatively high rate of change of the direction of the hole (e.g., known as dogleg severity) may introduce higher friction forces and introduce risk of stuck CT pipe20. For instance, the KOP114and the “snaking” geometry in the horizontal section inFIG.9would have relatively higher dogleg severity than the vertical section. Second, the valleys116in the horizontal section could collect deposits of sand or other downhole sedimentation. When POOH or sweeping across these valleys116, the CT pipe20can carry some of those deposits and pack itself in, progressively becoming stuck.

A CT operator may also consider a number of other aspects to fine-tune a pull test. For example, various aspects that the CT operator might consider are enumerated inFIG.10. The aspects illustrated inFIG.10are merely exemplary, and not intended to be limiting. For example, an experienced CT operator may choose to perform a pull test that was not prescribed if changes occur during the operation and/or unforeseen conditions indicate potential for stuck pipe. These changes or unforeseen conditions can vary widely. For example, the CT operator may be monitoring the sand in the returns during a cleanout and notice that the sand volume returned is diminishing. This could trigger a pull test up to the vertical section (in the context of cleanouts, this may be referred to as a long sweep) to ensure that the CT is still free. Similarly, if there are real-time downhole pressure data, an increase in downhole annular pressure could indicate settling of sand and that should trigger a pull test or sweep. Similarly, a loss of wellhead pressure could be indicating settling of sand. In the case, when a mechanical downhole tool would be actuated by pressure (e.g., an inflatable packer), a CT operator could also modify the pull test strategy to ensure that the downhole tool36is not prematurely setting, even partially. This is even truer when downhole conditions (e.g., pressure, temperature) notably vary compared to the values that were assumed for the design of a CT operation. An example of an unforeseen circumstance is when CT weights measured during RIH and/or POOH deviate significantly from the simulated RIH and POOH weights. A CT operator could identify this deviation, recognize the risk of stuck pipe, and adjust the pull test strategy.

In summary, as illustrated inFIG.11, a periodic pull test schedule98is a baseline recipe for pull testing but, ultimately, it takes an experienced CT operator to consider factors such as pipe fatigue profile (e.g., a fatigue model118), pipe integrity measurements120of the CT pipe20, a hole survey112, a completion schematic100, BHA characteristics122(e.g., of the BHA26described herein), and changing and/or unforeseen circumstances124to fine-tune the pull test schedule98(e.g., as illustrated inFIGS.11and12) to determine the executed pull tests or sweeps126. Besides requiring an experienced CT operator, this process is time-consuming, requires a CT operator's attention (e.g., the process may distract the CT operator from operating the CT unit), and will vary widely from one CT operator to the next.

FIG.12illustrates an example workflow128that includes CT job design considerations130versus CT job execution steps132. As illustrated, in certain embodiments, the CT job design considerations130that may be used to design a pull test schedule98may include CT pipe fatigue data118, locations of CT weak points84, surface condition data134(e.g., relating to surface conditions, for example, as detected by the surface sensors46described herein), downhole condition data136(e.g., relating to downhole conditions, for example, as detected by the downhole sensors40described herein), operational objectives138, completion data140, a hole survey112, among others.

Once the pull test schedule98has been designed, the CT job execution steps132may start with preparation to pull test (e.g., process step142), after which a determination may be made as to whether a particular section of CT pipe20is fatigued (e.g., decision block144). If the particular section of CT pipe20is fatigued, then the pull test may be revaluated (e.g., process step146) and the preparation for the pull test may be re-started (e.g., returning to process step142). Conversely, if the particular section of CT pipe20is not fatigued, then a determination may be made as to whether the CT pipe20is at a weak point84(e.g., decision block148). If the CT pipe20is at a weak point84, then the pull test may be revaluated (e.g., process step146) and the preparation for the pull test may be re-started (e.g., returning to process step142). Conversely, if the CT pipe20is not at a weak point84, then a determination may be made as to whether it is taking longer pull tests to reach nominal CT weight (e.g., decision block150). If it is taking longer pull tests to reach nominal CT weight, then the pull test may be revaluated (e.g., process step146) and the preparation for the pull test may be re-started (e.g., returning to process step142). Conversely, if it is not taking longer pull tests to reach nominal CT weight, then a determination may be made as to whether downhole conditions are changing (e.g., decision block152). If the downhole conditions are changing, then the pull test may be revaluated (e.g., process step146) and the preparation for the pull test may be re-started (e.g., returning to process step142). Conversely, if the downhole conditions are not changing, then the pull test may be executed (e.g., process step154) after which RIH may continue (e.g., process step156) until another downhole location of the CT pipe20is reached and preparation for another pull test is started (e.g., returning to process step142).

