Prediction of reservoir quality using well logs and seismic attributes analysis with an artificial neural network: A case study from Farrud Reservoir, Al-Ghani Field, Libya

• Published in: Journal of applied geophysics 2019-02, Vol.161, p.239-254
• Main Authors: Abdulaziz, Abdulaziz M., Mahdi, Hameeda A., Sayyouh, Mohamed H.
• Format: Article
• Language: English
• Subjects: Al-Ghani field; ANN analysis; Farrud reservoir; Reservoir quality; Seismic attributes
• ISSN: 0926-9851; 1879-1859
• DOI: 10.1016/j.jappgeo.2018.09.013

This paper presents an innovative technique that aims to predict the quality of a petroleum reservoir using Artificial Neural Networks (ANN) analysis of seismic, well logs and core data. A supervised Probabilistic Neural Network (PNN) was deployed to predict several reservoir properties, one at a time, through training and validation of the PNN to determine the seismic attributes that best fit a measured property in the well logs. The validated PPN models accurately converted the available 3D seismic data into shale volume, porosity, permeability and water saturation cubes. In addition, these predicted reservoir properties were integrated to define various reservoir grades using K-means Clustering algorithm of unsupervised classification ANN. This technique is applied to Al-Ghani Field and four grades of reservoir quality were classified as very good, good, bad, and very bad and their spatial distributions were displayed. The highest reservoir grade is characterized by good porosity and permeability and significantly low water saturation. Such information is highly valuable for optimum reservoir management and well placement. This not only maximizes reservoir profitability through production schemes, but also minimizes uncertainties in drilling, production, injection, and modeling processes. In addition, adopting the proposed methodology would decrease costs in well logging programs and improve the sweeping efficiency of water flooding operations.