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

BACKGROUND

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

This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present techniques, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as an admission of any kind.

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).

Various refinements of the features noted above may be undertaken in relation to various aspects of the present disclosure. Further features may also be incorporated in these various aspects as well. These refinements and additional features may exist individually or in any combination. For instance, various features discussed below in relation to one or more of the illustrated embodiments may be incorporated into any of the above-described aspects of the present disclosure alone or in any combination. The brief summary presented above is intended to familiarize the reader with certain aspects and contexts of embodiments of the present disclosure without limitation to the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects of this disclosure may be better understood upon reading the following detailed description and upon reference to the drawings, in which:

FIG. 1 illustrates a schematic diagram of an example coiled tubing (CT) system, in accordance with embodiments of the present disclosure;

FIG. 2 illustrates a well control system including a surface processing system to control the CT system of FIG. 1, in accordance with embodiments of the present disclosure;

FIG. 3 illustrates an exemplary CT system in which CT pipe is stored on a CT reel and passes through a gooseneck that aligns the CT pipe with chains of an injector head, in accordance with embodiments of the present disclosure;

FIG. 4 is a graph of a fatigue measurement and monitoring model of an exemplary CT pipe that has experienced fatigue from cycling, in accordance with embodiments of the present disclosure;

FIG. 5A is a picture of a pinhole in CT pipe, in accordance with embodiments of the present disclosure;

FIG. 5B is a picture of corrosion of CT pipe, in accordance with embodiments of the present disclosure;

FIG. 5C is a picture of gouging in CT pipe, in accordance with embodiments of the present disclosure;

FIG. 6A illustrates a graph that indicates magnetic flux leakage over (MFL) along CT pipe, in accordance with embodiments of the present disclosure;

FIG. 6B illustrates a graph that indicates MLF evolution over time, in accordance with embodiments of the present disclosure;

FIG. 6C illustrates a graph that indicates wall thickness of CT pipe over time, in accordance with embodiments of the present disclosure;

FIG. 7 illustrates a graph indicating a series of pull tests performed periodically during a run in hole (RIH) operation, in accordance with embodiments of the present disclosure;

FIG. 8 illustrates an exemplary completion schematic, in accordance with embodiments of the present disclosure;

FIG. 9 is an exemplary hole survey for a deviated well, in accordance with embodiments of the present disclosure;

FIG. 10 illustrates various aspects of pull test risk management, in accordance with embodiments of the present disclosure;

FIG. 11 illustrates various considerations for a pull test, in accordance with embodiments of the present disclosure;

FIG. 12 illustrates various inputs and/or decisions when designing and executing a pull tests, in accordance with embodiments of the present disclosure;

FIG. 13 illustrates an exemplary visualization of a pull test schedule, in accordance with embodiments of the present disclosure;

FIG. 14 illustrates an exemplary hardware architecture for implementing coiled tubing autonomous conveyance and/or pull test automation and optimization, in accordance with embodiments of the present disclosure;

FIG. 15 is a series of graphs that illustrate initial well conveyance trajectory with manually prescribed pull tests that produce an automated conveyance sequence with the pull tests, in accordance with embodiments of the present disclosure;

FIG. 16 illustrates an exemplary workflow to simulate CT pipe fatigue evolution from 0% to 100% based on historical run mission profiles, in accordance with embodiments of the present disclosure;

FIG. 17 is a graph of a run-on-run evolution of CT pipe fatigue based on homogeneity (σ²) metric for fixed interval pull tests and optimized pull tests, in accordance with embodiments of the present disclosure; and

FIG. 18 is a flow diagram of a method for automatically generating and/or performing a coiled tubing test, in accordance with embodiments of the present disclosure.

DETAILED DESCRIPTION

One or more specific embodiments of the present disclosure will be described below. These described embodiments are only examples of the presently disclosed techniques. Additionally, in an effort to provide a concise description of these embodiments, all features of an actual implementation may not be described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure.

When introducing elements of various embodiments of the present disclosure, the articles “a,” “an,” and “the” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. Additionally, it should be understood that references to “one embodiment” or “an embodiment” of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features.

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. 1 illustrates a schematic diagram of an example CT system 10. As illustrated, in certain embodiments, a CT string 12 may be run into a wellbore 14 that traverses a hydrocarbon-bearing formation 16 (i.e., reservoir). While certain elements of the CT system 10 are illustrated in FIG. 1, other elements of the CT system 10 (e.g., blow-out preventers, wellhead “tree”, etc.) may be omitted for clarity of illustration. In certain embodiments, the CT system 10 includes an interconnection of pipes, including vertical and/or horizontal casings, CT pipe 20, and so forth, that connect to a surface facility 22 at the surface 24 of the CT system 10. In certain embodiments, the CT pipe 20 extends inside drill pipe, tubing, casing, or liner (collectively referred to as a tubular 18) and terminates at a tubing head (not shown) at or near the surface 24. In addition, in certain embodiments, the tubular 18 contacts the wellbore 14 and terminates at a casing head (not shown) at or near the surface 24.

