The Choke as a Brainbox for Smart Wellhead Control (original) (raw)
Related papers
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...
Development of a New Comprehensive Model for Choke Performance Correlation in Iranian Oil Wells
2014
Multiphase flow occurs in all producing oil and Gas/Condensate wells. There is some Choke in flowing well to modulate the flowing rate. Some reasons are proposed for this modulating action: (1) to prevent enough back pressure to stop sand entry, (2) to safe surface equipment from high pressure, (3) to protect gas or water coning and (4) to keeping the reservoir at the optimum flow rate. Many mathematical models which correlate the rate of multiple phase flow through an orifice (choke) exist. The models offer empirical correlations which are based upon laboratory and field data. This article proposed a new empirical correlation pattern for under critical flow based on 76 production test points analyses collected for 10 wells in south Iran. The average error of sub-critical flow is about1% over entire range of oil production rates of 7000-28000 (), pressures of 100-1000 (psi), choke size of 42-98 (1/64th-inch) and gas oil ratios of 100-220 (). In this study, we use the data GLR. Because of this condition was lower than the value of the production rate has not trustworthy range.
Flow Measurement and Instrumentation, 2020
Takagi-Sugeno adaptive neuro-fuzzy inference system (TSFIS) model is developed and applied to a dataset of wellhead flow-test data for the Resalat oil field located offshore southern Iran, the objective is to assist in the prediction and control of multi-phase flow rates of oil and gas through the wellhead chokes. For this purpose, 182 test data points (Appendix 1) related to the Resalat field are evaluated. In order to predict production flow rate (Q L) expressed as stock-tank barrels per day (STB/D), this dataset includes four selected input variables: upstream pressure (Pwh); wellhead choke sizes (D64); gas to liquid ratio (GLR); and, base solids and water including some water-soluble oil emulsion (BS&W). The test data points evaluated include a wide range of oil flow rate conditions and values for the four input variables recorded. The TSFIS algorithm applied involves five data processing steps: a) pre-processing, b) fuzzification, c) rules base and adaptive neuro-fuzzy inference engine, d) defuzzification, and e) post-processing of the fuzzy model. The developed TSFIS model for the Resalat oil field database predicted oil flow rate to a high degree of accuracy (root mean square error = 247 STB/D, correlation coefficient = 0.9987), which improves substantially on the commonly used empirical algorithms used for such predictions. TSFIS can potentially be applied in wellhead choke fuzzy controllers to stabilize flow in specific wells based on real-time input data records.
Journal of Petroleum Exploration and Production Technology
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 productionflow 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 R 2 (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.
A NEW CHOKE CORRELATION TO PREDICT LIQUID FLOW RATE
Flow rate prediction is of prime importance for effectively managing and maintaining well productivity. Optimum flow rate prediction can prevent water/gas coning, sand entry, surface equipment problems and avoid formation damage due to imposing of excessive drawdown to reservoir. One of the most common wasy to achieve these goals is by controlling flow rate using wellhead chokes. The aim of this paper is to develop a new empirical Gilbert type correlation which is a function of flowing wellhead pressure, gas-liquid ratio and wellhead choke size. To achieve this, data from 1300 experimental production tests under multi-phase critical flow conditions from 120 Iranian offshore oil wells were used in a non-linear regression analysis. According to our results, predicted oil flow rates from new correlation are in excellent agreement with the observed data. Our results are also more accurate compared to those obtained from conventional methods. Furthermore, the accuracy of the proposed correlation was validated by cross plotting of synthetic and field data. The new correlation has an average relative deviation (ARD) of -0.18% and average absolute deviation (AAD) of 20.73%. The dataset covers a wide range of choke sizes (12/64 to 92/64 inches) and PVT parameters. Therefore, it is applicable in many Middle Eastern offshore oil wells mounted on satellite platforms where difficulties arise while performing productivity tests.