Various embodiments of the present disclosure provide improved methodologies for generating a pull test schedule98and/or implementing such pull tests. Such methodologies provide consistency in strategic and automated pull test placement to extend the life of the CT pipe20and to reduce the total cost of ownership of the CT pipe20. In certain embodiments, a priori data may be used to automate generation of a pull test schedule98. For instance, the pull test schedule98may indicate one or more intervals of CT pipe20for which one or more pull tests are encouraged to be implemented and/or one or more intervals of CT pipe20for which one or more pull tests are discouraged from being implemented, as well as indicating how long each particular pull test may be performed (e.g., an amount of time and/or CT pipe length the CT pipe20is pulled during the particular pull test). In certain embodiments, the a priori data may include (1) fatigue profiles and models for CT pipe20, (2) mechanical defect records that provide attributes, such as outer diameter (OD), ovality, wall thickness, corrosion, gouging, pinholes, and pitting along the CT pipe20; (3) hole surveys112that detail depth, inclination, and azimuth; (4) well completion schematics100that detail the material, size, and depth of tubing, casing, jewelry, fish, and generally provide completion geometry deployed via the CT pipe20; (5) a description of a BHA26, such as downhole mechanical tools36actuated by a flow and/or a pressure mechanism, whose premature actuation may generate stuck incidents; and/or (6) hydrodynamic models that estimate downhole static and flowing conditions, account for initial debris distributions in a wellbore14, account for transient redistributions of debris during a CT operation, and generally provide forces and effects of fluids; or the like.

Additionally, in certain embodiments, manual inputs may override and/or provide new data to be considered in the automated generation of intervals of CT pipe20where pull tests are encouraged, intervals of CT pipe20where pull tests are discouraged, or both. Manual inputs may include, for example, a prescribed schedule based on initial pull test depth, pull test intervals (e.g., distance to POOH when pull testing), pull test frequency (e.g., distance to RIH between pull tests), and/or pull test length/duration (e.g., amount of time/length a pull test is performed), or the like. The prescribed pull test schedule98may include different categories of pull tests, such as short pull tests and long pull tests, and the prescribed pull test schedule98may include pull test intervals that vary. In certain embodiments, different types of pull tests may be defined instead of just short and long pull tests. Further, the length of the pull test may be optimized depending on risk, keeping a pull test length to the minimum without compromising the effectiveness of the pull test or the objective of the pull test, thereby increasing operational efficiency of the pull test.

In certain embodiments, after the pull test schedule98has been automatically generated, the pull test schedule98may be manually adjusted by a CT operator. One or more pull test schedules98may also be exported to and/or imported from the well control system60and/or cloud storage50. In this way, stored pull tests may be reused and/or optimized between executions. Additionally, stored pull tests may be used in other applications. In certain embodiments, a user may also be notified of any updates or changes to a pull test, and the user may acknowledge, accept, and/or reject such updates or changes.

In certain embodiments, real-time operational data may be used to automatically and/or manually adjust or fine-tune the pull test schedule98(e.g., suggested pull test intervals and/or pull test lengths/durations). Such real-time operational data may include, for example, (1) surface condition data134(e.g., as measured by the surface sensors46described herein), such as wellhead pressure, circulation pressure, pumped flowrates, and fluid, gas, and/or solids returns; downhole condition data136(e.g., as measured by the downhole sensors40described herein), such as pressure, temperature, axial tension and/or compression, and torque; and (3) other data that may be measured by other sensors, produced by an application (e.g., being executed by the processing/control system42described herein), or provided by another computing system78in substantially real time during the CT operation; or the like. Additionally, in certain embodiments, the real-time operational data may include pipe integrity measurements120of the CT pipe20, such as OD, ovality, wall thickness corrosion, gouging, pinholes, and/or pitting, or the like (e.g., as illustrated inFIGS.5A through5C). In addition, in certain embodiments, the real-time operational data may be used to adjust a pull test type definition and/or placement (e.g., change a short pull test and/or a long pull test to three types of pull tests, and the placement of such pull tests).

