Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes (original) (raw)
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MIR Labs, 2013
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Optimization of choke size for two-phase flow using artificial intelligence
Journal of Petroleum Exploration and Production Technology, 2019
Currently, engineers are using numerical correlations to describe the flow of oil and gas through chokes. These numerical correlations are not 100% accurate, as indicated by other studies, so there is a need to find a better approach to describe and calculate the choke size. Artificial intelligence (AI) can be used for better results. In this study, AI was used to estimate the optimum choke size that is required to meet the desired flow rate. Four techniques are used in this study: artificial neural networks, fuzzy logic (FL), support vector machines, and functional networks. Results obtained using these techniques were compared. After researching each technique, FL was found to give the best predictions.
Applied Soft Computing, 2013
Multiphase flow meters (MPFMs) are utilized to provide quick and accurate well test data in numerous numbers of oil production applications like those in remote or unmanned locations topside exploitations that minimize platform space and subsea applications. Flow rates of phases (oil, gas and water) are most important parameter which is detected by MPFMs. Conventional MPFM data collecting is done in long periods; because of radioactive sources usage as detector and unmanned location due to wells far distance. In this paper, based on a real case of MPFM, a new method for oil rate prediction of wells base on Fuzzy logic, Artificial Neural Networks (ANN) and Imperialist Competitive Algorithm is presented. Temperatures and pressures of lines have been set as input variable of network and oil flow rate as output. In this case a 1600 data set of 50 wells in one of the northern Persian Gulf oil fields of Iran were used to build a database. ICA-ANN can be used as a reliable alternative way without personal and environmental problems. The performance of the ICA-ANN model has also been compared with ANN model and Fuzzy model. The results prove the effectiveness, robustness and compatibility of the ICA-ANN model.
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Journal of Natural Gas Science and Engineering, 2015
Thorough knowledge of PVT properties of oil and gas reservoirs plays an important role in forecasting the phase behavior of oil reservoirs and designing appropriate actions for optimized production from them. Among these PVT properties, some have a determinative role in gas and oil equilibrium in the hydrocarbon reservoirs. In this study, a powerful computational intelligent model is designed to develop a reliable model for predicting amount of dissolved gas in oil at reservoir conditions as one of the most important PVT properties of reservoir oils. To achieve this model, different Adaptive Neuro-Fuzzy Inference System (ANFIS) models (by changing the training optimization algorithms) are designed. Moreover, prediction accuracy of the developed models has been compared with the number of wellknown correlations in literature. The results show that the proposed model has a significantly improved performance in comparison with the other existing correlations.
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Journal of Natural Gas Science and Engineering, 2014
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Prediction of Two Phase Flow Rate through Wellhead Chokes in Oil Wells
2019
Wellhead assembly is an essential part of a producing oil or gas well, where it protects downstream facilities from the danger of high flow rates. An important part of this assembly is the choke that controls the flow rate of multiphase flux, in addition to protecting the hydrocarbon formation and surface equipment from probable fluctuation in pressure. Accurate prediction of flow rate through chokes is extremely helpful for assessing the reservoir performance and production forecasting. Furthermore, it is essential for establishing a controllable and stable flow in producing wells. Since flow meters are expensive and difficult in implementation for large fields, measuring the production rate of oil wells is hard. Furthermore, in fields with advanced well systems, multiple wells are connected to one manifold, and the flow rate reported from the manifold is for combined wells and not for individuals. In this work, we used machine-learning techniques to develop a reliable predictive m...
Application of artificial neural network for predicting production flow rates of gaslift oil wells
Tạp chí Khoa học Kỹ thuật Mỏ- Địa chất, 2022
In petroleum industry, the prediction of oil production flow rate plays an important role in tracking the good performance as well as maintaining production flow rate. In addition, a flow rate modelling with high accuracy will be useful in optimizing production properties to achieve the expected flow rate, enhance oil recovery factor and ensure economic efficiency. However, the oil production flow rate is traditionally predicted by theoretical or empirical models. The theoretical model usually gives predicted results with a wide variation of error, this model also requires a lot of input data that might be time-consuming and costly. The empirical models are often limited by the volume of data set used to construct the model, therefore predicted values from the applications of these models in practical condition are not highly accurate. In this research, the authors propose the use of an artificial neural network (ANN) to establish a better relationship between production properties and oil production flow rate and predict oil production flow rate. Using production data of 5 wells which use continuous gas lift method in X oil field, Vietnam, an ANN system was developed by using back-propagation algorithm and tansig function to predict production flow rate from the above data set. This ANN system is called a back-propagation neural network (BPNN). In comparison with the oil production flow rate data collected from these studied continuous gas lift oil wells, the predicted results from the constructed ANN achieved a very high correlation coefficient (98%) and low root mean square error (33.41 bbl/d). Therefore, the developed ANN models can serve as a practical and robust tool for oilfield prediction of production flow rate.
Journal of Petroleum Exploration and Production Technology, 2018
Petroleum exploration and production business thrives with in-depth knowledge and understanding of the subsurface. Technological advancement has helped in furnishing the industry with much information about the petroleum reservoir; however, a lot of uncertainties still exist due to the nature of the subsurface. The industry has strived to address this problem in diverse ways; regrettably, the classical methods relied upon have failed to provide a proper guide to management decision in exploiting these reservoirs. In recent times, artificial intelligence techniques, particularly Fuzzy Logic (FL), have been identified as a potential tool to deal with the uncertainties encountered in most exploration and production (E&P) operations. This research provides a review of FL applications in E&P operations under non-deterministic input parameters, possible challenges and solution procedures using FL sensitivity analysis and rule viewers. The focus is on reservoir characterization for reservoir evaluation, drilling/completion operations and stimulation treatment. The study also examines the extent FL could be applied to extract useful information from the large volume of historical oil and gas data already on the shelf and the future gaps to fill. A case study was presented which considered cost optimization in Liquefied Petroleum Gas (LPG) distribution operations using fuzzy logic.
Reservoir fluid PVT properties modeling using Adaptive Neuro-Fuzzy Inference Systems
Journal of Natural Gas Science and Engineering, 2014
Knowledge of prediction of PVT properties of reservoir oil is of primary importance in many petroleum engineering studies such as inflow performance calculations, production engineering studies, numerical reservoir simulations and design of proper improved oil recovery techniques. Ideally these parameters should be determined experimentally in laboratory under the reservoir conditions such as pressure and temperature. But owing to the fact that experimental methods are very expensive and time consuming, numerical models are developed for prediction of PVT properties. In this study several predictive models, based on a large data bank from different geographical regions were developed to predict the reservoir oil bubble point pressure as well as oil formation volume factor (OFVF) at bubble pressure. Developed models were successfully applied to the data set and the predicted values were in a good agreement with experimental values. Also a comparative study has been carried out to compare the result of this study to previously proposed correlations in terms of accuracy. Results show that the proposed models are more accurate than the available approaches.
An accurate estimation of sediment volume removal or in other word volume of flushing half-cone in pressure flushing technique is important for dam engineers for the rescue of power plant intakes and also recovery of the loss capacity of reservoirs. For achieving this purpose, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to investigate the characteristics of a flushing halfcone that develops at the vicinity of bottom outlet. The database for this modeling was provided from three different studies on pressure flushing operation.