Cuttings reinjection (CRI) is a globally accepted method for the disposal of drilling and production wastes, as it eliminates logistical challenges related to waste transport and storage, as well as the need for third-party disposal, which is often required by more conventional approaches. However, the CRI system must support penetration rates as high as 152.4 m/s (500 ft/hr). In arctic regions, the entire system must be winterized, while retaining ease of mobility for rig moves. Therefore, each CRI operation requires a customized design that correlates with drilling parameters, the local climate, and the scope of the drilling project.
On Sakhalin Island, an arctic area known for stringent regulations and a focus on highly optimized well construction, a CRI system was installed after intensive planning that accounted for the continued use of the original 2009 injection well, real-time cuttings collection in a winterized module, high capacities and process rates, continuous slurrification operations, and injection performed using cement pumps.
During a 14-month period, the customized CRI system successfully injected 95,867 m3 (602,988 bbl) of waste fluid from the six wells where the highest volumes were generated. The system delivered 100% operating efficiency throughout the project, keeping pace with the drilling activity, with a cuttings volume reaching up to 385 m3 (2,425 bbl) in one 24-hr period. Water-based cuttings and waste fluids from other rigs were also processed during this time. The system met all established performance criteria as outlined for the project's scope of work, including specified processing rates per hole section, slurries within specification, effective pumping schedule, and accurate and timely reporting.
Zero surface discharge projects present unique challenges that can be safely overcome with CRI technology. This paper describes essential steps in designing the site-specific system, best practices for managing sustained high-rate cuttings injection operations, and lessons learned concerning system optimization under harsh conditions.
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