In certain embodiments, an application may leverage the methodologies described herein to automatically propose or generate a pull test schedule98during CT job design130. For example, the application may reside on a local device (e.g., the processing/control system42described herein) and/or be provided as a cloud-based service (e.g., via the cloud storage50described herein). Additionally, in certain embodiments, the application may not be dependent on wellsite hardware. That is, the application may be executed on a device independent of wellsite hardware. In certain embodiments, the application may automatically provide sensitivity analysis for each input and generate a pull test schedule98based on one or more inputs. In addition, in certain embodiments, the application may be used in the CT job design phase130to automatically generate one or more optimal pull tests schedules160. The application may also provide the ability to overlay a pull test schedule160over one or more depth-gated visuals (e.g., a well completion schematic100, hole surveys112, fatigue profiles118, or the like).

In certain embodiments, an application may run in tandem or be integrated with CT acquisition software, and automatically advise a CT operator on where and how to pull test in substantially real time. The application may reside on a local device e.g., the processing/control system42described herein) and/or be provided as a cloud-based service (e.g., via the cloud storage50described herein). In certain embodiments, the application and its services may be embedded into existing acquisition software or can be run as a standalone application. In certain embodiments, the application may include a user interface through which the application may automatically provide feedback and/or suggested actions to a CT operator. Such feedback and/or suggested actions may include when to pull test, when not to pull test, how far to pull test, if a pull test was successful or not, if there are early indications of stuck CT pipe20, if there is stuck CT pipe20, or if the CT operation should no longer RIH, or the like.

In addition, in certain embodiments, an application may run in tandem or be integrated with CT acquisition software and automate one or more pull tests. For example, the application may automatically control one or more subsystems of the CT equipment such as the CT reel55and injector head54to automatically execute the CT operation. Furthermore, the application may automatically perform the pull tests as part of its conveyance and service objectives.

With the foregoing in mind, various embodiments of the present disclosure include systems, apparatuses, and methods for automatically generating a pull test schedule98, automatically implementing one or more pull tests (e.g., in accordance with the pull test schedule98), automatically optimizing one or more pull tests based on a priority and/or real-time data, automatically guiding a CT operator in performing a pull test, and the like. In certain embodiments, an algorithm (e.g., as part of the analysis modules62described herein) may gather information from a CT operator, such as any restriction locations in the well, requested pull frequency (Dint), maximum length of any pull tests (Dpt), and the depth of the run's objective. The algorithm may then integrate the received information with an existing fatigue profile118of a CT string12. From the fatigue profile118, the algorithm may note the presence of any welding locations (e.g., or other weak points84). In certain embodiments, both welding locations and restriction depths may be given a dead zone window and such depths may be avoided by the algorithm. As the calibrated and zeroed depth the CT operator uses is typically different from the calibrated and zeroed depth the fatigue model118uses, in certain embodiments, the algorithm may detect both the BHA26used and the distances between various surface equipment74to automatically perform all depth translations between the two.

Next, the algorithm may determine the feasibility of pull tests if performed at certain depths. From a starting depth, the model's fatigue data may be averaged out over the pull test length specified by the CT operator. The averaged fatigue value may then be recorded for the starting depth. This allows a quick reference to what a pull test fatigue profile118would be like if performed at a certain depth. The calculation may then be performed over the entire length of the CT pipe20. If during the averaging window, a dead zone window is reached from either a weak point84or a restriction, the proposed depth may be marked as invalid and be avoided.

The algorithm may then focus on finding optimal pull test locations based on the requested pull tests frequency from the CT operator. Using the pull test frequency depth as a starting point, the algorithm may search for a lowest averaged fatigued profile value that was computed earlier within a range of depth shallower or deeper than the proposed pull test. If no valid pull test profile exists within the depth window, such as in the case where the pull tests were requested in the middle of a restriction, the algorithm may increase the window and continue searching. However, there is an upper bound on the search window. If the search window hits the upper bound, the algorithm may inform the user and skip the proposed test. This process for finding the optimal pull test location for each proposed depth as defined by the pull test frequency is repeated until the run's main objective depth is reached. The list of proposed pull test depths may then be returned to the CT operator for review.

FIG.13illustrates an exemplary visualization158of a pull test schedule98having, for example, a graph of a hole survey112, a fatigue life profile118, a well completion schematic100, and an indicator of pull test recommendations160, all aligned relative to depth within the wellbore14. As illustrated, the algorithm may deal with “pull test not allowed” sections (e.g., as per the example inFIG.13). The algorithm may also be extended to proposed additional pull test locations based on the well survey and intervals with higher risks of stuck pipe events. This refers to the “pull test recommended” sections (e.g., as per the example inFIG.13).

