A comparative study of empirical, statistical, and soft computing methods coupled with feature ranking for the prediction of water saturation in a heterogeneous oil reservoir

• Autorzy: Jahan Labiba Nusrat , Munshi Tanveer Alam , Sutradhor Sree Sujon , Hashan Mahamudul

Identyfikatory

DOI

10.1007/s11600-021-00647-w

• Języki publikacji: EN

Abstrakty

EN

The present research applies six empirical, three statistical, and two soft computing methods to predict water saturation of an oil reservoir. The employed empirical models are ‘Archie (Trans AIME 146(1):54–62, 1942),’ ‘DeWitte (Oil Gas J 49(16):120–134, 1950),’ ‘Poupon et al. (J Petrol Technol 6(6):27–34, 1954),’ ‘Simandoux (Revue deI’Institut Francais du.Petrol, 1963),’ ‘Poupon and Leveaux (1971),’ and ‘Schlumberger (Log interpretation principles/applications, p. 235, 7th printing. Houston, 1998)’; statistical methods are ‘multiple variable regression,’ ‘fine tree, medium tree, coarse tree-based regression tree,’ and ‘bagged tree, boosted tree-based tree ensembles’; and soft computing techniques are ‘support vector machine (SVM)’ and ‘Levenberg–Marquardt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG)- based artificial neural network (ANN).’ In addition, log variables are ranked based on their significance in water saturation modeling. To achieve the goals, 521 data points are selected from three wells. Each data point has laboratory-derived core water saturation information and six well log features, such as gamma ray (GR), bulk density (RHOB), sonic travel time (DT), true resistivity (LLD), neutron porosity (φN), and Depth. Statistical indexes, namely regression coefficient, mean squared error, root mean squared error, average absolute percentage error, minimum absolute error percentage, and maximum absolute error percentage, are used to compare the prediction efficiency of study methods. Results show that the empirical models provide exceedingly poor prediction efficiency. Within the study models, fine tree, medium tree-based regression tree; bagged tree, boosted tree-based tree ensembles; fine Gaussian SVM; ANN with LM; and ANN with BR are very efficient predictive strategies. The log ranking reveals that GR and DT are the most important, whereas RHOB and φN are the least vital predictor variables in water saturation prediction.

Słowa kluczowe

EN

reservoir characteristic; water saturation; well log; support vector machine; artificial neural network; tree ensembles

PL

charakterystyka zbiornika; nasycenie wodą; geofizyka otworowa; maszyna wektorów nośnych; sztuczna sieć neuronowa; zespoły drzewne

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2021
• Tom: Vol. 69, no. 5
• Strony: 1697--1715
• Opis fizyczny: Bibliogr. 58 poz.

Twórcy

autor

Jahan Labiba Nusrat

• Department of Petroleum and Mining Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh

autor

Munshi Tanveer Alam

• Department of Petroleum and Mining Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh

autor

Sutradhor Sree Sujon

• Department of Petroleum and Mining Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh

autor

Hashan Mahamudul

• Department of Petroleum and Mining Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh

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