Coiled Tubing Underbalance Cleanout with Multiphase Flowmeter: Optimization Workflow and Lessons from the North Sea
Abstract: Coiled tubing (CT) cleanout interventions are complex operations that have significant potential for increased efficiency and reduced operational risks with the use of a multiphase flowmeter. Fluid return data provide valuable information that can revolutionize conventional operating processes. However, until now, there have been no practical recommendations available that summarize the experience, decision-making workflow, and guidelines when utilizing multiphase flowmeters during CT cleanout operations. … Show more
Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Cited by 3 publications
References 8 publications
Understanding Mechanisms of Wellbore Abrasion During Coiled Tubing Intervention
In 2018, coiled tubing (CT) interventions in the Norwegian sector witnessed a rise in the adoption of high-grade quench and tempered (Q&T) CT strings. These interventions primarily focused on multistage fracturing stimulation, involving tasks such as sliding sleeve manipulation, fishing, and, as the wells aged, underbalanced CT cleanouts using nitrified fluids due to reservoir depletion. Recent CT interventions in wells revealed significant wellbore abrasion, manifesting as longitudinal grooves, where dogleg severity builds. This study aims to challenge the perception that high-grade CT, with its increased material hardness, is the primary cause of wellbore abrasion damage. An analysis of over 360 CT runs in 22 wells and more than 30 recent caliper logs sought to identify patterns behind this abrasion. Wells experiencing erosion were categorized by intervention type, wellbore environment (wet or dry) during CT work, CT normal force profiles, abrasion severity from caliper logs, and CT grade used during the work scope. Weighting these data allowed for identifying the primary factor contributing to wellbore abrasion during CT interventions. The study found that high-grade CT is not the primary contributor to abrasion. Instead, the leading causes of wear are a dry or partially dry wellbore environment during interventions and high CT normal forces. Most affected wells with significant abrasion experienced reservoir pressure depletion, resulting in proppant instability and chalk debris entering the wellbore, hindering production. These wells used CT for chalk and proppant cleanout via underbalanced cleanout with nitrified fluids (base oil with nitrogen and/or gas). The wellbore became dry due to sub-hydrostatic conditions, and partially dry during nitrified cleanout. This dry environment increased the abrasion risk as the CT interacted unlubricated with the production tubing. Caliper logs revealed that abrasion primarily occurred at depths between 250-m and 500-m measured depth, where the well deviation and dogleg severity increased. Normal forces magnitude, tied to stiffness and trajectory, spiked at these shallow depths when running in, and amplified while pulling out of hole. This concentrated force, combined with the dry environment due to deeper liquid levels in sub-hydrostatic wells, compounded the abrasion issue. Furthermore, high overpull operations, like fishing or sleeve shifting at deeper depths, elevate normal forces at shallower depths, raising the abrasion risk. These findings sparked a significant shift in intervention planning and execution, with the development of local mitigation measures to reduce abrasion risk; these measurements and the analysis of normal forces are now integrated into the CT operations design process, influencing mature wells intervention planning, cleanout strategies, production management, and completion lifetime expectancy; and are considerations potentially influencing future field completion strategies, including well trajectory adjustments, and stimulation techniques selection. This study opens the potential of developing methods to quantify the abrasion rate for each CT run through data analysis and testing.
Understanding Mechanisms of Wellbore Abrasion During Coiled Tubing Intervention
In 2018, coiled tubing (CT) interventions in the Norwegian sector witnessed a rise in the adoption of high-grade quench and tempered (Q&T) CT strings. These interventions primarily focused on multistage fracturing stimulation, involving tasks such as sliding sleeve manipulation, fishing, and, as the wells aged, underbalanced CT cleanouts using nitrified fluids due to reservoir depletion. Recent CT interventions in wells revealed significant wellbore abrasion, manifesting as longitudinal grooves, where dogleg severity builds. This study aims to challenge the perception that high-grade CT, with its increased material hardness, is the primary cause of wellbore abrasion damage. An analysis of over 360 CT runs in 22 wells and more than 30 recent caliper logs sought to identify patterns behind this abrasion. Wells experiencing erosion were categorized by intervention type, wellbore environment (wet or dry) during CT work, CT normal force profiles, abrasion severity from caliper logs, and CT grade used during the work scope. Weighting these data allowed for identifying the primary factor contributing to wellbore abrasion during CT interventions. The study found that high-grade CT is not the primary contributor to abrasion. Instead, the leading causes of wear are a dry or partially dry wellbore environment during interventions and high CT normal forces. Most affected wells with significant abrasion experienced reservoir pressure depletion, resulting in proppant instability and chalk debris entering the wellbore, hindering production. These wells used CT for chalk and proppant cleanout via underbalanced cleanout with nitrified fluids (base oil with nitrogen and/or gas). The wellbore became dry due to sub-hydrostatic conditions, and partially dry during nitrified cleanout. This dry environment increased the abrasion risk as the CT interacted unlubricated with the production tubing. Caliper logs revealed that abrasion primarily occurred at depths between 250-m and 500-m measured depth, where the well deviation and dogleg severity increased. Normal forces magnitude, tied to stiffness and trajectory, spiked at these shallow depths when running in, and amplified while pulling out of hole. This concentrated force, combined with the dry environment due to deeper liquid levels in sub-hydrostatic wells, compounded the abrasion issue. Furthermore, high overpull operations, like fishing or sleeve shifting at deeper depths, elevate normal forces at shallower depths, raising the abrasion risk. These findings sparked a significant shift in intervention planning and execution, with the development of local mitigation measures to reduce abrasion risk; these measurements and the analysis of normal forces are now integrated into the CT operations design process, influencing mature wells intervention planning, cleanout strategies, production management, and completion lifetime expectancy; and are considerations potentially influencing future field completion strategies, including well trajectory adjustments, and stimulation techniques selection. This study opens the potential of developing methods to quantify the abrasion rate for each CT run through data analysis and testing.
