Abstract Wellbore tortuosity is an important metric of wellbore quality; however, it is not always an appropriate reflection of directional drilling performance. Drilling planned tortuous features will increase wellbore tortuosity, but this in itself says nothing about directional drilling performance. Not only is there a need for a metric of wellbore tortuosity, it isalso necessary to have a metric of "unplanned" wellbore tortuosity. The former provides information about wellbore quality, whereas the latteris reflective of directional drilling performance. The directional drilling literature contains metrics for both wellbore and unplanned tortuosity; however, they are largely unique and difficult to relate to one another. It is desirable for the planned and unplanned tortuosity metrics to be relatable. This would allow operators to not only quantify overall wellbore tortuosity in real time, but also to understand how much of that tortuosity is unplanned and possibly avoidable. This, then, opens up avenues for directional drilling performance improvement. In this paper, a new metric, the "Unplanned" Tortuosity Index is developed on the basis of an existing metric of wellbore tortuosity. This is done by systematically removing the effects of intended tortuous features from the wellbore tortuosity analysis, retaining only those tortuous features in the wellbore trajectory that are unplanned. The unplanned tortuosity index is then tested on two distinct sets of survey data from actual wells drilled. The results are compared between the sets and with the wellbore tortuosity metric from which the new index was derived. It is shown that thenewly developed unplanned tortuosity index canhelp operators and directional drilling companies discern their directional drilling performance, especially forwell paths with multiple, planned tortuous features.

Abstract Sophisticated drilling analysis software is available to provide drillers with advice on setting and modifying drilling parameters such as WOB, RPM, etc., but getting a driller to accept the recommendations of the software is still complicated. Additionally, it is not sufficient that a driller on one test rig accept the changes to the drilling techniques and modified workflows. The challenge is to scale across an operator’s mixed rig contractor fleet getting fleet-wide driller and stakeholder buy-in. The system used for this paper consists of a Rig-based Drilling Advisory System (RDAS) where new advisory information is displayed in the driller’s cabin running real-time pattern recognition algorithms to detect drilling dysfunctions. When a drilling dysfunction is encountered, a change in drilling parameters is suggested. Additionally, drilling parameters from offset wells are made automatically available for the driller’s use on the drilling screen. Through this process, we are entrusting the field personnel with a slightly higher level of technical responsibility. The team iteratively improved the system using feedback from drillers who used the RDAS. Two rigs were selected for testing on how the drillers and the wellsite supervisors utilize the system. Feedback from these two rigs pointed to the need for customization on a well by well basis. Working through the on-site drilling engineers on the test rigs, modifications were then made to the system to fine tune how they wanted the drilling advisory to behave. For example, a wellsite supervisor wanted the system to ignore mild stick slip in a short drill section - a rigid system with no customization could not provide an adequate solution. Being adaptable helped to improve the acceptance, as the driller now started seeing the advisory more as a support tool. Agile software development was critical to success and the rig personnel appreciated the quick modifications to the system. This also gave them confidence in the process, and made them more responsive to change. Comparing drilling performance from wells before and after deployment provided a way to quantify the benefits. Seven total systems were deployed over a five month period. The operator continues to deploy to additional rigs until all active rigs are covered. Change management is challenging. Many projects fail when this process is not properly executed. Presented here in detail is the process used by an operator to successfully implement a drilling advisory system across a mixed group of rig contractors. This process and the learnings presented in this paper serve as a case study for other companies embarking on such deployments.

The first step towards drilling optimization and automation is a reliable data acquisition and handling system. This includes receiving and processing different frequency data across multiple platforms and ensuring proper data quality. Once we implement such a platform, we can build different advisory solutions to improve drilling efficiency and move towards drilling automation. The developed Drilling Intelligence Guide (DIG) already enabled us to achieve the aforementioned goal and access different data (from contextual to high frequency) in real-time. In the next phase, different models (physical or data analytics) can be built in to optimize various stages of drilling operations. The drilling industry has made significant progress on different physical models that can be run offline (either for pre-job design or post-job analysis) in the previous decades. Given the developed data platform, we can now adopt and run these models in real-time for optimization and automation purposes. As an example, a real-time, computationally efficient hydraulic model that can account for drill string rotation and eccentricity would enable us to monitor ECD (to be used for wellbore stability, kick and lost circulation mitigation) and calculate pump pressure corresponding to different operational conditions (to be used for drill string washout/pump failure prediction and sensor calibration). The aim of this paper is to show the application of the hydraulic model for real-time monitoring and optimization purposes. An analytical hydraulic model including drill pipe rotation and eccentricity effects is used and compared against transient numerical simulations. In the next step, Pressure While Drilling (PWD) data are used to verify the accuracy of the model for real-time applications. After the verification steps, we explain the process to couple real-time data (e.g., flow rate, RPM), contextual data (e.g., mud properties, BHA and wellbore geometry) and the physical model using field examples. In addition to ECD monitoring at the bit and casing shoe, the real-time hydraulic model can also be used to monitor washout and pump failure events. The application of the model is shown using field examples.

