This paper presents a method for constructing a health indicator to detect neutron-generator faults in a multifunction logging-while-drilling (LWD) service and predict maintenance requirements due to wear. The method is based on extracting fetures from selected channels that hold information about the subsystem degradation with time. These features are used to build a decision-tree model which estimates the tool condition from the recorded data. The model demonstrates excellent value for both maintenance and field engineers due to the fact that in just a few minutes the physical condition of the neutron generator can be determined with high confidence. This work is part of a long-term project with the aim to construct a digital fleet management for drilling tools.
In the area of well construction, the tool reliability and the field environment are two contributing factors that influence drilling job efficiency and success. Either using high specification tools in low-risk environmental or applying tools of low reliability in harsh environments is inadvisable. Thus, how to select a suitable tool fitting the environment of an approaching drilling job is of great significance for tool planning. However, today, the tool selection decision is not optimized because it is often based on partial data availability and understanding.
This paper presents an indicator called tool compatibility index, which can support improved tool selection decision making. This index takes part reliability, part criticality, and field environment into consideration, and gives a score indicating the compatibility of the tool to a specific environment. Moreover, the tool compatibility index is computed based on a weighted average method, which is computation simple and can be easily deployed. This work is part of a long-term project aiming to construct a risk-based decision advisor for drilling and measurement tools.
The electronic boards in drilling and measurement (D&M) tools provide multiple functions, such as data acquisition, signal processing, operation control, and data storage. However, due to the harsh downhole operating conditions; i.e., high temperature, dynamic vibration, and extensive shocks, the boards are likely to suffer from complex failure modes and result in failed jobs. Estimating the risk level of the boards can tolerate and provide support for maintenance decision making and job planning, this paper presents a statistical method for risk assessment of the electronic boards. The method first selects relevant channels from D&M tool measurement data and extracts histogram features based on those selected channels. The histogram features are then enhanced based on a linear interpolation method and aggregated using weighted sum. Finally, hidden Markov models (HMMs) with different parameter settings are trained using the processed features. The best HMM is chosen according to the Akaike information criterion and Bayesian information criterion. The proposed HMM-based method is tested on a real-world data set of failed control processing unit boards that were assembled for a specific D&M tool. The experimental results show that this method is effective in estimating the risks as a sequence of events, which in turn, helps to achieve consistent risk estimation. The work presented in this paper is also part of a long-term project with the aim to construct a risk-based decision advisor for D&M tools used in the oil and gas industry.
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