New robust model to evaluate the total organic carbon using fuzzy logic

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

28 Scopus citations

Abstract

Total organic carbon (TOC) is an essential parameter used for unconventional shale resources evaluation. The current methods for TOC determination are based on conducting time-consuming laboratory experiments or by using empirical correlations; the later are developed based on assumptions that restrict their applicability. This study provides a new robust model for TOC estimation based on conventional well logs. The model was developed using an adaptive neuro-fuzzy inference system with subtractive clustering (ANFIS-SC). Four conventional well logs of deep resistivity, sonic transit time, gamma ray, and formation bulk density collected from Barnett shale formation were used to develop the ANFIS-SC model for TOC estimation. A dataset consists of 645 records of the four well logs and TOC were used to develop the new model. The model was optimized for the different combinations of the ANFIS-SC’ design parameters and for training/ testing data ratio. The optimum predictability of the ANFIS-SC TOC model was reached after 400 iterations using a cluster radius of 0.3 and a training/testing data ratio of 70/30. The statistical analysis showed that TOC is a strong function of the bulk density, moderate function of the sonic transit time and gamma ray, and a weak function of the deep resistivity. The training and testing results proved that the developed ANFIS-SC model is able to predict the TOC based on the four mentioned well log data with high accuracy. For the training data set, the TOC was estimated with an average absolute percentage error (AAPE) of 8.62%, a coefficient of determination (R2) of 0.91, and a correlation coefficient (R) of 0.96. For the testing data set, the associated AAPE in predicting the TOC is 9.57%, while R2 and R between the actual and predicted TOC are 0.89 and 0.94, respectively.

Original languageEnglish
Title of host publicationSociety of Petroleum Engineers - SPE Kuwait Oil and Gas Show and Conference 2019, KOGS 2019
PublisherSociety of Petroleum Engineers
ISBN (Electronic)9781613996744
DOIs
StatePublished - 2019

Publication series

NameSociety of Petroleum Engineers - SPE Kuwait Oil and Gas Show and Conference 2019, KOGS 2019

Bibliographical note

Publisher Copyright:
Copyright 2019, Society of Petroleum Engineers.

ASJC Scopus subject areas

  • Fuel Technology
  • Energy Engineering and Power Technology

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