Next-Gen Proppant Cleanout Operations: Machine Learning for Bottom-Hole Pressure Prediction
In proppant cleanout operations, it's crucial to utilize the optimum bottom-hole pressure to achieve enough annular velocity in the wellbore to lift solids to the surface, make sure no skin damage is created due to excess fluid losses, and avoid stuck-pipe situations. Machine learning models, which offer real-time on-site prediction of bottom-hole pressure, can be used to achieve this. The main goal of this study is to create machine learning-driven models capable of predicting bottom-hole pressure at the coiled tubing nozzle's exit while pumping nitrified fluids in cleanout operations. Nine machine learning and deep learning models were developed using readily available parameters typically gathered during cleanout operations, which include coiled tubing depth and inside diameter, bottom hole temperature at the coiled tubing nozzle, gel rate, nitrogen rate, and coiled tubing pressure at the surface as inputs. These models are trained utilizing measured bottom-hole pressure data acquired from deployed memory gauges, which serve as the model's outputs. Gradient Boosting, AdaBoost, Random Forest, Support Vector Machines (SVMs), Decision Trees, K-Nearest Neighbor (KNN), Linear Regression, Neural Network, and Stochastic Gradient Descent (SGD) are machine learning algorithms that were meticulously developed and optimized using an extensive data set derived from 48 wells. 33,453 data points make up this dataset, which was carefully divided into two subsets: 80% (26,763 data points) were used to train the algorithms, while 20% (6,690 data points) were used to test their predictive abilities. In addition, the performance of machine learning models is evaluated using the K-fold and random sampling validation procedures. When comparing predictions of coiled tubing nozzle outlet pressure to actual measurements, the results of the top-performing machine learning models, specifically Neural network, AdaBoost, Random Forest, K- Nearest Neighbor and Gradient Boosting show remarkably low mean absolute percent error (MAPE) values. These MAPE values are, in order, 1.7%, 1.6%, 2%, 2.5%, and 3.2%. Furthermore, these models have remarkably high correlation coefficients (R2), with respective values of 0.947, 0.943, 0.929, 0.918, and 0.878. Moreover, machine learning models offer a distinct advantage over conventional vertical lift performance correlations, as they do not necessitate routine calibration. Beyond this, these models demonstrated their ability to accurately predict bottom-hole pressure across a wide range of cleanout parameters. This paper introduces novel insights by demonstrating how using a machine learning model for predicting coiled tubing nozzle outlet pressure while pumping nitrified fluids in cleanout operations can enhance ongoing cleanout operations. Utilizing machine learning models offers a more efficient, rapid, real-time, and cost-effective alternative to calibrated vertical lift performance correlations and deployed memory gauges. Furthermore, these models excel at accommodating a wide spectrum of cleanout parameters and coiled tubing configurations. This was a challenge for single vertical lift performance correlation.
Multivariate Sensitivity Analysis and Real-Time Algorithms in Data-Driven Coiled Tubing Cleanouts Turn Insights into Action
Depleted wells require underbalanced coiled tubing cleanouts (CTCO) in which natural production from the reservoir assists solids transport. Reservoir pressures are often uncertain in these subhydrostatic environments, making CTCO design conditions difficult to predict. Under these conditions, sustaining an efficient cleanout is challenging, and risks include undesired leakoff, damage to the wellbore, and stuck pipe. New physics-based algorithms and workflows consume real-time data and output actionable feedback to optimize design, execution, and evaluation of CTCOs. A coiled tubing hydraulics (CTH) simulator with state-of-the-art flow and transport models improves CTCO design capabilities by sensitizing over every parameter, which generates a combinatorial number of scenarios. Once executed, this multivariate sensitivity analysis generates a large database of sensitized scenarios which delineate a safe and effective operational envelope. Meanwhile, a real-time execution advisor selects the sensitivity analysis scenario that best approximates actual conditions and guides coiled tubing (CT) operators to choose optimal liquid rates, nitrogen rates, and CT speed. This execution advisor is supported by an early inference algorithm (EIA), which assesses reservoir pressure during the run in hole (RIH), while surface testing flowmetering data are consumed by an annular velocity algorithm (AVA) to estimate solids transport efficiency, reservoir leakoff, and inflow in real time. EIA, AVA, and execution advisor run in real time to reduce operation time by up to 15% and nitrified fluid consumption by 10%, ultimately increasing hydrocarbon production by 50%. In addition to driving efficient workflows, the model reduces the risks of poor solids sweeping, formation damage due to reservoir leakoff, solids inflow from reservoir due to large drawdowns, and damage to the surface equipment. This study demonstrates that by combining extensive multivariate sensitivity analysis, advanced flow models, surface and downhole measurements with real-time interpretation and inference algorithms, CTCO operators can quickly assess multiple metrics of job performance, such as downhole solids sweeping efficiency, reservoir leakoff and inflow, and drawdown, and react accordingly to significantly improve operational outcomes. This first use of these real-time execution advisors paves the way to a step change in the efficiency and safety of CT interventions worldwide.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
Contact Info
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Product
Resources
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.
