| 1 | A new robust predictive model for lost circulation rate using convolutional neural network:A case study from Marun Oilfield显示文摘A major cause of some of serious issues encountered in a drilling project,including wellbore instability,formation damage,and drilling string stuck e which are known to increase non-productive time(NPT)and hence the drilling cost e is what we know as mud loss.The mud loss can be prevented or at least significantly reduced by taking proper measures beforehand provided the position and intensity of such loss can be properly predicted using an accurate predictor model.Accordingly,in this study,we used the convolutional neural network(CNN)and hybridized forms of multilayer extreme learning machine(MELM)and least square support vector machine(LSSVM)with the Cuckoo optimization algorithm(COA),particle swarm optimization(PSO),and genetic algorithm(GA)for modeling the mud loss rate based on drilling data,mud properties,and geological information of 305 drilling wells penetrating the Marun Oilfield.For this purpose,we began by a pre-processing step to attenuate the effect of noise using the Savitzky-Golay method.The whole set of available data was divided into the modeling(including 2300 data points)and the validation(including 483 data points)subsets.Next,the second generation of the non-dominated sorting genetic algorithm(NSGA-Ⅱ)was applied to the modeling data to identify the most significant features for estimating the mud loss.The results showed that the prediction accuracy increased with the number of selected features,but the increase became negligible when the number of selected features exceeded 9.Accordingly,the following 9 features were selected as input to the intelligent algorithms(IAs):pump pressure,mud weight,fracture pressure,pore pressure,depth,gel 10 min/gel 10 s,fan 600/fan 300,flowrate,and formation type.Application of the hybrid algorithms and simple forms of LSSVM and CNN to the training data(80%of the modeling data,i.e.1840 data points)showed that all of the models tend to underestimate the mud loss at higher mud loss rates,although the CNN exhibited lower underestimation levels.Error analysis on different models showed that the CNN provided for a significantly higher degree of accuracy,as compared to other models.The more accurate outputs of the hybrid LSSVM model than those of the simple LSSVM indicated the large potentials of metaheuristic algorithms for achieving optimal solutions.The lower error levels obtained with the CNN model in the testing phase highlighted the excellent generalizability of this model for unseen data.The more accurate predictions obtained with this model,rather than the other models,in the validation phase further proved this latter finding.Therefore,application of this method to other wells in the same field is highly recommended. | Farshad Jafarizadeh Babak Larki Bamdad Kazemi Mohammad Mehrad Sina Rashidi Jalil Ghavidel Neycharan Mehdi Gandomgoun Mohammad Hossein Gandomgoun | 2023 | Petroleum2023，9，3： | 0 |
| 2 | Prediction of permeability from well logs using a new hybrid machine learning algorithm显示文摘Permeability is a measure of fluid transmissibility in the rock and is a crucial concept in the evaluation of formations and the production of hydrocarbon from the reservoirs.Various techniques such as intelligent methods have been introduced to estimate the permeability from other petrophysical features.The efficiency and convergence issues associated with artificial neural networks have motivated researchers to use hybrid techniques for the optimization of the networks,where the artificial neural network is combined with heuristic algorithms.This research combines social ski-driver(SSD)algorithm with the multilayer perception(MLP)neural network and presents a new hybrid algorithm to predict the value of rock permeability.The performance of this novel technique is compared with two previously used hybrid methods(genetic algorithm-MLP and particle swarm optimization-MLP)to examine the effectiveness of these hybrid methods in predicting the permeability of the rock.The results indicate that the hybrid models can predict rock permeability with excellent accuracy.MLP-SSD method yields the highest coefficient of determination(0.9928)among all other methods in predicting the permeability values of the test data set,followed by MLP-PSO and MLP-GA,respectively.However,the MLP-GA converged faster than the other two methods and is computationally less expensive. | Morteza Matinkia Romina Hashami Mohammad Mehrad Mohammad Reza Hajsaeedi Arian Velayati | 2023 | Petroleum2023，9，1： | 0 |
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