Evaluation of empirical relationships in carbonates by developing a 1D mechanical earth model in an oil field in Southwestern Iran
Accurate determination of rock mechanical parameters is crucial for safe and efficient drilling, production, and reservoir development in these complex formations. While numerous empirical relationships have been proposed to estimate these properties, there is no reliable way to guide the selection...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302500876X |
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| Summary: | Accurate determination of rock mechanical parameters is crucial for safe and efficient drilling, production, and reservoir development in these complex formations. While numerous empirical relationships have been proposed to estimate these properties, there is no reliable way to guide the selection of suitable models in new studies. This study develops a one-dimensional mechanical earth model (1D MEM) for a carbonate reservoir in southwestern Iran to systematically evaluate existing empirical relationships for carbonate rocks. First, laboratory tests were performed on the obtained core samples. The results were then integrated with petrophysical data to establish new empirical correlations and convert dynamic measurements into static elastic and rock strength parameters. The study involved estimating Biot's effective stress coefficient, calculating the pore pressure and in-situ stresses profiles, and calibrating them using field data. Additionally, this study reviewed existing methods and empirical relationships for estimating key physico-mechanical rock parameters. The predictions of these relationships were statistically compared with results from the current research as reference data. Using the Taylor diagram as an innovative method the relationships were ranked based on their prediction accuracy, providing a guideline for selecting optimal relationships to use in future studies on similar lithologies. The comprehensive statistical analysis on more than 200 existing empirical relationships showed that the effectiveness of predictive relationships is influenced by factors such as lithology, geological environment, Eq. forms, and data acquisition. Relationships developed specifically for carbonate rocks particularly limestone produce more reliable results than those intended for mixed or non-carbonate rocks. |
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| ISSN: | 2590-1230 |