Artificial Neural Network-Based Caprock Structural Reliability Analysis for CO 2 Injection Site—An Example from Northern North Sea

Sajjad Ahmadi Goltapeh, Md Jamilur Rahman, Nazmul Haque Mondol and Helge Hellevang
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Sajjad Ahmadi Goltapeh: Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, Norway
Md Jamilur Rahman: Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, Norway
Nazmul Haque Mondol: Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, Norway
Helge Hellevang: Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, Norway

Energies, 2022, vol. 15, issue 9, 1-16

Abstract: In CO 2 sequestration projects, assessing caprock structural stability is crucial to assure the success and reliability of the CO 2 injection. However, since caprock experimental data are sparse, we applied a Monte Carlo (MC) algorithm to generate stochastic data from the given mean and standard deviation values. The generated data sets were introduced to a neural network (NN), including four hidden layers for classification purposes. The model was then used to evaluate organic-rich Draupne caprock shale failure in the Alpha structure, northern North Sea. The train and test were carried out with 75% and 25% of the input data, respectively. Following that, validation is accomplished with unseen data, yielding promising classification scores. The results show that introducing larger input data sizes to the established NN provides better convergence conditions and higher classification scores. Although the NN can predicts the failure states with a classification score of 97%, the structural reliability was significantly low compare to the failure results estimated using other method. Moreover, this indicated that during evaluating the field-scale caprock failure, more experimental data is needed for a reliable result. However, this study depicts the advantage of machine learning algorithms in geological CO 2 storage projects compared with similar finite elements methods in the aspect of short fitting time, high accuracy, and flexibility in processing different input data sizes with different scales.

Keywords: machine learning; neural network; caprock reliability; caprock integrity; Monte Carlo; gaussian formalism; northern North Sea; alpha prospect; Smeaheia (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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