... 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...

... drilling data (i.e., weight-on-bit, rotation speed, torque, rate of penetration, flowrate, and standpipe pressure) are used to derive the model. In the data preprocessing stage, we labeled drilling and well logging data based on formation tops in the drilling plan and performed data cleansing to remove...

..., time sensitive and resource-intensive. Therefore, field-wide estimates are often dependent on production data history-matching, which has a high degree of uncertainty. In this paper, we present a new attempt to apply machine learning approach to estimate coal permeability using drilling data. We first...

... process optimization drilling performance individual well Exhibition drilling operation coal seam gas drilling data mean connection time ROP drilling data Ilt australia NPT BCT best composite time removable time society of petroleum engineers rpm Introduction The raw data...

... are increasingly using data analytics to improve drilling performance. This paper provides an example of using a business intelligence (BI) tool to analyze drilling data in the Permian Basin. The BI tool helped to improve operation decisions through the use of a visual report. A database, consisting of massive...

... Abstract Drilling process has a nature of surprise and uncertainty. It is urgent for us to decrease the drilling problems and to increase drilling speed. The scale of drilling data is becoming larger and larger. However there are plentiful and valuable knowledge hidden behind them...