Numerical-Based Model for Calculating the Risk of Well Integrity Failures in Mature Fields Operated by Gas Lift

Salem, Adel M.; Yakoot, Mostafa S.; Mahmoud, Omar

doi:10.1007/s11668-022-01353-5

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…As a result, it may be difficult to predict where landslides will occur after an earthquake [7]. Over the last 20 years, many prediction models have been developed to pinpoint locations vulnerable to landslides caused by earthquakes, and they can be divided into two categories: (1) models with a physical and numerical foundation [8]; and (2) models for determining susceptibility [9].…”

Section: Introductionmentioning

confidence: 99%

2-D Deep Convolutional Neural Network for Predicting the Intensity of Seismic Events

Turarbek¹,

Adetbekov²,

Bektemesov³

2023

IJACSA

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Machine learning has advanced rapidly in the last decade, promising to significantly change and improve the function of big data analysis in a variety of fields. When compared to traditional methods, machine learning provides significant advantages in complex problem solving, computing performance, uncertainty propagation and handling, and decision support. In this paper, we present a novel end-to-end strategy for improving the overall accuracy of earthquake detection by simultaneously improving each step of the detection pipeline. In addition, we propose a Conv2D convolutional neural network (CNN) architecture for processing seismic waveforms collected across a geophysical system. The proposed Conv2D method for earthquake detection was compared to various machine-learning approaches and state-of-the-art methods. All of the methods used were trained and tested on real data collected in Kazakhstan over the last 97 years, from 1906 to 2022. The proposed model outperformed the other models with accuracy, precision, recall, and f-score scores of 63%, 82.4%, 62.7%, and 83%, respectively. Based on the results, it is possible to conclude that the proposed Conv2D model is useful for predicting realworld earthquakes in seismic zones.

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Section: Introductionmentioning

confidence: 99%

2-D Deep Convolutional Neural Network for Predicting the Intensity of Seismic Events

Turarbek¹,

Adetbekov²,

Bektemesov³

2023

IJACSA

View full text Add to dashboard Cite

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Measuring Maturity of Well Integrity Management - Analysis of Well Integrity in Brownfield Using Maturity Models for Prolonged Well Lifecyle

Yakoot

Salem

Mahmoud

2023

Day 1 Mon, March 13, 2023

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Well integrity (WI) is a growing concern in the oil and gas (O&G) industry as fields mature and WI problems increase. In project management, maturity denotes having perfect condition to attain the organization's objectives. Applying the same principle to O&G industry, provides a pathway and basis to achieve excellence in WI management in O&G fields. Maturity of WI management has a direct impact on performance, assurance, compliance, and most importantly operation safety. A case study from a brown field has been conducted to evaluate the effect of WI management maturity on field performance. The application of well integrity management system (WIMS) in an offshore brown oilfield has been studied. The journey of WI maturity has been presented in detail. All maturity phases have been analyzed starting from program initiation, up to predicting WI failures using machine learning (ML) algorithms. Three maturity models, that have been developed by different organizations and individuals, were selected for benchmarking of measured maturity level in the WIMS applied. They were selected as they show independency from industry/organization's type. The three models are; organizational project management maturity model (Opm3), capability maturity model integration (CMMI), and Kerzner project management maturity model (KPMMM). The attained results indicated that K-PMMM provides the best description and level determination of maturity level with WIMS applied. Moreover, it is highly recommended to implement maturity models by including all processes and subsystems in the WIMS, to have better resolution of system gaps.

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Numerical-Based Model for Calculating the Risk of Well Integrity Failures in Mature Fields Operated by Gas Lift

Cited by 2 publications

References 9 publications

2-D Deep Convolutional Neural Network for Predicting the Intensity of Seismic Events

2-D Deep Convolutional Neural Network for Predicting the Intensity of Seismic Events

Measuring Maturity of Well Integrity Management - Analysis of Well Integrity in Brownfield Using Maturity Models for Prolonged Well Lifecyle

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