Blowout Risk in Drilling and Production

Khan, Faisal; Abimbola, Majeed; Hashemi, Seyed Javad

doi:10.1002/9781118476406.emoe356

Cited by 2 publications

(2 citation statements)

References 43 publications

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“…By sensitivity analysis, key influencing factor can be determined [22]. Another study describes the blowout scenarios and important risk factors that should be taken into account for blowout risk assessment [23]. Similarly the risk and time assessments of the pipe-stuck incidents are required to overcome non-productive time due to human errors, mechanical failure or with other petro physical properties.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

et al. 2020

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Stuck pipe incidents are one of the contributors to non-productive time (NPT), where they can result in a higher well cost. This research investigates the feasibility of applying machine learning to predict events of stuck pipes during drilling operations in petroleum fields. The predictive model aims to predict the occurrence of stuck pipes so that relevant drilling operation personnel are warned to enact a mitigation plan to prevent stuck pipes. Two machine learning methodologies were studied in this research, namely, the artificial neural network (ANN) and support vector machine (SVM). A total of 268 data sets were successfully collected through data extraction for the well drilling operation. The data also consist of the parameters with which the stuck pipes occurred during the drilling operations. These drilling parameters include information such as the properties of the drilling fluid, bottom-hole assembly (BHA) specification, state of the bore-hole and operating conditions. The R programming software was used to construct both the ANN and SVM machine learning models. The prediction performance of the machine learning models was evaluated in terms of accuracy, sensitivity and specificity. Sensitivity analysis was conducted on these two machine learning models. For the ANN, two activation functions—namely, the logistic activation function and hyperbolic tangent activation function—were tested. Additionally, all the possible combinations of network structures, from [19, 1, 1, 1, 1] to [19, 10, 10, 10, 1], were tested for each activation function. For the SVM, three kernel functions—namely, linear, Radial Basis Function (RBF) and polynomial—were tested. Apart from that, SVM hyper-parameters such as the regularization factor (C), sigma (σ) and degree (D) were used in sensitivity analysis as well. The results from the sensitivity analysis demonstrate that the best ANN model managed to achieve an 88.89% accuracy, 91.89% sensitivity and 86.36% specificity, whereas the best SVM model managed to achieve an 83.95% accuracy, 86.49% sensitivity and 81.82% specificity. Upon comparison, the ANN model is the better machine learning model in this study because its accuracy, sensitivity and specificity are consistently higher than those of the best SVM model. In conclusion, judging from the promising prediction accurateness as demonstrated in the results of this study, it is suggested that stuck pipe prediction using machine learning is indeed practical.

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Section: Literature Reviewmentioning

confidence: 99%

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

et al. 2020

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“…Although, a number of predictable risk assessment approaches and barriers have played important role in identifying major risk in drilling operation, and on other oil and gas facilities [9][10]. Although, most of these barriers have several advantages and disadvantages due to their structural limitations [11].…”

Section: Introductionmentioning

confidence: 99%

Application of Hazop and Bowtie-Xp Methodologies of Risk Assessment on the Macondo Oilwell Blowout

Igbani¹,

Ketebu²,

Digieneni³

2019

IJASRE

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The application of HAZOP and BowtieXP methodologies of risk assessment using the Macondo oil well blowout as a case study has been investigated. This study is aimed, to compare and contrast between the HAZOP and BowtieXP methodologies of risk management using the Macondo oil well blowout as a case study. Hence, to accomplish the set aim of the study, the study envisions the Macondo oil well blowout as a scenario, which objectively vector: to evaluate risk management of the Macondo blowout using the Bowtie methodology; to assess risk management of the Macondo blow out using the HAZOP methodology; to prioritize risk assessment methodology within the risk management process; and to show the causes, failed controls, top event, impact, and consequences of the Macondo oil well blowout. To perform the Bowtie Analysis, several steps were followed to ensure an accurate representation of the Bowtie Diagram. Firstly, The TOP EVENT was established. This was the most critical step in Bowtie XP Risk tool. The choice of the TOP EVENT was guided by some questions: What is the risk event? At what point was the safety control lost? At what point did the root causes lead to, and that eventually results in the top event? Next, the preceding events and root causes threats were established. The choices made addressed answered the aforementioned questions relating to the risk controls which failed, the human factors and behaviour which played a role in the elevation of danger and the root causes of the Top Event. This study concludes that the major event of drilling an oil well is a BLOWOUT, which may lead to a catastrophe, and this was identified by both the HAZOP and BowtieXP as STUDY NODES and TOP EVENT, respectively. The consequences of a failed control blowout are Fire, Explosion, Fatal loss of lives, and environmental pollution.

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Blowout Risk in Drilling and Production

Cited by 2 publications

References 43 publications

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

Application of Hazop and Bowtie-Xp Methodologies of Risk Assessment on the Macondo Oilwell Blowout

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