Scholarlink

<p>Source rock characterization (SRC) is one of the important approaches in the exploration of geology. The SRC in a sedimentary basin is economically crucial in conventional and unconventional hydrocarbon resources. This investigation uses the integration of seismic data inversion methods and geochemical data to evaluate an accurate total organic carbon (TOC) of the Kazhdumi Formation in NW of the Persian Gulf. The Kazhdumi Formation with Cretaceous age is an important source rock in the NW of the Persian Gulf and Zagros region. The main purpose of this study is to evaluate the TOC values as one of the important geochemical parameters of the Kazhdumi Formation by integrating seismic data inversion results, and well logs analysis. This study uses the artificial neural network (ANN) algorithm and well logs e.g., sonic, neutron, density, gamma-ray, and resistivity to evaluate the TOC log along with the Rock–Eval pyrolysis to analyze the actual data. The performance of the ANN algorithm was evaluated using correlation analysis as a cross-validation method by the bind core data sample. The simultaneous, and model-based seismic data inversion methods were evaluated as seismic attributes, and the seismic data inversion attributes and the TOC log from the well logs analysis were used in the TOC volume evaluation procedure. Integrating seismic inversion attributes with high lateral resolution and well log with high vertical resolution creates a more accurate TOC value than the conventional methods. The multi-attribute regression (MAR) and artificial neural network (ANN) methods were utilized to estimate the TOC content of the Kazhdumi Formation. The obtained results of the MAR and ANN methods using RMSE and MAE algorithm analysis are evaluated. The correlation of evaluated TOC between the actual TOC and TOC volume is 0.89. The obtained results and data evaluation procedure of this investigation can provide useful information for the geological and geochemical studies in this oil field, and this method can be used in other geological properties in the blind area.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Source rock characterization using seismic data inversion and well log analysis; a case study from Kazhdumi Formation, NW Persian Gulf

  • 期刊论文

  • 发表日期：

    2025年2月4日

查看全文
  • Mehran Rahimi,
  • Bahram Alizadeh,
  • Seyed Mohsen Seyedali

摘要

Source rock characterization (SRC) is one of the important approaches in the exploration of geology. The SRC in a sedimentary basin is economically crucial in conventional and unconventional hydrocarbon resources. This investigation uses the integration of seismic data inversion methods and geochemical data to evaluate an accurate total organic carbon (TOC) of the Kazhdumi Formation in NW of the Persian Gulf. The Kazhdumi Formation with Cretaceous age is an important source rock in the NW of the Persian Gulf and Zagros region. The main purpose of this study is to evaluate the TOC values as one of the important geochemical parameters of the Kazhdumi Formation by integrating seismic data inversion results, and well logs analysis. This study uses the artificial neural network (ANN) algorithm and well logs e.g., sonic, neutron, density, gamma-ray, and resistivity to evaluate the TOC log along with the Rock–Eval pyrolysis to analyze the actual data. The performance of the ANN algorithm was evaluated using correlation analysis as a cross-validation method by the bind core data sample. The simultaneous, and model-based seismic data inversion methods were evaluated as seismic attributes, and the seismic data inversion attributes and the TOC log from the well logs analysis were used in the TOC volume evaluation procedure. Integrating seismic inversion attributes with high lateral resolution and well log with high vertical resolution creates a more accurate TOC value than the conventional methods. The multi-attribute regression (MAR) and artificial neural network (ANN) methods were utilized to estimate the TOC content of the Kazhdumi Formation. The obtained results of the MAR and ANN methods using RMSE and MAE algorithm analysis are evaluated. The correlation of evaluated TOC between the actual TOC and TOC volume is 0.89. The obtained results and data evaluation procedure of this investigation can provide useful information for the geological and geochemical studies in this oil field, and this method can be used in other geological properties in the blind area.