Automatic Identification of Formation Iithology from Well Log Data: A Machine Learning Approach

Seyyed Mohsen Salehi, Bizhan Honarvar;


Determination of the hydrocarbon content and also the successful drilling of petroleum wells are highly contingent upon the lithology of the underground formation. Conventional lithology identification methods are either uneconomical or of high uncertainties.The main aim of this study is to develop an intelligent model based on Least Squares Support Vector Machine (LSSVM) and Coupled Simulated Annealing (CSA) algorithm simply called CSA-LSSVM for predicting the lithology in one of the Iranian oilfields. To this end, photoelectric index (PEF) values were simulated by CSA-LSSVM algorithm based on valid well logging data generally known as lithology indicators. Model predictions were compared to the real data obtained from well logging operation and the overall Correlation Coefficient (R2) of 0.993 and Average Absolute Relative Deviation (AARD) of 1.6% were obtained for the total dataset (3243 data points) which shows the robustness of the CSA-LSSVM algorithm in predicting accurate PEF values. In order to check the validity of the employed well log data,value statistical method was implemented in this study for detecting the possible outliers. However, diagnosing only one single data point as the suspected data or probable outlier reveals the validity of recorded data points and shows high applicability domain of the proposed model.


Lithology; Least Squares Support Vector Machine (LSSVM); Coupled Simulated Annealing (CSA); Outlier

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