Understanding friction at diamond–rock interfaces is crucial to increase the energy efficiency of drilling operations. Harder rocks usually are usually more difficult to drill; however, poor performance is often observed for polycrystalline diamond compact (PDC) bits on soft calcite-containing rocks, such as limestone. Using macroscale tribometer experiments with a diamond tip, we show that soft limestone rock (mostly calcite) gives much higher friction coefficients compared to hard granite (mostly quartz) in both humid air and aqueous environments. To uncover the physicochemical mechanisms that lead to higher kinetic friction at the diamond–calcite interface, we employ nonequilibrium molecular dynamics simulations (NEMD) with newly developed reactive force field (ReaxFF) parameters. In the NEMD simulations, higher friction coefficients are observed for calcite than quartz when water molecules are included at the diamond–rock interface. We show that the higher friction in water-lubricated diamond–calcite than diamond–quartz contacts is due to increased interfacial bonding in the former. For diamond–calcite, the interfacial bonds mostly form through chemisorbed water molecules trapped between the tip and the substrate, while mainly direct tip–surface bonds form inside diamond–quartz contacts. For both rock types, the rate of interfacial bond formation increases exponentially with pressure, which is indicative of a stress-augmented thermally activated process. The friction force is shown to be linearly dependent on the number of interfacial bonds during steady-state sliding. The agreement between the friction behavior observed in the NEMD simulations and tribometer experiments suggests that interfacial bonding also controls diamond–rock friction at the macroscale. We anticipate that the improved fundamental understanding provided by this study will assist in the development of bit materials and coatings to minimize friction by reducing diamond–rock interfacial bonding.
Summary The oil-and-gas drilling industry has developed a large body of knowledge about methods for drilling directional wells with steerable motors. Experience indicates that more-aggressive drill bits are harder to steer. This is commonly attributed to the fact that bits with higher aggressivity produce larger torque changes for a given change in bit weight. The actual mechanics, however, of tool-face disorientation during slide events is poorly understood. This paper reports on tests conducted on a full-size drill rig aimed at understanding the mechanics of tool-face control. Tool-face orientation and other data were measured downhole at 100 Hz. Nine different bits ranging from polycrystalline diamond compact (PDC) to hybrid to roller-cone bits were tested on an adjustable-kick-off (AKO) motor bottomhole assembly (BHA) in slide mode. These tests confirm the common industry notions about the effect of aggressivity on tool-face control. They also show that angular motion of the BHA while sliding is overdamped. Tool-face orientation consequently follows the average of the torque signal generated by the bit. Furthermore, the tool-face orientation is more easily disoriented by a torque signal at a frequency near to or less than the torsional natural frequency of the drillstring. PDC bits excite this more readily than bits with rolling cones. We also identify a tool-face disorientation anomaly that we call a fast torque anomaly (FTA). FTAs occur because the bent motor/AKO has a preferred angular orientation in the borehole. FTAs have not been previously recognized, probably because they are not identifiable in mud-pulse signals. A BHA suffering FTAs would simply appear as a chaotic and profound loss of tool-face control in mud-pulse data. Hybrid and roller-cone bits caused fewer FTAs than PDC bits.
Drilling operations rely on learned expertise in monitoring the drilling performance data and the rock data to assess the dull condition of the drill bit. While human learning can subjectively pick up the indicators based on rig surface data streams, this information is highly convoluted with changes in rock and drilling data. Recent approaches for bit wear estimation also include model-based and traditional supervised machine learning methods, which are usually costly and time-consuming. In this study, we developed a bi-directional long short-term memory-based variational autoencoder (biLSTM-VAE) to project raw drilling data into a latent space in which the real-time bit-wear can be estimated. The proposed deep neural network was trained in an unsupervised manner, and the bit-wear estimation is demonstrated as an end-to-end process.
This paper describes the development of an improved polycrystalline diamond cutter (PDC) drilling performance simulation in 3D which includes worn cutters. A polygonal mesh model is used to improve the geometric data needed for the drilling performance model calculations. Methods for active face identification have been developed to consistently identify a variety of polygonal face types. The simple wear flat definition used for data input is shown to adequately capture the geometry compared to field worn bit scans and provide valuable insights with active face identification. Drilling performance calculation and results from the calibration to laboratory drilling data and validation to both laboratory and field data are shown. Additionally, including a velocity parameter in the drilling performance model reduced model error.
This study presents a hybrid approach that combines data-driven and physics models for worn and sharp drilling simulation of polycrystalline diamond compact (PDC) bit designs and field learning from limited downhole drilling data, worn state measurements, formation properties, and operating environment. The physics models include a drilling response model for cutting forces, worn or rubbing elements in the bit design. Decades of pressurized drilling and cutting experiments validated these models and constrained the physical behaviour while some coefficients are open for field model learning. This hybrid approach of drilling physics with data learning extends the laboratory results to application in the field. The field learning process included selecting runs in a well for which rock properties model was built. Downhole drilling measurements, known sharp bit design, and measured wear geometry were used for verification. The models derived from this collaborative study resulted in improved worn bit drilling response understanding, and quantitative prediction models, which are foundational frameworks for drilling and economics optimization.
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