Prescriptive Analytics for Real-Time Optimization of Deepwater Casing Exits

Abstract

Casing exits provide operators with an additional wellbore path in which subsequent well operations may be performed. In many instances, a casing exit operation provides an operator with an economic method for accessing a known reservoir zone from an existing wellbore. This is especially true in deepwater settings, which have higher baseline costs. However, milling casing in a deepwater application is often cumbersome and unreliable. The downhole environment is complex, the nature of the formation is often unknown, and directional steering capability is ineffective in casing. Therefore, optimizing casing exits is essential to minimize costs of rig operation, especially in high-cost and high-risk deepwater settings like the Gulf of Mexico. This paper presents a real-time optimization solution using data analytics to improve casing exit efficiency, quality, and consistency. In this respect, a prescribed milling schedule is developed using advanced analytics on historical job data. Further, the use of downhole data and telemetry tools enables the collection and transmission of real-time downhole measurements. Finally, a surface acquisition system provides real-time readouts of the downhole measurements to ensure BHA parameters are optimized in real-time. This optimization methodology was successfully employed in the Gulf of Mexico in collaboration with an operator. Using prescriptive analytics, a milling schedule was provided before a casing exit. During operation, the schedule for downhole weight-on-bit (WOB) and surface rotary speed (RPM) was followed and adjustments were made using real-time downhole measurements. The 13 5/8-in casing exit was successfully performed in a single run and resulted in a high-quality window. Vibration and window drag were minimal, and the milling time was reduced by 10% compared to the average for similar casing exits.