Novel approaches in geomechanical parameter estimation using machine learning methods and conventional well logs

Today, geomechanics plays a crucial role in the oil industry, particularly in enhancing production and ensuring well stability. To achieve optimal results, accurate estimation of geomechanical parameters is essential. One of the low-cost and accurate methods for estimating geomechanical parameters is the use of intelligent methods. In this research, geomechanical parameters are estimated using conventional data logs using intelligent methods. The aim of this study is to introduce a new machine learning algorithm to estimate geomechanical parameters using conventional data logs in one of the hydrocarbon field wells in southwest Iran. In this article, the shear wave velocity and uniaxial compressive strength (UCS) were estimated using machine learning algorithms. Subsequently, other geomechanical parameters were calculated based on these estimated parameters derived from machine learning algorithms. For shear wave velocity (Vs) prediction using MLP and CLM (CNN+LSTM+MLP) algorithms, First, effective features were selected using the Auto-encoder deep learning algorithm. The selected features for Vs input into the algorithms were Vp, RHOB, CALIPER, and NPHI, and then the Vs is estimated with MLP and CLM algorithm. To evaluate the results, the model was assessed using metrics such as MAE, MAPE, MSE, RMSE, NRMSE, and R2 on the train, test, and blind datasets. The CLM algorithm consistently demonstrated superior performance across all datasets, including training, testing, and blind data sets. The R2 values for blind data were RMLP2=0.8727, RCLM2=0.9274, respectively. These outputs are crucial for estimating subsequent studies. Next, Elastic Young's moduli and Poisson's ratio were calculated, and the dynamic brittleness index was computed using dynamic Young's modulus and Poisson's ratio. Subsequently, UCS values were predicted using machine learning algorithms. Since there were 12 laboratory core samples of UCS available, UCS was initially calculated using relevant empirical relations and data from some available well logs. This process aimed to extrapolate these core results to cover the entire target depth range from 3551.072 to 3799.789 m. Subsequently, a relationship between UCS derived from well logs and laboratory results was established for the depths corresponding to where the laboratory samples were recorded. Following that, an autoencoder deep network was utilized to select effective features for predicting UCS. The selected features for UCS input into the algorithms were Vp, RHOB, and CALIPER. Subsequently, UCS was estimated using MLP and CLM algorithms. To evaluate the results, the model performance was assessed using metrics such as MAE, MAPE, MSE, RMSE, NRMSE, and R2 on the train, test, and blind datasets. Furthermore, the results were checked and evaluated using a set of statistical measures calculated for the train, test, and blind datasets, that RMLP2=0.9305,RCLM2=0.9953 were obtained for blind UCS data. The results demonstrate that CLM achieves high accuracy in estimating these parameters, with deep learning algorithms showing higher determination coefficients and lower errors compared to MLP. Furthermore, UCS and tensile strength were calculated, followed by the computation of static brittleness index. The relationship between dynamic and static brittleness index was also investigated. Overall, the findings indicate that machine learning algorithms are robust and accurate methods for estimating Vs, UCS, and other geomechanical parameters using conventional logs.