In certain embodiments, a bottom hole assembly (“BHA”) 26 may be run inside the tubular 18 by the CT pipe 20. As illustrated in FIG. 1, in certain embodiments, the BHA 26 may include a downhole motor 28 that operates to rotate a drill bit 30 (e.g., during drilling operations) or other downhole tools. In certain embodiments, the downhole motor 28 may be driven by hydraulic forces carried in fluid supplied from the surface 24 of the CT system 10. In certain embodiments, the BHA 26 may be connected to the CT pipe 20, which is used to run the BHA 26 to a desired location within the wellbore 14. It is also contemplated that, in certain embodiments, the rotary motion of the drill bit 30 may be driven by rotation of the CT pipe 20 effectuated by a rotary table or other surface-located rotary actuator. In such embodiments, the downhole motor 28 may be omitted.

In certain embodiments, the CT pipe 20 may also be used to deliver fluid 32 to the drill bit 30 through an interior of the CT pipe 20 to aid in the drilling process and carry cuttings and possibly other fluid or solid components in return fluid 34 that flows up the annulus between the CT pipe 20 and the tubular 18 (or via a return flow path provided by the CT pipe 20, in certain embodiments) for return to the surface facility 22. It is also contemplated that the return fluid 34 may include remnant proppant (e.g., sand) or possibly rock fragments that result from a hydraulic fracturing application, and flow within the CT system 10. Under certain conditions, fracturing fluid and possibly hydrocarbons (oil and/or gas), proppants and possibly rock fragments may flow from the fractured formation 16 through perforations in a newly opened interval and back to the surface 24 of the CT system 10 as part of the return fluid 34. In certain embodiments, the BHA 26 may 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 BHA 26 may include a tractor or agitating system that is capable of improving reach and WOB of the BHA 26 during CT operations.

As such, in certain embodiments, the CT system 10 may include a downhole well tool 36 that is moved along the wellbore 14 via the CT pipe 20. In certain embodiments, the downhole well tool 36 may include a variety of drilling/cutting tools coupled with the CT pipe 20. In the illustrated embodiment, the downhole well tool 36 includes the drill bit 30, which may be powered by the downhole motor 28 (e.g., a positive displacement motor (PDM), or other hydraulic motor) of the BHA 26. In certain embodiments, the wellbore 14 may be an openhole wellbore or a cased wellbore defined by the tubular 18. In addition, in certain embodiments, the wellbore 14 may be vertical or horizontal or inclined. It should be noted the downhole well tool 36 may be part of various types of BHAs 26 coupled to the CT pipe 20.

As also illustrated in FIG. 1, in certain embodiments, the CT system 10 may include a downhole sensor package 38 having multiple downhole sensors 40. In certain embodiments, the sensor package 38 may be mounted along the CT string 12, although certain downhole sensors 40 may be positioned at other downhole locations in other embodiments. In addition, in certain embodiments, downhole sensors 40 disposed on the CT pipe 20 may be configured to detect downhole flow rates, downhole temperatures, and downhole pressures, and so forth, in the wellbore 14. In addition, in certain embodiments, downhole sensors 40 disposed on the tubular 18 may 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 wellbore 14.

In certain embodiments, data from the downhole sensors 40 may be relayed uphole to a processing/control system 42 (e.g., a computer-based processing system) disposed at the surface 24 and/or other suitable location of the CT system 10. In certain embodiments, the data may be relayed uphole in substantially real time (e.g., relayed while it is detected by the downhole sensors 40 during operation of the downhole well tool 36) via a wired or wireless telemetric control line 44, and this real-time data may be referred to as edge data. In certain embodiments, the telemetric control line 44 may 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 line 44 may be routed along an interior of the CT pipe 20, within a wall of the CT pipe 20, or along an exterior of the CT pipe 20. In addition, as described in greater detail herein, additional data (e.g., surface data) may be supplied by surface sensors 46 and/or stored in a memory location 48. By way of example, historical data and other useful data may be stored in the memory location 48 such as a cloud storage 50.

As illustrated, in certain embodiments, the CT pipe 20 may be deployed from a CT reel 55 of a CT unit 52 and delivered downhole via an injector head 54. In certain embodiments, the injector head 54 may be controlled to slack off or pick up the CT pipe 20 so as to control the tubing string weight and, thus, the weight-on-bit (WOB) acting on the drill bit 30 (or the downhole well tool 36). In certain embodiments, the downhole well tool 36 may be moved along the wellbore 14 via the CT pipe 20 under control of the injector head 54 so as to apply a desired tubing weight and, thus, to achieve a desired rate of penetration (ROP) as the drill bit 30 is operated. Depending on the specifics of a given application, various types of data may be collected downhole, and transmitted to the processing/control system 42 in substantially real time to facilitate improved operation of the downhole well tool 36. 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, fluid 32 may be delivered downhole under pressure from a pump unit 56. In certain embodiments, the fluid 32 may be delivered by the pump unit 56 through the downhole motor 28 to power the downhole motor 28 and, thus, the drill bit 30. In certain embodiments, the return fluid 34 is returned uphole, and this flow back of the return fluid 34 is controlled by suitable flowback equipment 58. In certain embodiments, the flowback equipment 58 may include chokes and other components/equipment used to control flow back of the return fluid 34 in a variety of applications, including well treatment applications.

