Integration of Seismic Attributes and Wellbore Data of Ghar Formation in the Hendijan and Bahregansar Oilfields

Safarpour, Mehrdad; Riahi, Mohammad Ali; Rahimi, Mehran

doi:10.22050/ijogst.2022.336225.1634

Integration of Seismic Attributes and Wellbore Data of Ghar Formation in the Hendijan and Bahregansar Oilfields

Document Type : Research Paper

Authors

Mehrdad Safarpour ¹

Mohammad Ali Riahi ²

Mehran Rahimi ³

¹ M.S. Student, Faculty of Mining Engineering, Science and Research Branch, University of Tehran, Tehran, Iran

² Professor, Institute of Geophysics, University of Tehran, Tehran, Iran

³ Ph.D. Candidate, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran

https://doi.org/10.22050/ijogst.2022.336225.1634

Abstract

The primary purpose of this paper is to estimate and evaluate the petrophysical properties of the Ghar formation in the Hendijan and Bahregansar oilfields using a combination of seismic and well-log data. This study follows a step-by-step regression approach. First, sonic, density, and porosity well-log data are collected. Second, seismic attributes, including amplitude, phase, frequency, and acoustic impedance, are extracted from the seismic lines intersecting the wellbore locations. Then, using the MFLN and PNN intelligent systems, a relationship between porosity, shale volume, saturation, and seismic attributes is established. Using this relationship, the physical and petrophysical properties of the reservoir in the Ghar formation are estimated and evaluated. We estimated the reservoir porosity to be between 15% and 20%, higher in the Hendijan oilfield than in the Bahregansar oilfield. The water saturation in the Ghar formation varied from 25% to 30%. On the other hand, the clay content and shale volume of the Ghar formation in the Hendijan field were higher than those of the Bahregansar oil field.

Highlights

An appropriate attribute selection process is a vital step in neural network implantation.
Seismic attributes have shown a perfect correlation with petrophysical parameters in the studied area.
Using petrophysical information derived from seismic attributes, one can identify high hydrocarbon potential zones in the seismic section area.

Keywords

Bahregansar

Ghar Formation

Hendijan

Neural Networks

Seismic Attributes

Subjects

Petroleum Engineering – Exploration

Abdel-Fattah, M. I., Pigott, J. D., and El-Sadek, M. S., Integrated Seismic Attributes and Stochastic Inversion for Reservoir Characterization: Insights from Wadi Field (NE Abu-Gharadig Basin, Egypt). Journal Of African Earth Sciences, Vol. 161, 103661 P., 2020.

Abdulaziz, A. M., Mahdi, H. A., and Sayyouh, M. H., Prediction of Reservoir Quality Using Well Logs and Seismic Attributes Analysis with An Artificial Neural Network: A Case Study from Farrud Reservoir, Al-Ghani Field, Libya. Journal Of Applied Geophysics, Vol. 161, p. 239–254, 2019.

Abdulaziz, A. M., The Effective Seismic Attributes in Porosity Prediction for Different Rock Types: Some Implications from Four Case Studies. Egyptian Journal of Petroleum, Vol. 29, No. 1, p. 95–104. Https://Doi.Org/10.1016/J.Ejpe.2019.12.001, 2020.

Abdulaziz, A.M., Mahdi, H.A., and Sayyouh, M.H., Prediction of Reservoir Quality Using Well Logs and Seismic Attributes Analysis with An Artificial Neural Network: A Case Study from Farrud Reservoir, Al-Ghani Field, Libya. J. Appl. Geophys., 161 (2019), p. 239–254, 2019.

Alsharhan, A. E. and Nairn, M., A Review of the Cretaceous Formations in the Arabian Peninsula and Gulf: Part I, Lower Cretaceous (Thamama Group), Stratigraphy and Paleogeography, Journal of Petroleum Geology, Vol. 9, p. 365–392, 1986.

Berthold, M.R., Diamond, J., Constructive Training of Probabilistic Neural Networks. Neurocomputing, Vol. 19, p. 167–183, 1998.

Brown, A., Understanding Seismic Attributes, Geophysics, Vol. 66, No. 1, p. 47–49, 2001.

Faraji, M. A., Kadkhodaie, A., Rezaee, R., and Wood, D. A., Integration of Core Data, Well Logs, And Seismic Attributes for Identification of the Low Reservoir Quality Units with Unswept Gas in the Carbonate Rocks of the World’s Largest Gas Field. Journal Of Earth Science, Vol. 28, No. 5, p. 857–866, 2017.

Hampson, D.P., Schuelke, J.S., and Quirein, J.A., Use of Multi Attributes Transform to Predict Log Properties from Seismic Data. Geophysics, Vol. 66, p. 230–236, 2001.

