Method for optimizing coiled tubing operations by combining pre-job sensitivity analysis, real-time measurement, and inference

Abstract

Embodiments presented provide for a method for optimizing coiled tubing operations. In embodiments, a pre-job sensitivity analysis is coupled with real-time field measurements to arrive at a decision-making process to enhance the decision making.

Description

METHOD FOR OPTIMIZING COILED TUBING OPERATIONS BY COMBINING PREJOB SENSITIVITY ANALYSIS, REAL-TIME MEASUREMENT, AND INFERENCECROSS REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority to United States Provisional Patent Application 63 / 595,384, filed on November 2, 2023, the entirety of which is incorporated by reference.FIELD OF THE DISCLOSURE

[0002] Aspects of the disclosure relate to coiled tubing operations. More specifically, aspects of the disclosure relate to a method for optimizing coiled tubing operations by combining pre-job sensitivity analysis with real-time measurements.BACKGROUND

[0003] Coiled Tubing (CT) is a long, continuous, flexible, steel pipe on a spool used to perform drilling, completion, and intervention operations in the oil and gas industry. During the process of using CT, fluids and chemicals can be pumped from the surface to a downhole environment. In addition to the use of the fluids and chemicals, mechanical devices may be activated at various depths into the wellbore, making CT applicable to various operations such as drilling, milling, solids cleanout, matrix acidizing, hydraulic fracturing, well kick-off, and kill operations. CT operations are often complex because their outcomes depend on many parameters such as downhole conditions, wellbore geometry, pumping schedule, CT movement, and multiphase fluid flow with solid transport.

[0004] To plan, design, and analyze CT operations, engineers often use modeling software that can simulate all the relevant physical phenomena that impacts job design and outcome. Today, a considerable number of CT operators use data tables to summarize simulation results, compare CT design options, and find the optimum design for a job. These processes are not only extensive, tedious, and time-consuming, but also require domain expertise and experience to reach a meaningful conclusion and designchoice. This typically implies that only a limited number of simulation scenarios are considered during the job design phase and the risk of defining a poor operation plan is greater than the proposed workflow with an exhaustive database of scenarios to handle all the possible uncertainty that comes with designing such interventions. In addition, as the CT operation proceeds, and real-time measurements are performed that may indicate operational issues or loss of performance, there is no seamless workflow engineers may follow to:1 . Identify the issue.2. Identify options that may help solve the issue.3. Assess the potential effect of each option on the issue.4. Rank all the options to decide the next best action to take.

[0005] There is a need to provide an apparatus and methods that allow identification of issues applicable to coiled tubing operations.

[0006] There is a further need to provide apparatus and methods that do not have the drawbacks discussed above, namely the use of highly trained individuals that have significant domain expertise in order to make relevant decisions pertaining to coiled tubing operations.

[0007] There is a still further need to reduce economic costs associated with operations and apparatus described above with coiled tubing operations such that coiled tubing operations may be conducted in the numerous possible environments in a cost efficient and safe manner.

[0008] There is a further need to allow engineers and wellbore planners to effectively utilize available data to achieve efficiency that has not been achieved by previous conventional operations.

[0009] There is a further need to allow engineers and wellbore planners with flexibility of operations for the numerous possible environments and situational requirements that are encountered in coiled tubing operations.

[0010] There is a further need to allow field operations to be enhanced through computational analysis that has previously been unavailable for operators in order to enable increased factor of safety operations while reducing economic consequences of incorrect decision making.SUMMARY

[0011] So that the manner in which the above recited features of the present disclosure can be understood in detail, a more particular description of the disclosure, briefly summarized below, may be had by reference to embodiments, some of which are illustrated in the drawings. It is to be noted that the drawings illustrate only typical embodiments of this disclosure and are therefore not to be considered limiting of its scope, for the disclosure may admit to other equally effective embodiments without specific recitation. Accordingly, the following summary provides just a few aspects of the description and should not be used to limit the described embodiments to a single concept.