As described in greater detail herein, the CT unit 52, the injector head 54, the pump unit 56, and the flowback equipment 58 may include advanced surface sensors 46, 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 system 42. In certain embodiments, as described in greater detail herein, the surface sensors 46 may include flow rate, pressure, and fluid rheology sensors 46, 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 unit 56 and the flowback equipment 58, respectively, among other types of actuators.

In certain embodiments, surface sensors 46 of the CT unit 52 may be configured to detect positions of the CT pipe 20, weights of the CT pipe 20, and so forth. In addition, in certain embodiments, surface sensors 46 of the injector head 54 may be configured to detect wellhead pressure, and so forth. In addition, in certain embodiments, surface sensors 46 of the pump unit 56 may be configured to detect pump pressures, pump flow rates, and so forth. In addition, in certain embodiments, surface sensors 46 of the flowback equipment 58 may be configured to detect fluids production rates, solids production rates, and so forth.

FIG. 2 illustrates a well control system 60 that may include the processing/control system 42 to control the CT system 10 described herein. In certain embodiments, the processing/control system 42 may include one or more analysis modules 62 (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 modules 62 may execute on one or more processors 64 of the processing/control system 42, which may be connected to one or more storage media 66 of the processing/control system 42. Indeed, in certain embodiments, the one or more analysis modules 62 may be stored in the one or more storage media 66.

In certain embodiments, the computer-executable instructions of the one or more analysis modules 62, when executed by the one or more processors 64, may cause the one or more processors 64 to generate one or more models (e.g., including the FM described in greater detail herein). Such models may be used by the processing/control system 42 to 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 processors 64 may include a microprocessor, a microcontroller, a processor module or subsystem, a programmable integrated circuit, a programmable gate array, a digital signal processor (DSP), or another control or computing device. In certain embodiments, the one or more processors 64 may include machine learning and/or artificial intelligence (AI) based processors. In certain embodiments, the one or more storage media 66 may be implemented as one or more non-transitory computer-readable or machine-readable storage media. In certain embodiments, the one or more storage media 66 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); or other types of storage devices. Note that the computer-executable instructions and associated data of the analysis module(s) 62 may be provided on one computer-readable or machine-readable storage medium of the storage media 66, 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 media 66 may 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) 64 may be connected to a network interface 68 of the processing/control system 42 to allow the processing/control system 42 to communicate with the multiple downhole sensors 40 and surface sensors 46 described herein, as well as communicate with the actuators 70 and/or PLCs 72 of the surface equipment 74 (e.g., the CT unit 52, the injector head 54, the pump unit 56, the flowback equipment 58, and so forth) and of the downhole equipment 76 (e.g., the BHA 26, the downhole motor 28, the drill bit 30, the downhole well tool 36, and so forth) for the purpose of controlling operation of the CT system 10, as described in greater detail herein. In certain embodiments, the network interface 68 may also facilitate the processing/control system 42 to communicate data to the cloud storage 50 (or other wired and/or wireless communication network) to, for example, archive the data or to enable external computing systems 78 to access the data and/or to remotely interact with the processing/control system 42.

It should be appreciated that the well control system 60 illustrated in FIG. 2 is only one example of a well control system, and that the well control system 60 may have more or fewer components than shown, may combine additional components not depicted in the embodiment of FIG. 2, and/or the well control system 60 may have a different configuration or arrangement of the components depicted in FIG. 2. In addition, the various components illustrated in FIG. 2 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. Furthermore, the operations of the well control system 60 as 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. 3 illustrates an exemplary CT system 10. In particular, FIG. 3 illustrates how CT pipe 20 may be spooled (e.g., stored) on a CT reel 55 and be run in hole (RIH) by an injector head 54, which has chains capable of pushing and pulling the CT pipe 20. In certain embodiments, a gooseneck 80 may align the CT pipe 20 with the chains of the injector head 54.

CT Pipe Fatigue Based on Plastic Deformation

When the CT pipe 20 is RIH or pulled out of the hole (POOH), the CT pipe 20 may experience plastic deformation as the CT pipe 20 bends and/or straightens through the core of the CT reel 55 and gooseneck 80 because the radii of the core of the CT reel 55 and the gooseneck 80 are lower than the limit for plastic deformation. Such plastic deformation induces fatigue on the CT pipe 20. Certain models may estimate fatigue at each section of the CT pipe 20 based on how many times the CT pipe 20 has been cycled (e.g., how many times the CT pipe 20 has been RIH and/or POOH) and other factors, such as pressures and corrosion of pumped fluids. When a section of CT pipe 20 is sufficiently fatigued, the CT pipe 20 is expected to fail (e.g., result in a parted pipe) at the section. In some pipe fatigue models, a section of CT pipe 20 is only allowed to be used until the section reaches 100% fatigue, at which point the section of CT pipe 20 may be cut off if the section is near an endpoint or the entire CT pipe 20 reaches an end-of-life (EOL) status and is retired.

