Production optimization under waterflooding with long short-term memory and metaheuristic algorithm

In petroleum domain,optimizing hydrocarbon production is essential because it does not only ensure the economic prospects of the petroleum companies,but also fulfills the increasing global demand of energy.However,applying numerical reservoir simulation(NRS)to optimize production can induce high computational footprint.Proxy models are suggested to alleviate this challenge because they are computationally less demanding and able to yield reasonably accurate results.In this paper,we demonstrated how a machine learning technique,namely long short-term memory(LSTM),was applied to develop proxies of a 3D reservoir model.Sampling techniques were employed to create numerous simulation cases which served as the training database to establish the proxies.Upon blind validating the trained proxies,we coupled these proxies with particle swarm optimization to conduct production optimization.Both training and blind validation results illustrated that the proxies had been excellently developed with coefficient of determination,R2 of 0.99.We also compared the optimization results produced by NRS and the proxies.The comparison recorded a good level of accuracy that was within 3%error.The proxies were also computationally 3 times faster than NRS.Hence,the proxies have served their practical purposes in this study.

Corrigendum to“Production optimization under waterflooding with long short-term memory and metaheuristic algorithm”Petroleum 9(2023)53-60

The authors regret the error in equation 14 of the above-mentioned article.Corrected version is as given below.The authors would like to emphasize that the corrected version was actually used in the calculation and analysis in the article.Therefore,the results remain unchanged.

Bottom hole pressure estimation using hybridization neural networks and grey wolves optimization

An effective design and optimum production strategies of a well depend on the accurate prediction of its bottom hole pressure(BHP)which may be calculated or determined by several methods.However,it is not practical technically or economically to apply for a well test or to deploy a permanent pressure gauge in the bottom hole to predict the BHP.Consequently,several correlations and mechanistic models based on the knownsurfacemeasurementshave beendeveloped.Unfortunately,all these tools(correlations&mechanistic models)are limited to some conditions and intervals of application.Therefore,establish a global model that ensures a large coverage of conditions with a reduced cost and high accuracy becomes a necessity.In this study,we propose new models for estimating bottom hole pressure of vertical wells with multiphase flow.First,Artificial Neural Network(ANN)based on back propagation training(BP-ANN)with 12 neurons in its hidden layer is established using trial and error.The next methods correspond to optimized or evolved neural networks(optimize the weights and thresholds of the neural networks)with Grey Wolves Optimization(GWO),and then its accuracy to reach the global optima is compared with 2 other naturally inspired algorithms which are the most used in the optimization field:Genetic Algorithm(GA)and Particle Swarms Optimization(PSO).The models were developed and tested using 100 field data collected from Algerian fields and covering a wide range of variables.The obtained results demonstrate the superiority of the hybridization ANN-GWO compared with the 2 other hybridizations or with the BP learning alone.Furthermore,the evolved neural networks with these global optimization algorithms are strongly shown to be highly effective to improve the performance of the neural networks to estimate flowing BHP over existing approaches and correlations.

Predicting wax deposition using robust machine learning techniques

Accurate prediction of wax deposition is of vital interest in digitalized systems to avoid many issues that interrupt the flow assurance during production of hydrocarbon fluids.The present investigation aims at establishing rigorous intelligent schemes for predicting wax deposition under extensive production conditions.To do so,multilayer perceptron(MLP)optimized with Levenberg-Marquardt algorithm(MLPLMA)and Bayesian Regularization algorithm(MLP-BR)were taught using 88 experimental measurements.These latter were described by some independent variables,namely temperature(in K),specific gravity,and compositions of C1eC3,C4eC7,C8eC15,C16eC22,C23eC29 and C30þ.The obtained results showed that MLP-LMA achieved the best performance with an overall root mean square error of 0.2198 and a coefficient of determination(R2)of 0.9971.The performance comparison revealed that MLP-LMA outperforms the prior approaches in the literature.

Application of hybrid support vector regression artificial bee colony for prediction of MMP in CO2-EOR process

Minimum miscibility pressure(MMP)is a key parameter in the successful design of miscible gases injection such as CO2 flooding for enhanced oil recovery process(EOR).MMP is generally determined through experimental tests such as slim tube and rising bubble apparatus(RBA).As these tests are time-consuming and their cost is very expensive,several correlations have been developed.However,and although the simplicity of these correlations,they suffer from inaccuracies and bad generalization due to the limitation of their ranges of application.This paper aims to establish a global model to predict MMP in both pure and impure CO2-crude oil in EOR process by combining support vector regression(SVR)with artificial bee colony(ABC).ABC is used to find best SVR hyper-parameters.201 data collected from authenticated published literature and covering a wide range of variables are considered to develop SVR-ABC pure/impure CO2-crude oil MMP model with following inputs:reservoir temperature(TR),critical temperature of the injection gas(Tc),molecular weight of pentane plus fraction of crude oil(MWC5+)and the ratio of volatile components to intermediate components in crude oil(xvol/xint).Statistical indicators and graphical error analyses show that SVR-ABC MMP model yields excellent results with a low mean absolute percentage error(3.24%)and root mean square error(0.79)and a high coefficient of determination(0.9868).Furthermore,the results reveal that SVR-ABC outperforms either ordinary SVR with trial and error approach or all existing methods considered in this work in the prediction of pure and impure CO2-crude oil MMP.Finally,the Leverage approach(Williams plot)is done to investigate the realm of prediction capability of the new model and to detect any probable erroneous data points.