Hydraulic fracturing is a widespread well completion technique for Oil and Gas production enhancement in both conventional and unconventional reservoirs. However, it is exposed to various risks, which can result in a most adverse consequence – the premature screen-out, which is a condition that occurs due to the proppant bridging across the perforations or similar restricted flow areas. The goal of this work is to propose a novel and complete approach that makes it possible to predict the risk of screen-out occurrence, identify the riskiest scenarios and determine the best risk reduction strategies. The premature screen-out problem is addressed within a Risk Management and Control Process. The qualitative assessment of the early screen-out risk has been carried out by the Features, Events and Processes (FEP) Analysis, and the quantitative assessment has been implemented with a Bayesian Belief Network (BBN). The input values of the BBN model, i.e. the probabilities of the model’s variables, have been evaluated using various expert elicitation methods due to shortfall in data, and the log-likelihood method has turned out to provide the best results.

The risk assessment and management of premature screen-out during hydraulic fracturing based on the bayesian belief network model

Zio E.;
2020-01-01

Abstract

Hydraulic fracturing is a widespread well completion technique for Oil and Gas production enhancement in both conventional and unconventional reservoirs. However, it is exposed to various risks, which can result in a most adverse consequence – the premature screen-out, which is a condition that occurs due to the proppant bridging across the perforations or similar restricted flow areas. The goal of this work is to propose a novel and complete approach that makes it possible to predict the risk of screen-out occurrence, identify the riskiest scenarios and determine the best risk reduction strategies. The premature screen-out problem is addressed within a Risk Management and Control Process. The qualitative assessment of the early screen-out risk has been carried out by the Features, Events and Processes (FEP) Analysis, and the quantitative assessment has been implemented with a Bayesian Belief Network (BBN). The input values of the BBN model, i.e. the probabilities of the model’s variables, have been evaluated using various expert elicitation methods due to shortfall in data, and the log-likelihood method has turned out to provide the best results.
Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference
978-981-14-8593-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1181272
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