Efficient prediction for porosity only using logging data: a case study of lacustrine carbonate reservoirs of IARA oilfield. (30th April 2020)

• Record Type: Journal Article
• Authors: Gu, Yufeng; Bao, Zhidong; Rui, Zhenhua; Song, Xinmin
• Is Part Of: International journal of oil, gas and coal technology. Volume 25:Number 2(2020)
• Page Start: 133
• Page End: 160
• Publication Date: 2020-04-30
• Subjects: logging interpretation -- porosity prediction -- N-way analysis of variance -- multivariate linear fitting -- fitting correction; Petroleum as fuel -- Periodicals; Gas as fuel -- Periodicals; Coal -- Periodicals; Biomass energy -- Periodicals; Hydrogen as fuel -- Periodicals; 662.6
• Languages: English
• ISSNs: 1753-3309

Abstract

Statistical methods are capable of revealing variation trends of study cases by only processing source data. N-way analysis of variance and multivariate linear fitting are a few excellent examples. Then in order to cast off the reliance on using physical model parameters, a statistical method combined N-way analysis of variance and multivariate linear fitting is proposed. This article discloses the function of proposed method in terms of porosity prediction when only logging data is available. The data used for verification derives from the lacustrine carbonate reservoirs of IARA oilfield. Five experiments are well-designed to analyse capability of the method on porosity prediction. Experiment results prove that the proposed method can only utilise logs to predict porosity, and the predicted outcomes are accurate. The proposed method can be viewed as a new tool to obtain porosity data when only logs are handled. [Received: March 8, 2018; Accepted: February 14, 2019]