Real-Time Drilling Fluid Measurements Provide Value Via Data-Driven Decision Making for Cost-Sensitive Unconventional Environments

Abstract

Abstract A new approach to real-time automated drilling fluid measurement systems leads to enhanced collaboration and decision making between field and remote operations personnel. The approach is based on straight forward technology that can easily be understood by all, operated by field personnel, and robust enough for fast paced unconventional operations. The sensor information offers real-time actionable data without the demand to replicate the traditional daily mud report, streamlining wellsite activities for drilling fluid treatment as fluid conditions change. The automated drilling fluid measurement system (mud skid) was designed for reliability and simplicity. The idea is to use trending data over the single data point mud check to drive real time decision making across teams. Rather than replace the fluids specialist on location it enhances their role to drive operational excellence and consistency in their day-to-day rig support. Everyday across multiple rigs, drilling fluids specialists use the data to adjust treatment schedules, optimize activities, and capture unplanned events as early as possible to lower treatment cost with the support of the remote operations center (ROC). The ROC's monitoring activities drive fluid enhancements across multiple locations via data transparency and analysis, sharing of best practices, and event detection. With a few proven and fit for purpose sensors, the mud skid adds value within the cost limits of the unconventional well market. There is an opportunity for value generation via the use of economical drilling fluid sensors where costs traditionally limit full-scale automated drilling fluid measurements. The mud skid unit provides sufficient data to generate value without prohibitive expenses. The mud skid unit and the data collected are now part of standard operations across multiple rigs and basins. The data streams, available in real-time to everyone from the derrick hand to the drilling engineer, have created a new workflow for decision-making, fluid maintenance, trouble-shooting, and early event detection. As more events are captured by the sensors, there are additional opportunities to train machine learning algorithms and develop predictive models. This paper will review the development process, deployment, and delivery of the mud skid unit with case histories demonstrating the advantages and opportunities for simple, real-time data streams.