[0012] In one example embodiment, a method for optimizing coiled tubing operations is disclosed. The method may comprise using a coiled tubing intervention simulator to generate, prior to intervention, a database of scenarios, based on intervention parameters uncertainty. The method may further comprise developing an initial action plan for intervention of a coiled tubing in the wellbore by ranking the database scenarios. The method may further comprise executing the initial action plan for the intervention of the coiled tubing wellbore. The method may further comprise, during the executing of the initial action plan, acquiring data related to the coiled tubing wellbore. The method may further comprise performing at least one microservice using at least a portion of theacquired data related to the coiled tubing wellbore. The method may further comprise performing a job diagnostic new inference based upon the at least one microservice. The method may further comprise performing a query to determine if the initial action plan is in need of change. The method may further comprise continuing with plan execution when the initial action plan is not in need of change. The method may further comprise when the initial action plan is in need of change, performing a new scenario ranking, developing a new action plan, and then performing the new action plan.

[0013] In another example embodiment, an article of manufacture having a non-volatile memory configured to provide a list of computer readable instructions, the list of computer readable instructions configured to optimize coiled tubing operations is disclosed. The method contained in the article of manufacture further comprises using a coiled tubing intervention simulator to generate, prior to intervention, a database of scenarios based on intervention parameters uncertainty. The method may further comprise developing an initial action plan for intervention of a coiled tubing in the wellbore by ranking the database scenarios. The method contained in the article of manufacture further comprises executing the initial action plan for the drilling of the coiled tubing wellbore. The method contained in the article of manufacture further comprises during the executing of the initial action plan, acquiring data related to the drilling of the coiled tubing wellbore. The method contained in the article of manufacture further comprises performing at least one microservice using at least a portion of the acquired data related to the drilling of the coiled tubing wellbore. The method contained in the article of manufacture further comprises performing a job diagnostic new inference based upon the at least one microservice. The method contained in the article of manufacture further comprises performing a query to determine if the initial action plan is in need of change. The method contained in the article of manufacture further comprises continuing with plan execution when the initial action plan is not in need of change. The method contained in the article of manufacture further comprises when the initial action plan is in need of change,performing a new scenario ranking, developing a new action plan, and then performing the new action plan.

[0014] In another example embodiment, a method for optimizing coiled tubing operations, is disclosed. The method may comprise obtaining a coiled tubing intervention simulator. The method may further comprise inputting data into the coiled tubing intervention simulator to achieve results. The method may further comprise developing an initial action plan related to a coiled tubing wellbore based upon the results. The method may further comprise executing the initial action plan of the coiled tubing wellbore. The method may further comprise during the executing of the initial action plan, acquiring data related to the coiled tubing wellbore. The method may further comprise performing at least one microservice using at least a portion of the acquired data related to the coiled tubing wellbore. The method may further comprise performing a job diagnostic new inference based upon the at least one microservice. The method may further comprise performing a query to determine if the initial action plan is in need of change. The method may further comprise continuing with plan execution when the initial action plan is not in need of change. The method may further comprise when the initial action plan is in need of change, performing a new scenario ranking, developing a new action plan and then performing the new action plan.BRIEF DESCRIPTION OF THE DRAWINGS

[0015] So that the manner in which the above recited features of the present disclosure can be understood in detail, a more particular description of the disclosure, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the drawings. It is to be noted; however, that the appended drawings illustrate only typical embodiments of this disclosure and are therefore not be considered limiting of its scope, for the disclosure may admit to other equally effective embodiments.

[0016] FIG. 1 is a method for optimizing coiled tubing operations by combining pre-job sensitivity analysis, real-time measurement, and inference, in one example embodiment of the disclosure.

[0017] FIG. 2 is a method for sensitivity analysis in another example embodiment of the disclosure.

