A mathematical casing cutting model and operation parameters optimization of a large-diameter deepwater hydraulic cutter

Abstract An important part of subsea abandonment operations on gas and oil wellheads is the use of hydraulic cutters. To save time and guarantee security of an operation, so it is desirable to predict their operating parameters with a mathematical model. To determine the relations between cutting force, structure, and operating parameters of a hydraulic cutter to optimize its design and operation, the casing cutting mechanism of a hydraulic cutter was analyzed. Cutting parameters were analyzed based on the characteristics of real-life cutting by a hydraulic cutter, and a mathematical casing cutting model of a large-diameter hydraulic cutter in deep water was established according to the classic empirical formulas of cutting and grinding forces. The model parameters were defined, and the mathematical model was verified by combining it with site data from abandonment operations. An optimization mathematical model of cutting parameters was also established for cutting efficiency, operation parameters were optimized by particle swarm optimization, and the pump output was also optimized. Results indicate that casing cutting is a combination of cutting and grinding. Cutting forces calculated by the mathematical casing cutting model were within 7% of actual cutting forces. The requirements of engineering applications were met. We found that cutting efficiency is greatly improved by adopting an optimal speed and feed, so the research results are pertinent and important to real-life practice.

[1]  Daniel O.B. Jones,et al.  A multi-criteria decision approach to decommissioning of offshore oil and gas infrastructure , 2014 .

[2]  Li Da Zhu,et al.  Detailed modeling of cutting forces in grinding process considering variable stages of grain-workpiece micro interactions , 2017 .

[3]  Zbigniew Michalewicz,et al.  Analysis of Stability, Local Convergence, and Transformation Sensitivity of a Variant of the Particle Swarm Optimization Algorithm , 2016, IEEE Transactions on Evolutionary Computation.

[4]  Mark J. Kaiser,et al.  Decommissioning cost estimation for deepwater floating structures in the US Gulf of Mexico , 2015 .

[5]  Tibérius O. Bonates,et al.  Integrated optimization model for location and sizing of offshore platforms and location of oil wells , 2016 .

[6]  Salem Y. Lakhal,et al.  An “Olympic” framework for a green decommissioning of an offshore oil platform , 2009 .

[7]  Gao Jun-wei The Disposal of Abandoned Offshore Oil Platform , 2010 .

[8]  Zheng Liang,et al.  Research on cutting mechanics of window bit cutting teeth , 2016 .

[9]  Renzhong Tang,et al.  Pareto fronts of machining parameters for trade-off among energy consumption, cutting force and processing time , 2017 .

[10]  David White,et al.  Engineering and legal considerations for decommissioning of offshore oil and gas infrastructure in Australia , 2017 .

[11]  Abdulazeez Abdulraheem,et al.  A hybrid particle swarm optimization and support vector regression model for modelling permeability prediction of hydrocarbon reservoir , 2017 .

[12]  Mark J. Kaiser,et al.  Rigless well abandonment remediation in the shallow water U.S. Gulf of Mexico , 2017 .

[13]  Xuanxuan Yang,et al.  Prediction of cutting forces in ball-end milling of 2.5D C/C composites , 2016 .

[14]  Witold Habrat,et al.  Effect of Bond Type and Process Parameters on Grinding Force Components in Grinding of Cemented Carbide , 2016 .

[15]  P. Srihari,et al.  International Conference On DESIGN AND MANUFACTURING, IConDM 2013 Influence of cutting parameters on cutting force and surface finish in turning operation , 2013 .

[16]  Issam Hanafi,et al.  Application of Particle Swarm Optimization for Optimizing the Process Parameters in Turning of PEEK CF30 Composites , 2016 .