Challenges and Strategies in Modeling Thin-Bedded Carbonate Reservoirs Based on Horizontal Well Data: A Case Study of Oilfield A in the Middle East

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Abstract
Thin-bedded carbonate reservoirs face significant challenges in characterization and development due to their thin formation thickness, strong interlayer heterogeneity, and rapid sedimentary transformation. In recent years, horizontal wells have played an increasingly important role in improving the productivity of thin-bedded carbonate reservoirs. However, building accurate geological models from horizontal well data is a major challenge for geoscientists. Using Middle East Oilfield A as a case study, this paper analyzes the specific challenges of horizontal well geomodeling and proposes a dedicated strategy for integrating horizontal well-derived constraints into the geological modeling workflow. To address the challenges of structural modeling constrained by horizontal well data, this study proposes three methodologies: stratigraphic layer iteration, virtual control point generation, and localized grid refinement. These techniques collectively enable the construction of a higher-fidelity structural framework that rigorously honors hard well data constraints while incorporating geological plausibility. To address the challenges posed by the spatial configuration of vertical and horizontal wells and the dominant trajectory patterns of horizontal wells, this study introduces two complementary approaches: the exclusion of horizontal well section data (relying solely on vertical wells) and the selective extraction of representative horizontal well section data for variogram derivation. These methods collectively enable the construction of a geologically realistic reservoir model that accurately captures the spatial distribution of reservoir properties. These methodologies not only effectively leverage the rich geological information from horizontal wells but also mitigate spatial clustering effects inherent to such data. Validation through development well production data confirms robust performance, providing transferable insights for reservoir characterization in analogous fields worldwide.
Funding Information
  • the Major Science and Technology Project of PetroChina (2023ZZ19, 2025ZD1406400)
  • National Science and Technology Major Project (2023ZZ19, 2025ZD1406400)

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