It should be noted that the embodiments described herein are not limited to any one CT application or any one type of pull test. Inserting any tubular of reasonable length (e.g., drill pipe, tubing, casing, liners, or screens, or the like) in a well may introduce similar risks of fatigue, failure, damage, becoming stuck, etc. as described above for CT. Likewise, applications beyond cleanouts, such as milling, drilling, fluid circulation, setting and retrieving plugs, shifting sleeves, and more, are subject to the same or comparable conditions of requiring the CT to be POOH for intervals. In some cases, POOH intervals may exclusively address the risk of stuck CT pipe20(e.g., known as a pull test). In other cases, the POOH intervals may address a combination of risks, such as cleanout properties and stuck CT pipe20(e.g., known as sweeps). The embodiments described herein may also be applied to other POOH intervals and applications. Further, it should be understood that certain features of the various embodiments described herein may be combined.

System Architecture

FIG.14illustrates a diagram162of an exemplary configuration of system architecture that facilitates pull test automation and optimization in accordance with various embodiments described herein. In particular, in certain embodiments, an edge intelligence device164(e.g., as part of the well control system60described herein with respect toFIG.2) with real-time simulation capabilities and advanced orchestration capabilities may enable autonomous CT conveyance (e.g., as performed by the various CT equipment166described herein, for example, with respect toFIGS.1-3). As described in greater detail herein, various inputs of the system may include the well geometry, completion tubulars and jewelry, CT geometry, BHA configuration, and high-level objectives. The automation software executed by the edge intelligence device164and/or the processing/control system42may use one or more of these inputs to create a list of tasks to achieve the conveyance goals168at an optimal speed without sacrificing safety or operation integrity. For instance, well restrictions and weight safety limits may be considered to ensure conveyance does not put the completion, CT string12, or BHA26at risk during RIH or POOH.

As part of the high-level objectives, a CT operator170may provide a conveyance target depth with desired pull test intervals, pull test lengths, pull test durations, and so forth. For instance, the CT operator170might input a desired pull test interval of 500 meters with pull test lengths of 15 meters. As a first step, the autonomous solution may inject those pull tests into the conveyance plan without any consideration of CT pipe fatigue, weak points, or downhole restrictions (seeFIG.15).

Returning toFIG.14, the autonomous conveyance solution and infrastructure162may contain valuable information172about well restrictions and CT fatigue from acquisition software174, which it can use to strategically plan and execute pull tests. As a result, the autonomous conveyance solution162may run a pull test optimization solution. The pull test optimizer (e.g., optimizer algorithms) works primarily by analyzing the CT pipes' fatigue profile118. When the CT operator170provides a desired pull test interval (Dint) and length (Dpt), the optimizer may search for intervals close to the proposed pull test depths that would minimize cycling highly fatigued sections of the CT pipe20. The optimizer may adjust the pull test depths and inject them in the automated conveyance plan. The optimizer may also consider weak points84in the CT pipe20and well-specific restrictions. If a pull test occurs in or near such a feature, the optimizer may place the pull test elsewhere, prioritizing sections of the CT pipe20that are fatigued less.

On every run, the pull test optimizer may ingest the information172relating to CT pipe fatigue, weak points, and well completion, and automate the pull test strategy with the objectives of redistributing the pull test placement to decelerate overall fatigue of the CT pipe20, avoiding weak points84, and avoiding restrictions in the completion. A dedicated communication channel between the edge intelligence device164and the acquisition software174that runs the fatigue modeling and monitoring software may provide the optimizer with the latest CT pipe fatigue data. Therefore, every pull test sequence is bespoke to the latest run and the latest conditions of the CT pipe20.

For example,FIG.16illustrates an exemplary workflow176to simulate CT pipe fatigue evolution from 0% to 100% based on historical run mission profiles. As illustrated, the workflow176may begin by initializing new CT pipe20with 0 runs and 0% fatigue (e.g., subroutine178). Then, based on data relating to CT pipe fatigue versus depth (e.g., a fatigue profile118), a determination may be made as to whether fatigue for the CT pipe20has reached 100% (e.g., decision block180). If the fatigue for the CT pipe20has reached 100%, then a total number of runs 182 to 100% fatigue may be set as the current number of runs. However, if the fatigue for the CT pipe20has not reached 100%, then the pull test methods described in greater detail herein may be determined (e.g., decision block184). In particular, if the automated pull test optimizer described above it utilized, then optimized pull test locations186may be calculated by the automated pull test optimizer (e.g., subroutine188), whereas a fixed pull test interval (Dint)190may be used if a pull test schedule is determined manually (e.g., by a CT operator170). Regardless, a run mission profile192(e.g., including depths, pressures, weights, and so forth) may be merged with the optimized pull test locations186(e.g., subroutine194), the run may be simulated and the fatigue may be updated (e.g., subroutine196), and the run number may be updated (e.g., subroutine198). At this point, another run cycle of the workflow176may be iteratively performed.

