Development of Tools for the Analysis of Pre-Emergency Situations on the Drilling Rig Based on Neural Network Technologies

Abu-Abed, Fares

doi:10.1051/e3sconf/20184101025

Cited by 12 publications

(4 citation statements)

References 6 publications

(8 reference statements)

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“…In [16], plant safety management using new information technologies and computer decision-making tools is considered.…”

Section: Methods Usedmentioning

confidence: 99%

The Mathematical Approach to the Identification of Trouble-Free Functioning of Mining Facilities

Abu-Abed

2020

E3S Web Conf.

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Modern drilling rigs are complexes of high-tech equipment operated in difficult climatic and technological conditions, characterized by sudden spasmodic changes in the process of drilling a well, which contributes to increased wear of drilling components and increases the likelihood of pre-emergency and emergency situations. Drilling equipment has a wide range of characteristics and technological parameters, the values of which are available during drilling due to the use of modern software and hardware systems for processing geological and technological information. In order to single out the most frequent pre-emergency situations in practice and to preliminarily determine the set of signs necessary for their recognition, a corresponding analysis of the complications arising during well drilling has been carried out.

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“…In [16], plant safety management using new information technologies and computer decision-making tools is considered.…”

Section: Methods Usedmentioning

confidence: 99%

The Mathematical Approach to the Identification of Trouble-Free Functioning of Mining Facilities

Abu-Abed

2020

E3S Web Conf.

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“…As the parameters of the learning algorithm were used [2,4,16,17,18,20,21]: • К -the number of repetitions of the search procedure from various starting points of the minimum value of the neural network learning error; • Rmax -the maximum radius of the search for the global minimum of learning errors.…”

Section: Investigation Of the Influence Of Algorithm Parameters On The Accuracy Of Recognition Of Pre-emergency Situationsmentioning

confidence: 99%

Investigation of the Effectiveness of the Method for Recognizing Pre-Emergency Situations at Mining Facilities

Abu-Abed

2020

E3S Web Conf.

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In previous reports, an analysis of the basic mathematical methods used to solve the pattern recognition problem was carried out. The inappropriateness of applying the Bayesian classification and cluster analysis to solve the problem of recognizing pre-emergency situations in the process of drilling a well is shown. As a mathematical apparatus for solving the problem of determining the current state of an object of research by a given set of features, a pattern recognition method based on an artificial neural network is selected. In this paper, an analysis is made of existing approaches to improving the quality of education aimed at improving the efficiency of its functioning. The results obtained in this paper will improve the quality of work of the previously developed modified algorithm for training the pre-emergency classifier based on the back propagation method, which differs from the classical one by the procedure for finding the global minimum of the error function, and its software implementation has been implemented. The work is an integral part of previously published developments presented in the materials of articles in 2-nd, 3-rd and 4-th International innovative mining symposiums (2017-2019).

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“…Compared to traditional empirical formula methods, this approach demonstrates greater accuracy, objectivity, and efficiency, finding extensive applications in various sectors of the petroleum industry [19][20][21][22][23][24][25]. For example, Fares Abu-Abed [4][5][6] introduced a pattern recognition approach utilizing artificial neural networks for the identification and prediction of intricate scenarios within the drilling process. The approach employs accident statistical data extracted from the database as its input and enables the prediction of accidents, including blowouts, well leaks, and wellbore collapses.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Prediction Method for Pore Pressure in Abnormally High-Pressure Blocks Based on Machine Learning

Li,

Tan,

Deng

et al. 2023

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In recent years, there has been significant research and practical application of machine learning methods for predicting reservoir pore pressure. However, these studies frequently concentrate solely on reservoir blocks exhibiting normal-pressure conditions. Currently, there exists a scarcity of research addressing the prediction of pore pressure within reservoir blocks characterized by abnormally high pressures. In light of this, the present paper introduces a machine learning-based approach to predict pore pressure within reservoir blocks exhibiting abnormally high pressures. The methodology is demonstrated using the X block as a case study. Initially, the combination of the density–sonic velocity crossplot and the Bowers method is favored for elucidating the overpressure-to-compact mechanism within the X block. The elevated pressure within the lower reservoir is primarily attributed to the pressure generated during hydrocarbon formation. The Bowers method has been chosen to forecast the pore pressure in well X-1. Upon comparison with real pore pressure data, the prediction error is found to be under 5%, thus establishing it as a representative measure of the reservoir’s pore pressure. Intelligent prediction models for pore pressure were developed using the KNN, Extra Trees, Random Forest, and LightGBM algorithms. The models utilized five categories of well logging data, sonic time difference (DT), gamma ray (GR), density (ZDEN), neutron porosity (CNCF), and well diameter (CAL), as input. After training and comparison, the results demonstrate that the LightGBM model exhibits significantly superior performance compared to the other models. Specifically, it achieves R2 values of 0.935 and 0.647 on the training and test sets, respectively. The LightGBM model is employed to predict the pore pressure of two wells neighboring well X-1. Subsequently, the predicted data are juxtaposed with the actual pore pressure measurements to conduct error analysis. The achieved prediction accuracy exceeds 90%. This study delivers a comprehensive analysis of pore pressure prediction within sections exhibiting anomalously high pressure, consequently furnishing scientific insights to facilitate both secure and efficient drilling operations within the X block.

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Development of Tools for the Analysis of Pre-Emergency Situations on the Drilling Rig Based on Neural Network Technologies

Cited by 12 publications

References 6 publications

The Mathematical Approach to the Identification of Trouble-Free Functioning of Mining Facilities

The Mathematical Approach to the Identification of Trouble-Free Functioning of Mining Facilities

Investigation of the Effectiveness of the Method for Recognizing Pre-Emergency Situations at Mining Facilities

A Comprehensive Prediction Method for Pore Pressure in Abnormally High-Pressure Blocks Based on Machine Learning

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