Development of a Integrated Approach to the Risk Analysis of a Blow-out Accident

Blotto, Paola; Bonuccelli, Michele; Morale, Gianni; Dellarole, E.; Falcitelli, M.; Podenzani, F.

doi:10.2118/86704-ms

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…Where 𝑊 (0) and 𝑊 (1) denote the weight matrices of the first two layers of the convolutional network, respectively, ReLU is the rectified linear unit, and SoftMax is the normalization function.…”

Section: Improved Gcnmentioning

confidence: 99%

“…As a major source of energy for the country, oil and gas are vital to national economy and the people's livelihood, but the exploitation of oil and gas resources is accompanied by a variety of accidents, the most dangerous of which is the blowout accident [1]. When a well blowout occurs, it will make the environment hot and high-pressure, and the violent and harmful nature of the blowout accident makes it impossible for rescue and relief personnel to take rapid and effective measures to control the accident at close range [2], leading to its intensification and the spewing of harmful substances from the strata, posing a threat to human health, a burden to the environment and a fatal blow to flora and fauna [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

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Research on Measurement and Prediction of Well Blowout Fluid Flow Rate based on Machine Vision

Cai,

Zhang

2024

FSD

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A machine vision-based method for measuring and predicting well blowout fluid flow rate is proposed to solve the problems of limited quantitative identification of well blowout fluid flow rate, inability to install traditional measurement equipment in the field, and inability to accurately measure and predict blowout fluid flow rate. This method establishes an image processing model based on ORB-BF-RANSAC for blowout sequence images, feature extraction and feature identification are performed on the blowout images from consecutive frames in order to obtain the coordinates of the feature points within the well blowout images. The pixel displacement of the feature point pairs is then calculated. Subsequently, the true displacement of the feature points is calculated using the pinhole imaging principle. Finally, the true displacement is divided by the number of frames to calculate the blowout fluid flow rate. Compared to the speed of mass flow meter calibration, the method achieves a measurement accuracy of 91%. Based on the above method, a well blowout fluid flow rate prediction method is designed by introducing Spatial Attention Mechanism (SAM) to improve the Graph Convolutional Networks (GCN). The prediction model proposed in this paper is more effective compared with other prediction methods, and the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are 12.39%, 18.72% and 9.16%.

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Section: Improved Gcnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Measurement and Prediction of Well Blowout Fluid Flow Rate based on Machine Vision

Cai,

Zhang

2024

FSD

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The CFD Approach for the Risk Analysis of a Blowout Event

Borello

Bonuccelli²,

Morale³

2007

All Days

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The uncontrolled blow-out of a well is one of the most critical accidents that can occur during both exploration and exploitation of hydrocarbon fields. Significant HSE issues are associated to this event that introduces safety risks for field operators, potential health injury for the population and impacts on the environment. Blowouts need to be evaluated through specific models: commercial packages may lead to a non-exhaustive analysis and underestimate the effects. Some phenomena that occur during a blow-out event are not sufficiently treated in literature, so that experimental or CFD investigations are sometime necessary to allow an exhaustive analysis and the expansion of a specific database, useful to develop and validate simplified models. ENI E&P has been carrying out a specific R&D project since 1998. This paper describes some blow-out jet simulations, and the associated methodology, carried out applying the CFD approach. The jet development into atmosphere affects strongly the cloud evolution, in terms of shape and concentration, and then the flammable/toxic gas dispersion as well as heat radiation. Different release conditions have been analyzed:horizontal and vertical jet, both ignited and un-ignited;free and restrained jet;heavy and neutral gas dispersion. Jet direction has a strong effect on cloud formation and dispersion: for example if a horizontal jet take place near the ground, it can remain glued on it (KOANDA effect), resulting in high concentration. This phenomena take place also in case of ignited jet: the flame remains glued to the ground, resulting in high temperature and radiation level. Moreover, totally restrained jet could generate a heavy-gas cloud, resulting in high concentration in the near-field. In this case, the cloud formation happens in different consequent stages, and a transition analysis is necessary to reproduce the event correctly, in terms of risk area evaluation. "Blow-out Project" Objectives e Work Phases Eni E&P is developing the methodology, for the simulation of a blow-out event, in the framework of three subsequent specific R&D project phases, in order to allow the complete analysis of the phenomenology associated to the blow-out event:Phase 1: 1998 - 2000: "Blow-out 1";Phase 2: 2001 - 2004: "Blow-out 2";Phase 3: 2005 - 2007: "Blow-out 3". The main objectives of the project have been:Provide tools to evaluate the consequences of a blow-out during the various drilling stages and exploitation activities.Supply information on the evolution of the event that can be used as a basis to:Support authorization phases, providing important and reliable information direct to the authorities;Optimize the location of the well area taking into account the possible consequences of a blow-out just from the first step in the drilling activities;Plan and manage the emergency actions to mitigate real blow-out situations, providing decision making tools to key people;Produce the data required to prepare a Contingency Plan. The Methodology includes all the kind of impact associated with a blow-out event and with pipeline releases accidents also including underwater discharges, oil spill analysis and transient releases (typical of a pipeline leakage). "Blow-out 1" and "Blow-out 2" phases allowed to obtain two PC softwares (for the "Short-Cut" and "Standard" Approaches) for an exhaustive simulation of the consequences of a blow-out. The software has just been presented in other Conferences,5,6,7. Presently, Eni E&P and TEA Group are carrying out a 3rd phase of the project (2005–2007) "Blow-out 3". This activity will allow to improve the model and the methodology, taking into account problems, results and the know-how resulting from a great number of applications performed and scenarios analyzed. CFD investigation is one of the most important activities of the "Blow-out 3" phase.