Metrics of Pull Test Performance

Traditionally, there are two metrics that may be used to evaluate the performance of a pull test strategy: the number of runs until reaching end of life (EOL) of CT pipe20, and the maximum fatigue on the CT pipe20after any given run. The issue with the metric of “number of runs until reaching the EOL” is that, barring extrapolation, one does not have access to this number in practice (i.e., outside of a simulated environment) until the CT pipe20has reached 100% and retired. To gauge how well the fatigue is evolving on a run-basis, one can monitor the maximum fatigue in the CT pipe's fatigue profile. However, maximum fatigue on its own does not provide a CT job designer and CT operator with feedback about how effectively pull tests are being redistributed along the CT pipe20. The ideal cycling of CT pipe20may result in a fatigue profile118that grows as a single, flat front toward 100%. In practice, a perfectly flat fatigue profile118is improbable due to operational requirements, fatigue hotspots, and weak points, but it is still qualitatively evident that an optimized redistribution of pull test placement may lead to a flatter fatigue profile as compared to fixed intervals, which results in a greater number of runs before reaching 100% fatigue.

To determine “flatness” on a run-basis, a new metric is proposed based on a similar challenge and solution used to characterize the homogeneity of injection distributions in water injection wells. First, the CT pipe fatigue profile may be normalized:

fn=fn+1-fnDn+1-Dn⁢∀n∈[1,total⁢number⁢of⁢monitored⁢section⁢on⁢CT⁢pipe],where:fi=CT pipe fatigue at a section, %;Di=distance from CT bottom hole end for that section, m; and{circumflex over (f)}t=change in CT pipe fatigue normalized per unit length in that section, %/m.

Some CT pipe fatigue models monitor the fatigue in 1.5-m (5-ft) sections; other monitor every 15 m (50 ft). There is no standard sampling frequency across models and service providers. By normalizing fatigue over each monitored section, the homogeneity metric may be used to compare CT pipes20with differing fatigue sampling intervals. The new metric for CT pipe fatigue “flatness”, or homogeneity, is the sample variance of the normalized CT pipe fatigue:

A perfectly flat fatigue profile118would yield a homogeneity of σ2=0.FIG.17is a graph of run-on-run homogeneity, which starts at σ2=0 (normal for a new CT pipe20with 0% from end to end) and grows exponentially. The traditional fixed interval pull test method's σ2increases at a faster rate than the optimized pull test method. At EOL, the traditional method is less homogeneous (i.e., less “flat”) (σ2=3.5) than the optimized method (σ2=2.5). Therefore, the optimized pull test placement is effective at distributing the pull test load throughout the life of the CT pipe20.

Additional Embodiments

In certain embodiments, the pull test optimizer can use a pump schedule (e.g., of the pump unit56described herein with respect toFIG.1) to avoid pull testing during periods of time during which it expects to see circulating pressures. For example, the presence of circulating pressure in the CT pipe20will generally accelerate fatigue when the CT pipe20is cycled. Alternatively, the pull test optimizer may suggest a plan where pull tests and establishing circulation occur asynchronously (i.e., at different times). During execution, the automation solution might warn the CT operator170of ongoing pumping and advise them to either stop pumping prior to commencing a pull test, or to forego that pull test altogether.

In certain embodiments, other information172may be used, in addition to completion restrictions and fatigue profiles, to determine areas where pull tests should be avoided. For example, such a priori information may include the hole survey112and the intervention objectives. In certain embodiments, the hole survey112may be automatically analyzed by the digital solution to identify intervals where there is higher likelihood of the CT pipe20becoming stuck (e.g., the KOPs114and valleys116described herein). These insights may be used to place pull tests in intervals where there is a higher risk of stuck CT pipe20. Similarly, the digital solution could leverage knowledge of operational objectives (for instance, the expected depth of a first plug to be milled, or the depth of a fish) to place pull tests right before these reference events.

