In the current drilling climate, efficiency is king: do more with less. This motivation drives disruptive technological innovations in automation of the drilling process. Drilling automation can contribute to this efficiency specifically by automating the sliding process. The paper discusses a case history involving one operator's deployment and results of an automated sliding system. The goal for automating the sliding process was to reduce personnel on location, drive consistency, increase wellbore quality, and shift the focus from an ROP-focused mindset. The operator had initially used bit guidance software for approximately one year, which was a significant backbone component of the automated sliding system. The automated sliding software was installed and tested on the rig, and then deployed on a six-well pad for initial observation and analysis. After deployment, the automated sliding system successfully completed slides in all four surface sections on the pad. The first complete well on the pad, drilled to total depth, successfully completed slides in the vertical, curve and lateral sections. The rate of automated sliding exceeded initial goals, and the rig proceeded to drill several more wells at near-100% utilization rates. The automated decision-making system compiled detailed drilling set points and specifications used to form the most consistent and efficient method to drill the well, formation by formation. The total number of third party directional drillers was reduced, increasing overall safety and lowering costs. Automating the sliding process, with this degree of accuracy and lack of human intervention through automated decision-making, represents a significant step change in the drilling industry milestones on the road to full automation. Best practices regarding adoption and deployment of automation technology will contribute to ensuring success in the ever-increasing field of drilling automation.
The driller on the rig performs a number of complex tasks including engaging and disengaging the bit, determining subsurface bit location in real-time, orienting the toolface to steer the bit, adjusting pumps and rotary, and deciding set-points to maximize performance, all while managing the rig crew to ensure safe operations. There is always a potential for dynamic dysfunctions that, if not addressed quickly, could have destructive outcomes. Stick-slip, bit whirl, excessive downhole vibration, and oversteering can each quickly lead to problems. Human response time to address these phenomena varies greatly. Further, the methods in which drillers address these dysfunctions are not standardized. This human variability can increase well costs, decrease production potential, and increase safety risks. Under the direction of experienced drillers, a suite of software applications has been developed to provide a holistic, automated solution to many of the tasks previously performed manually by humans. Reducing human variability bridges the gap between past performance and maximum theoretical performance. Deployment of these "apps" within an autonomous drilling platform enables operators to easily study and improve the drilling process and, in turn, accelerate and improve well programs.Utilizing multiple automation technologies simultaneously improves consistency, reduces operating costs and lowers risk potential. Higher quality wellbores are delivered with maximum hydrocarbon production potential. New processes have been developed to deploy the apps in a coordinated way, changing roles both in the field and in 24/7 remote support centers. The apps automate or augment many processes, such as making connections, making slide/rotate decisions, determining bit position in relation to local geology, reengaging the bit to bottom, and controlling both rotating and sliding sequences in an efficient manner. Individual automation technologies have demonstrated measured benefits independently. However, the utilization of an entire suite of automation tools designed to work together within new workflows has demonstrated a substantially higher benefit potential to the operator not typically achievable by individual automation technologies in isolation. These human/machine workflows were refined in pilot deployments and are now being deployed at various levels across a uniform fleet of rigs. Individual tasks can be automated in relation to acquiring and analyzing data, making decisions, and implementing those decisions as part of the drilling processes. Automation empowers the operator by allowing significantly larger volumes of data to be digested and interpreted more rapidly than is possible by humans alone, while taking economic factors into account during the automated decision making. The deployment of the automation technologies presented in this paper requires novel work processes both in the field and in the office that are only possible across a uniform suite of rigs, demonstrating the value of scaling and leveraging expertise and experience.
An indispensable item for every roughneck is the tally book, used to measure and count the drill pipe entering and exiting the wellbore. The current practice is for a crew member to measure the pipe with a pipe strap and enter the information, each time while tripping, into their tally book. This manual entry is prone to error, leading to potential mistakes in the calculated drilling depth and poorly sequenced lithologies, which in turn may contribute to an unsafe environment and drill bit damage due to inaccurate drillstring length. These mistakes often require an additional trip out of hole and increase the amount of nonproductive time. Computer vision technology has shown promise in other industries with its ability to automate similar recognition and counting tasks. A dual-use system has been developed where the same cameras for pipe counting can be used for red zone entry detection, holding the potential to enhance the overall safety of the drilling process. A pilot application has been created serving dual applications: both counting and measuring the pipe entering the wellbore and detecting personnel movement in the red zone during pipe delivery operations. Each stage of the design process was intently developed, considering requirements for both functionalities of the system. Neural network detection algorithms, 3D localization, and drilling data signal processing all combined to interpret rig state and use the appropriate computer vision algorithms at the correct time. System practicalities such as camera placement, hardware and software robustness, and field-tested accuracy were considered. The system has been deployed for field testing in West Texas. The system succeeded in both accurately maintaining a drill pipe count and detecting personnel in the red zone. The system is designed so that the neural network algorithms can be updated using newly collected data as new scenarios are encountered, such as new weather conditions, lighting environments, additional people on the rig floor, and other dynamic factors. This computer vision technology is the first of its kind on a drilling rig. No other system has been developed that accomplishes not only one of the functions, but also two. Just as we have seen rapid improvements each year in driver assistance technology, the time has come to apply recent advancements in computer vision capabilities to increase the efficiency and overall safety of the rig.
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