[0018] To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures (“FIGS”). It is contemplated that elements disclosed in one embodiment may be beneficially utilized on other embodiments without specific recitation.DETAILED DESCRIPTION

[0019] In the following, reference is made to embodiments of the disclosure. It should be understood; however, that the disclosure is not limited to specific described embodiments. Instead, any combination of the following features and elements, whether related to different embodiments or not, is contemplated to implement and practice the disclosure. Furthermore, although embodiments of the disclosure may achieve advantages over other possible solutions and / or over the prior art, whether or not a particular advantage is achieved by a given embodiment is not limiting of the disclosure. Thus, the following aspects, features, embodiments and advantages are merely illustrative and are not considered elements or limitations of the claims except where explicitly recited in a claim. Likewise, reference to “the disclosure” shall not be construed as a generalization of inventive subject matter disclosed herein and should not be considered to be an element or limitation of the claims except where explicitly recited in a claim.

[0020] Although the terms first, second, third, etc., may be used herein to describe various elements, components, regions, layers and / or sections, these elements,components, regions, layers and / or sections should not be limited by these terms. These terms may be only used to distinguish one element, components, region, layer or section from another region, layer or section. Terms such as “first”, “second”, and other numerical terms, when used herein, do not imply a sequence or order unless clearly indicated by the context. Thus, a first element, component, region, layer or section discussed herein could be termed a second element, component, region, layer or section without departing from the teachings of the example embodiments.

[0021] When an element or layer is referred to as being “on”, “engaged to”, “connected to”, or “coupled to” another element or layer, it may be directly on, engaged, connected, coupled to the other element or layer, or interleaving elements or layers may be present. In contrast, when an element is referred to as being “directly on”, “directly engaged to”, “directly connected to”, or “directly coupled to” another element or layer, there may be no interleaving elements or layers present. Other words used to describe the relationship between elements should be interpreted in a like fashion. As used herein, the term “and / or” includes any and all combinations of one or more of the associated listed terms.

[0022] Some embodiments will now be described with reference to the figures. Like elements in the various figures will be referenced with like numbers for consistency. In the following description, numerous details are set forth to provide an understanding of various embodiments and / or features. It will be understood; however, by those skilled in the art, that some embodiments may be practiced without many of these details, and that numerous variations or modifications from the described embodiments are possible. As used herein, the terms “above” and “below”, “up” and “down”, “upper” and “lower”, “upwardly” and “downwardly”, and other like terms indicating relative positions above or below a given point are used in this description to more clearly describe certain embodiments.

[0023] As will be understood, aspects of the disclosure may be performed, at least in part, by function of a computer. These functions may be encoded into a list of instructions that may be read by a computer for operation. Aspects of methods described may be included onto a non-volatile memory system. For definitional purposes, a non-volatile memory system may be a memory system that does not wipe clean after termination of electrical power to the system. Examples of non-volatile memory systems may be compact disks, solid-state drives, and universal serial bus devices. These memory systems may be used to store program executable method steps for a computer, server or computing arrangement.

[0024] Aspects of the disclosure provide for a method that enables use of a simulator that acts in conjunction with field operations to enable efficient coiled tubing operations. In embodiments, a simulator, defined as a coiled tubing intervention simulator, hereinafter known as “CTIS” is provided. In embodiments, the CTIS has the ability to simulate coiled tubing operations in a number of different environments. For example, the CTIS has the ability for a user to input different geologically significant layers that the coiled tubing will transition through during operations. Each individual layer may have different values such as specific gravity, cohesion, shear strength, saturation level, as well as other values. Layers may have different thicknesses and orientations. Layers may be horizontally configured, vertically configured or inclined.

[0025] In embodiments, the CTIS is capable of simulating the CT operation in consideration. The CTIS simulates the effect that all relevant job parameters have on the job outcome. In embodiments, the CTIS takes many parameters as input describing, for instance, the wellbore, CT, and the reservoir. Some of these parameters are uncertain at the time of the pre-job design. For terms of definition, an intervention may be defined as a clean out, milling, drilling or cementing of a wellbore.

[0026] In embodiments, a pre-job Sensitivity Analysis (SA) on all uncertain job parameters is performed. The CTIS is run with multiple scenarios for various values that the uncertain parameter may have, to investigate their impact on the job outcome. All the simulation outputs are stored in a Sensitivity Analysis Database (SADB). In embodiments, the SA results are analyzed by additional algorithms that are capable of ranking the scenarios according to some user-defined criteria or objective function. The ranking accounts for both operation success criteria and operational constraints that must be respected.