FIG. 4 is a graph 82 of a pipe fatigue measurement and monitoring model of an exemplary CT pipe 20 that has experienced 32% fatigue from cycling. As illustrated in FIG. 4, the periodic spikes 84 relate to weak points in the CT pipe 20 (e.g., weld points at which welds exist in the CT pipe 20), which are considered weaker points and, thus, are originally derated compared to other portions of the CT pipe 20. The weak points (e.g., relating to the spikes 84) are usually where fatigue is most advanced and/or where fatigue accelerates the fastest.

CT Pipe Mechanical Defects and Pipe Integrity Measurements

The CT pipe 20 may have other weak points besides the welds and sections with fatigue due to cycling. For example, FIGS. 5A-5C show pictures of various examples of mechanical defects that could occur to CT pipe 20. FIG. 5A is a picture of a pinhole 86 in CT pipe 20, FIG. 5B is a picture of corrosion 88 of CT pipe 20, and FIG. 5C is a picture of gouging 90 in CT pipe 20. Such defects can occur for a multitude of reasons. For instance, gouging 90 may occur when sediments or foreign objects become trapped between CT pipe wraps on a CT reel 55, the CT pipe 20, and chains of the injector head 54, and/or CT pipe 20 and the downhole environment. Corrosion 88 may occur as a result of corrosive fluids inside or outside of the CT pipe 20, including pumped fluids or naturally-occurring H₂S. Corrosion 88 and erosion can reduce the wall thickness of the CT pipe 20 or embrittle it. Reduced wall thickness and embrittlement can lead to pinholes 86 in the CT pipe 20 or parted pipe.

Pipe integrity measurement devices can measure physical attributes of the CT pipe 20 to 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 pipe 20, 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-6C illustrate various graphs of exemplary measurements that can detect mechanical defects. In particular, FIG. 6A illustrates a graph 92 that indicates magnetic flux leakage over (MFL) along CT pipe 20, FIG. 6B illustrates a graph 94 that indicates MLF evolution over time, and FIG. 6C illustrates a graph 96 that indicates wall thickness of CT pipe 20 over time.

Pull Tests to Minimize Risk of Stuck CT Pipe

Stuck CT pipe 20 is one of the costliest incidents that can occur in CT operations. To minimize and mitigate the risk of CT pipe 20 becoming stuck, a CT operator may perform pull tests as the CT pipe 20 is RIH to a target depth (D_td). FIG. 7 illustrates a pull test schedule 98 indicating a series of pull tests performed periodically during a CT operation when CT pipe 20 is RIH. Generally, the first pull test will take place after an initial minimum depth (D_i) has been reached and the pull test will cover a prescribed interval (D_pt). After finishing the pull test, the operation will continue the RIH operation and perform more pull tests at prescribed intervals (D_int).

Following a pull test schedule 98 (e.g., as illustrated in FIG. 7), there will be a total number of pull tests:

$N_{pt} = 1 + FLOOR [\frac{D_{td} - D_{i}}{D_{int}}] .$

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

$T_{pt} = (N) (\frac{v_{RIH}}{D_{pt}} + \frac{v_{POOH}}{D_{pt}} + 2 T_{reset})$

Table 1 provides typical values for an example pull test schedule 98.

TABLE 1

Typical values for pull tests schedules.

Variable
Value
Units

D_td
15000
ft

D_i
500
ft

D_int
1000
ft

D_pt
150
ft

N_pt
15
ea

ν_RIH
60
ft/min

ν_POOH
30
ft/min

T_reset
0.3
min

T_pt
18
min

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 pipe 20, and detecting stuck CT pipe 20 situations. Related to the last point (i.e., stuck CT pipe 20), the “pull test length” optimization also enables access to early identification of stuck CT pipe 20 (e.g., faster than is currently possible) and enables automation of the detection of stuck CT pipe 20 (i.e., automated stuck CT pipe 20 avoidance and automated stuck CT pipe 20 detection).

Time spent pull testing is productive if it helps minimize and mitigate the risk of stuck CT pipe 20. However, prescribed periodic pull test schedules (e.g., as illustrated in FIG. 7) do not necessarily distinguish or address intervals where there is a higher risk of CT pipe 20 becoming stuck. Therefore, some pull tests may be deemed less productive than others.

Additionally, pull tests also introduce fatigue to CT pipe 20. For example, a pull test may introduce approximately 1% fatigue to a section of CT pipe 20. In the example illustrated in Table 1, the string will have incurred an approximately 1% increase in fatigue in 15 sections of the CT pipe 20. These sections will, thus, incur accelerated fatigue after each run and could reach 100% fatigue, at which point the CT pipe 20 would 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 in FIG. 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 pipe 20.

