Cloud-Based Planning and Real-Time Algorithms Improve Coiled Tubing Cleanout Efficiency
Authors: Bardsen Johnny , Nilsen Bjørn Engvald Staveland , Froyland Tormod , Ramondenc Pierre , Segura Jordi , Gabdullin Ablay , Kotlyar Lev , Fonseca Santiago Hässig 
Published: March 2024

Abstract
Depleted wells require underbalanced coiled tubing cleanouts (CTCO) in which natural production from the reservoir assists solids transport. Conventional cleanout methods relying on fluid circulation pose a risk of fluid loss, reducing annular velocity and increasing the risk of formation damage or stuck CT pipe incidents. The use of nitrified fluids addresses some of those risks, but also introduces a new set of challenges. In addition to technical challenges, cleanout operations face logistics and operational constraints, which directly impact the feasibility and viability of the intervention.
Digital tools provide a path toward increased efficiency and success rate of CTCO, but the suite of legacy software often used in CT operations relies on monolithic implementations, which limit the possible optimization of the planning and the connection between design and execution data. More generally, reliance on manual operations (whether during the design or execution phases) often leads to missing on potential optimization opportunities. The transformation of CTCO leveraging a new cloud-based CT hydraulics (CTH) simulator, real-time execution advisors, and autonomous conveyance brings a new level of flexibility and interconnectivity to the design and execution phases.
CTH features state-of-the-art flow and transport models, which improve CTCO design capabilities, providing the required insights during execution time to optimize the cleanout operation. During the design phase of underbalanced CTCO, the designer needs to work with uncertainty on several parameters, such as reservoir pressure or PI distribution of the horizontal section. The architecture of the CTH allows sensitizing over every parameter, which generates a combinatorial number of scenarios, driving a larger-than-usual processing demand. The cloud-based service's processing capacity meets these demands during the job design phase to generate a large database of sensitized scenarios and delineate a safe and effective operational envelope. Two case studies show how CTH can be used during the design phase to ensure more efficient job execution in two horizontal oil wells in the Valhall brownfield. In the first one, the simulator was used to guarantee that the cleanout execution would be possible even if contingency plans due to gas lift valve failure had to be triggered. In the second, sensitivity analysis was conducted over the pumping rate and formation pressure, identifying a safe operating envelope that, once coupled with an adequate execution approach, led to 20% oil base savings.
Efficiency of CTCO operations is further improved by implementing autonomous conveyance execution during the operations. This includes automatic control of depth and speed, achieving more than 10% more efficient speeds during run-in-hole and pull-out-of-hole activities. Pull tests need to be performed at set intervals during conveyance to ensure that the pipe is not getting stuck, which accelerates fatigue of the CT pipe. The autonomous system also includes a pull test optimizer that accounts for the pipe's fatigue profile, weld locations, and completion data to strategically adjust the pull test schedule. This reduces the effect of these tests on pipe fatigue by up to 28% over its lifetime and lowers the risks linked with running across downhole restrictions. Besides, autonomous conveyance and pull test execution liberates the CT operator to concentrate on other crucial aspects of the operation. These include managing and monitoring the CT unit, fluid pumps, remote-operated choke, and downhole tools, controlling real-time parameters, updating the job log, and managing the crew.
This study demonstrates that by combining extensive cloud computing, advanced flow models, surface and downhole measurements with real-time interpretation and inference algorithms, and autonomous operations, CTCO operations can be conducted safer and more efficiently, in a repeatable manner, therefore reducing the operating time, fluid pumped, pipe fatigue, and greenhouse emissions, and allowing to raise the success rate of those operations to a new industry benchmark level.