[0027] In embodiments, a set of Real-Time Measurements (RTM) are performed. Some sensors provide real-time data such as pressures, temperatures, pump rates, and flow rates at various depths along the wellbore and at the surface. A set of Real-Time Services (RTS) are created to use the RTM as inputs and provide a Parameter Inference (PI) on some initially uncertain job parameters. The real-time acquisition data of the above sensors, various RTS consisting of numerical simulators, calculators, and algorithms are capable of reducing the amount of uncertainty of the originally uncertain parameters.

[0028] In embodiments, a design workflow combining all the above information provides CT operators with real-time decisions on the best actions to be taken to optimize job performance. Through the method provided, uncertainty is reduced using the PI from the RTS. The results are stored in the SDAB are revisited to provide the CT operator with the optimum action plan, in real-time.Pre-Job Sensitivity Analysis

[0029] In embodiments, the CTIS may be used for sensitivity analysis. The sensitivity analysis may be performed to focus on the uncertainty of input parameters. This uncertainty comes from the fact that some parameters are not known with a sufficient degree of accuracy before the operation starts and that may have a significant impact onthe performance of the operation. Such parameters may be linked, for instance, to the following parameters:• the properties of the reservoir;• the wellbore geometry and its completion hardware; and• the fluids and solids initially present in the wellbore and the reservoir.

[0030] In embodiments, the uncertainty is managed by running a large number of simulations with the CTIS using cloud computing. Cloud computing allows a large number of simulations to be run in parallel and thus in a smaller amount of time. Each simulation represents a scenario where one of the uncertain parameters has a different value than that in another scenario. All the simulation results of all the scenarios are stored in the SADB. Although described as being applicable to cloud computing, other possibilities exist. For example, computing may be performed on a localized personal computer, a bank of connected computers, web-servers, or mobile computing devices, as non-limiting embodiments.Ranking of Pre-Job Sensitivity Analysis

[0031] Per the above-identified steps, once all the scenarios of the SA are computed, a method is performed to perform specific steps. These steps, in some non-limiting embodiments, may include defining the scenarios that meet all operational constraints. These constraints define the set of actions that cannot be performed during the intervention, for whatever reasons (e.g. pumping liquids at a rate that cannot be reached by the pumps) or the set of outcomes that must be prevented (e.g. maximum liquid volume to be handled at the surface is exceeded, maximum wellhead pressure is exceeded, CT collapse pressure is reached). In embodiments, the method may rank various parameters using user-defined performance criteria, e.g. minimum volume of injected fluid and nitrogen during a CT Cleanout operation or the maximum solids rate handled at the surface. For example, in the context of CT cleanout operations, success criteria may include:• Sufficient annular velocity to carry solids to the surface at all times.• Drawdown below a maximum user-defined acceptable value.• Injected fluid losses below a user-defined acceptable volume.• Minimum CO2 emission.As will be understood, different parameters may be ranked differently.

[0032] CT operators can create a range of constraints and criteria. In some embodiments, scenarios could be filtered out by applying the user-defined constraints automatically. Similarly, CT operators can define a target function to rank sensitivity cases by selecting the relevant simulation output parameters and their relative weight on the job success rating. Target function criteria may be based on:• Operation duration.• Volume of fluid handled.• CO2 emissions.• Amount of leak-off into reservoir.• Amount of solids removed.The above identified target function criteria should not be considered limiting.

[0033] Based on the ranking discussed above, an initial intervention plan is defined as well as an operational envelope. An operational envelope is the range of values some operational parameters may take while maintaining acceptable job performance and while constraints are met. Such operation parameters may include:• Liquid pump rates.• Gas pump rates.• CT depth and speed.• Choke settings at wellhead.• Gas lift rates.Updating the Optimal Design Using Real-Time Measurement and Inference

[0034] Several acquisition data sets (ACTive, DHPG, WHP) may be obtained during the first run-in hole (RIH). Such measuring may be accomplished without pumping to help determine uncertain downhole conditions such as:• Initial wellbore fluid distribution.• Fluid interface.• Average reservoir pressure.• Solid fill location.