Correlations Developed To Predict Two-Phase Flow Through Wellhead Chokes
Journal of Canadian Petroleum Technology, 1991
The predictive accuracy of ten critical two-phase flow correlations in combination with four PVT property correlations, is tested against field measured production data, from 210 well tests, covering a broad range of production rates, choke sizes, upstream pressures, gas-liquid ratios and oil API gravities. Test data are divided into four selected categories based on choke size (D): D<6, 6~D<10, JO~D<30, and D?:.30/64". The average per cent error, absolute average per cent error and standard deviation are computed for each correlation combination. It is observed that the choice of empirical PVT correlation appears to have only a minor effect on final calculated statistical results. Also it is found that most of the compared correlations yielded unsatisfactory results, therefore an attempt is made to find correlations that best fit the measured data. As a result, four new correlations are developed (a correlation for each diameter category).. Based on the statistical results, the new correlations clearly outperformed the original correlations.
Joint CINTI-MACRo, 2022
One of the main duties of production engineers is to maintain the reservoir productivity and keep it at a desirable level during the production time by preventing extra production and controlling the production through wellhead chokes. Wellhead chokes are tools that are installed in flowing pipes to resist the pressure, limit and control the production, prevent water and gas coning, and control the pressure in order to maintain the wellhead equipment in good working order. Wellhead chokes, which are installed in well flow, are divided into two main groups: positive or fixed chokes and adjustable or variable chokes. Passing flow rate through wellhead chokes is a function of wellhead pressure, choke diameter, before choke temperature, and water production rate. The objective of this work is to propose a new model for the estimation of the oil rate passing through wellhead chokes. In this study, 180 actual tested data for 5 wells from a heavy crude oil field were used to develop a new model for estimating oil rate passing through wellhead chokes. The proposed model has an average relative error of about 5.8%.
Optimization of waterflooding operations, whether reducing water cut (WC) or increasing ultimate oil recovery, has been a great challenge. With advancing technology, intelligent wells with controllable downhole chokes have provided the petroleum industry with efficient tools. Several techniques originating from different industries have been applied within the petroleum industry to address such challenges. This paper presents the advantages of using intelligent well valves compared to the base case of conventional wells with simultaneous use of a next-generation reservoir simulator and a user-friendly, robust optimization tool by maximizing net present value (NPV) and cumulative oil production. Recovery has been enhanced using dynamic and smart control of interval control valves (ICVs) (Brouwer et al. 2002). ICVs provide more control and real-time action than that of inflow control devices (ICDs). Integrating a thorough understanding of reservoir physics expressed in analytical terms as key performance indicators, it is now feasible to use smart field technology proactively, rather than reactively. The additional recovery as a result of integrating key performance indicators with ICVs is presented through reservoir simulation. The difference between static and dynamic optimization methods used to date within the industry is also outlined. This paper additionally demonstrates the pros and cons of using intelligent well chokes for various optimization and uncertainty scenarios using a model of wells under rate and pressure-constraint simulations and parameters of uncertainty.
New multiphase choke correlations for a high flow rate Iranian oil field
Mechanical Sciences, 2012
The multiphase flow through wellhead restrictions of an offshore oil field in Iran is investigated and two sets of new correlations are presented for high flow rate and water cut conditions. The both correlations are developed by using 748 actual data points, corresponding to critical flow conditions of gas-liquid mixtures through wellhead chokes. The first set of correlations is a modified Gilbert equation and predicts liquid flow rates as a function of flowing wellhead pressure, gas-liquid ratio and surface wellhead choke size. To minimize error in such condition, in the second correlation, free water, sediment and emulsion (BS & W) is also considered as an effective parameter. The predicted oil flow rates by the new sets of correlations are in the excellent agreement with the measured ones. These results are found to be statistically superior to those predicted by other relevant published correlations. The both proposed correlations exhibit more accuracy (only 2.95 % and 2.0 % average error, respectively) than the existent correlations. These results should encourage the production engineer which works at such condition to utilize the proposed correlations for future practical answers when a lack of available information, time, and calculation capabilities arises.