Hollis, C., Diagenetic Controls on Reservoir Properties of Carbonate Successions Within the Albian–Turonian of the Arabian Plate. Petroleum Geoscience, Vol. 17, No. 3, p. 223–241, 2011.

Kadkhodaie-Ilkhchi, A., Rezaee, M.R., Rahimpour-Bonab, H., and Chehrazi, A., Petrophysical Data Prediction from Seismic Attributes Using Committee Fuzzy Inference System. Computers and Geosciences, Vol. 35, No. 12, p. 2314–2330. Https://Doi.Org/10.1016/J.Cageo.2009.04.010, 2009.

Lang, X. and Grana, D., Geostatistical Inversion of Prestack Seismic Data for the Joint Estimation of Facies and Impedances Using Stochastic Sampling from Gaussian Mixture Posterior Distributions. Geophysics, Vol. 82, No. 4, p. 55–65, 2017.

Lapponi, G. Casini, I. Sharp, W., Blendinger, N., Fernandez, I., Romaire, and Hunt, D., From Outcrop To 3D Modelling: A Case Study of a Dolomitized Carbonate Reservoir, Zagros Mountains, Iran, Petroleum geoscience, Vol. 17, p. 283–307, 2011.

Mao, K.Z., Tan, K. C., and Ser, W., Probabilistic Neural-Network Structure Determination for Pattern Classification. IEEE Trans. Neural Networks, Vol.11, p. 1009–1016, 2000.

Maurya, S. P., Singh, K. H., Kumar, A., and Singh, N. P., Reservoir Characterization Using Post-Stack Seismic Inversion Techniques Based on Real Coded Genetic Algorithm. Jour. Of Geophysics, Vol. 39, No. 2, 2018.

Mohebali, B., Tahmassebi, A., Meyer-Baese, A., and Gandomi, A. H., Handbook of Probabilistic Models. p. 347–367, https://Doi.Org/10.1016/B978-0-12- 816514-0.00014-X, 2020.

Rastegarnia, M., and Kadkhodaie, A., Estimation of Flow Zone Indicator Distribution by Using Seismic Data: A Case Study from A Central Iranian Oilfield, Iranian Journal of Oil & Gas Science and Technology, Vol. 2, No. 4, p. 12–25, 2013.

Russell, B. H., Introduction to Seismic Inversion Methods. Course Notes Series, No. 2, Society of Exploration Geophysicists, 1988.

Russell, B. H., The Application of Multivariate Statistics and Neural Networks to the Prediction of Reservoir Parameters Using Seismic Attributes. Ph.D. Dissertation, University of Calgary, Department of Geology and Geophysics, 2004.

Russell, B.H., and Hampson, D., The Old and the New in Seismic Inversion, CSEG Recorder, Vol. 31, No. 10, p. 5–11, 2006.

Setudehnia, A., The Mesozoic Sequence in Southwest Iran and Adjacent Area, Journal of Petroleum Geology, Vol.1, No.1, p. 3–42, 1978.

Sharifi, J., Sabeti, N., Saberi, M.R., and Sokouti Diarjan, M.R., Identify and Correction of The Borehole Effect on Petrophysical Logs, 17th Iranian Geophysical Conference, Tehran, Iranian Geophysical Association, 2016.

Sharland P. R., Archer R., Casey D. M., Davis R. B., Hall S. H., Heward A. P., Horbury A. D., and Simmons M. D. Arabian Plate Sequence Stratigraphy, Geoarabia Special Pub., Gulf Petro Link, Bahrain., 371 P., 2001.

Sheriff, R. E., Encyclopedic Dictionary of Applied Geophysics, SEG, 2002.

Shiri, S. and Falahat, R., Rock Physics Modeling And 4D Seismic Feasibility Study in One of the Iranian Carbonate Reservoirs. Journal of Applied Geophysics, 103855, 2019.

Taner M. T., Schuelke J. S., O’Doherty R., and Baysal E., Seismic Attributes Revisited 64th SEG Annu. Int. Meeting Vol. 13, 1104 P, 1994.

White, R.E., Properties of Instantaneous Seismic Attributes, The Leading Edge, Vol. 10, No. 7, p. 26–32, 1991.

Xu, Z., Hu, S., Wang, L., Zhao, W., Cao, Z., Wang, R., and Jiang, L., Seismic Sedimentologic Study of Facies and Reservoir in Middle Triassic Karamay Formation of the Mahu Sag, Junggar Basin, China. Marine and Petroleum Geology, Vol. 107, p. 222–236, 2019.

Ziegler, M.A., Late Permian to Holocene Paleofacies Evolution of the Arabian Plate and its Hydrocarbon Occurrences. Geoarabia, Vol. 6, p. 445–504, 2001.