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 wellbore 14, including junk and tools that were dropped or left in the hole and never recovered). FIG. 8 illustrates a simplified completion schematic 100 that depicts jewelry, such as sliding sleeves 102, a no-go profile 104, and a mule shoe 106 where the tubing ends. As illustrated in FIG. 8, the simplified completion schematic 100 also includes production tubing 108, tubular 18, and an open hole 110 at the bottom. Such information may be useful for fine-tuning pull tests to ensure that the CT pipe 20 does 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 wellbore 14. FIG. 9 is an exemplary hole survey 112 for a deviated well with a kickoff point (KOP) 114 and two valleys 116 in 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 pipe 20. For instance, the KOP 114 and the “snaking” geometry in the horizontal section in FIG. 9 would have relatively higher dogleg severity than the vertical section. Second, the valleys 116 in the horizontal section could collect deposits of sand or other downhole sedimentation. When POOH or sweeping across these valleys 116, the CT pipe 20 can 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 in FIG. 10. The aspects illustrated in FIG. 10 are 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 tool 36 is 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 in FIG. 11, a periodic pull test schedule 98 is 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 model 118), pipe integrity measurements 120 of the CT pipe 20, a hole survey 112, a completion schematic 100, BHA characteristics 122 (e.g., of the BHA 26 described herein), and changing and/or unforeseen circumstances 124 to fine-tune the pull test schedule 98 (e.g., as illustrated in FIGS. 11 and 12) to determine the executed pull tests or sweeps 126. 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. 12 illustrates an example workflow 128 that includes CT job design considerations 130 versus CT job execution steps 132. As illustrated, in certain embodiments, the CT job design considerations 130 that may be used to design a pull test schedule 98 may include CT pipe fatigue data 118, locations of CT weak points 84, surface condition data 134 (e.g., relating to surface conditions, for example, as detected by the surface sensors 46 described herein), downhole condition data 136 (e.g., relating to downhole conditions, for example, as detected by the downhole sensors 40 described herein), operational objectives 138, completion data 140, a hole survey 112, among others.

Once the pull test schedule 98 has been designed, the CT job execution steps 132 may start with preparation to pull test (e.g., process step 142), after which a determination may be made as to whether a particular section of CT pipe 20 is fatigued (e.g., decision block 144). If the particular section of CT pipe 20 is fatigued, then the pull test may be revaluated (e.g., process step 146) and the preparation for the pull test may be re-started (e.g., returning to process step 142). Conversely, if the particular section of CT pipe 20 is not fatigued, then a determination may be made as to whether the CT pipe 20 is at a weak point 84 (e.g., decision block 148). If the CT pipe 20 is at a weak point 84, then the pull test may be revaluated (e.g., process step 146) and the preparation for the pull test may be re-started (e.g., returning to process step 142). Conversely, if the CT pipe 20 is not at a weak point 84, then a determination may be made as to whether it is taking longer pull tests to reach nominal CT weight (e.g., decision block 150). If it is taking longer pull tests to reach nominal CT weight, then the pull test may be revaluated (e.g., process step 146) and the preparation for the pull test may be re-started (e.g., returning to process step 142). 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 block 152). If the downhole conditions are changing, then the pull test may be revaluated (e.g., process step 146) and the preparation for the pull test may be re-started (e.g., returning to process step 142). Conversely, if the downhole conditions are not changing, then the pull test may be executed (e.g., process step 154) after which RIH may continue (e.g., process step 156) until another downhole location of the CT pipe 20 is reached and preparation for another pull test is started (e.g., returning to process step 142).