[0035] Through the above, the fluid density distribution may be tracked along the wellbore with the Bottom Hole Assembly (BHA) annular pressure measurement. By plotting the measured fluid density, the interface between two fluids of different densities can be identified with a downhole gauge measurement. Also, this leads to the estimate of the average reservoir pressure by extrapolation with hydrostatic calculation. In the workflow, real-time measurement and inference results will be consumed by CT design software. CT engineers can validate their CT operation design with these inferred parameters in real-time during the first RIH and find the optimal design.

[0036] During CT operations, several sensors located at various depths along the wellbore, from surface to downhole, or located on the Bottom Hole Assembly (BHA) of the CT, or located on the surface flow lines, may provide real-time measurements on, for instance:• Pressure.• Temperature.• Liquid and gas flow rates.• Solids mass rates.• CT depth and speed.

[0037] Such data may be sensitive to some of the uncertain parameters mentioned above, and for which the SA was conducted. The workflow uses real-time services thatcan predict how the acquired data sets are relevant to the value of the uncertain parameters. In embodiments, the method runs the real-time service in inverse mode to infer what the actual value of the initially uncertain parameters is. For instance, if a service can predict the value acquired by a pressure sensor mounted on the CT when it knows the uncertain reservoir pressure, then, in inverse mode, the service can be used to infer what the actual reservoir pressure is when it knows the pressure value at CT the sensor.

[0038] Once the uncertainty is reduced by the real-time services’ inference, the list of relevant scenarios in the SADB may be automatically reduced to only those scenarios that have the actual inferred values. Consequently, the same set of constraints and ranking system used during the pre-job SA may be used to automatically rank the reduced set of scenarios. This new ranking may provide the CT operator with a new plan forward, in real-time, to optimize the objective set by the CT operator. The overall process is illustrated FIG. 1 and FIG. 2.

[0039] Referring to FIG. 1 , a method 100 in one non-limiting embodiment is illustrated. The method 100 may include providing a coiled tubing intervention simulator 102 that interfaces with a sensitivity analysis database 104. The output of the sensitivity analysis database 104 produces a scenario ranking 106 that may be used in an initial action plan 108. At 110, a job or project may start. The start of the job or project may interface with the initial action plan 108 such that a plan execution is undertaken at 112. During plan execution, sensor data may be acquired at 114. Data obtained at 114 may be analyzed under micro service 1 118 through micro service N 120. These micro services 118, 120, may create data that allows for a job diagnostic inference 122 to be established. A query is performed at 116 to determine if a change is needed with the plan, as being executed at 112. If a change is needed, as determined at 116, a new sensitivity analysis database scenario ranking may occur at 124. A new action plan may be developed based upon the new sensitivity analysis database, with the plan looping back to plan execution at 112. In embodiments, a notification may be provided to an operator if a new action plan has beengenerated. If a change is not needed at 116, the method may loop back to continue plan execution at 112 directly from 116.

[0040] Referring to FIG. 2, a series of method for sensitivity analysis 200 is illustrated. The sensitivity analysis 200 may include a pre-job sensitivity analysis at 202, accompanied by a ranking of the pre-job sensitivity analysis at 204. Optimal design may be updated, at 206, using real-time measurement and inference developed by the ranking of the pre-job sensitivity analysis 204.

[0041] Through the embodiments shown, the apparatus and methods allow identification of issues applicable to coiled tubing operations. Updates to actions plans may be generated in response to the identification of issues applicable to the coiled tubing operations.

[0042] Through the embodiments, the many drawbacks discussed as applicable to conventional analyses are avoided. Such drawbacks avoided are; for example, the required use of highly trained individuals, that have significant domain expertise, in order to make relevant decisions pertaining to coiled tubing operations.

[0043] As disclosed above, economic costs associated with operations and apparatus for coiled tubing drilling are reduced such that operations may be conducted in the numerous possible environments in a cost efficient and safe manner.

[0044] The applications of the methods disclosed allow engineers and wellbore planners to effectively utilize available data to achieve efficiency that has not been achieved by previous operations.

