An efficient optimization process for hydrocarbon production in presence of geological uncertainty using a clustering method: A case study on Brugge field

Foroud, Toomaj; Seifi, Abbas; Aminshahidy, Babak

doi:10.1016/j.jngse.2016.04.059

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…Also, a deviation method of semi-variance was included in geological and economical uncertainty defined by a set of geological system realizations and a set of different oil price scenarios to optimize the worst cases [17,19]. Foroud, et al, andSiraj, et al (2016, 2017) stressed the geological system as a primary cause of uncertainty in the simulation of petroleum reservoirs that can lower the reliability of optimization process outcomes of simulation. The clustering algorithms such as the Kernel K-means Method (KKM) were suggested to pick a generic subset of geological systems and reduce the overall calculation cost during the process of simulation.…”

Section: Methodsmentioning

confidence: 99%

Quick Review

Akhavan

Hosseini

Bahrami

2023

Teh. glas. (Online)

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The notable increase in petroleum demand, together with a decline in discovery rates, has highlighted the desire for efficient production of existing oil wells worldwide. Mainly, the productivity of the existing large oil fields makes us consider the principles of managing reservoirs to make the most of extraction. At the same time, many different uncertainties in the course of the developing oil field, including geological, operational, and economic uncertainties, have a detrimental impact on the reservoir's effective production, which is why dealing with uncertainty is crucial for maximizing output. There is a broad variety of studies on managing oil reservoirs under uncertainty information in the literature. In this study a short review of earlier works has been done on optimization strategies and management of uncertainty in reservoir production.

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Section: Methodsmentioning

confidence: 99%

Quick Review

Akhavan

Hosseini

Bahrami

2023

Teh. glas. (Online)

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“…K-Means involved in this data preprocessing stage by reducing the large number of data, and categorizing them into clusters. Foroud et al studied on production optimization under the state of geological uncertainty (14). The geological model was identified as the main source of uncertainty that diminished the viability of simulation results.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Use of machine learning method (Bao & Guan, 2016) No Oil production prediction K-Meansdata discretion (Foroud, et al, 2016) No Optimize production by looking at geological models K-Meansdata discretion (Awoleke & Lane, 2011) No Well water production prediction SOMsee how data are clustered K-Meansdetermine number of clusters Neural Networkprediction (Hu, et al, 2015) No Oil production prediction K-Meansdata discretion (G. (Popa, et al, 2015) No Perforation strategy optimization C-Meanscluster log data (Grieser, et al, 2008) No Overall well investigation SOMdata clustering (Cremaschi, et al, 2015) No Flow velocity estimation in pipelines K-Meansdata clustering (Shin & Cremaschi, 2014) No Flow velocity estimation in pipelines K-Meansdata clustering (Ding, et al, 2015) No Investigate high-permeability zone C-Meansdata clustering (Cui, et al, 2016) No Oil recovery improvement for high water-cut reservoirs K-Meanscluster/group the subjects (Liu, et al, 2009) No Measurement of water content in crude oil K-Meansdata preprocessing for prediction (Singh, et al, 2014) No Measurement for forecasted oil recovery K-Meansdata discretion…”

Section: Purposementioning

confidence: 99%

A Review on “Management by Exception” Surveillance for Well Management: To maximize Oil Production

Malik¹,

Hasan²,

Jasamai³

2018

IJET

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The business processes and decisions of oil and gas operations generate large amounts of data, which causes surveillance engineers to spend more time gathering, and analyzing them. To do this manually is inefficient. Hence, this study is proposed to leverage on data driven surveillance by adopting the principle of management by exception (MBE). The study aims to minimize the manual interaction between data and engineers; hence will focus on monitoring well production performance through pre-determined parameters with set of rules. The outcome of this study is a model that can identify any deviations from the pre-set rules and the model will alert user for deviations that occur. The model will also be able to predict on when the well be offline if the problem keeps on persisting without immediate action from user. The objective of this paper is to present a literature review on the prediction and management by exception for the above mentioned well management. The results presented in this paper will help in the development of the proposed prediction and management model. The literature review was conducted based on structured literature review methodology, and a comparative study among the collected works is analyzed and presented in this paper.

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“…Optimization under geological uncertainties have been proposed in several studies (Dang et al, 2016;Foroud et al, 2016;Nguyen et al, 2016;Nwachukwu et al, 2018;Welkenhuysen et al, 2017). However, these studies emphasize on optimizing oil production performance rather than CO2 sequestration.…”

Section: Introductionmentioning

confidence: 99%