Multivariate Sensitivity Analysis and Real-Time Algorithms in Data-Driven Coiled Tubing Cleanouts Turn Insights into Action

Abstract

Abstract 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.