Abstract
Determination of the rock elastic parameters is essential in geomechanical studies. Among the elastic parameters, Young’s modulus (YM) and Poisson’s ratio (PR) have many applications in wellbore stability analysis, hydraulic fracturing, casing design, and sand production. In this study, the machine learning methods, including adaptive Neuro-Fuzzy inference system (ANFIS), artificial neural network (ANN), and support vector machine (SVM) are used to predict the rock elastic parameters. Using these models requires measuring the static elastic parameters, so 34 laboratory tests are used to develop empirical correlations between static elastic and dynamic elastic parameters. Then, the static elastic parameters at all logged intervals are calculated by applying the suggested empirical correlations. To demonstrate the capabilities of the ANFIS, ANN, and SVM methods, DT, RHOB, and NPHI data are used as inputs, and YM and PR data are used as outputs. The performance of single models can be enhanced using ensemble models, such as simple averaging ensemble (SAE), weighted averaging ensemble (WAE), and neural network ensemble (NNE). The results of the single models showed that ANN models performed better overall than other single models. The results also showed that ensemble models predicted elastic parameters better than single models. This shows that NNE model with R2 of 0.998 and 0.993, MAPE error values of 0.0041 and 0.0010, and RMSE error values of 0.58 and 0.0029 for the training data of YM and PR is more accurate and reliable than SAE, WAE and single models.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by MRAE, AH and MS. The first draft of the manuscript was written by MRAE and all authors commented on previous versions of the manuscript. All authors discussed the results and contributed to the final manuscript.
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Aghakhani Emamqeysi, M.R., Fatehi Marji, M., Hashemizadeh, A. et al. Prediction of elastic parameters in gas reservoirs using ensemble approach. Environ Earth Sci 82, 269 (2023). https://doi.org/10.1007/s12665-023-10958-4
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DOI: https://doi.org/10.1007/s12665-023-10958-4