Innovative Approach of Drilling Risk Identification and Mitigation Using Drilling Automation Services: Case Studies

Abstract

In the increasingly complex and cost sensitive drilling environment of today, data gathered using downhole and surface real-time sensor systems must work in unison with physics-based models to facilitate early indication of drilling hazards, allowing timely action and mitigation. Identification of opportunities for reduction of invisible lost time (ILT) is similarly critical. Many similar systems gather and analyze either surface or downhole data on a standalone basis but lack the integrated approach towards using the data in a holistic decision-making manner. These systems can either paint an incomplete picture of prevailing drilling conditions or fail to ensure system messages result in parameter changes at rigsite. This often results in a hit or miss approach in identification and mitigation of drilling problems. The automated software system architecture is described, detailing the physics-based models which are deployed in real-time consuming surface and downhole sensor data and outputting continuous, operationally relevant simulation results. Measured data from either surface, for torque & drag, or downhole for ECD & ESD is then automatically compared both for deviation of actual-to-plan, and for infringement of boundary conditions such as formation pressure regime. The system is also equipped to model off-bottom induced pressures; swab & surge, and dynamically advise on safe, but optimum tripping velocities for the operation at hand. This has dual benefits; both the avoidance of costly NPT associated with swab & surge, as well as being able to visually highlight running speed ILT. All processing applications are coupled with highly intuitive user interfaces. Three successful deployments all onshore in the Middle East are detailed. First a horizontal section where real-time model vs. actual automatic comparison of torque & drag samples, validated with PWD data allowed early identification of poor hole cleaning. Secondly, a vertical section where again the model vs. actual algorithmic automatically identified inadequate hole cleaning in a case where conventional human monitoring did not. Finally, a case is exhibited where real-time modelling of swab and surge, as well as intuitive visualization of the trip speeds within those boundary conditions led to a significant increase in average tripping speeds when compared to offset wells, reducing AFE for the operator. Common for all three deployments was an integrated well services approach, with a single service company providing the majority of services for well construction, as well as an overarching remote operations team who were primary users of the software solutions deployed.