The in-flight measurement of particle parameters (size, velocity, temperature, and local number density) can provide insight into the plasma processing of solid materials. A measurement technique for simultaneously obtaining the size, velocity, and temperature of particles entrained in high-temperature How fields is described. Particle size and velocity are obtained from a combination laser-particlesizing system and laser Doppler velocimeter (LDV). The particle temperature is determined by a two-color pyrometry technique and the data rate is a measure of relative particle number density. Typical measured temperatures and velocities for the 5-100 pm particles used in plasma spraying are 1600-3500 K and 100-300 m/s, respectively. Since particle size, velocity, and temperature are measured simultaneously, cold particles (< 1600 K) are identified and their relative number density can be quantified. Data from two plasma spray systems, a metal (NI-AI) and a metal oxide (A1203), are presented and their application to understanding the plasma spray-coating process illustrated.

A measurement technique for simultaneously obtaining the size, velocity and temperature of particles entrained in thermal plasma jets is described. Particle size and velocity are obtained from a combination laser sizing system and laser Doppler velocimeter (LDV). The particle temperature is determined by two-colour pyrometry and the data rate is a measure of relative particle number density. Since particle size, velocity and temperature are measured simultaneously, cold, solid particles or unmelts are identified and their relative number density can be quantified. Typical data are presented.

To avoid severe vibrations, different drillstem (or drillstring) vibration models have been used to predict and avoid resonance regions by selecting bottom hole assembly (BHA) components and operating parameters such as weight on bit (WOB) and RPM. The overall efficiency of the drilling operations can be evaluated using either a mechanical specific energy model or an inverted rate of penetration (ROP) model. The key output from the analysis described herein is a method to improve drilling efficiency by incorporating a drillstem vibration model with an ROP model. A data set including vibrational data was collected from a section of a well drilled in the North Sea to analyze the level of drilling efficiency for the drilled section. A drillstem vibration model was created using nonlinear finite strain theory, including coupled axial, torsional and lateral vibration modes. To optimize the drilling process, the vibration model was integrated with an ROP model. The vibration model calculates the critical speeds for a given BHA at a given depth, supplied with an operational window for WOB and RPM combinations that gives the optimal combination in therms of ROP. By analyzing the drilling variables such as WOB, rotational speed, and ROP, the drilling operational effectiveness was evaluated. The operational window with low vibration level and with the potential maximum instantaneous ROP was chosen to be the optimum drilling scenario for the target hole section. The model was verified with the analysis of the collected field data where the level of drilling efficiency was obtained for the different sections drilled. This paper presents a new methodology to increase drilling performance by means of drillstem vibration and ROP modeling.

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Ever since the first commercial well was spudded, operators have looked for ways to drill wells faster without sacrificing safety or incurring huge costs. While saving time and money through efficient drilling is not a new concept, the more recent adoption of drilling optimization and automation services has certainly become one of the biggest drivers to achieving those goals. As the current downturn has shown limited signs of recovery, it has continued to evolve in ways never imagined, and the effects are taking their toll in every facet of the oil and gas industry. While rigs and drilling equipment can be set aside to ride out the storm, what about the drilling teams who are working on the rigs and in remote operation centers? As these teams are being removed from the field, the expectation is that many of them won’t return for a myriad of reasons. So, what happens when that experience is lost? The exodus of seasoned crews, otherwise known as the “great crew change,” has been discussed for several years, but recent conditions could expedite the process. Considering the recent shutdown of rigs and the loss of personnel, the question remains whether we will see a noticeable gap in knowledge and experience once crews return to the drilling rigs in full force. The lack of individual skills can be offset over time with hands-on experience, but a drilling crew needs to operate at the highest level possible, preferably with few to no gaps in experience. To assist the drilling process, NOV’s M/D Totco division recently launched its KAIZEN intelligent drilling optimization application, which performs as an adaptive autodriller. The system features continuous learning capabilities, enabling it to provide proactive drilling dysfunction mitigation while maximizing rate of penetration (ROP) and optimizing mechanical specific energy. It also reduces human dependence in the drilling process, lowering the risk of slow or incorrect responses to drilling dysfunction. In turn, the system assesses wellbore conditions and drilling performance, then automatically applies appropriate parameters to mitigate those dysfunctions. Intelligent Drilling Optimizer When faced with distinct interbedded formations, drillers often encounter drilling dysfunction due to varying formations, and optimal setpoints are required to identify and proactively mitigate dysfunction. While drillers are inundated with large amounts of data, the system takes the human dependence away and employs artificial intelligence (AI) to continuously optimize the drilling process. Utilizing an array of machine-learning algorithms and a digital twin that is updated each second, the AI system builds a store of knowledge that the drilling application leverages to make more accurate and timely decisions. This automated parameter application approach enables the system to remove distractions from the driller so their focus can be on critical items such as keeping the crew safe and the well under control, while the system instantly responds to changing conditions and provides optimal weight on bit (WOB) and revolutions per minute (rev/min) setpoints. The AI and machine-learning feature stores thousands of hours of processed drilling data. This capability allows the system to recommend surface parameters that deliver the best expected performance as well as select the correct dataset to mitigate changes detected in drilling dynamic behaviors.