Abstract
Reservoir classification and evaluation is the base for gas reservoir description. Well logging interpretation while drilling technique collects drilling logging signal in real-time through the sensor module, and transmits to the database server wirelessly. Well logging interpretation model is applied to reservoir information analysis, which is important to describe gas reservoirs accurately. Because of complicated geological conditions, there is a deviation in single well logging interpretation model. To solve the problem, a reservoir well logging evaluation while drilling method based on fuzzy comprehensive evaluation is proposed. Key parameters affecting reservoir evaluation, such as porosity, permeability and gas saturation are considered. Fully mining the information contained in GR, SP, AC and RT well logging data. Firstly, the reservoir is divided into gas, poor-gas, dry layer and water layer. For each well logging method, statistical method is used to calculate the subordinate intervals of each reservoir’s parameters, and the membership degree is calculated to form the evaluation matrix of the well logging method. Then, the weight of each parameter is selected to form the comprehensive evaluation weight matrix, and fuzzy comprehensive evaluation result of well logging is computed. Finally, the comprehensive evaluation results of different well logging methods are composed to evaluation matrix, and fuzzy comprehensive evaluation method is used again to get the final reservoir evaluation category, so as to provide scientific basis for gas field development decision making.
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This work has been supported by National Science and Technology Major Project (No. 2016ZX05047003).
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Zhou, Z., Shi, S., Ma, S., Fu, J. (2018). Application of Fuzzy Comprehensive Evaluation Method for Reservoir Well Logging Interpretation While Drilling. In: Hu, J., Khalil, I., Tari, Z., Wen, S. (eds) Mobile Networks and Management. MONAMI 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 235. Springer, Cham. https://doi.org/10.1007/978-3-319-90775-8_7
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DOI: https://doi.org/10.1007/978-3-319-90775-8_7
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