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<p>Conventional techniques, which depend on geostatistical modeling, frequently fail to capture reservoir variability, especially when well data are sparse. To overcome this limitation, we develop a combined approach that integrates Multi-Seismic Attribute Transformation (MSAT) and Probabilistic Neural Network (PNN) techniques. By leveraging data from eight wells and post-stack seismic data from the Poseidon 3D area, gas-saturated deltaic-fluvial settings, we analyzed twelve seismic attributes, among which acoustic impedance low-frequency attribute, relative geological time, amplitude envelope, and amplitude-weighted frequency emerge as the most significant. Using the MSAT approach, we established strong connections between these attributes and porosity. Six attributes provide a correlation coefficient of 0.65 with the target log. To enhance precision, PNN and fine-tuning the sigma factor using well drop-out cross-validation analysis was utilized. This leads to a significant enhancement in model accuracy, reaching 76%. In addition, when the model training is concentrated within a 10-millisecond range around the Plover reservoir zone, the accuracy increases significantly to 89%. Our approach has proven to be highly effective, with a success rate of 73% as evidenced by validation through well drop-out analysis. This demonstrates that our method surpasses traditional methods. This innovative integration of seismic attribute-driven methodologies has indicated a major leap forward in identifying and characterizing reservoir heterogeneity. The results demonstrate that it enables more efficient future well-planning strategies and deepens our comprehension of deltaic-fluvial reservoir dynamics.</p>

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Optimizing petrophysical property prediction in fluvial-deltaic reservoirs: a multi-seismic attribute transformation and probabilistic neural network approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月4日

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  • Muhammad Khan,
  • Andy Anderson Bery,
  • Yasir Bashir,
  • Sya’rawi Muhammad Husni Sharoni,
  • Joseph Gnapragasan,
  • Qazi Sohail Imran

摘要

Conventional techniques, which depend on geostatistical modeling, frequently fail to capture reservoir variability, especially when well data are sparse. To overcome this limitation, we develop a combined approach that integrates Multi-Seismic Attribute Transformation (MSAT) and Probabilistic Neural Network (PNN) techniques. By leveraging data from eight wells and post-stack seismic data from the Poseidon 3D area, gas-saturated deltaic-fluvial settings, we analyzed twelve seismic attributes, among which acoustic impedance low-frequency attribute, relative geological time, amplitude envelope, and amplitude-weighted frequency emerge as the most significant. Using the MSAT approach, we established strong connections between these attributes and porosity. Six attributes provide a correlation coefficient of 0.65 with the target log. To enhance precision, PNN and fine-tuning the sigma factor using well drop-out cross-validation analysis was utilized. This leads to a significant enhancement in model accuracy, reaching 76%. In addition, when the model training is concentrated within a 10-millisecond range around the Plover reservoir zone, the accuracy increases significantly to 89%. Our approach has proven to be highly effective, with a success rate of 73% as evidenced by validation through well drop-out analysis. This demonstrates that our method surpasses traditional methods. This innovative integration of seismic attribute-driven methodologies has indicated a major leap forward in identifying and characterizing reservoir heterogeneity. The results demonstrate that it enables more efficient future well-planning strategies and deepens our comprehension of deltaic-fluvial reservoir dynamics.