Various embodiments of the present disclosure provide improved methodologies for generating a pull test schedule 98 and/or implementing such pull tests. Such methodologies provide consistency in strategic and automated pull test placement to extend the life of the CT pipe 20 and to reduce the total cost of ownership of the CT pipe 20. In certain embodiments, a priori data may be used to automate generation of a pull test schedule 98. For instance, the pull test schedule 98 may indicate one or more intervals of CT pipe 20 for which one or more pull tests are encouraged to be implemented and/or one or more intervals of CT pipe 20 for 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 pipe 20 is pulled during the particular pull test). In certain embodiments, the a priori data may include (1) fatigue profiles and models for CT pipe 20, (2) mechanical defect records that provide attributes, such as outer diameter (OD), ovality, wall thickness, corrosion, gouging, pinholes, and pitting along the CT pipe 20; (3) hole surveys 112 that detail depth, inclination, and azimuth; (4) well completion schematics 100 that detail the material, size, and depth of tubing, casing, jewelry, fish, and generally provide completion geometry deployed via the CT pipe 20; (5) a description of a BHA 26, such as downhole mechanical tools 36 actuated 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 wellbore 14, 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 pipe 20 where pull tests are encouraged, intervals of CT pipe 20 where 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 schedule 98 may include different categories of pull tests, such as short pull tests and long pull tests, and the prescribed pull test schedule 98 may 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 schedule 98 has been automatically generated, the pull test schedule 98 may be manually adjusted by a CT operator. One or more pull test schedules 98 may also be exported to and/or imported from the well control system 60 and/or cloud storage 50. 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 schedule 98 (e.g., suggested pull test intervals and/or pull test lengths/durations). Such real-time operational data may include, for example, (1) surface condition data 134 (e.g., as measured by the surface sensors 46 described herein), such as wellhead pressure, circulation pressure, pumped flowrates, and fluid, gas, and/or solids returns; downhole condition data 136 (e.g., as measured by the downhole sensors 40 described 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 system 42 described herein), or provided by another computing system 78 in substantially real time during the CT operation; or the like. Additionally, in certain embodiments, the real-time operational data may include pipe integrity measurements 120 of the CT pipe 20, such as OD, ovality, wall thickness corrosion, gouging, pinholes, and/or pitting, or the like (e.g., as illustrated in FIGS. 5A through 5C). 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 schedule 98 during CT job design 130. For example, the application may reside on a local device (e.g., the processing/control system 42 described herein) and/or be provided as a cloud-based service (e.g., via the cloud storage 50 described 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 schedule 98 based on one or more inputs. In addition, in certain embodiments, the application may be used in the CT job design phase 130 to automatically generate one or more optimal pull tests schedules 160. The application may also provide the ability to overlay a pull test schedule 160 over one or more depth-gated visuals (e.g., a well completion schematic 100, hole surveys 112, fatigue profiles 118, 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 system 42 described herein) and/or be provided as a cloud-based service (e.g., via the cloud storage 50 described 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 pipe 20, if there is stuck CT pipe 20, 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 reel 55 and injector head 54 to 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 schedule 98, automatically implementing one or more pull tests (e.g., in accordance with the pull test schedule 98), 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 modules 62 described herein) may gather information from a CT operator, such as any restriction locations in the well, requested pull frequency (D_int), maximum length of any pull tests (D_pt), and the depth of the run's objective. The algorithm may then integrate the received information with an existing fatigue profile 118 of a CT string 12. From the fatigue profile 118, the algorithm may note the presence of any welding locations (e.g., or other weak points 84). 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 model 118 uses, in certain embodiments, the algorithm may detect both the BHA 26 used and the distances between various surface equipment 74 to 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 profile 118 would be like if performed at a certain depth. The calculation may then be performed over the entire length of the CT pipe 20. If during the averaging window, a dead zone window is reached from either a weak point 84 or 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. 13 illustrates an exemplary visualization 158 of a pull test schedule 98 having, for example, a graph of a hole survey 112, a fatigue life profile 118, a well completion schematic 100, and an indicator of pull test recommendations 160, all aligned relative to depth within the wellbore 14. As illustrated, the algorithm may deal with “pull test not allowed” sections (e.g., as per the example in FIG. 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 in FIG. 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 pipe 20 (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 pipe 20 (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. 14 illustrates a diagram 162 of 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 device 164 (e.g., as part of the well control system 60 described herein with respect to FIG. 2) with real-time simulation capabilities and advanced orchestration capabilities may enable autonomous CT conveyance (e.g., as performed by the various CT equipment 166 described herein, for example, with respect to FIGS. 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 device 164 and/or the processing/control system 42 may use one or more of these inputs to create a list of tasks to achieve the conveyance goals 168 at 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 string 12, or BHA 26 at risk during RIH or POOH.

As part of the high-level objectives, a CT operator 170 may provide a conveyance target depth with desired pull test intervals, pull test lengths, pull test durations, and so forth. For instance, the CT operator 170 might 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 (see FIG. 15).

Returning to FIG. 14, the autonomous conveyance solution and infrastructure 162 may contain valuable information 172 about well restrictions and CT fatigue from acquisition software 174, which it can use to strategically plan and execute pull tests. As a result, the autonomous conveyance solution 162 may run a pull test optimization solution. The pull test optimizer (e.g., optimizer algorithms) works primarily by analyzing the CT pipes' fatigue profile 118. When the CT operator 170 provides a desired pull test interval (D_int) and length (D_pt), the optimizer may search for intervals close to the proposed pull test depths that would minimize cycling highly fatigued sections of the CT pipe 20. The optimizer may adjust the pull test depths and inject them in the automated conveyance plan. The optimizer may also consider weak points 84 in the CT pipe 20 and 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 pipe 20 that are fatigued less.

On every run, the pull test optimizer may ingest the information 172 relating 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 pipe 20, avoiding weak points 84, and avoiding restrictions in the completion. A dedicated communication channel between the edge intelligence device 164 and the acquisition software 174 that 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 pipe 20.

For example, FIG. 16 illustrates an exemplary workflow 176 to simulate CT pipe fatigue evolution from 0% to 100% based on historical run mission profiles. As illustrated, the workflow 176 may begin by initializing new CT pipe 20 with 0 runs and 0% fatigue (e.g., subroutine 178). Then, based on data relating to CT pipe fatigue versus depth (e.g., a fatigue profile 118), a determination may be made as to whether fatigue for the CT pipe 20 has reached 100% (e.g., decision block 180). If the fatigue for the CT pipe 20 has 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 pipe 20 has not reached 100%, then the pull test methods described in greater detail herein may be determined (e.g., decision block 184). In particular, if the automated pull test optimizer described above it utilized, then optimized pull test locations 186 may be calculated by the automated pull test optimizer (e.g., subroutine 188), whereas a fixed pull test interval (D_int)190 may be used if a pull test schedule is determined manually (e.g., by a CT operator 170). Regardless, a run mission profile 192 (e.g., including depths, pressures, weights, and so forth) may be merged with the optimized pull test locations 186 (e.g., subroutine 194), the run may be simulated and the fatigue may be updated (e.g., subroutine 196), and the run number may be updated (e.g., subroutine 198). At this point, another run cycle of the workflow 176 may 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 pipe 20, and the maximum fatigue on the CT pipe 20 after 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 pipe 20 has 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 pipe 20. The ideal cycling of CT pipe 20 may result in a fatigue profile 118 that grows as a single, flat front toward 100%. In practice, a perfectly flat fatigue profile 118 is 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:

