NPT is thought to account for upwards of 30% of the upstream costs in O&G production, half of which can be attributed to downhole drilling problems, within which hole cleaning is arguably the most prominent offender, accountable for tens of millions of dollars in lost time costs. The objective of this paper is to outline a modern, holistic, automated approach to hole cleaning that closely replicates human experience within a suite of software applications, allowing the early identification, and therefore early mitigation and remediation of this significant drilling issue. The principles of hole cleaning as a drilling process are generally considered to be well understood, and both the accurate diagnosis during the drilling operation and the means to either mitigate or remediate the issue exist. While significant technological advances regarding detection of hole cleaning dysfunctions have been made in various individual approaches, the best certainty in identification has still been achieved where a human aggregates the individual results of these. The paper explores how such a holistic approach can be automated through an innovative digital solution. This paper describes the individual advances made in individual system components, including real-time engineering modelling, surface and downhole direct measurements with intelligent agents, and offset experience. We then look at how these various components can be aggregated by an automated higher-level process to produce a result akin to that of an experienced human at the wellsite. Case studies from initial system deployments demonstrate both the validity and the financial impact of such an approach. Several of the ‘component’ services are described in more detail, including topics within torque and drag and pressure regime modelling are advances on current practice. Additionally, the concept of exploring aggregated multiple point-source findings in an application, just as a human would, is a framework that has significant potential when automating the detection of other drilling dysfunctions. These concepts are expected to greatly reduce drilling risks and achieve predictable drilling performance at scale, in various drilling environments.

Tripping operations can take up a significant portion of well construction time and the associated cost. In the last decade, there has been extensive development and deployment of real-time tripping applications to optimize tripping parameters while maintaining formation integrity. This paper presents a system that utilizes transient modeling of tripping behavior to determine the optimum parameters that safeguard the integrity of the formation and the mechanical equipment at the rig site. The system delivers tripping boundaries to automated drilling control systems (ADCS) for every stand. A digital twin of the wellbore, equipped with physics-based transient models, estimates the permissible axial velocities and accelerations developed when running drillstring in and out the wellbore. These motions develop pressure waves which travel along the wellbore and which can compromise formation integrity. The digital twin, prepared in the planning phase and deployed in the real-time drilling environment, uses smart triggering algorithms to automatically update the models and refine simulation results. Automation systems consume the predicted limits via an aggregation layer to refine fit-for-purpose tripping applications. The automation system finds optimum proposals of tripping limits and updates them directly in the rig control system in real-time. The trip monitoring system automatically and continuously publishes optimum velocity and acceleration tripping limits per stand and transmits them as set points to the ADCS to define a safe operating envelope (SOE). This approach can greatly reduce the overall tripping time in comparison to non-automated deployments. Furthermore, the reduction of invisible lost time (ILT) takes place while maintaining the integrity of the formation, and the integrity of the surface equipment. Finally, reduction of the energy required to perform the tripping process consequently decreases the amount of carbon emissions involved in the process. A set of case studies confirm the effectiveness of the approach and illustrate its benefits. A case study addresses the topic of adoption of drilling automation applications such as the tripping advisor. Another case presents the concept of interoperability using as example a deployment on a rig simulator setup in Europe to perform closed-loop control using the tripping application to write velocity and acceleration limits continuously to the ADCS.

A significant part of well construction is invested in tripping drill pipe. This type of operation is considered not productive in terms of drilled wellbore but is however necessary for the well construction process. The associated cost of tripping operations can be as high as 30% of the overall well CAPEX and poses an attractive optimization case to reduce spending by means of increasing efficiency. Furthermore, a byproduct of increased efficiency is a cleaner operation in terms of carbon emissions. Increasing efficiency for tripping operations concerns two main components: reducing connection time and optimizing the motion of the string while tripping in and out of the wellbore. The connection time can be reduced by means of machine automation to deliver repetable and safer handling of the drill pipe during connections. Optimizing the tripping parameters to move the string requires a more complex approach, where physics-based modeling plays a key role in determining a safe operating envelope (SOE) to move the string without harming the formation or the surface equipment in the process. The system described in this paper touches upon this problem and includes the concept of interfacing to automated drilling control systems (ADCS) to achieve closed-loop control of tripping operations. The solution proposed deploys a hydraulic digital twin of the wellbore that estimates the permissible axial velocities and accelerations to use when running drillstring in and out the wellbore. The same digital twin is used during pre-job modeling to verify proposed tripping plans, and later on in real-time to update the tripping limits for velocity and acceleration for every stand as the tripping process continues. The results produced in real-time are published to a data aggregation layer to serve as input for a tripping automation application to refine fit-for-purpose monitoring and control algorithms. The automation system finds optimum proposals of tripping limits and updates them directly in the rig control system in real-time. The trip monitoring system automatically and continuously publishes optimum velocity and acceleration tripping limits per stand and transmits them as set points to the ADCS to define a safe operating envelope (SOE). This approach can greatly reduce the overall tripping time in comparison to non-automated deployments. Furthermore, the reduction of invisible lost time (ILT) takes place while maintaining the integrity of the formation, and the integrity of the surface equipment. A set of case studies confirm the effectiveness of the approach and illustrate its benefits. A case study from the Middle East addresses the topic of adoption of drilling automation applications such as the tripping advisor. Another case presents the concept of interoperability using as example a deployment on a rig simulator setup in Europe to perform closed-loop control using the tripping application to write velocity and acceleration limits continuously to the ADCS.