Most traditional polycrystalline diamond compact (PDC) cutting elements have a flat polycrystalline diamond table at the end of cylindrically shaped tungsten carbide body. During drilling, the flat diamond table engages the formation and shears the rock layer by layer. A new ridge-shaped diamond cutting element (RDE) has a similar cylindrical tungsten carbide base; however, the diamond table is shaped like a saddle with an elongated ridge running through the center of the diamond table and normal to the cutter axis. The intended cutting portion, the "ridge," engages the formation to fracture and shear the rock at the same time. The design intent was to create a unique cutting element that could combine the crush action of a traditional roller cone insert and the shearing action of a conventional PDC cutter. The new cutting elements were tested in the laboratory against standard flat PDC cutters in a rock-cutting evaluation, and later the new elements were applied to PDC bits and run under real drilling conditions. The laboratory rock-scrape tests indicated that the new cutting element not only enables the cutter to efficiently shear formation in the same way as a conventional PDC cutter, but also delivers a crushing action similar to a roller cone insert. Preliminary results indicated a reduction of roughly 40% in both cutting force and vertical force on the new ridged diamond element cutters (RDE) over a conventional PDC cutter. Similar findings were also observed during the rock-shearing test on a vertical turret lathe (VTL). Subsequent field tests in multiple areas in North America have produced faster rates of penetration (ROP) in most of the cases. The trials indicate that the new cutting element is efficient at removing rock, and a bit equipped with these elements requires less mechanical specific energy (MSE) during drilling than does a bit with a conventional PDC cutter. In addition, the reduced cutting forces reduces bit torque and thus improves the drilling tools’ life and the bit directional performance. Field data has proven this technology improves drilling performance in terms of ROP and footage over the current PDC bits fitted with traditional flat PDC cutters.

In recent years, the phenomenon of drill string torsional oscillation at frequencies over 50 Hz has been well documented. This high frequency torsional oscillation (HFTO) creates cyclic fatigue loading on bits and drilling tools within the bottom hole assembly (BHA) and thus limits tool life and drilling performance. However, few models exist which can predict occurrence of HFTO and its severity. To our knowledge; none of these models consider the entire drilling system including the bit-rock interaction, downhole drive(s), BHA design, and surface drilling parameters, and hence there is a need to develop a system model for HFTO mitigation. A 3D transient drilling dynamics model has been extended to study the severity of HFTO and cyclical loading to drilling tools. The accuracy of the model was validated by theoretical calculation, and high frequency downhole data. An example analysis was conducted to evaluate drilling system design performance in terms of HFTO risks. Good correlation was found between the analysis and field data collected from the Permian Basin. Advanced models were developed for mud motors and rotary steerable system (RSS) tools. After conducting a full drilling simulation, the drilling system behavior under HFTO can be fully described. Cyclical torque loading of differing magnitudes and frequencies were observed for different BHA components depending on HFTO vibration mode, HFTO severity and BHA design. PDC cutters were subjected to different cyclical loading depending on bit design, formation and HFTO conditions. The mud motor power section was found to undergo high frequency cyclical loading which could accelerate its rubber degradation. Since the failure of PDC cutters and the degradation of mud motor power sections have a critical effect on drilling performance, the importance of mitigating HFTO cannot be underestimated. By evaluating the loading conditions, an optimized drilling system can be selected. Field data has proved the validity of this approach. The methodology presented in this paper offers a new way for the industry to systematically mitigate HFTO by considering the rock drilled, bit design, mud motor utilized, the mechanics of RSS and other tools in the BHA, as well as drilling parameters. The usage of this approach can reduce premature drilling component failure and improve drilling performance, especially in the high energy drilling applications found in North America Land and other areas.

Downhole vibration is detrimental to drilling efficiency and can cause MWD/LWD tool failure, drillstring fatigue, bit/PDM damage. Preventing or mitigating BHA instability is critical to improve drilling efficiency, increase ROP and reduce drilling costs. To prevent dynamic instability requires an in-depth understanding of the BHA's downhole behavior in terms of the actual phenomenon and ability to determine root cause. Downhole RPM and vibration data sent to surface from MWD/LWD usually have very low frequency limited by telemetry. Because vibrations are measured at a distance from the bit, the data may not capture detailed bit behavior patterns. The low frequency data is typically used in combination with steady-state response or static analysis models developed to predict interaction between the drillstring and wellbore. Results produced by these type systems have been less than optimal. To obtain superior quality downhole vibration data, a high-frequency drilling dynamics measurement module (DDMM) was recently developed to capture and measure bit/BHA dynamic response. The authors will describe the application of the new module to measure and record downhole RPM, acceleration at bit/BHA at sampling frequencies up to 2048-Hz. Analysis verified the high-frequency data produces more specific details about dynamic behavior compared to standard data. The module can be positioned at any location within the BHA or directly above the bit. The high-frequency data is used in conjunction with a unique time-based dynamic simulation system to verify and confirm the modeling's prediction of ROP/RPM, acceleration and drillstring instability including stick-slip/whirl. When drilling dysfunction is observed, the time-based modeling system has the potential to ascertain the root cause not typically identified in measured data. This system approach is cost effective and can be highly effective at preventing or mitigating complex instability issues. Three DDMM field tests are presented that document the capability of the combined testing and modeling system approach to achieve better understanding of downhole dynamics: Case Study 1 Rotary BHA with one DDMM positioned at top of bit. Analysis of actual downhole data from DDMM shows large RPM variation at the bit which was confirmed by the time-based modeling system. The two data plots showed good correlation between axial/lateral acceleration (acceleration/g's vs time/sec). Case Study 2 Motor BHA with two DDMMs installed. One positioned at top of motor another at top of the bit. Analysis of actual data documents stick-slip at the bit and top of motor which is confirmed by the modeling system. Detailed analysis of the two data plots revealed good correlation between actual and modeled data with both depicting negative RPM at the top of the motor with increased acceleration at the occurrence of stick-slip (RPM vs time/sec). Case Study 3 Motor BHA with two DDMMs installed. One positioned at top of motor, another at top of bit. Analysis of actual data documents stick slip at the top of motor which was confirmed by the modeling system. Further analysis of the modeling data plot identified stick-slip coupled with BHA whirl. The case studies document accurate modeling predictions of ROP/RPM and acceleration corroborated by the measured data. Modeling also successfully predicted dynamic instability issues identified by actual DDMM measurement and gave specific details about coupling of stick-slip and BHA whirl not observed in the measured data.

