Objectives/Scope

The paper describes what steps have been taken towards improved monitoring and control of the offshore drilling fluid process. These steps include the following:

  • Two automatic density and rheology sensors,

  • Developing a new predictive rheology model

  • Software integration, data transfer from offshore to cloud to shore

  • Onshore laboratory experiments

  • Full-scale tests

Methods, Procedures, Process

The predictive viscosity model tracks the active fluids, calculates additives concentrations in the flow loop, analyses sensor data, calibrates viscosity response to additives, and estimates what is needed to obtain the set target rheology. Laboratory and full-scale tests both onshore and offshore have been performed using the automated rheology and density sensor. To prepare for automating the drilling fluid conditioning process, the predictive rheology model was integrated with the drilling fluid control system and sensor data. Additionally, integration with database and cloud service was implemented to facilitate transfer of data from the rig to the onshore.

Results, Observations, Conclusions

During laboratory scale experiments, the predictive rheology model represented topology flexibly and was configured to represent the active flow loops in laboratory scale experiments, full scale onshore test, and offshore test with two sensors in the loop. The data processing algorithm with data quality assessment and model calibration was tested with onshore full-scale data. Many challenges were addressed and solved by cooperating partners in the integrated fluid laboratory, which included two sensors, the predictive rheology model, drilling fluid control system and a flow loop with multiple active tanks and mixing lines with dosing units for adding liquids and powders. The first response tests for the calculated additive rate set-point were successfully performed. Full-scale onshore tests were completed while adding different additives and recording the rheology data. The calibrated predictive model showed good correlation with the recorded data. Temperature effects were also considered and modelled accurately. Two sensors were installed offshore, recording data during regular drilling operations.

Novel/Additive Information

New elements required to automate the drilling fluid treatment process have been implemented and tested, including a predictive rheology model and data integration between different vendor's software/control systems and equipment.

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