Integration of deep neural networks and ensemble learning machines for missing well logs estimation

• Published in: Flow measurement and instrumentation Vol. 73; p. 101748
• Main Authors: Jian, Han, Chenghui, Lu, Zhimin, Cao, Haiwei, Mu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-06-2020
• Subjects: Deep neural networks (DNN); Ensemble learning machine (ELM); Soft logging; Well logs estimation; Well logs estimation; Soft logging; Deep neural networks (DNN); Ensemble learning machine (ELM)
• ISSN: 0955-5986; 1873-6998
• DOI: 10.1016/j.flowmeasinst.2020.101748

Geophysical logging is one of the most important measurement techniques for the oil/gas development and exploration industry. In practice, missing well logs estimation/prediction or soft logging is one of the effective ways to save oil/gas exploration costs. Due to the structural complexity and heterogeneous of the geological reservoir, there must be strong nonlinear relationships among different well logs. In order to reveal one or more of these relationships, multiple linear regressions, Bayesian learning and traditional machine learning methods (ANN, SVM, etc.) are always employed in the literature. However, in practice, it is impossible to obtain a compact data set that accurately reflects these nonlinear relationships. Therefore, falling into local optimum solution is the fatal defect of these traditional methods. In order to address this problem for a certain extent, we propose to integrate deep neural networks (DNN) and several ensemble learning machines (ELM) to reveal these relationships more accurately. Experiential results illustrated that the proposed method can really estimate missing logs more accurately than traditional ones, and the performance is promising.
•Well logs can obtain a variety of important reservoir geological parameters.•Deep learning or ensemble learning fails to take advantage of low-quality incomplete data.•Integrating several hierarchical machine learners can benefit the final results.