Abstract The drilling industry has made significant progress on physics-based torque and drag (T&D) models that can run either offline (pre-job or post-job) or in real-time. Despite its numerous benefits, real-time T&D analysis is not prevalent since it requires merging real-time and contextual data of dissimilar frequency and quality, along with repeated calibration whose results are not easily accessible to the user. Our goal is to implement a rig-based T&D advisory system which overcomes these obstacles. The first step towards real-time T&D analysis is a reliable data acquisition and processing system at the rig site. This includes the ability to receive and process data of different frequency and merge it with contextual data. Once this was accomplished, the system was implemented on more than 20 rigs in North America. We then adopted a soft-string T&D model to be used for various purposes including the automatic detection of overpull/underpull events and the depths where these occur, open-hole and casing friction factor determination, sensor calibration and real-time broomstick plotting and field data comparison for subsequent casing run design. In this paper, we demonstrate the field and office application and usage of a real-time T&D model. The system on which the model is run must be able to merge both real-time (hook load, torque, rig state, etc.) and contextual (BHA composition and specifications, wellbore design and trajectory, mud weight, etc.) data. Given the developed infrastructure, the drilling engineers have access to automated model calibration in real-time which enhance the reliability and repeatability of results and also contribute to time/cost savings. Using the embedded rig state identification engine, different real-time data points can be classified (e.g., slack-off, pick-up and rotating off-bottom) and used in T&D calibration. In addition to traditional broomstick plots, the algorithm uses probabilistic data analytics approaches to identify troublesome zones (e.g., overpull/underpull locations). In a fully automated manner, the platform generates predictions based on calibrated friction factors to enhance subsequent casing run as well. The outputs are used in both field and office in a variety of ways to improve drilling performance and safety. Using the developed platform, we automated the process of T&D analysis and reduced/eliminated the time/cost required to run physical models offline. Using data from multiple BHA runs and one casing run from an exemplary well in North America, we were able to demonstrate the benefits of the automated real-time application in comparison to the traditional offline use of torque and drag analysis.

Abstract Data exchanges between different electronic data recorder (EDR) systems and personnel occur on a regular basis in a well drilling operation. A significant portion of this data is derived; i.e., calculated or manipulated after sensor measurements. Currently, derived data calculations are poorly documented; therefore, the usefulness of this data diminishes through data transfer. The objective of this work is to define a meta-data framework for derived data. In this paper, we focus our efforts on one derived data channel, the rate of penetration (ROP) and identify the meta-data required to fully understand the values transferred to the end user. We start by identifying the different types of ROP and document the calculation procedure for each type. Part of the meta-data that needs to be captured involves data transformations that occur when this data stream is moved from one EDR to the next. We interviewed various EDR providers in an attempt to understand their current process. The different types of ROP calculations and their use in different types of drilling performance analysis are described in this paper. The calculation procedures were implemented and tested on an operator's data aggregation system. This effort also documents different EDR systems and how they handle sensed data required for ROP calculations. A meta-data framework is able to capture not just the calculation used, but also data transformations that occur as data hops from one EDR system to the next. Different data transfer protocols such as WITS0, WITSML, and OPC/UA necessitates a broad meta-data framework. While much of the meta-data can be embedded in the data transfer channel itself, a document describing all relevant meta-data is equally effective in communicating the information. Lastly, the meta-data framework developed here can also be applied to other forms of derived data (such as Hole depth, Bit Depth, WOB, etc.). This meta-data framework improves the transparency by providing guidelines to data aggregation providers on the type of information that should be supplied to end users. It also provides insights into how data gets transformed from its point of origin (sensor) to its point of consumption. Finally, it also documents the various type of ROPs and their appropriateness for the analysis that is performed using them.