Cost reduction by 4D maintenance planning

Explore how a systematic analysis and identification of best practice for asset operation and maintenance can lead to significant improvements of the operational performance. DHRTC’s 4D maintenance project works on use of 4D models for preparation of maintenance operations with the potential to lead to improved maintenance and reduction of costs.

Modularization deployed on maintenance and operation in the DUC.

Current maintenance philosophies often fail to regard maintenance activities on an adequate level of detail, which have led to a situation where each equipment are treated as being unique and no one can maintain the big picture. Therefore, the same maintenance activity are carried out in various ways with various efficiency and safety.

The 4D maintenance project at DHRTC seeks to develop a methodology for describing and comparing heterogeneous maintenance activities on a high level. This is to enable identification the best practices, ensure consistency across assets and create a fact basis for taking strategical decisions for maintenance activities. The steps forward include

  • Making inhomogeneous maintenance operations comparable by modelling their physical, operational and performance dimensions
  • Compare various maintenance scenarios to identify best performing principles and quantify financial potential

CTR2 4D Figur 1

Figure 1: Scope of 4D study (Click here to se the figure)

In order to gain experiences of modularization within Oil & Gas, two pilot projects have been carried out; Lean Well Head platforms as well as modular external corrosion management (paint).In the lean wellhead platform it was the purpose to investigate if modularization could enable further cost savings compared to the so‐called SLIC concept. By systematically investigating functions across the different assets and by conscious calculation of OPEX and CAPEX is was possible to save additional 11% of total cost at 35USD/BBL. In the external corrosion management project, more than 500.000 hours of paint operations were analyzed. Data from SAP and RiscM were systematically correlated. Preliminary findings are that there are more than 30% differences in painting efficiency between the best and worst performing asset. Furthermore, small teams are more effective than large teams.

CTR2 4D Figur 2

Figure 2: Modelling of painting operations (Click here to see the figure)

From the two pilot studies the following conclusions can be made 1) It is possible to identify tangible cost savings both on assets and maintenance activities 2) It is possible to build up data models that enable correlation of data that has not previously been related, such as operations (how activities are carried out), the asset and performance (cost & time) 3) Data is not well structured, but by cleaning and restructuring it can be utilized It is possible to carry out research that can reduce cost on both assets and maintenance.