Implementation of artificial intelligence and support vector machine learning to estimate the drilling fluid density in high-pressure high-temperature wells

Syah, Rahmad; Ahmadian, Naeim; Elveny, Marischa; Alizadeh, Seyed Mehdi Seyed; Hosseini, Meysam; Khan, Afrasyab

doi:10.1016/j.egyr.2021.06.092

Cited by 21 publications

(9 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to their powerful capability to map correlation among data and find solution for different problems, ML models' application has become much popular in various fields of science and engineering over the last few decades (Choubin et al, 2019;Ghalandari et al, 2019;Qasem et al, 2019;Torabi et al, 2019;Ahmadi et al, 2020;Band et al, 2020;Mosavi et al, 2020;Shabani et al, 2020;H Ghorbani and Davarpanah, 2021). For instance, ML methods have been applied for tackling a variety of challenges in petroleum engineering such as petrophysical (Rajabi et al, 2022c;Jafarizadeh et al, 2022;Tabasi et al, 2022;Zhang et al, 2022), reservoir characterization (Hassanpouryouzband et al, 2020;Abad et al, 2021a;Hassanpouryouzband et al, 2021;Zhang et al, 2021;Kamali et al, 2022;Kamali et al, 2022;Rajabi et al, 2022d;Hassanpouryouzband et al, 2022;Ibrahim et al, 2022;Zhang et al, 2022), production (Mirzaei-Paiaman andSalavati, 2012;Ghorbani et al, 2020;Abad et al, 2021b) drilling (Soares and Gray, 2019;Syah et al, 2021;Beheshtian et al, 2022;Pang et al, FIGURE 5 Flowchart for LSSVM-GA/PSO models used for prediction of fracture density. 2022).…”

Section: Methodsmentioning

confidence: 99%

Prediction of fracture density in a gas reservoir using robust computational approaches

Gao

Hazbeh²,

Davoodi³

et al. 2023

Front. Earth Sci.

View full text Add to dashboard Cite

One of the challenges that reservoir engineers, drilling engineers, and geoscientists face in the oil and gas industry is determining the fracture density (FVDC) of reservoir rock. This critical parameter is valuable because its presence in oil and gas reservoirs boosts productivity and is pivotal for reservoir management, operation, and ultimately energy management. This valuable parameter is determined by some expensive operations such as FMI logs and core analysis techniques. As a result, this paper attempts to predict this important parameter using petrophysics logs routinely collected at oil and gas wells and by applying four robust computational algorithms and artificial intelligence hybrids. A total of 6067 data points were collected from three gas wells (#W1, #W2, and #W3) in one gas reservoir in Southwest Asia. Following feature selection, the input variables include spectral gamma ray (SGR); sonic porosity (PHIS); potassium (POTA); photoelectric absorption factor (PEF); neutron porosity (NPHI); sonic transition time (DT); bulk density (RHOB); and corrected gamma ray (CGR). In this study, four hybrids of two networks were used, including least squares support vector machine (LSSVM) and multi-layer perceptron (MLP) with two optimizers particle swarm optimizer (PSO) and genetic algorithm (GA). Four robust hybrid machine learning models were applied, and these are LSSVM-PSO/GA and MLP-PSO/GA, which had not previously used for prediction of FVDC. In addition, the k-fold cross validation method with k equal to 8 was used in this article. When the performance accuracy of the hybrid algorithms for the FVDC prediction is compared, the revealed result is LSSVM-PSO > LSSVM-GA > MLP-PSO > MLP-GA. The study revealed that the best algorithm for predicting FVDC among the four algorithms is LSSVM-PSO (for total dataset RMSE = 0.0463 1/m; R2 = 0.9995). This algorithm has several advantages, including: 1) lower adjustment parameters, 2) high search efficiency, 3) fast convergence speed, 4) increased global search capability, and 5) preventing the local optimum from falling. When compared to other models, this model has the lowest error.

show abstract

Section: Methodsmentioning

confidence: 99%

Prediction of fracture density in a gas reservoir using robust computational approaches

Gao

Hazbeh²,

Davoodi³

et al. 2023

Front. Earth Sci.

View full text Add to dashboard Cite

show abstract

“…RBFNN is an artificial neural network which can approximate any nonlinear complex continuous function with any accuracy. It has the advantage of fast convergence speed and unique optimal approximation [14], which can solve the problem of local minimum. e basic structure of RBFNN is shown in Figure 1, which consists of input layer, hidden layer, and output layer.…”

Section: Rbfnn Modelmentioning

confidence: 99%

Analysis and Research on Nonlinear Complex Function Approximation Problem Based on Deep Learning

Zhou

2022

Scientific Programming

View full text Add to dashboard Cite

Shallow models have limited ability to express high-dimensional nonlinear complex functions. Based on deep learning, a Gaussian radial basis function neural network (RBFNN) is proposed, which is an analysis method of nonlinear complex function approximation based on the Gaussian-RBFNN model. The proposed method can approximate single-variable and binary complex nonlinear functions, and the approximation error is less than 0.1, which can achieve the ideal approximation effect. Finally, the proposed method is applied to analyze the nonlinear complex stock prediction approximation problem, and the effectiveness and practicality of the method are further verified. It can be seen that compared with the BP neural network model, this model performs better in average training time, training standard error, Ratt, RATR, and trmse For RBFNN; the training only needs 50 iterations, while the traditional BP needs 7100 training to gradually converge. In the actual function test, after 275 times of training, the convergence speed gradually tends to be stable, and the predicted value is closer to the actual value. Therefore, the model can be used to analyze practical nonlinear complex function approximation problems.

show abstract

“…SVM is a machine learning mechanism. The essence of machine learning is to obtain the relationship estimation between input and output of the system from a given finite sample [44]. Thus, it can make a more accurate prediction of the unknown output.…”

Section: Theoretical Basismentioning

confidence: 99%

Prediction of Uniaxial Compression Strength of Limestone Based on the Point Load Strength and SVM Model

Wang

Xie

2021

Minerals

View full text Add to dashboard Cite

Uniaxial compression strength (UCS) is a fundamental parameter to carry out geotechnical engineering design and construction. It is simple and efficient to predict UCS using point load strength (PLS) at engineering sites. However, the high dispersion of rock strength limits the accuracy of traditional fitting prediction methods. In order to improve the UCS prediction accuracy, 30 sets of regular cylindrical specimen tests between PLS and UCS are conducted on limestone mines. The correlation relationship between PLS and UCS is found by using four basic fitting functions. Then, a prediction model is established by using SVM algorithm. Multiple training test data are used to achieve high-precision prediction of UCS and the results show it is less different from the actual values. Especially, the R2 coefficient reached 0.98. The SVM model prediction performance is significantly better than the traditional fitting function. The constructed SVM model in this study can accurately predict the UCS using the PLS obtained in the field, which has a great significance to the rock stability judgment in the actual construction environment.

show abstract

Implementation of artificial intelligence and support vector machine learning to estimate the drilling fluid density in high-pressure high-temperature wells

Cited by 21 publications

References 59 publications

Prediction of fracture density in a gas reservoir using robust computational approaches

Prediction of fracture density in a gas reservoir using robust computational approaches

Analysis and Research on Nonlinear Complex Function Approximation Problem Based on Deep Learning

Prediction of Uniaxial Compression Strength of Limestone Based on the Point Load Strength and SVM Model

Contact Info

Product

Resources

About