PERMEABILITY ESTIMATION IN CARBONATE RESERVOIRS USING ELECTROFACIES IN AN OIL FIELD IN THE SOUTHWEST OF IRAN

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KAYHANI H.R.; RIAHI M.A.; NOUROZI GH.H.

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Journal: PETROLEUM RESEARCH Year:2015 | Volume:24 | Issue:80 Page(s): 28-40

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Title

PERMEABILITY ESTIMATION IN CARBONATE RESERVOIRS USING ELECTROFACIES IN AN OIL FIELD IN THE SOUTHWEST OF IRAN

Author(s)

KAYHANI H.R. | RIAHI M.A. | NOUROZI GH.H. | Issue Writer Certificate

Keywords

PERMEABILITYQ2

ELECTROFACIESQ2

PRINCIPAL COMPONENTS ANALYSISQ2

ALTERNATING CONDITIONAL EXPECTATIONSQ2

SUPPORT VECTOR MACHINEQ2

ARTIFICIAL NEURAL NETWORKSQ2

Abstract

An ELECTROFACIES in defined by a similar set of log responses that characterize a spe-cific bed and allow it to be distinguished from other beds. ELECTROFACIES character-ization is a simple and cost-effective approach to obtaining PERMEABILITY estimates in heterogeneous carbonate reservoirs using commonly available well logs. For-mation PERMEABILITY is often measured directly from core samples in the laboratory or evaluated from the well test data. The first method is very expensive. Moreover, the well test data or core data are not available in every well in a field; however, the majority of wells are logged. We propose a two-step approach to PERMEABILITY prediction from well logs that uses non parametric regression in conjunction with multivariate statistical analysis. First, we classify the well-log data into electrofa-cies types. This classification does not require any artificial subdivision of the data population and it follows naturally based on the unique characteristics of well-log measurements reflecting minerals and lithofacieswithin the logged interval. A com-bination of PRINCIPAL COMPONENTS ANALYSIS (PCA), model-based cluster analysis (MCA), and discriminant analysis is used to characterize and identify ELECTROFACIES types. Second, we apply nonpararnetric regression techniques to predict perme-ability using well logs within each ELECTROFACIES. Three nonparametric approaches are examined, namely ALTERNATING CONDITIONAL EXPECTATIONS (ACE), support vector machine (SVM), and ARTIFICIAL NEURAL NETWORKS (ANN), and the relative advantages and disadvantages are explored. For PERMEABILITY predictions, the ACE model ap-pears to outperform the other non parametric approaches. We applied the proposed technique to a highly heterogeneous carbonate reservoir in the southwest of Iran.

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APA: Copy

KAYHANI, H.R., RIAHI, M.A., & NOUROZI, GH.H.. (2015). PERMEABILITY ESTIMATION IN CARBONATE RESERVOIRS USING ELECTROFACIES IN AN OIL FIELD IN THE SOUTHWEST OF IRAN. PETROLEUM RESEARCH, 24(80), 28-40. SID. https://sid.ir/paper/114948/en

Vancouver: Copy

KAYHANI H.R., RIAHI M.A., NOUROZI GH.H.. PERMEABILITY ESTIMATION IN CARBONATE RESERVOIRS USING ELECTROFACIES IN AN OIL FIELD IN THE SOUTHWEST OF IRAN. PETROLEUM RESEARCH[Internet]. 2015;24(80):28-40. Available from: https://sid.ir/paper/114948/en

IEEE: Copy

H.R. KAYHANI, M.A. RIAHI, and GH.H. NOUROZI, “PERMEABILITY ESTIMATION IN CARBONATE RESERVOIRS USING ELECTROFACIES IN AN OIL FIELD IN THE SOUTHWEST OF IRAN,” PETROLEUM RESEARCH, vol. 24, no. 80, pp. 28–40, 2015, [Online]. Available: https://sid.ir/paper/114948/en

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