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

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.

Experimental Analysis on the Use of Counterflow Jets as a System for the Stabilization of the Spatial Hydraulic Jump

This study presents an investigation on the use of submerged counterflow jets as a means for stabilizing the spatial hydraulic jump occurring in abruptly expanding channels. The characteristics of the flow downstream from the stilling basin and the main parameters influencing the effectiveness of the device in improving flow uniformity and reducing scouring potential are examined in laboratory tests, under several geometric configurations and hydraulic boundary conditions. The position within the stilling basin and the jet density (i.e., the number of orifices issuing the counterflow jets) were found to be important parameters influencing the performance of the device. Overall, the results indicate that this dissipation system has promising capabilities in forcing the transition from supercritical to subcritical flow, by significantly shortening the protection length needed to limit the phenomena of instability associated with spatial hydraulic jumps.

Prediction of Surface Oil Rates for Volatile Oil and Gas Condensate Reservoirs Using Artificial Intelligence Techniques

Allocated well production rates are crucial to evaluate the well performance. Test separators and flow meters were replaced with choke formulas due to economic and technical issues special for high gas-oil ratio (GOR) reservoirs. This study implements Adaptive network-based fuzzy logic (ANFIS), and functional networks (FN) techniques to predict the oil rate through wellhead chokes. A set of data containing 1,200 wells was obtained from actual oil fields in the Middle East. The dataset included GOR, upstream and downstream pressure, choke size and actual oil and gas rates based on well test. GOR varied from 1,000 to 9,265 scf/stb, while oil rates ranged between 1,156 and 7,982 stb/d. Around 650 wells were flowing under critical flow conditions, while the rest were subcritical. Seventy percent of the data was used to train the AI models, while thirty percent of the data was used to test and validate these models. The developed AI models were then compared against the previous formulas. For subcritical flow conditions, rate prediction was correlated to both upstream and downstream pressures. While at critical flow conditions, changes in the downstream pressure did not affect the prediction of the production rates. For each AI method, two models were developed for subcritical flow and critical flow conditions. The average absolute percent error (AAPE) in the case of subcritical flow for ANFIS and FN were 0.88, and 1.01%, respectively. While in the case of critical flow, the AAPE values were 1.07, and 1.3% for ANFIS and FN models, respectively. All developed AI models outperform the published formulas, where the AAPE values for published formulas were higher than 34%. The results from this study will greatly assist petroleum engineers to predict the oil and gas rates based on available data from wellhead chokes in real-time with no need for additional operational costs or field intervention.