The purpose of this paper is to demonstrate the power and business benefits of leveraging online analytical processing (OLAP) cubes in the utilization of high-level data analytics and data dashboards from an established drilling record system (DRS). The DRS contains over 1.4 million wells, including 75,000 offshore wells drilled worldwide since 1980 with nearly 5 million total bottomhole assembly (BHA) runs from over 100 countries. Since 2009, over 1.5 million BHA runs drilling 2.6 billion feet of formation have been captured. Being able to visualize and understand the drilling data allows for increased efficiencies, reducing the days on wells for operators from deepwater to inland barge and land drilling worldwide. The development of the OLAP cubes required a multidisciplinary team consisting of software developers, business managers, domain champions, field-based engineers, and data scientists. The OLAP cubes consist of multidimensional databases built from relational and algorithmic interpretations of DRS transaction data. These algorithms are generated and developed by an iterative cycle of continuous improvement, development, and utilization of the OLAP cubes in parallel to improve the functionality and business impact for performance analysis, sales, product development, product reliability, and marketing. The data can be analyzed and visualized in the Microsoft Office suite by directly querying the DRS OLAP cubes. This also allows for dashboards to be updated in real time as data are added to DRS. OLAP cubes have been developed to analyze the performance of drill bits, motors, reamers, rotary steerable tools, and many more downhole tools. The DRS cubes assist in identifying failure causes on bits to identify high-risk intervals to better target products and parameters to reduce costly nonproductive time. Fit-for-purpose OLAP cubes have been developed to understand drilling efficiencies and strategies in multibit versus single-bit sections using variable trip speeds and field performance. Traditional business reports were made more efficient and auto-updated and dashboards were built to identify major business trends to equip business managers. This OLAP cube development has allowed for increased usage of the world's largest drilling record database and has made it easier to access and analyze the data. Ultimately, the techniques and development described in this paper help answer business questions to make better business decisions through data-driven analytics.

Ensuring a proper apple to apple comparison is a challenge in drilling performance evaluation. When assessing the effect of a particular drilling technology, such as bit, bottomhole assembly (BHA) or mud type, on the rate of penetration (ROP) or other drilling performance criteria, all other factors must be fixed to truly isolate the effect. Traditionally, performance evaluation starts with manual identification of reasonably similar entities, such as drilling runs or well sections by means of numerous selection criteria; e.g., location, depths, inclinations, drilling conditions, tools, etc. The selected drilling performance metrics are then compared using statistical analysis techniques with various extents of thoroughness. Such analyses are laborious and are usually limited to just a handful of cases due to practical reasons and time constraints. Furthermore, the analyses are difficult to apply to large data sets of hundreds or thousands of wells, and there is always a risk of missing an important combination of factors where the effect is important. Therefore, conclusions based on these analyses may well be insufficiently justified or even confirmation biased, leading to suboptimal technical and business decisions. This paper presents a combined machine learning and statistical analysis workflow addressing these challenges. The workflow a) discovers similar entities (wells, intervals, runs) in big datasets; b) extracts subsets of similar entities (i.e., "apples") for evaluation; c) applies rigorous statistical tests to quantify the effect (mud type, BHA type, bit type) on a metric (ROP, success rate) and its statistical significance; and, finally, d) returns information on areas, sets of conditions where the effect is pronounced (or not). In the statistical analysis workflow, the user first specifies the drilling technology of interest and drilling performance metrics, and then defines factors and parameters to be fixed to better isolate the effect of the drilling technology. The historical data on thousands of entities are then preprocessed, and the entities are clustered by similarities in the multitude of factors by the k-means algorithm. Statistical tests are performed automatically on each cluster, quantifying the magnitude of technology effect on performance criteria, and calculating p-values as the measure of statistical significance of the effect. The results are presented in a series of clustering observations that summarize the effects and allow for zooming into the clusters to review drilling parameters and to perform further in-depth analysis, if necessary. All steps of the workflow are presented in this paper, including data processing details, and reasons for selecting specific clustering algorithms and statistical tests. Several examples of the successful applications of the workflow to actual drilling data for thousands of wells are provided, focusing on the effects of BHA, steering tools, and drilling muds on drilling performance. This unique approach can be used to improve other drilling performance evaluation workflows.