Prediction of Poisson's ratio for hydraulic fracturing operations in the Oligocene formations in the Bach Ho field

Tóm tắt

In rock geomechanics analysis, Poisson's ratio is one of the critical factors that affect mechanical properties of rocks and soils, wellbore stability, in situ stress, drilling efficiency, and hydraulic fracturing design. There are two conventional methods for measuring Poisson's ratio, they are called acoustic wave method and compression testing of core sample. In the first, the Poisson's ratio is determined based on well-log data known as dynamic values. Conversion formulas need to be established for different geological conditions to obtain reliable computational results. However, the determination of each suitable conversion formula is time and money-consuming, as well as the process, is relatively complicated. The latter method must be performed in the laboratory with high accuracy equipment and requires the availability of core samples obtained through the coring process with high expenditure. To overcome the limitations of these two methods, the authors used the Artificial Intelligence technique to establish correlations between the value of Poisson's ratio and drilling parameters (e.g., weight on bit, flow rate, torque, annulus velocity, pressure losses) in the Oligocene formation of the Bach Ho field. Two machine learning algorithms including Random Forest (RF) and Decision Tree (DT) were applied in this study. On the other hand, the offset data from Well A and Well B penetrated through the Oligocene formation of the Bach Ho field were used to build, train, and verify the accuracy of the artificial intelligence simulations. Both wells have similarities in lithological characteristics and composition. The results indicated that the Artificial Intelligence models are highly accurate in predicting the value of Poisson's ratio, with correlation coefficient results for the RF model and the DT model being at 0.79 and 0.76 respectively