An artificial neural network visible mathematical model for real-time prediction of multiphase flowing bottom-hole pressure in wellbores

Accurate prediction of multiphase flowing bottom-hole pressure(FBHP)in wellbores is an important factor required for optimal tubing design and production optimization.Existing empirical correlations and mechanistic models provide inaccurate FBHP predictions when applied to real-time field datasets because they were developed with laboratory-dependent parameters.Most machine learning(ML)models for FBHP prediction are developed with real-time field data but presented as black-box models.In addition,these ML models cannot be reproduced by other users because the dataset used for training the machine learning algorithm is not open source.These make using the ML models on new datasets difficult.This study presents an artificial neural network(ANN)visible mathematical model for real-time multiphase FBHP prediction in wellbores.A total of 1001 normalized real-time field data points were first used in developing an ANN black-box model.The data points were randomly divided into three different sets;70%for training,15%for validation,and the remaining 15%for testing.Statistical analysis showed that using the Levenberg-Marquardt training optimization algorithm(trainlm),hyperbolic tangent activation function(tansig),and three hidden layers with 20,15 and 15 neurons in the first,second and third hidden layers respectively achieved the best performance.The trained ANN model was then translated into an ANN visible mathematical model by extracting the tuned weights and biases.Trend analysis shows that the new model produced the expected effects of physical attributes on FBHP.Furthermore,statistical and graphical error analysis results show that the new model outperformed existing empirical correlations,mechanistic models,and an ANN white-box model.Training of the ANN on a larger dataset containing new data points covering a wider range of each input parameter can broaden the applicability domain of the proposed ANN visible mathematical model.

SCKE:Combining Logic- with Object-Oriented Paradigm

A new implementation(SCKE—Stractured Communication Knowledge Entity)has been proposed towards combining the logic-with the object-oriented paradigm of computing.It is intended to explore the advantages in these two paradigms in a structured,natural and efficient manner for large-scale know- ledge processing.The SCKE model supports modularity and protection for the structured development of knowledge systems.It also introduces the concepts that are typical for the object-oriented systems in the logic-oriented paradigm,without losing its advantages as a declarative language.Various inheritance hier- archies are supported in the SCKE model.They provide the semantics basis for various knowledge in AI systems.The M-entity/K-entity/Instance inheritance captures the relationship among the control, procedural and factual knowledge in AI systems,And,the super-entity/entity/instance inheritance shows the concepts of data abstraction in the knowledge of a particular domain.In addition,the SCKE model is not simply supported on top of Prolog like other attempts to integrate the object-into the log- ic-oriented paradigm.The SCKE model is a tighltly coupled model of the logic-and the object-oriented paradigm and its interpreter uniformly interprets the logic semantics and the object-oriented semantics.

Compute extremely low-frequency electromagnetic field exposure by 3-D impendance method

A 3-D impedance method has been introduced to compute the electric currents induced in a human body exposed to extremely low-frequency electromagnetic field. The 3-D impedance method has been deduced from Maxwell equations and is put into the computation and simulation effectively to the visible human body model, which has 196×114×626 cells and more than 40 types of tissues. As the result, two representative cases are investigated. One is exposure of the human body to 100 μT （1 000 mG）, the limit recommended by the International Commission on Non-Ionizing Radiation Protection for the public and the other one is the exposure of human body to 0.4 laT （4 mG）, the level at which a statistical link appears with a doubled risk of development of childhood leukaemia. The distribution of induced current density can be obtained and the maximum of induced current are found to be 16 mA/m^2 and 0.07 mA/m^2.