$f_{n} = \frac{f_{n + 1} - f_{n}}{D_{n + 1} - D_{n}} \forall n \in [1, total number of monitored section on CT pipe],$

- where:
- f_i=CT pipe fatigue at a section, %;
- D_i=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-f_t) sections; other monitor every 15 m (50 f_t). 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 pipes 20 with differing fatigue sampling intervals. The new metric for CT pipe fatigue “flatness”, or homogeneity, is the sample variance of the normalized CT pipe fatigue:

$σ^{2} = \frac{\sum {(\hat{f} - {\hat{f}}^{'})}^{2}}{n - 1}$

A perfectly flat fatigue profile 118 would yield a homogeneity of σ²=0. FIG. 17 is a graph of run-on-run homogeneity, which starts at σ²=0 (normal for a new CT pipe 20 with 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 pipe 20.

Additional Embodiments

In certain embodiments, the pull test optimizer can use a pump schedule (e.g., of the pump unit 56 described herein with respect to FIG. 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 pipe 20 will generally accelerate fatigue when the CT pipe 20 is 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 operator 170 of 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 information 172 may 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 survey 112 and the intervention objectives. In certain embodiments, the hole survey 112 may be automatically analyzed by the digital solution to identify intervals where there is higher likelihood of the CT pipe 20 becoming stuck (e.g., the KOPs 114 and valleys 116 described herein). These insights may be used to place pull tests in intervals where there is a higher risk of stuck CT pipe 20. 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 operator 170 to define a static pull test length (D_pt). 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 reel 55, gooseneck 80, and injector head 54 relative to each other. Such inputs may already be included in the fatigue model 118.

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 pipe 20 out of the well, which would introduce unnecessary fatigue to the CT pipe 20 as well as create non-productive time, and it would eliminate the scenario of pulling insufficient CT pipe 20 out of the well, which would expose the operation to the risk of stuck CT pipe 20. In the non-ideal case where the CT pipe 20 is already becoming stuck, this approach would alert the CT operator 170 with greater immediacy and confidence of onset of stuck CT pipe 20 given 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 pipe 20 that should be targeted and/or avoided to accumulate fatigue, extending the useful life of CT pipe 20, reducing the total cost of ownership of CT pipe 20, reducing Scope 3 emissions 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 pipe 20.

For example, the useful life of CT pipe 20 can be extended by successfully implementing a pull test optimization strategy that redistributes where and how the CT pipe 20 is cycled. In particular, if CT pipe 20 lasts 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

$\frac{(Y - X)}{X} .$

Equivalently, the total cost of ownership may be reduced by

$1 - \frac{X}{Y} .$

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 pipe 20 depend on many assumptions, including mining, refining, production process; transport of raw steel; transport of milled CT pipe 20; 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 pipe 20 is extended by

$\frac{(Y - X)}{X},$

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

$1 - \frac{X}{Y} .$

Using the same numbers of the previous example (100 runs versus 120 runs to EOL), and assuming 25-ton CT pipe 20, 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 pipe 20 is RIH. Risks associated with this approach include repeated pull tests at an already fatigued section, which accelerates fatigue of the CT pipe 20, 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 schedule 98, 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 pipes 20; these pipes averaged 108 runs to end of life. Periodic pull tests with these CT pipes 20 accounted for up to 20% of their fatigue. The pull test optimization methodology diverts fatigue to lesser-affected sections of the CT pipe 20, 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 unit 52. 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 points 84 where 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 operator 170 to focus on other critical elements of the operation such as running and monitoring the unit and downhole tools 36, 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 pipe 20 has a positive impact on reducing both the total cost of ownership and Scope 3 emissions.

In summary, the embodiments described herein provide a methodology wherein a priori data educates an automated determination of intervals of CT pipe 20 where pull tests are encouraged and other intervals of the CT pipe 20 where pull tests are discouraged (also known as the pull test schedules 98 described herein). In certain embodiments, the a priori data may include fatigue models 118 for CT pipe 20; mechanical defect records that provide attributes such as OD, ovality, wall thickness, corrosion, gouging, pinholes, and pitting along the CT pipe 20; hole surveys 112 that detail depth, inclination, and azimuth through a wellbore 14; well completion schematics 100 that detail the material, size, and depth of tubing, casing, jewelry, fish, and generally provide completion geometry of completion equipment deployed via the CT pipe 20; description 122 of the BHA 26 that may include downhole mechanical tools 36 actuated by flow and/or pressure mechanisms, whose premature actuation may generate stuck incidents; hydrodynamic models that estimate downhole static and flowing conditions within the wellbore 14, accounting for initial debris distributions in the wellbore 14, 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 pipe 20 where pull tests are encouraged and the intervals of the CT pipe 20 where 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 schedule 98 may be enabled by the system. In addition, the system may enable the exporting and importing of pull test schedules 160, which enables the reuse and polishing of pull test schedules 98 from one run to the next. This feature also enables consuming the pull test schedules 98 in other applications. In certain embodiments, the user (e.g., CT operator 170) 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 schedules 98. For example, such real-time operational data may include surface measurements (e.g., as measured by the surface sensors 46 described herein) such as wellhead pressure, circulation pressure, pumped flowrates, and fluid/gas/solids returns; downhole measurements (e.g., as measured by the downhole sensors 40 described 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 system 42 described herein), or provided by another computing system 78 in real-time during the CT operation. In certain embodiments, the real-time operational data may include pipe integrity measurements 120 such as OD, ovality, wall thickness, corrosion, gouging, pinholes, and pitting of the CT pipe 20. 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 schedule 98 during a CT job planning phase 130. The application may reside on a local computer (e.g., the processing/control system 42 described herein), on an edge intelligence device 164, and/or as a cloud-based service (e.g., the cloud storage 50 described 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 schedule 98 for each combination of inputs described herein. In addition, in certain embodiments, the application may be used exclusively in the CT job planning phase 130 to generate optimal pull test schedules 98. In addition, the application may include the ability to overlay the pull test schedule 98 over one or more depth-gated visuals (e.g., well completion schematic 100, hole survey 112, fatigue profile 118, etc.), as illustrated in FIG. 13.

