Estimation of Shear Wave Velocity for Shallow Depth Using Artificial Neural Network Technique: A Case Study in Rumaila oil field

Abstract

In Rumaila oilfield, the lost circulation problem is a challenging issue. The geological and geomechanical properties of subsurface formations have a role in causing circulation loss. One of the natural features related to mud loss against these formations is an unconformity surface. Inferring surface unconformity needs to be understood how the mechanical properties of rocks are distributed across the entire reservoir. The result is identifying areas of loss for solving most loss problems. Using well logs, a geomechanical model can be constructed to identify the surface unconformity. Shear wave velocity is the most crucial factor in determining mechanical properties. They are not frequently recorded during well logging for time and cost-saving purposes. save time and cost. To overcome this challenge, an ANNs model was developed to estimate the missing Vs data for the Hasa and Aruma groups in the Rumaila oil field for interested wells from the south and north domes (extending from the top of Dammam to the bottom of Hartha). The performance of the new model was tested through calibration. The outcomes showed that measured depth (MD), bulk density (RHOB), and compressional velocity (Vp) are key parameters for creating the ANN model utilizing. This study has proven that the basic systematic equations are accurately anticipate shear velocity (Vs) from conventional well logs. The correlation coefficient (R²) and the root mean square error were 0.956) and0.118, respectively. The optimum number of hidden neurons was 3 neurons). The presented model is closely resemble the measured Vs data when dataset from another well was used to check the accuracy of that predictive model. This study presents an effective, simple, and cost-effective technique, which can be used in the absence of rock tests and DTs.