Dynamic risk assessment of reservoir production using data-driven probabilistic approach

Mamudu, Abbas; Khan, Faisal; Zendehboudi, Sohrab; Adedigba, Sunday

doi:10.1016/j.petrol.2019.106486

Cited by 30 publications

(13 citation statements)

References 39 publications

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“…A recent survey showed that deep learning, support vector machine and random forest had lately become more popular in the application of hazard prediction [50]. Mamudu et al [43], [44] developed hybrid models based on neural network and Bayesian network algorithms that not only served as a risk monitoring system but also as product optimization. Roy et al [54] utilized ANFIS for predicting fracture toughness to prevent rock failure during drilling.…”

Section: Machine Learning In Oil and Gasmentioning

confidence: 99%

A Data-Driven Decision Support Tool for Offshore Oil and Gas Decommissioning

Vuttipittayamongkol¹,

Tung

Elyan

2021

IEEE Access

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A growing number of oil and gas offshore infrastructures across the globe are approaching the end of their operational life. It is a major challenge for the industry to plan and make a decision on the decommissioning as the processes are resource exhaustive. Whether a facility is completely removed, partially removed or left in-situ, each option will affect individual parties differently. Stakeholders' concerns and needs are collected and analyzed to obtain the most compromised decommissioning decision. Engaging with hundreds of stakeholders is extremely complicated, hence time-consuming and costly. This issue can be addressed using a predictive model to provide suggested decommissioning options based on the data of previously approved projects. However, the lack of readily available relevant datasets is the main hindrance of such an approach. In this paper, we introduce a new oil and gas decommissioning dataset extensively covering all types of offshore infrastructures in the UK landscape over a 21-year period. An experimental framework using several learning algorithms on the new dataset for predicting the decommissioning option is presented. Various resampling methods were applied to tackle the imbalanced class distribution of the dataset for improved classification. Promising results were achieved despite the exclusion of some stakeholder-related features used in the traditional approach. This shows signs of a potential solution for the industry to significantly reduce time and cost spent on a decommissioning project, and encourages more efforts put into researching on this timely topic.

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Section: Machine Learning In Oil and Gasmentioning

confidence: 99%

A Data-Driven Decision Support Tool for Offshore Oil and Gas Decommissioning

Vuttipittayamongkol¹,

Tung

Elyan

2021

IEEE Access

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“…However, it usually requires a large amount of real and actual exploration data, and this method is mainly suitable for medium and high degree exploration areas. At present, the data-driven method is widely applied, but there is still great room for improvement in improving the accuracy of model prediction [17,18,24,25].…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution Prediction of Oil and Gas Based on Bayesian Network with Case Study

Ren

Wang

Ren

et al. 2020

Mathematical Problems in Engineering

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Effectively predicting the spatial distribution of oil and gas contributes to delineating promising target areas for further exploration. Determining the location of hydrocarbon is a complex and uncertain decision problem. This paper proposes a method for predicting the spatial distribution of oil and gas resource based on Bayesian network. In this method, qualitative dependency relationship between the hydrocarbon occurrence and key geologic factors is obtained using Bayesian network structure learning by integrating the available geoscience information and the current exploration results and then using Bayesian network topology structure to predict the probability of hydrocarbon occurrence in the undiscovered area; finally, the probability map of hydrocarbon-bearing is formed by interpolation method. The proposed method and workflow are further illustrated using an example from the Carboniferous Huanglong Formation (C2hl) in the eastern part of the Sichuan Basin in China. The prediction results show that the coincidence rate between the results of 248 known exploration wells and the predicted results reaches 89.5%, and it has been found that the gas fields are basically located in the high value area of the hydrocarbon-bearing probability map. The application results show that the Bayesian network method can effectively predict the spatial distribution of oil and gas resources, thereby reducing exploration risks, optimizing exploration targets, and improving exploration benefits.

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“…Применение цифровых технологий моделирования в нефтяной промышленности позволяет в настоящее время решать сложные задачи. Например, цифровые модели используются для прогнозирования рисков, автоматизации добычи, оптимизации систем заводнения и состояния месторождения [1][2][3][4][5][6][7][8][9][10]. Среди существующих разработок следует отметить системы, основанные на моделях нейронных сетей, вероятностных методах и нечеткой логике [11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionunclassified

Development of Mathematical Models for Predicting Oil Production and Transportation Processes Under Conditions of Uncertainty

Degtyarev¹,

Sagdatullin²

2020

Bulletin of St PbSIT(TU)

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The structure of production at the field, the structure of data obtained, as well as the features of their interpretation and modeling, were investigated. A mathematical model was developed for predicting oil production and transportation processes under conditions of uncertainty based on the results of an analysis of production equipment. The existing data model for individual oil field facilities was analyzed. An algorithm for the operation of a neural network model was proposed to predict an important characteristic of an objectprofitability. The algorithm was improved based on the optimization block, which served to classify and identify features in existing data based on the p-criterion. The proposed algorithms are designed to make decisions when performing various types of operations in the field. The proposed model for predicting oil production and transportation processes under conditions of uncertainty showed the efficiency of profitability projection at the level of 73.5%.

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Dynamic risk assessment of reservoir production using data-driven probabilistic approach

Cited by 30 publications

References 39 publications

A Data-Driven Decision Support Tool for Offshore Oil and Gas Decommissioning

A Data-Driven Decision Support Tool for Offshore Oil and Gas Decommissioning

Spatial Distribution Prediction of Oil and Gas Based on Bayesian Network with Case Study

Development of Mathematical Models for Predicting Oil Production and Transportation Processes Under Conditions of Uncertainty

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