Estimation of a Maturity Index from Well Log and Seismic Data via Ant Colony Optimization Technique in Mansuri Oil Field

Document Type : Research Paper

Authors

1 Department of Geology, Faculty of Science, University of Sistan and Baluchestan, Zahedan, Iran

2 Department of Geology, Faculty of Natural Sciences, University of Tabriz, Iran

3 Department of Geology, National Iranian South Oil Company (NISOC), Ahvaz, Iran

Abstract

Thermal maturity is the primary geological factor in petroleum exploration and source rock assessment.  Besides, Thermal maturity is an index to determine the maximum temperature that the source rock which has endured at the different stages of  an evaluation of hydrocarbon. Measurement of this parameter requires conducting geochemical analyses on cutting samples which is expensive and time consuming, therefore the main objective of the research is development of a new method for direct estimation of this parameter from well log and seismic using a metaheuristic algorithm called Ant Colony Algorithm. In this research, 2D-seismic and petrophysical data of the Pabdeh Formation from 3 wells of the Mansuri Oil field were used. Also, a Maturity Index Equation at the well location was used to predict Maturity Index values from well logs; such as, neutron, resistivity, sonic and density. These calculated values were used as inputs for a Multi Attribute Analysis. Seismic inversion was performed based on a Neural Networks Algorithm because of its high accuracy;  moreover, the resulting acoustic impedance was utilized as an external attribute. Afterwards, a Probabilistic Neural Network was trained using the set of predicting attributes derived from multiple  regressions. Subsequently, MI was estimated using seismic attributes with a correlation coefficient of 87%. In the next step, the nonlinear Ant Colony Optimization technique was utilized as an intelligent tool to estimate and product a Maturity Index seismic section. It calculates weight factors for each of seismic attributes. The correlation coefficient between the input and output data was estimated 91% by nonlinear ACO. Finally, MI seismic section was produced. The comparison of the results from PNN with ACO methods revealed that the accuracy of ACO model was higher than the PNN.
 

Keywords

References

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Journal of Petroleum Research
Volume 26, 95-6 - Serial Number 91
March and April 2017
Pages 136-147

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