Search

Mud motor failure is a significant contributor to non-productive time in North American land drilling operations. Currently, there is limited work on mud motor failure analysis using surface drilling data. The objective of this work was to apply data analytics methodologies on historical drilling datasets to identify features and trends in surface data that contribute most to indicating impending mud motor failure. The methodology involved investigating a large dataset of mud motor runs for certain on-bottom and off-bottom drilling events that are generally known to be leading causes of motor failure. Spikes in differential pressure, pick up practices, drill-off time, etc., were all investigated. Additionally, the impact of temperature on mud motor failure was studied. A combination of the investigated features was analyzed using statistical measures and supervised machine learning algorithms to determine the leading contributors to motor failure. The dataset consisted of 32 motor runs drilled in the lateral section of wells from early-to-mid 2019. These motor runs represented a mix of both failure and non-failure cases. The motor stalls were categorized as either low, medium, or high impact depending on the severity of the differential pressure spike. It was observed that high impact motor stalls and the rate of stalling correlated strongly with eventual failure. Likewise, prolonged exposure to high bottom-hole temperature (exceeding the motor temperature limit) increased the risk of motor damage, most likely through thermal expansion of the stator elastomer. The drill-off time was also found to have some influence on motor failure for the investigated cases. Using these features, statistical significance tests and supervised machine learning models were trained on the dataset to determine the features that contribute most to motor failure. This work shows the feasibility of performing mud motor failure analysis using readily available surface data. It is expected to inform drilling engineers on data analysis methodologies for efficient failure analysis and help them plan future wells more efficiently.

Since the dawn of their discipline, archaeologists have used surface data to assess the information potential of archaeological sites. Excavation and surface assemblages are used as the foundation for the reconstruction of past histories. Given the importance of surface assemblages in archaeology, it is necessary to examine their reliability. One way of determining the reliability of inferences made by using surface assemblages is through repeated examinations of the same archaeological site over a period of time. The information gathered with each examination of MU 125, south of the Grand Canyon, Arizona, provides such an opportunity. Three years’ of surface ceramic data to was in order to determine if MU 125 was indeed a Kayenta Anasazi site. The final results of this analysis involve a comparison of four wares that were found within all three surface ceramic assemblages. Tusayan Grayware exhibited a higher number of sherds than all others. Of the 5,498 surface sherds located on MU 125, about 50 percent were assigned to Tusayan Grayware. In this case, the predominant of Tusayan Grayware at MU 125 associates it with the Kayenta Anasazi culture. To gain a better idea of surface data reliability, it is the task of archaeologists to understand the processes that affect the surface material and how they impact the archaeological record. Numerous agents transform artifacts from one state to another within the archaeological context. The formation processes themselves display patterns that can distort the archaeological record. It is important to understand these processes and their disturbances on the archaeological record. Without acknowledging these processes, poor inferences may be made in archaeological interpretations. However, in the case of MU 125 one can conclude that these processes did not affect the reliability of the surface ceramic data. Thus, inferences made from the data can be deemed reliable.