TY - GEN
T1 - Machine intelligence vs. human intelligence in geological interpretation of seismic data
AU - Farfour, Mohammed
AU - Mesbah, Mostefa
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/8
Y1 - 2020/11/8
N2 - Seismic data are rich in information about the structure of geological formations and their pore content. Seismic attributes are any information that can be derived or computed from seismic data. Over the past decade, the increasing number of seismic attributes itself becomes a challenge for interpreter and even for machines to accomplish a reliable interpretation. As in oil exploration wrong decisions may result in costly dry wells, a good understanding of the seismic attributes and the limitations of the machine is of great importance. In this paper, we conduct a brief comparison between deterministic approaches by human and statistical approaches established by machine intelligence. Then, a real example from North Western Australia is presented where seismic attributes are used to find hydrocarbon-saturated rocks and discriminate them from their surrounding rocks. Machine learning, namely, artificial neural network (ANN) is used to combine the seismic attributes to produce hydrocarbon probability cube from seismic data. A number of seismic attributes are used in the training of the neural network. Further, we propose a new seismic attribute combining the gradient and the scaled-Poisson reflectivity which are both sensitive to fluid. The new attribute showed a better detection than the neural network. To benefit from the statistical relationship determined by the ANN and from the proposed deterministic attribute we added the attribute to the ANN training. A better detection of the gas-saturated reservoir is achieved. We conclude that that machine learning algorithms can reduce the workload and save computation time but they need assistance from interpreter to make good decisions.
AB - Seismic data are rich in information about the structure of geological formations and their pore content. Seismic attributes are any information that can be derived or computed from seismic data. Over the past decade, the increasing number of seismic attributes itself becomes a challenge for interpreter and even for machines to accomplish a reliable interpretation. As in oil exploration wrong decisions may result in costly dry wells, a good understanding of the seismic attributes and the limitations of the machine is of great importance. In this paper, we conduct a brief comparison between deterministic approaches by human and statistical approaches established by machine intelligence. Then, a real example from North Western Australia is presented where seismic attributes are used to find hydrocarbon-saturated rocks and discriminate them from their surrounding rocks. Machine learning, namely, artificial neural network (ANN) is used to combine the seismic attributes to produce hydrocarbon probability cube from seismic data. A number of seismic attributes are used in the training of the neural network. Further, we propose a new seismic attribute combining the gradient and the scaled-Poisson reflectivity which are both sensitive to fluid. The new attribute showed a better detection than the neural network. To benefit from the statistical relationship determined by the ANN and from the proposed deterministic attribute we added the attribute to the ANN training. A better detection of the gas-saturated reservoir is achieved. We conclude that that machine learning algorithms can reduce the workload and save computation time but they need assistance from interpreter to make good decisions.
KW - fluid detection
KW - geophysics
KW - machine learning
KW - seismic attributes
UR - http://www.scopus.com/inward/record.url?scp=85100543907&partnerID=8YFLogxK
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U2 - 10.1109/DASA51403.2020.9317032
DO - 10.1109/DASA51403.2020.9317032
M3 - Conference contribution
AN - SCOPUS:85100543907
T3 - 2020 International Conference on Decision Aid Sciences and Application, DASA 2020
SP - 996
EP - 999
BT - 2020 International Conference on Decision Aid Sciences and Application, DASA 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Decision Aid Sciences and Application, DASA 2020
Y2 - 7 November 2020 through 9 November 2020
ER -