[0045] Engineers and wellbore planners are provided with flexibility of operations for the numerous possible environments and situational requirements that are encountered in coiled tubing operations through the embodiments described.

[0046] In embodiments of the disclosure, field operations are enhanced through computational analysis that has previously been unavailable for wellbore owners to enable an increased factor of safety operations while reducing economic consequences of incorrect decision making.

[0047] Example embodiments of the claims are described next. The scope of the disclosure should not be considered limited by the recitation. In one example embodiment, a method for optimizing coiled tubing operations is disclosed. The method may comprise using a coiled tubing intervention simulator to generate, prior to intervention, a database of scenarios based on intervention parameters uncertainty. The method may further comprise developing an initial action plan for a intervention of a coiled tubing wellbore by ranking the database scenarios. The method may further comprise executing the initial action plan for the coiled tubing wellbore. The method may further comprise during the executing of the initial action plan, acquiring data related to an intervention of the coiled tubing wellbore. The method may further comprise performing at least one microservice using at least a portion of the acquired data related to the drilling of the coiled tubing wellbore. The method may further comprise performing a job diagnostic new inference based upon the at least one microservice. The method may further comprise performing a query to determine if the initial action plan is in need of change. The method may further comprise continuing with plan execution when the initial action plan is not in need of change. The method may further comprise when the initial action plan is in need of change, performing a new scenario ranking, developing a new action plan, and then performing the new action plan.

[0048] In another example embodiment, the method may be performed wherein the acquiring of the data is through at least one downhole sensor.

[0049] In another example embodiment, the method may be performed wherein the new scenario ranking includes creating a new sensitivity analysis database scenario ranking.

[0050] In another example embodiment, the method may be performed wherein acquired sensor data is stored in a computer readable format that is addressable from a remote computer arrangement.

[0051] In another example embodiment, the method may further comprise displaying at least one of the initial action plan and the new action plan.

[0052] In another example embodiment, the method may be performed wherein the displaying of the at least one of the initial action plan and the new action plan is on a display monitor.

[0053] In another example embodiment, an article of manufacture having a non-volatile memory configured to provide a list of computer readable instructions, the list of computer readable instructions configured to optimize coiled tubing operations is disclosed. The method contained in the article of manufacture comprises using a coiled tubing intervention simulator to generate, prior to intervention, a database of scenarios based on intervention parameters uncertainty. The method may also comprise developing an initial action plan for intervention of a coiled tubing in the wellbore by ranking the database scenarios. The method contained in the article of manufacture further comprises executing the initial action plan for the drilling of the coiled tubing wellbore. The method contained in the article of manufacture comprises during the executing of theinitial action plan, acquiring data related to the drilling of the coiled tubing wellbore. The method contained in the article of manufacture comprises performing at least one microservice using at least a portion of the acquired data related to the drilling of the coiled tubing wellbore. The method contained in the article of manufacture comprises performing a job diagnostic new inference based upon the at least one microservice. The method contained in the article of manufacture comprises performing a query to determine if the initial action plan is in need of change. The method contained in the article of manufacture comprises continuing with plan execution when the initial action plan is not in need of change. The method contained in the article of manufacture comprises when the initial action plan is in need of change, performing a new scenario ranking, developing a new action plan, and then performing the new action plan.

[0054] In another example embodiment, the article of manufacture may be configured wherein the list of instructions includes that the acquiring of the data is through at least one downhole sensor.

[0055] In another example embodiment, the article of manufacture may be configured wherein the list of instructions includes that the new scenario ranking includes creating a new sensitivity analysis database scenario ranking.

[0056] In another example embodiment, the article of manufacture may be configured wherein the list of instructions includes that the acquired sensor data is stored in a computer readable format that is addressable from a remote computer arrangement.

[0057] In another example embodiment, the article of manufacture may be configured wherein the article of manufacture is one of a compact disk, a solid-state memory arrangement and a computer hard drive.