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An Innovative Approach for Stochastic Analysis of Blowout Inventories and Impacts

Dellarole¹,

Bonuccelli²,

Frattini³

et al. 2016

SPE International Conference and Exhibition on Health, Safety, Security, Environment, and Social Responsibility

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The uncontrolled eruption of a well is one of the most critical accidents that could happen in the oil and gas industry. Thanks to challenging R&D activities and collaborations, eni Upstream and Technical Services has developed and implemented Rainbow, an integrated methodology for blow-out risk analyses. As the Methodology has been applied in a wide range of HSE studies, the database of the analyzed blowout scenarios has been increasing for about 15 years, including for example release rates, total oil spill volumes and impact parameters (e.g. beached volumes for offshore scenarios and rainout area extension for onshore blowouts).Considering, for example, oil rates (BOPD), they could extend in a range as wide as 5 orders of magnitude, therefore a probabilistic analysis has been performed in order to evaluate the stochastic distribution of the above mentioned impact parameters.Applying short-cut models included in the Rainbow Methodology, the database of the analyzed blowout events has been integrated in order to include all potential scenarios. Moreover, in order to take into account both the local parameters affecting the well blowout frequency and the different flow paths (i.e. in-well geometry) distribution, a complete frequency assessment has been carried out.The stochastic distributions of the following parameters have been obtained as results:• total spill volume (all the scenarios).• extension of impacted areas involved in the oil fallout (for onshore wells).• length of impacted shoreline, oil volumes beached and open sea impacted area extension (for offshore wells, considering both underwater and atmospheric releases).The distribution curves have been clustered distinguishing among:• HPHT and normal wells.• Subsea and atmospheric.• Crude oil and gas condensate wells.The above information can be useful for several purposes, e.g.:• To categorize wells and potential blowouts in a rational risk ranking.• To support the Company clustering procedure focused on the cost evaluation of the blowout events. • To compare the blowout impact distribution with other accidents associated to hydrocarbon releases in the environment (e.g. tanker/ship, pipeline, storage, etc.)The comparison between the stochastic distribution curves and some real accidents (including Macondo blowout) occurred in the oil and gas industry is also presented in the Paper.

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Development of a Integrated Approach to the Risk Analysis of a Blow-out Accident

Cited by 6 publications

References 3 publications

Research on Measurement and Prediction of Well Blowout Fluid Flow Rate based on Machine Vision

Research on Measurement and Prediction of Well Blowout Fluid Flow Rate based on Machine Vision

The CFD Approach for the Risk Analysis of a Blowout Event

An Innovative Approach for Stochastic Analysis of Blowout Inventories and Impacts

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