Certain embodiments of the pull test optimization solution require the CT operator170to define a static pull test length (Dpt). In such embodiments, the pull test digital solution may automatically calculate the minimum pull test based on certain inputs, such as the placement and distances of the CT reel55, gooseneck80, and injector head54relative to each other. Such inputs may already be included in the fatigue model118.

In certain embodiments, the pull test digital solution may automatically adjust pull test length on each pull test. In such embodiments, CT pipe elongation models may be integrated in the pull test solution to predict the minimum pull test length where CT slack is eliminated, the system is in dynamic frictions, and the nominal weight is recovered. This would eliminate pulling excessive CT pipe20out of the well, which would introduce unnecessary fatigue to the CT pipe20as well as create non-productive time, and it would eliminate the scenario of pulling insufficient CT pipe20out of the well, which would expose the operation to the risk of stuck CT pipe20. In the non-ideal case where the CT pipe20is already becoming stuck, this approach would alert the CT operator170with greater immediacy and confidence of onset of stuck CT pipe20given that the CT weight did not return to nominal values when the model predicted it should.

Technical Advantages and Benefits

As mentioned herein, the embodiments described herein provide a number of advantages and benefits over conventional systems and methods for implementing pull tests. For instance, such advantages and benefits include reducing operational time by eliminating unnecessary pull tests, reducing the rate at which CT pipe fatigues, optimizing sections of CT pipe20that should be targeted and/or avoided to accumulate fatigue, extending the useful life of CT pipe20, reducing the total cost of ownership of CT pipe20, reducing Scope3emissions associated with CT pipe manufacturing, improving the consistency and effectiveness of executed pull tests, improving the effectiveness and efficiency of sweeps by optimizing sweep placements, length, and frequency, and reducing the risk of stuck CT pipe20.

For example, the useful life of CT pipe20can be extended by successfully implementing a pull test optimization strategy that redistributes where and how the CT pipe20is cycled. In particular, if CT pipe20lasts X runs to reach 100% fatigue when using a traditional pull test method, and it takes the CT pipe Y runs to reach 100% when using the optimized pull test method, then its useful life is extended by

Equivalently, the total cost of ownership may be reduced by

For instance, if the traditional pull test approach yields 100 runs until retirement or end-of-life (EOL), and the optimized approach yields 120 runs until EOL, then the life has been extended by 20% (in terms of additional runs) or, equivalently, the CT pipe's depreciation run-on-run has been reduced by 17%.

Additionally, estimates for the carbon dioxide emissions (CO2e) related to the manufacturing of CT pipe20depend on many assumptions, including mining, refining, production process; transport of raw steel; transport of milled CT pipe20; and EOL recovery or recycling rates. Ignoring transport and EOL rates, a relatively low-end estimate of greenhouse gas emissions (GHG) is 2.3 tons of CO2e per ton of steel. Other estimates exceed 3.0 tons of CO2e per ton of steel when considering the use of chemicals for manufacturing; power conversion; and cradle-to-gate transportation. By similar logic as that used to calculate the reduction in total cost of ownership, if the useful life of CT pipe20is extended by

the amortized CO2e associated with CT pipe's manufacturing is reduced by

Using the same numbers of the previous example (100 runs versus 120 runs to EOL), and assuming 25-ton CT pipe20, amortized emissions associated with the CT manufacturing process would reduce from 0.58 to 0.48 tons CO2e per run, or a reduction by 17%.

Further, traditional pull tests are executed at fixed intervals while the CT pipe20is RIH. Risks associated with this approach include repeated pull tests at an already fatigued section, which accelerates fatigue of the CT pipe20, and pull tests executed in wellbore sections with completion restrictions. The embodiments described herein address such risks by systematically combining completion information and the CT pipe's fatigues profile to strategically adjust the pull test schedule98, reducing the impact of those tests on pipe fatigue and the risks associated with drifting across downhole jewelry.

Testing Results

Certain embodiments described herein were testing using historical runs from one 1.50-in and two 1.75-in CT pipes20; these pipes averaged 108 runs to end of life. Periodic pull tests with these CT pipes20accounted for up to 20% of their fatigue. The pull test optimization methodology diverts fatigue to lesser-affected sections of the CT pipe20, slowing down the rate at which the fatigue approaches 100%. By decelerating the fatigue growth, the modeled strings could last 118 runs before reaching 100% fatigue. Therefore, certain embodiments described herein can extend CT pipe life by at least 10%.

