Real time risk analysis of kick detection: Testing and validation

Oil and gas development is moving into harsh and remote locations where the highest level of safety is required. A blowout is one of the most feared accidents in oil and gas developments projects. The main objective of this paper is to test and validate the kick detection of blowout risk assessment model using uniquely developed experimental results. Kick detection is a major part of the blowout risk assessment model. The accuracy and timeliness of kick detection are dependent on the monitoring of multiple downhole parameters such as downhole pressure, fluid density, fluid conductivity and mass flow rate. In the present study these four parameters are considered in different logical combinations to assess the occurrence of kick and associated blowout risk. The assessed results are compared against the experimental observations. It is observed that simultaneous monitoring of mass flow rate combined with any one the three parameters provides most reliable detection of kick and potential blowout likelihood. The current work presents the framework for a dynamic risk assessment and management model. Upon success testing of this approach at the pilot and field levels, this approach could provide a paradigm shift in drilling safety.

[1]  Per Holand.  SINTEF Offshore Blowout Database , 1997 .

[2]  Faisal Khan,et al.  SHIPP methodology: Predictive accident modeling approach. Part I: Methodology and model description , 2011 .

[3]  Tove Loberg,et al.  KickRisk—A Well Specific Approach to the Quantification of Well Control Risks , 2009 .

[4]  Louise K. Comfort,et al.  The Exxon Valdez and BP Deepwater Horizon Oil Spills , 2012 .

[5]  Lasse B. Andersen,et al.  Stochastic modelling for the analysis of blowout risk in exploration drilling. , 1998 .

[6]  Jon Espen Skogdalen,et al.  Developing safety indicators for preventing offshore oil and gas deepwater drilling blowouts , 2011 .

[7]  Jamal Ghodousi,et al.  Safety barriers analysis of offshore drilling system by employing Fuzzy Event Tree Analysis , 2015 .

[8]  Faisal Khan,et al.  Quantitative risk analysis of offshore drilling operations: A Bayesian approach , 2013 .

[9]  Marvin Rausand,et al.  A safety barrier-based accident model for offshore drilling blowouts , 2013 .

[10]  Ramachandran Venkatesan,et al.  Monitoring of down-hole parameters for early kick detection , 2016 .

[11]  Roy A. Lindley,et al.  Early kick detection methods and technologies , 2014 .

[12]  Greg Welch,et al.  An Introduction to Kalman Filter , 1995, SIGGRAPH 2001.

[13]  Per Holand.  Offshore Blowouts: Causes and Control , 1997 .

[14]  Faisal Khan,et al.  Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network , 2013 .

[15]  Michael Nikolaou,et al.  Evaluation of control methods for drilling operations with unexpected gas influx , 2013 .

[16]  Robert D. Grace,et al.  Blowout and Well Control Handbook , 2003 .

[17]  Stephen Butt,et al.  Safety and risk analysis of managed pressure drilling operation using Bayesian network , 2015 .

[18]  Jon Espen Skogdalen,et al.  Quantitative risk analysis of oil and gas drilling, using Deepwater Horizon as case study , 2012, Reliab. Eng. Syst. Saf..

[19]  Havard Brandt,et al.  Well-specific Blowout Risk Assessment , 2012 .

[20]  Vikram Garaniya,et al.  Risk analysis of deepwater drilling operations using Bayesian network , 2015 .

[21]  S. Sillem,et al.  Towards an integrated risk model for hydrocarbon industry operation , 2011 .

[22]  Yonghong Liu,et al.  Application of Bayesian Networks in Quantitative Risk Assessment of Subsea Blowout Preventer Operations , 2013, Risk analysis : an official publication of the Society for Risk Analysis.

[23]  Mohammad Ataei,et al.  Fault tree analysis of failure cause of crushing plant and mixing bed hall at Khoy cement factory in Iran☆ , 2014, Case studies in engineering failure analysis.