TY - CHAP
T1 - Porosity Prediction from Seismic Using Machine Learning
T2 - 1st International conference on Mediterranean Geosciences Union, MedGU 2021
AU - Al Sarmi, Mohamed
AU - Farfour, Mohamed
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - Porosity has always been an essential property for determining reservoirs’ volumetric; however, determining porosity with an acceptable range of certainty carries many challenges. The deep-water Plover reservoir in Poseidon area, North Western Australia exhibits a special depositional environment, unique interior structure, and a complex porosity distribution that make predicting reliable reservoir properties challenging. This study aims to enhance the characterization of the Poseidon reservoir in terms of porosity using a combination of reservoir geophysics (seismic attributes and petrophysics) through machine learning (ML) techniques. Three methods of porosity estimation from seismic data have been implemented and compared: (1) multi-linear regression (MLR), (2) probabilistic neural network (PNN), and (3) deep feed-forward neural networks (DFNN). The seismic data available is a post-stack volume inverted to derive P-impedance. After the inversion, training data at well locations were analyzed, and statistical relationships were established between the porosity log, the seismic data, and the seismically derived P-impedance. Cross-validation was used to assess the reliability of the derived relationships. The probabilistic neural network (PNN) showed promising results far better than other comparative methods. Apart from PNN, the deep feed-forward neural network (DFNN) was also tested but gave limited success due to the scarcity of labeled data. The lack of labeled data has limited the optimum prediction of subsurface properties to a large extent. The predicted porosity from PNN has revealed geological features that otherwise are not seen in simple seismic inversions or seismic attribute analysis.
AB - Porosity has always been an essential property for determining reservoirs’ volumetric; however, determining porosity with an acceptable range of certainty carries many challenges. The deep-water Plover reservoir in Poseidon area, North Western Australia exhibits a special depositional environment, unique interior structure, and a complex porosity distribution that make predicting reliable reservoir properties challenging. This study aims to enhance the characterization of the Poseidon reservoir in terms of porosity using a combination of reservoir geophysics (seismic attributes and petrophysics) through machine learning (ML) techniques. Three methods of porosity estimation from seismic data have been implemented and compared: (1) multi-linear regression (MLR), (2) probabilistic neural network (PNN), and (3) deep feed-forward neural networks (DFNN). The seismic data available is a post-stack volume inverted to derive P-impedance. After the inversion, training data at well locations were analyzed, and statistical relationships were established between the porosity log, the seismic data, and the seismically derived P-impedance. Cross-validation was used to assess the reliability of the derived relationships. The probabilistic neural network (PNN) showed promising results far better than other comparative methods. Apart from PNN, the deep feed-forward neural network (DFNN) was also tested but gave limited success due to the scarcity of labeled data. The lack of labeled data has limited the optimum prediction of subsurface properties to a large extent. The predicted porosity from PNN has revealed geological features that otherwise are not seen in simple seismic inversions or seismic attribute analysis.
KW - Deep feed neural network (DFNN)
KW - Deep marine reservoir
KW - Machine learning
KW - Porosity
KW - Probabilistic neural network (PNN)
UR - http://www.scopus.com/inward/record.url?scp=85189350549&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85189350549&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-43218-7_85
DO - 10.1007/978-3-031-43218-7_85
M3 - Chapter
AN - SCOPUS:85189350549
SN - 9783031432170
T3 - Advances in Science, Technology and Innovation
SP - 363
EP - 366
BT - Recent Research on Geotechnical Engineering, Remote Sensing, Geophysics and Earthquake Seismology - Proceedings of the 1st MedGU, Istanbul 2021 Volume 3
A2 - Çiner, Attila
A2 - Ergüler, Zeynal Abiddin
A2 - Bezzeghoud, Mourad
A2 - Ustuner, Mustafa
A2 - Eshagh, Mehdi
A2 - El-Askary, Hesham
A2 - Biswas, Arkoprovo
A2 - Gasperini, Luca
A2 - Hinzen, Klaus-Günter
A2 - Karakus, Murat
A2 - Comina, Cesare
A2 - Karrech, Ali
A2 - Polonia, Alina
A2 - Chaminé, Helder I.
PB - Springer Nature
Y2 - 25 November 2021 through 28 November 2021
ER -