Artificial intelligence model for predicting geomechanical characteristics using easy-to-acquire offset logs without deploying logging tools  

作　　者：Temitope F.Ogunkunle Emmanuel E.Okoro Oluwatosin J.Rotimi Paul Igbinedion David I.Olatunji 

机构地区：[1]Department of Petroleum Engineering,Covenant University,Ota,Nigeria

出　　处：《Petroleum》2022年第2期192-203,共12页油气（英文）

基　　金：The authors would like to thank Covenant University centre for Research Innovation and Discovery Ota,Nigeria for its support in making the publication of this research possible.

摘　　要：This study focuses on predicting acoustic and mechanical rock properties using random forest and feed forward neural network models to evaluate the likelihood of developing efficient ways of handling absence of rock properties at offset locations.The Random Forest algorithm was used for direct prediction of the sonic data without considering the depth range of the facies;while Feed forward Neural network was used to predict the sonic data with emphasis on the lithofacies depths.The accuracy of these approaches was used in choosing the best and the most robust model for predicting sonic data when estimating formation strength and mechnical properties.Acoustic log was predicted after training a combination of caliper log,gamma log,depth,density log and resistivity log from offset wells.5 hidden layers that accounts for the data structural complexities was included in the model architecture.A multilayer perceptron network was adopted for the Random forest algorithm to handle linear combinations of input data set.Diverse error computations were used to evaluate the performance of the model.Lastly,mechanical properties and sanding potential was evaluated using standard relations and appropriate depositional conditions.Random forest algorithm gave the best prediction accuracy of more than 96%,but the Feed forward network has the lower mean absolute error and mean squared error of 2.75 and 5.93 respectively.Generally,the predicted compressive and shear wave velocity show increase of values with depth,a behavior that is capable of identifying payzone characteristics.This was validated by the distinction seen within the 200 feet gas sand formation in the deeper portion of the studied well(9600e9800 feet).Potential failure portions of the wells,a common feature in the field,were inferred from the sanding potential computed using the predicted mechanical properties value.

关 键 词：Shear wave velocity Mechanical properties Random forest Feed forward neural network Sanding potential 

分 类 号：TE151[石油与天然气工程—油气勘探]