ABSTRACT
One of the main challenges in screening of enhanced oil recovery (EOR) techniques is the class imbalance problem, where the number of different EOR techniques is not equal. This problem hinders the generalization of the data-driven methods used to predict suitable EOR techniques for candidate reservoirs. The main purpose of this paper is to propose a novel approach to overcome the above challenge by taking advantage of the Power-Law Committee Machine (PLCM) technique optimized by Particle Swam Optimization (PSO) to combine the output of five cutting-edge machine learning methods with different types of learning algorithms. The PLCM method has not been used in previous studies for EOR screening. The machine learning models include the Artificial Neural Network (ANN), CatBoost, Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The CatBoost is used for the first time in this work for screening of EOR methods. The role of the PSO is to find the optimal values for the coefficients and exponents of the power-law model. In this study, a bigger dataset than those in previous studies, including 2563 successful worldwide EOR experiences, was gathered. A bigger dataset improves the generalization of the data-driven methods and prevents overfitting. The hyperparameters of the individual machine-learning models were tuned using the fivefold cross-validation technique. The results showed that all the individual methods could predict the suitable EOR method for unseen cases with an average score of 0.868. Among the machine learning models, the KNN and SVM had the highest scores with a value of 0.894 and 0.892, respectively. Nonetheless, after combining the output of the models using the PLCM method, the score of the predictions improved to 0.963, which was a substantial increase. Finally, a feature importance analysis was conducted to find out the most influential parameters on the output. The novelty of this work is having shown the ability of the PLCM technique to construct an accurate model to overcome the class-imbalance issue in EOR screening by utilizing different types of data-driven models. According to feature importance analysis, oil gravity and formation porosity were recognized as the most influential parameters on EOR screening.
ABSTRACT
Microalgae in the Middle East can theoretically address food security without competing for arable land, but concerns exist around scalability and durability of production systems under the extreme heat. Large-scale Chlorella sorokiniana production was developed in outdoor raceway ponds in Oman and monitored for 2 years to gather data for commercial production. Biological and technical challenges included construction, indoor/outdoor preculturing, upscaling, relating productivity to water temperature and meteorological conditions, harvesting, drying, and quality control. Small cultivation systems required cooling for initial scale-up, but, despite maximum temperatures of 49.7 °C, water temperatures were at acceptable levels by evaporative cooling in larger raceway ponds. Contamination with Vampirovibrio chlorellavorus was identified by 16S rDNA amplicon sequencing and addressed by culture replacement. Productivities ranged from 8 to 30 g-dry weight m-2d-1, with estimated annual productivity of 16 g-dry weight m-2d-1 as functions of solar intensity and water temperature, confirming that the region is suitable for commercial microalgae production.