Pull test optimization and automation were integrated into an automation-enabled CT unit52. The automation eliminated the human factor and the risk of repeating pull tests at the same locations on each run; it also minimized pull tests at the weakest points, which include the weak points84where cycle fatigue is accelerated; and it eliminated pull tests at restrictions, reducing the risk of abrading or damaging completion jewelry. Finally, besides standardizing pull test placement, the combination of the pull test optimization and automation frees the CT operator170to focus on other critical elements of the operation such as running and monitoring the unit and downhole tools36, managing the crew, and engaging the customer representative. Thus, optimizing and automating pull test placement removes user bias and eliminates technique and competency sensitivity by introducing a consistent fit-for-run approach. The 10% increase in the life of CT pipe20has a positive impact on reducing both the total cost of ownership and Scope3emissions.

In summary, the embodiments described herein provide a methodology wherein a priori data educates an automated determination of intervals of CT pipe20where pull tests are encouraged and other intervals of the CT pipe20where pull tests are discouraged (also known as the pull test schedules98described herein). In certain embodiments, the a priori data may include fatigue models118for CT pipe20; mechanical defect records that provide attributes such as OD, ovality, wall thickness, corrosion, gouging, pinholes, and pitting along the CT pipe20; hole surveys112that detail depth, inclination, and azimuth through a wellbore14; well completion schematics100that detail the material, size, and depth of tubing, casing, jewelry, fish, and generally provide completion geometry of completion equipment deployed via the CT pipe20; description122of the BHA26that may include downhole mechanical tools36actuated by flow and/or pressure mechanisms, whose premature actuation may generate stuck incidents; hydrodynamic models that estimate downhole static and flowing conditions within the wellbore14, accounting for initial debris distributions in the wellbore14, accounting for transient redistributions of debris during a CT operation, and generally providing forces and effects of fluids.

In certain embodiments, the system may define as many types of pull tests as it deems necessary (not limited to just short and long pull test) and optimize the pull test lengths depending on risk, keeping the pull test length to a minimum without compromising the pull test's effectiveness or objective, therefore increasing operational efficiency.

In addition, in certain embodiments, manual inputs may override or provide new data to be considered in the automated generation of the intervals of CT pipe20where pull tests are encouraged and the intervals of the CT pipe20where pull tests are discouraged. In addition, in certain embodiments, the manual inputs may include a prescribed schedule based on initial pull test depth, and pull test intervals (what distance to POOH when pull testing), and pull test frequency (what distance to RIH between pull tests). This schedule may include different categories of pulls tests, such as short pull test and long pull test, where the pull test interval would vary. As such, in certain embodiments, manual adjustment of the pull test schedule98may be enabled by the system. In addition, the system may enable the exporting and importing of pull test schedules160, which enables the reuse and polishing of pull test schedules98from one run to the next. This feature also enables consuming the pull test schedules98in other applications. In certain embodiments, the user (e.g., CT operator170) may be notified of any updates and changes to the pull test schedule, and may acknowledge, accept, and/or reject the updates and changes.

In addition, the embodiments described herein provide a methodology wherein real-time operational data may be utilized to automatically adjust or fine-tune the suggested pull test intervals in the pull test schedules98. For example, such real-time operational data may include surface measurements (e.g., as measured by the surface sensors46described herein) such as wellhead pressure, circulation pressure, pumped flowrates, and fluid/gas/solids returns; downhole measurements (e.g., as measured by the downhole sensors40described herein) such as pressure, temperature, axial tension/compression, and torque; and other data that may be measured by a sensor, produced by an application (e.g., being executed by the processing/control system42described herein), or provided by another computing system78in real-time during the CT operation. In certain embodiments, the real-time operational data may include pipe integrity measurements120such as OD, ovality, wall thickness, corrosion, gouging, pinholes, and pitting of the CT pipe20. In addition, in certain embodiments, the real-time operational data may be used to automatically adjust the pull test type definition and placement (e.g., change from short/long pull test to three types of pull test, and the placement of these pulls tests).

In addition, the embodiments described herein provide an application that leverages the described methodologies to propose a pull test schedule98during a CT job planning phase130. The application may reside on a local computer (e.g., the processing/control system42described herein), on an edge intelligence device164, and/or as a cloud-based service (e.g., the cloud storage50described herein). In addition, the application does not have to be dependent on any particular wellsite hardware. In addition, in certain embodiments, the application may provide sensitivity analysis for every input and generate a pull test schedule98for each combination of inputs described herein. In addition, in certain embodiments, the application may be used exclusively in the CT job planning phase130to generate optimal pull test schedules98. In addition, the application may include the ability to overlay the pull test schedule98over one or more depth-gated visuals (e.g., well completion schematic100, hole survey112, fatigue profile118, etc.), as illustrated inFIG.13.

