Utilizing machine learning for flow zone indicators prediction and hydraulic flow unit classification

Tengku Astsauri; Muhammad Habiburrahman; Ahmed Farid Ibrahim; Yuzhu Wang

doi:10.1038/s41598-024-54893-1

Utilizing machine learning for flow zone indicators prediction and hydraulic flow unit classification

Sci Rep. 2024 Feb 20;14(1):4223. doi: 10.1038/s41598-024-54893-1.

Authors

Tengku Astsauri¹, Muhammad Habiburrahman¹, Ahmed Farid Ibrahim^{2

3}, Yuzhu Wang^{1

4}

Affiliations

¹ Department of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia.
² Department of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia. ahmed.ibrahim@kfupm.edu.sa.
³ Center for Integrative Petroleum Research, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia. ahmed.ibrahim@kfupm.edu.sa.
⁴ Center for Integrative Petroleum Research, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia.

Abstract

Reservoir characterization, essential for understanding subsurface heterogeneity, often faces challenges due to scale-dependent variations. This study addresses this issue by utilizing hydraulic flow unit (HFU) zonation to group rocks with similar petrophysical and flow characteristics. Flow Zone Indicator (FZI), a crucial measure derived from pore throat size, permeability, and porosity, serves as a key parameter, but its determination is time-consuming and expensive. The objective is to employ supervised and unsupervised machine learning to predict FZI and classify the reservoir into distinct HFUs. Unsupervised learning using K-means clustering and supervised algorithms including Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were employed. FZI values from RCAL data formed the basis for model training and testing, then the developed models were used to predict FZI in unsampled locations. A methodical approach involves 3 k-fold cross-validation and hyper-parameter tuning, utilizing the random search cross-validation technique over 50 iterations was applied to optimize each model. The four applied algorithms indicate high performance with coefficients determination (R²) of 0.89 and 0.91 in training and testing datasets, respectively. RF showed the heist performance with training and testing R² values of 0.957 and 0.908, respectively. Elbow analysis guided the successful clustering of 212 data points into 10 HFUs using k-means clustering and Gaussian mixture techniques. The high-quality reservoir zone was successfully unlocked using the unsupervised technique. It has been discovered that the areas between 2370-2380 feet and 2463-2466 feet are predicted to be high-quality reservoir potential areas, with average FZI values of 500 and 800, consecutively. The application of machine learning in reservoir characterization is deemed highly valuable, offering rapid, cost-effective, and precise results, revolutionizing decision-making in field development compared to conventional methods.

Keywords: Flow zone indicators; Hydraulic flow unit; Machine learning; Reservoir characterization.