Two-phase flow rate estimation of liquid and gas flow through wellhead chokes is essential for determining and monitoring production performance from oil and gas reservoirs at specific well locations. Liquid flow rate (Q_L) tends to be nonlinearly related to these influencing variables, making empirical correlations unreliable for predictions applied to different reservoir conditions and favoring machine learning (ML) algorithms for that purpose. Recent advances in deep learning (DL) algorithms make them useful for predicting wellhead choke flow rates for large field datasets and suitable for wider application once trained. DL has not previously been applied to predict Q_L from a large oil field. In this study, 7245 multi-well data records from Sorush oil field are used to compare the Q_L prediction performance of traditional empirical, ML and DL algorithms based on four influencing variables: choke size (D₆₄), wellhead pressure (P_wh), oil specific gravity (γ_o) and gas–liquid ratio (GLR). The prevailing flow regime for the wells evaluated is critical flow. The DL algorithm substantially outperforms the other algorithms considered in terms of Q_L prediction accuracy. The DL algorithm predicts Q_L for the testing subset with a root-mean-squared error (RMSE) of 196 STB/day and coefficient of determination (R²) of 0.9969 for Sorush dataset. The Q_L prediction accuracy of the models evaluated for this dataset can be arranged in the descending order: DL > DT > RF > ANN > SVR > Pilehvari > Baxendell > Ros > Glbert > Achong. Analysis reveals that input variable GLR has the greatest, whereas input variable D₆₄ has the least relative influence on dependent variable Q_L.

None of the various published models used to predict oil production rates through wellhead chokes from fluid composition and pressures can be considered as a universal model for all regions. Here, a model is provided to predict liquid production-flow rates for the Reshadat oil field offshore southwest Iran, applying a customized genetic optimization algorithm (GA) and standard Excel Solver non-linear and evolutionary optimization algorithms. The dataset of 182 records of wellhead choke measurements spans liquid flow rates from < 100 to 30,000 stock tank barrels/day. Each data record includes measurements of five variables: liquid production rate (QL), wellhead pressure, choke size, basic sediment and water, and gas–liquid ratio. 70% of the dataset (127 data records) was used for training purposes to establish the prediction relationships, and 30% of the dataset (55 data records) was utilized for independently testing the accuracy of the derived relationships as predictive tools. The methodology applying either the customized GA or standard Solver optimization algorithms, demonstrates significant improvements in QL-prediction accuracy with the lowest APD (− 7.72 to − 2.89), AAPD (7.33–8.51), SD (288.77–563.85), MSE (91,871–316,429), and RMSE (303.1–562.52); and the highest R² (greater than 0.997) compared to six previously published liquid flow-rate prediction models. As a general result, the novel methodology is easily applied to other field/reservoir datasets, to achieve rapid practical flow prediction applications, and is consequently of worldwide significance.

Enhanced oil recovery methods are used to recover the percents of oil, which are not naturally recoverable from reservoirs. Water injection, as a secondary recovery, is used to maintain the pressure in water-drive reservoirs. An important point for having a successful injection is the compatibility of injection and formation waters. This article presents the predicting of calcium carbonate precipitation in formation water, injection water, and mixing of injection water with formation water at different pressures. The experimentally measured chemical analyses of formation water and injection water were used by OLI ScaleChem software to determine the amount of CaCO3 scale that can be formed. This software has been applied to investigate the potential of calcium carbonate scale precipitation in Iranian oilfields, either in onshore or offshore fields where water injection is being performed for desalting units' water disposal purpose or as a method of secondary recovery or reservoir pressure maintenance. As pressure was increased, the amount of CaCO3 precipitation decreased, as was predicted by OLI ScaleChem software.