In addition, the embodiments described herein provide an application that runs in tandem or integrated with CT acquisition software and advise a CT operator 170 on where and how to pull test in substantially real time. Again, the application may reside on a local computer (e.g., the processing/control system 42 described herein), on an edge intelligence device 164, and/or as a cloud-based service (e.g., the cloud storage 50 described 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 operator 170 may 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 reel 55 and injector head 54 to automatically execute the CT operation. Furthermore, the application may automatically perform the pull tests as part of its conveyance and service objectives.

FIG. 18 is a flow diagram of a method 200 for automatically generating and/or performing a coiled tubing test (e.g., utilizing the well control system 60 illustrated in FIG. 2), as described in greater detail herein. As illustrated, in certain embodiments, the method 200 may include performing a CT operation including deploying a string 12 of CT pipe 20 into a wellbore 14 (block 202). In addition, in certain embodiments, the method 200 may include receiving a priori data relating to the CT operation (block 204). In addition, in certain embodiments, the method 200 may include automatically generating a pull test schedule 98 based on the a priori data (block 206). In general, the pull test schedule 98 defines a first set of intervals of the string 12 of the CT pipe 20 where pull tests are encouraged and a second set of intervals of the string 12 of the CT pipe 20 where pull tests are discouraged. In addition, in certain embodiments, the pull test schedule 98 also defines pull lengths for respective pull tests. In addition, in certain embodiments, the method 200 may include automatically adjusting operational parameters of the CT operation based on the pull test schedule 98. 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 models 118 for the CT pipe 20. In addition, in certain embodiments, the a priori data may include one or more mechanical defect records relating to the CT pipe 20. In addition, in certain embodiments, the a priori data may include one or more weak points in the CT pipe 20. In addition, in certain embodiments, the a priori data may include one or more hole surveys 112 relating to the wellbore 14. In addition, in certain embodiments, the a priori data may include one or more well completion schematics 100 that define geometries of completion equipment deployed into the wellbore 14 via the string 12 of the CT pipe 20. In addition, in certain embodiments, the a priori data may include a description of a BHA 26 deployed into the wellbore 14 via the string 12 of the CT pipe 20.

In addition, in certain embodiments, the method 200 may 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 schedule 98 based on the real-time operational data. In certain embodiments, the real-time operational data may include surface measurements 134 (e.g., as measured by the surface sensors 46 described herein) relating to operation of surface equipment 74 during the CT operation. In addition, in certain embodiments, the real-time operational data may include downhole measurements 136 (e.g., as measured by the downhole sensors 40 described herein) relating to operation of downhole equipment 76 during the CT operation. In addition, in certain embodiments, the real-time operational data may include pipe integrity measurements 120 for the CT pipe 20. In addition, in certain embodiments, the real-time operational data may include a CT pipe fatigue profile 118 that 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 pipe 20. 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 pipe 20 to be POOH for intervals. In some cases, these POOH intervals exclusively address the risk of stuck CT pipe 20 (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).

The specific embodiments described above have been illustrated by way of example, and it should be understood that these embodiments may be susceptible to various modifications and alternative forms. It should be further understood that the claims are not intended to be limited to the particular forms disclosed, but rather to cover all modifications, equivalents, and alternatives falling within the spirit and scope of this disclosure.

The techniques presented and claimed herein are referenced and applied to material objects and concrete examples of a practical nature that demonstrably improve the present technical field and, as such, are not abstract, intangible or purely theoretical. Further, if any claims appended to the end of this specification contain one or more elements designated as “means for [perform]ing [a function] . . . ” or “step for [perform]ing [a function] . . . ”, it is intended that such elements are to be interpreted under 35 U.S.C. § 112(f). However, for any claims containing elements designated in any other manner, it is intended that such elements are not to be interpreted under 35 U.S.C. § 112(f).

	Number	Date	Country
	63581840	Sep 2023	US
	63610492	Dec 2023	US

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

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