[0058] In another example embodiment, a method for optimizing coiled tubing operations, is disclosed. The method may comprise obtaining a coiled tubing intervention simulator. The method may further comprise inputting data into the coiled tubing intervention simulator to achieve results. The method may further comprise developing an initial action plan for drilling a coiled tubing wellbore based upon the results. The method may further comprise executing the initial action plan for the drilling of the coiled tubing wellbore. The method may further comprise during the executing of the initial action plan, acquiring data related to the drilling of the coiled tubing wellbore. The method may further comprise performing at least one microservice using at least a portion of the acquired data related to the drilling of the coiled tubing wellbore. The method may further comprise performing a job diagnostic new inference based upon the at least one microservice. The method may further comprise performing a query to determine if the initial action plan is in need of change. The method may further comprise continuing with plan execution when the initial action plan is not in need of change. The method may further comprise when the initial action plan is in need of change, performing a new scenario ranking, developing a new action plan and then performing the new action plan.

[0059] In another example embodiment, the method may be performed wherein the inputting of the data is from at least one of a preexisting database and a downhole sensor.

[0060] In another example embodiment, the method may further comprise displaying at least one of the initial action plan and the new action plan.

[0061] In another example embodiment, the method may further comprise saving at least one of the initial action plan and the new action plan to a non-volatile memory.

[0062] In another example embodiment, the method may be performed wherein the method includes artificial intelligence.

[0063] In another example embodiment, the method may be performed wherein the artificial intelligence is configured with at least two node layers.

[0064] In another example embodiment, the method may be performed wherein the method is one of performed remotely from the wellbore and at the wellbore.

[0065] In another example embodiment, the method may be performed wherein the method of optimizing the coiled tubing operations is remote from the wellbore and the performing of the new action plan is performed at the wellbore from a remote location.

[0066] In another example embodiment, the method may be performed wherein in a case of a new action plan, providing a notification to an operator of the new action plan.

[0067] The foregoing description of the embodiments has been provided for purposes of illustration and description. It is not intended to be exhaustive or to limit the disclosure. Individual elements or features of a particular embodiment are generally not limited to that particular embodiment, but, where applicable, are interchangeable and can be used in a selected embodiment, even if not specifically shown or described. The same may be varied in many ways. Such variations are not to be regarded as a departure from the disclosure, and all such modifications are intended to be included within the scope of the disclosure.

[0068] While embodiments have been described herein, those skilled in the art, having benefit of this disclosure, will appreciate that other embodiments are envisioned that do not depart from the inventive scope. Accordingly, the scope of the present claims or any subsequent claims shall not be unduly limited by the description of the embodiments described herein.

Claims

METHOD FOR OPTIMIZING COILED TUBING OPERATIONS BY COMBINING PREJOB SENSITIVITY ANALYSIS, REAL-TIME MEASUREMENT, AND INFERENCECLAIMSWhat is claimed is:1 . A method for optimizing coiled tubing operations, comprising: using a coiled tubing intervention simulator to generate, prior to intervention, a database of scenarios based on intervention parameters uncertainty; develop an initial action plan for intervention of a coiled tubing in the wellbore; executing the initial action plan for the intervention of the coiled tubing in the wellbore by ranking the database scenarios; during the executing of the initial action plan, acquiring data related to the intervention of the coiled tubing in the wellbore; performing at least one microservice using at least a portion of the acquired data related the coiled tubing intervention in the wellbore; performing a job diagnostic new inference based upon the at least one microservice; performing a query to determine if the initial action plan is in need of change; continuing with plan execution when the initial action plan is not in need of change; and when the initial action plan is in need of change, performing a new scenario ranking, developing a new action plan, and then performing the new action plan.

2. The method according to claim 1 , wherein the acquiring of the data is through at least one downhole sensor.20SUBSTITUTE SHEET (RULE 26)METHOD FOR OPTIMIZING COILED TUBING OPERATIONS BY COMBINING PREJOB SENSITIVITY ANALYSIS, REAL-TIME MEASUREMENT, AND INFERENCE3. The method according to claim 1 , wherein the new scenario ranking includes creating a new sensitivity analysis database scenario ranking.

4. The method according to claim 1 , wherein acquired sensor data is stored in a computer readable format that is addressable from a remote computer arrangement.

