Machine Learning Applications to Predict Surface Oil Rates for High Gas Oil Ratio Reservoirs

Abstract

Well-performance investigation highly depends on the accurate estimation of its oil and gas flow rates. Testing separators and multiphase flow meters are associated with many technical and operational issues. Therefore, this study aims to implement the support vector machine (SVM), and random forests (RF) as machine learning (ML) methods to estimate the well production rate based on chokes parameters for high GOR reservoirs. Dataset of 1,131 data points includes GOR, upstream and downstream pressures (PU, and PD), choke size (D64), and actual data of oil and gas production rates. The data have GOR was up to 9,265 SCF/STB, the oil rate varied from 1,156 and 7,982 BPD. SVM and RF models were built to estimate the production rates. The ML models were trained using seventy percent of the dataset, while the models were tested and validated using thirty percent of the dataset. The dataset was classified to 622 wells that were flowing at critical flow compared to 509 wells that were flowing at subcritical conditions based on a PD/PU ratio of 0.55. Four machine learning models were developed using SVM and RF for subcritical flow and critical flow conditions. Different performance indicators were applied to assess the developed models. SVM and RF models revealed average absolute percent error (AAPE) of 1.3, and 0.7%, respectively in the case of subcritical flow conditions. For critical flow conditions, the AAPE was found to be 1.7% in the SVM model, and 0.8% in the RF model. The developed models showed a coefficient of determination (R2) higher than 0.93. All developed ML models perform better than empirical correlations. These results confirm the capabilities to predict the oil rates from the choke parameters in real-time without the requirement of instrument installation of wellsite intervention.

Deep learning models for improved accuracy of a multiphase flowmeter

“…), [23]. Different investigations have been made on measuring individual phases in multiphase flow rates across the pipeline using soft computing techniques [30][31][32][33][34][35][36][37][38][39][40][41]. Soft computing input parameters can be mainly divided into five groups: 1) gamma-ray densitometer [18], 2) electrical signals [42], 3) ultrasonic data [43], 4) pressure signals [44] 5) fluid properties (such as temperature, density, and viscosity).…”

“…Machine learning methods have been used in several investigations of multiphase flow measurements [39] and flow over a choke [41]. Besides, extreme learning machine (ELM) is used for various types of problems (e.g.…”