Bayesian regularization BP Neural Network model for predicting oil-gas drilling cost

Yue, Zhao; Zhao, Shengchuan; Liu, Tianshi

doi:10.1109/icbmei.2011.5917952

Cited by 22 publications

(10 citation statements)

References 4 publications

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“…The interrelating neural weights used in the objective function are obtained as illustrated in Eq. ( 10) (Yue et al, 2011;Rostami et al, 2018):…”

Section: Bayesian Regularization Algorithmmentioning

confidence: 99%

“…In order to minimize the F(ω), the computations are conveyed to the LM phase; as a result, it will be possible to update the weight space. If the stopping condition is not satisfied, this process continues to estimate the new β and α values (Yue et al, 2011).…”

Section: Bayesian Regularization Algorithmmentioning

confidence: 99%

“…In order to tune the weight space, the conventional backpropagation method leads to the most negative descent direction. The decrease in the performance function would be faster in this way, but it might not satisfy the requirement for the highest convergence speed (Yue et al, 2011).…”

Section: Scaled Conjugate Gradient Algorithmmentioning

confidence: 99%

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Application of soft computing approaches for modeling annular pressure loss of slim-hole wells in one of Iranian central oil fields

et al. 2023

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In order to have a better control over the drilling process and reduce the overall cost of this drilling operation, engineers have tried to use soft computing (SC) techniques to conduct the preestimation of drilling events. It is critically important to estimate the annular pressure losses (APL) for non-Newtonian drilling muds within annulus in order to specify pump rates and also to be able to choose the most appropriate mud pump systems while conducting the drilling operations. To develop the vigorous and exact models to enable the prediction of APL, two popular models were employed, i.e., multilayer perceptron (MLP) [optimized by levenbergmarquardt (LM), bayesian regularization (BR), scaled conjugate gradient (SCG), Resilient back propagation (RB), and broyden fletcher goldfarb shanno (BFGS)] and radial basis function (RBF). Subsequently, applying a committee machine intelligent system (CMIS), the four top models were combined into a unit paradigm. Several tools such as error distribution diagram, cross plot, trend analysis, and cumulative frequency diagram were used in conjunction with statistical calculation to assess the efficiency of models. Consequently, the CMIS model was introduced as the most exact technique which has the greatest coefficient of determination (R 2 close to one) as well as the lowest root mean square error (RMSE close to zero) for the tested dataset.

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“…The interrelating neural weights used in the objective function are obtained as illustrated in Eq. ( 10) (Yue et al, 2011;Rostami et al, 2018):…”

Section: Bayesian Regularization Algorithmmentioning

confidence: 99%

Section: Bayesian Regularization Algorithmmentioning

confidence: 99%

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Application of soft computing approaches for modeling annular pressure loss of slim-hole wells in one of Iranian central oil fields

et al. 2023

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“…Accurate prediction of liquid-phase diffusion coefficients based on Bayesian neural networks was performed by Mariani et al (2020). Yue et al (2011) employed Bayesian regularized back propagation networks to predict the oil-gas drilling costs. Sun et al (2020) designed a Bayesian optimization neural network model based on physical constraints for reconstructing fluid flow in sparse and noisy data.…”

Section: Model Optimization-based Embedding Mechanismmentioning

confidence: 99%

Intelligent modeling with physics-informed machine learning for petroleum engineering problems

Xie

Wang

et al. 2023

Adv. Geo-Energy Res.

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The advancement in big data and artificial intelligence has enabled a novel exploration mode for the study of petroleum engineering. Unlike theory-based solution methods, the data-driven intelligent approaches demonstrate superior flexibility, computational efficiency and accuracy for dealing with complex multi-scale, and multi-physics problems. However, these intelligent models often disregard physical laws in pursuit of error minimization, which leads to certain uncertainties. Therefore, physics-informed machine learning approaches have been developed based on data, guided by physics, and supported by machine learning models. This study summarizes four embedding mechanisms for introducing physical information into machine learning models, including input databased embedding, model architecture-based embedding, loss function-based embedding, and model optimization-based embedding mechanism. These "data + physics" dualdriven intelligent models not only exhibit higher prediction accuracy while adhering to physic laws, but also accelerate the convergence to improve computational efficiency. This paradigm will facilitate the guide developments in solving petroleum engineering problems toward a more comprehensive and efficient direction.

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“…According to this idea in the related field has been studied by domestic and foreign scholars in related fields, and certain progress has been made. Zhao et al used a Bayesian regularized neural network to predict drilling costs [11]. Five different kinds of cost categories were considered in that model, and well structure factors are also involved.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of Drilling Costs for Ultra-Deep Wells Based on GA-BP Neural Network

Wang¹,

Zhu²,

Wei³

et al. 2023

Energy Engineering

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Drilling costs of ultra-deep well is the significant part of development investment, and accurate prediction of drilling costs plays an important role in reasonable budgeting and overall control of development cost. In order to improve the prediction accuracy of ultra-deep well drilling costs, the item and the dominant factors of drilling costs in Tarim oilfield are analyzed. Then, those factors of drilling costs are separated into categorical variables and numerous variables. Finally, a BP neural network model with drilling costs as the output is established, and hyper-parameters (initial weights and bias) of the BP neural network is optimized by genetic algorithm (GA). Through training and validation of the model, a reliable prediction model of ultra-deep well drilling costs is achieved. The average relative error between prediction and actual values is 3.26%. Compared with other models, the root mean square error is reduced by 25.38%. The prediction results of the proposed model are reliable, and the model is efficient, which can provide supporting for the drilling costs control and budget planning of ultra-deep wells.

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Bayesian regularization BP Neural Network model for predicting oil-gas drilling cost

Cited by 22 publications

References 4 publications

Application of soft computing approaches for modeling annular pressure loss of slim-hole wells in one of Iranian central oil fields

Application of soft computing approaches for modeling annular pressure loss of slim-hole wells in one of Iranian central oil fields

Intelligent modeling with physics-informed machine learning for petroleum engineering problems

Prediction Model of Drilling Costs for Ultra-Deep Wells Based on GA-BP Neural Network

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