5. The method according to claim 1 , further comprising displaying at least one of the initial action plan and the new action plan.

6. The method according to claim 5, wherein the displaying of the at least one of the initial action plan and the new action plan is on a display monitor.

7. An article of manufacture having a non-volatile memory configured to provide a list of computer readable instructions, the list of computer readable instructions configured to optimize coiled tubing operations, the method comprising: using a coiled tubing intervention simulator to generate, prior to intervention, a database of scenarios based on intervention parameters uncertainty. developing an initial action plan for the intervention of a coiled tubing in the wellbore by ranking the database scenarios; executing the initial action plan for the intervention of the coiled tubing in the wellbore; during the executing of the initial action plan, acquiring data related to the intervention of the coiled tubing in the wellbore; performing at least one microservice using at least a portion of the acquired data related to the intervention of the coiled tubing in the wellbore; performing a job diagnostic new inference based upon the at least one microservice;21SUBSTITUTE SHEET (RULE 26)METHOD FOR OPTIMIZING COILED TUBING OPERATIONS BY COMBINING PREJOB SENSITIVITY ANALYSIS, REAL-TIME MEASUREMENT, AND INFERENCE performing a query to determine if the initial action plan is in need of change; continuing with plan execution when the initial action plan is not in need of change; and when the initial action plan is in need of change, performing a new scenario ranking, developing a new action plan, and then performing the new action plan.

8. The article of manufacture according to claim 7, wherein the list of instructions includes that the acquiring of the data is through at least one downhole sensor.

9. The article of manufacture according to claim 7, wherein the list of instructions includes that the new scenario ranking includes creating a new sensitivity analysis database scenario ranking.

10. The article of manufacture according to claim 7, wherein the list of instructions includes that the acquired sensor data is stored in a computer readable format that is addressable from a remote computer arrangement.11 . The article of manufacture according to claim 7, wherein the article of manufacture is one of a compact disk, a solid-state memory arrangement and a computer hard drive.

12. A method for optimizing coiled tubing operations, comprising: obtaining a coiled tubing intervention simulator; inputting data into the coiled tubing intervention simulator to achieve results; developing an initial action plan for the intervention of a coiled tubing in the wellbore based upon the results;22SUBSTITUTE SHEET (RULE 26)METHOD FOR OPTIMIZING COILED TUBING OPERATIONS BY COMBINING PREJOB SENSITIVITY ANALYSIS, REAL-TIME MEASUREMENT, AND INFERENCE executing the initial action plan for the coiled tubing intervention in the wellbore; during the executing of the initial action plan, acquiring data related to the coiled tubing intervention in the wellbore; performing at least one microservice using at least a portion of the acquired data related to the intervention of the coiled tubing in the wellbore; performing a job diagnostic new inference based upon the at least one microservice; performing a query to determine if the initial action plan is in need of change; continuing with plan execution when the initial action plan is not in need of change; and when the initial action plan is in need of change, performing a new scenario ranking, developing a new action plan and then performing the new action plan.

13. The method according to claim 12, wherein the inputting of the data is from at least one of a preexisting database and a downhole sensor.

14. The method according to claim 12, further comprising displaying at least one of the initial action plan and the new action plan.

15. The method according to claim 12, further comprising saving at least one of the initial action plan and the new action plan to a non-volatile memory.

16. The method according to claim 12, wherein the method includes artificial intelligence.23SUBSTITUTE SHEET (RULE 26)METHOD FOR OPTIMIZING COILED TUBING OPERATIONS BY COMBINING PREJOB SENSITIVITY ANALYSIS, REAL-TIME MEASUREMENT, AND INFERENCE17. The method according to claim 16, wherein the artificial intelligence is configured with at least two node layers.

18. The method according to claim 12, wherein the method is one of performed remotely from the wellbore and at the wellbore.

19. The method according to claim 12, wherein the method of optimizing the coiled tubing operations is remote from the wellbore and the performing of the new action plan is performed at the wellbore from a remote location.

20. The method according to claim 12, wherein in a case of a new action plan, providing a notification to an operator of the new action plan.24SUBSTITUTE SHEET (RULE 26)

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