Real-Time Transient Model–Based Leak Detection

Henrie, Morgan; Carpenter, Philip; Nicholas, R. Edward

doi:10.1016/b978-0-12-802240-5.00004-2

Cited by 5 publications

(4 citation statements)

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“…In addition to the complexity of the mathematical description of oil production processes, in practice there are problems of uncertainty arising from the shortage and fuzziness of the initial information necessary for developing models and optimizing these processes (12,13). Such problems are associated with the fact that complex processes, such as oil production, are difficult to describe quantitatively.…”

Section: Methodsmentioning

confidence: 99%

A method for extracting oil from a field with hard-to-recover reserves based on systems analysis methodology

Ospanov,

Smailova,

Mukhametov

et al. 2024

E3S Web Conf.

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The article examines the problems of solving the problem of optimizing oil production processes from a field with hard-to-recover oil reserves, characterized by shortages and fuzziness of the initial information. Equations for the continuity of the mass of matter in an oil reservoir have been derived for various cases. As a result of the study, by combining and modifying various optimality principles for fuzzy conditions, a formulation of the problem of multi-criteria optimization of oil production processes was obtained and an effective method for the resulting problem in a fuzzy environment was proposed. The effectiveness and novelty of the proposed heuristic algorithm lies in the fact that for the maximum use of the collected fuzzy information, it ensures high adequacy of solving fuzzy problems that arise in oil production practice. The sections of the studied multi-layer oil field Kenbai and the main properties of its layers are analyzed and described.

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Section: Methodsmentioning

confidence: 99%

A method for extracting oil from a field with hard-to-recover reserves based on systems analysis methodology

Ospanov,

Smailova,

Mukhametov

et al. 2024

E3S Web Conf.

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“…However, all fluid transmissions are subjected to the principle of fluid dynamics based on the conservation of energy, mass and linear momentum [45]. As such, the state equation (any data-driven equation) can be used to define the fluid phase behaviour [46,47] by measuring the fluid properties, such as temperature, flow, pressure, and density.…”

Section: Computational Fluid Mechanicsmentioning

confidence: 99%

“…These measurements are performed in real-time and represent the state information of the pipeline under all conditions. One of the important considerations in using RTTM involves deciding the boundary conditions, i.e., the input data, signal calculation, and processing [47]; this has a significant impact on the obtained results.…”

Section: Real-time Transient Modelmentioning

confidence: 99%

Development and Analysis of a Distributed Leak Detection and Localisation System for Crude Oil Pipelines

Ahmed

Mouël

Stouls

et al. 2023

Sensors

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Crude oil leakages and spills (OLS) isare some of the problems attributed to pipeline failures in the oil and gas industry’s midstream sector. Consequently, they are monitored via several leakage detectiondetection and localisation techniques (LDTs) comprising classical methods and, recently, Internet of Things (IoT)-based systems via wireless sensor networks (WSNs). Although the latter techniques are proven to be more efficient, they are susceptible to other types of failures such as high false alarms or single point of failure (SPOF) due to their centralised implementations. Therefore, in this work, we present a hybrid distributed leakage detection and localisation technique (HyDiLLEch), which combines multiple classical LDTs. The technique is implemented in two versions, a single-hop and a double-hop version. The evaluation of the results is based on the resilience to SPOFs, the accuracy of detection and localisation, and communication efficiency. The results obtained from the placement strategy and the distributed spatial data correlation include increased sensitivity to leakage detection and localisation and the elimination of the SPOF related to the centralised LDTs by increasing the number of node-detecting and localising (NDL) leakages to four and six in the single-hop and double-hop versionversions, respectively. In addition, the accuracy of leakages is improved from 0 to 32 m in nodes that were physically close to the leakage points while keeping the communication overhead minimal.

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“…Penelitian ini memperhatikan kesetimbangan material yang masuk dan keluar sistem. Metode ini terbatas pada pendekatan dasar seperti penyeimbang massa dimana massa yang masuk ΔMI dalam interval satuan waktu Δt dan keluar ke massa ΔMO dalam interval satuan waktu [13]. Kebocoran massa dihitung menggunakan persamaan nomor 1 sebagai berikut,…”

Section: B Kebocoran Pipa Dengan Pendekatan Metode Kesetimbanganunclassified

Deteksi Kebocoran Pipa Air Menggunakan Machine Learning dengan Jaringan Nirkabel IEEE 802.15.4

Saputra¹,

Rasyid

Hadi³

2022

ISI

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Pipa adalah cara paling ekonomis dan paling aman dalam mendistribusikan hasil produk seperti air, petrokimia, gas, dan cairan lainnya. Terlepas dari manfaat tersebut, ternyata pipa memiliki ancaman yaitu potensi kebocoran. Artikel ini membahas pendeteksian kebocoran pipa air menggunakan parameter debit aliran. Pengujian dilakukan pada dua format dataset, menggunakan raw dataset dan process dataset menggunakan metode volume balance. Pada proses pembelajaran ada beberapa hal yang perlu disoroti seperti pemilihan tipe dataset, pre-processing dengan menormalisasi dataset, dan menerapkan metode fungsi kernel untuk meningkatkan kinerja akurasi prediksi ukuran dan lokasi kebocoran pipa. Dataset dilatih menggunakan algortima SVM untuk mengklasifikasikan ukuran dan lokasi kebocoran pipa. Hasil klasfikasi ukuran kebocoran dengan fungsi kernel polynomial pada raw dataset mencapai akurasi sebesar 98,25%, recall 99,1%, presisi 99,8%, dan F-measure 99,5%. Sedangkan fungsi kernel Radial Basis Function pada process dataset mencapai akurasi tertinggi sebesar 89,7%, recall 94,4%, presisi 95,4%, dan F-measure 94,6%. Dalam hal mengidentifkasikan lokasi kebocoran, fungsi kernel polynomial pada raw dataset meningkatkan akurasi sebesar 88,96%, recall 94,7%, presisi 91,5%, dan F-measure 92,8%. Sedangkan fungsi kernel polynomial pada process dataset mencapai akurasi sebesar 74,42%, recall 74,1%, presisi 72,8%, dan F-measure 71,3%.

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Real-Time Transient Model–Based Leak Detection

Cited by 5 publications

References 0 publications

A method for extracting oil from a field with hard-to-recover reserves based on systems analysis methodology

A method for extracting oil from a field with hard-to-recover reserves based on systems analysis methodology

Development and Analysis of a Distributed Leak Detection and Localisation System for Crude Oil Pipelines

Deteksi Kebocoran Pipa Air Menggunakan Machine Learning dengan Jaringan Nirkabel IEEE 802.15.4

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