In addition, the embodiments described herein provide an application that runs in tandem or integrated with CT acquisition software and advise a CT operator170on where and how to pull test in substantially real time. Again, the application may reside on a local computer (e.g., the processing/control system42described herein), on an edge intelligence device164, and/or as a cloud-based service (e.g., the cloud storage50described herein). In addition, in certain embodiments, the application and its services may be embedded into existing acquisition software or can be run as a standalone application. In addition, in certain embodiments, the application may include a user interface through which a CT operator170may be provides feedback and suggested actions. This may include when to pull test, when not to pull test, how far to pull test, if a pull test was successful or not, if there are early indications of stuck pipe, if there is stuck pipe, or if the CT operator should no longer RIH.

In addition, the embodiments described herein provide an application that runs in tandem or integrated to CT automation software and automates the pull tests. For example, the application may automatically control (e.g., automatically adjust operational parameters of) one or more subsystems of the CT equipment such as the CT reel55and injector head54to automatically execute the CT operation. Furthermore, the application may automatically perform the pull tests as part of its conveyance and service objectives.

FIG.18is a flow diagram of a method200for automatically generating and/or performing a coiled tubing test (e.g., utilizing the well control system60illustrated inFIG.2), as described in greater detail herein. As illustrated, in certain embodiments, the method200may include performing a CT operation including deploying a string12of CT pipe20into a wellbore14(block202). In addition, in certain embodiments, the method200may include receiving a priori data relating to the CT operation (block204). In addition, in certain embodiments, the method200may include automatically generating a pull test schedule98based on the a priori data (block206). In general, the pull test schedule98defines a first set of intervals of the string12of the CT pipe20where pull tests are encouraged and a second set of intervals of the string12of the CT pipe20where pull tests are discouraged. In addition, in certain embodiments, the pull test schedule98also defines pull lengths for respective pull tests. In addition, in certain embodiments, the method200may include automatically adjusting operational parameters of the CT operation based on the pull test schedule98. For example, in certain embodiments, operational parameters relating to fluid pumping and operational parameters relating to the pull tests described herein may be automatically adjusted, whether at the same time or, more typically, at different times.

In certain embodiments, the a priori data may include one or more fatigue models118for the CT pipe20. In addition, in certain embodiments, the a priori data may include one or more mechanical defect records relating to the CT pipe20. In addition, in certain embodiments, the a priori data may include one or more weak points in the CT pipe20. In addition, in certain embodiments, the a priori data may include one or more hole surveys112relating to the wellbore14. In addition, in certain embodiments, the a priori data may include one or more well completion schematics100that define geometries of completion equipment deployed into the wellbore14via the string12of the CT pipe20. In addition, in certain embodiments, the a priori data may include a description of a BHA26deployed into the wellbore14via the string12of the CT pipe20.

In addition, in certain embodiments, the method200may include receiving real-time operational data relating to the CT operation in substantially real time during performance of the CT operation; and automatically adjusting the pull test schedule98based on the real-time operational data. In certain embodiments, the real-time operational data may include surface measurements134(e.g., as measured by the surface sensors46described herein) relating to operation of surface equipment74during the CT operation. In addition, in certain embodiments, the real-time operational data may include downhole measurements136(e.g., as measured by the downhole sensors40described herein) relating to operation of downhole equipment76during the CT operation. In addition, in certain embodiments, the real-time operational data may include pipe integrity measurements120for the CT pipe20. In addition, in certain embodiments, the real-time operational data may include a CT pipe fatigue profile118that is updated in real time by one or more fatigue models, as described in greater detail herein.

The embodiments described herein are not limited to any one CT application or any one type of “pull test”. For example, inserting any tubular of reasonable length (e.g., drillpipe, tubing, casing, liners, screens) in a well may introduce similar risks of fatigue, failure, damage, becoming stuck as described above for CT pipe20. Likewise, applications beyond cleanouts, such as milling, drilling, fluid circulation, setting and retrieving plugs, shifting sleeves, and more, are subject to the same or comparable conditions of requiring the CT pipe20to be POOH for intervals. In some cases, these POOH intervals exclusively address the risk of stuck CT pipe20(known as a pull test). In other cases, these POOH intervals address a combination of risks such as cleanout properties and stuck pipe (known as sweeps).