KEYWORDS: Data conversion, Data storage, Clouds, Databases, Data processing, Nomenclature, Analytical research, Software development, Feature extraction, Data modeling
In the research of petroleum exploration and development, original wellbore files collected by researchers are characterized by the massive data volume, diverse file types, and inconsistent file naming methods, which leads to time-consuming data format rearrangement for researchers. This paper proposed an automatic recognition method of wellbore data based on the Levenshtein distance similarity and TF-IDF (Term Frequency Inverse Document Frequency), which can automatically identify and process data of the wellhead, well trajectory, well interval division, mud logging lithology, and well logs of various wellbore file types and convert them into a unified standard format for storage. Compared with manual data sorting, the proposed methods deliver a reduction of data processing time of about 60% and greatly improve the data processing efficiency, also laying a foundation for subsequent data management.
KEYWORDS: Data modeling, Clouds, Optimization (mathematics), Performance modeling, Data conversion, Detection and tracking algorithms, Data processing, Process modeling, Feature selection
In private cloud environments, resource usage is determined based on experience, which often leads to excessive resource allocation and low resource utilization. This paper proposes a private cloud resource consumption prediction algorithm based on combinatorial optimization algorithm. Based on the virtual machine performance data collected from the private cloud platform, the key features are selected by recursive feature elimination method (SVM-RFE), and then the model is trained by eXtrem Gradient Boosting (XGBoost) algorithm for model training to predict the resource usage. Compared with Random Forest, LSTM and other algorithms, the proposed algorithm has higher prediction accuracy and smaller prediction error. This paper adopts a data-driven approach to achieve intelligent prediction of resource usage in private cloud environments, which improves resource utilization and provides decision support for optimal allocation of resources.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.