Methodology-selection by Fuzzy Analytic Hierarchy Process for Studying Net Pays Pedram Masoudi1,3, Mohammad Ataei2, Tahar Aïfa1*, Hossein Memarian3* 1*: Géosciences-Rennes, CNRS UMR6118, Univ. de Rennes 1, France, tahar.aifa@univ-rennes1.fr 2: School of Mining, Petroleum and Geophysics Eng., Shahrood Univ. of Tech., Iran. 3*: School of Mining Eng., Univ. of Tehran, Iran, memarian@ut.ac.ir

Why methodology selection? Ex. of software selection Software programs differ in: Functionalities and performance Precision Being user-friendly/ complexity of parameters Speed of calculations Simulating geological conditions … Projects goals vary: Research oriented Industrial applications Project scale Addressing to which organization … 6-8 November 2016 – Tehran International Exhibition Center

Definition of net pay and methods Table of content Definition of net pay and methods AHP and FAHP Comparison matrices Results and conclusion 6-8 November 2016 – Tehran International Exhibition Center

What is net pay? Modified from Worthington (2010) Modified from Bashari (2007) 6-8 November 2016 – Tehran International Exhibition Center

Net pay detection methodologies Cut-off (Worthington 2010): conventional method Diffusivity equation (Masoudi et al. 2011): MSc Bayesian classifier (Masoudi et al. 2012): MSc Dempster rule of combination (Masoudi et al. 2014): IOOC Artificial Neural Networks (ANN) (Masoudi et al. 2014): IOOC 6-8 November 2016 – Tehran International Exhibition Center

Net pay detection: cut-off If-then rules on shale, porosity, saturation, permeability, … Sarvak Formation, the Abadan Plain (Masoudi et al. 2012) 6-8 November 2016 – Tehran International Exhibition Center

Net pay detection: Diffusivity equation Calculating a continuous “productivity index” based on solving diffusivity equation Sarvak Formation, the Abadan Plain (Masoudi et al. 2012) 6-8 November 2016 – Tehran International Exhibition Center

Net pay detection: Bayesian classifier Training a based on well-test results of well 3 Sarvak Formation, the Abadan Plain (Masoudi et al. 2012) 6-8 November 2016 – Tehran International Exhibition Center

Net pay detection: Bayesian classifier Training a based on well-test results of well 4 Sarvak Formation, the Abadan Plain (Masoudi et al. 2012) 6-8 November 2016 – Tehran International Exhibition Center

Net pay detection: Sugeno-Bayesian Fusion of Bayes3 and Bayes4 by Sugeno integral Sarvak Formation, the Abadan Plain (Masoudi et al. 2012) 6-8 November 2016 – Tehran International Exhibition Center

Net pay detection: Dempster rule Combining the information of shale content, porosity and saturation Mishrif reservoir, the Persian Gulf (Masoudi et al. 2014) 6-8 November 2016 – Tehran International Exhibition Center

Net pay detection: Artificial Neural Network Combining the information of shale content, porosity and saturation Backpropagation due to well-test Mishrif reservoir, the Persian Gulf (Masoudi et al. 2014) 6-8 November 2016 – Tehran International Exhibition Center

Net pay detection: Dempster rule Combining the information of shale content, porosity and saturation Burgan reservoir, the Persian Gulf (Masoudi et al. 2014) 6-8 November 2016 – Tehran International Exhibition Center

Net pay detection: Artificial Neural Network Combining the information of shale content, porosity and saturation Backpropagation due to well-test Burgan reservoir, the Persian Gulf (Masoudi et al. 2014) 6-8 November 2016 – Tehran International Exhibition Center

Table of comparison IOOC report (Masoudi 2013) Precision   Precision Generalization Fuzziness Simplicity of Method User-friendly Speed Cut-off acceptable in carbonate, poor in sandstone acceptable discrete very simple too simple time consuming ANN 86% in carbonate; 94% in sandstone not checked complex difficult rather time consuming Bayes 74% in carbonate; 81% in sandstone fuzzy: too weak crisp: very good simple quick DST very good continuous very complex IOOC report (Masoudi 2013) 6-8 November 2016 – Tehran International Exhibition Center

Hierarchy: linking alternatives to the goals 6-8 November 2016 – Tehran International Exhibition Center

Analytical Hierarchy Process (AHP) A decision making technique Organizes human’s thoughts and psyches (Saaty 1987) by : comparing alternatives due to criteria comparing criteria due to goals Algorithm: Graph of hierarchy Comparison matrices for each level: Subjective pairwise comparison (Saaty 1977,1987): 1- Reciprocal 2- homogeneity 3- dependency 4- expectations and 5- hypothetical syllogism/ cardinal consistency Weighting: (logarithmic) least square method, eigenvector or approximate method Multiplication and scoring Inconsistency rate and modification of comparison matrices Final score 6-8 November 2016 – Tehran International Exhibition Center

Fuzzy AHP 6-8 November 2016 – Tehran International Exhibition Center

Comparison matrices between alternatives: 6 matrices Comparison matrix of precision with: IR=0.0076<0.10   Cut-off ANN Bayes DST [1/2,1,2] [1/5,1/4,1/3] [1/4,1/3,1/2] [3,4,5] [1,2,3] [2,3,4] [1/3,1/2,1] weights 0.14 0.43 0.29 Comparison matrix of generalization with: IR=0.0000<0.10   Cut-off ANN Bayes DST [1/2,1,2] [1/6,1/5,1/4] [4,5,6] weights 0.22 0.33 6-8 November 2016 – Tehran International Exhibition Center

Comparison matrices between criteria: 3 matrices   Precision Group Application Group Precision Generalization Fuzziness Simplicity of Method User-friendly Speed Comparison matrix for industrial mode with Inconsistency Rate: IR=0.0448 Preciseness [1/2,1,2] [2,3,4] [8,9,9] [4,5,6] [6,7,8] [1/4,1/3,1/2] [1/9,1/9,1/8] [1/8,1/7,1/6] [1/6,1/5,1/4] Weights 0.31 0.19 0.00 0.12 0.06 Comparison matrix for research mode with Inconsistency Rate: IR=0.0600 [3,4,5] [5,6,7] [1/5,1/4,1/3] [1/7,1/6,1/5] 0.25 6-8 November 2016 – Tehran International Exhibition Center

Final scores for different modes   Precision Generalization Fuzziness Simplicity of Method User-friendly Speed Final Score General mode weights 0.24 0.09 Cut-off 0.14 0.22 0.50 0.43 0.00 0.23 ANN 0.17 0.25 Bayes 0.29 0.33 0.26 DST Industrial mode 0.31 0.19 0.12 0.06 0.21 0.27 Research mode 6-8 November 2016 – Tehran International Exhibition Center

Thank you Conclusion None of the methods could be prioritized But cut-off method is always ranked 3rd Having different algorithms is not useless at all Acknowledgement: This work has been supported by the Center for International Scientific Studies & Collaboration (CISSC) and French Embassy in Iran through PHC Gundishapur Program. Mahta Gholizadeh Ansari for sharing her experience about fuzzy AHP Conclusion   General Mode Industrial Mode Research Mode 1st Bayes, DST ANN, DST ANN, Bayes 2nd ANN Bayes DST 3rd Cut-off Thank you