Drilling Mud Process Control

A step-change is needed in drilling process control, to enable consistently high performance, accurate pressure control and reduced risk of unwanted effects. Advances in process control have been made but risk reduction and optimization of the drilling process is still a major challenge.

A missing piece in this process-control puzzle is the drilling mud. Without real-time knowledge of, or control over the mud, a control system cannot reliably maintain bottom hole pressure, detect kicks, nor ensure a smooth flow of cuttings.

Therefore, it is important to understand, analyze, model, and predict the hydraulics during a given drilling process. These parameters are strongly dependent on the mud properties, well stability and hole cleaning situation. In addition, a given percentage of solid additives are required in the fluid (mud) system to achieve good mud properties, but overcapacity could lead to disastrous situations. The tracing of the exact mud composition allows more accurate predictions for different drilling parameters such as the required critical hole cleaning in combination with an optimal ROP. In addition, it enhances early kick detection.

To achieve this requires an improvement in the monitoring and control of the mud and cuttings parameters such as density, rheology and cuttings size distribution for a better management of the hydraulics hole cleaning, ROP etc. in a drilling operation. This approach is not only important for conventional drilling operation, but also for MPD on floaters.

This project proposes a full-scale demonstration at Huisman’s test facility. Novel technologies for monitoring of cuttings and mud properties work in tandem with a mud treatment system and real-time wellbore flow and cuttings tracing models. These are to be integrated with an MPD system on a test rig. The rig is designed from the bottom-up to provide both automatic pipe handling and heave compensation and the rig will itself undergo heave motions at the test facility.

By bringing mud monitoring and treatment into the process control, this demo project addresses the integration gap in closed loop drilling optimization which was identified by OG21 TTA3. The automatic pipe handling will serve to demonstrate how this can be translated into consistently fast drilling.

The largest value creation potential may however become apparent when the rig undergoes heave and then simulates an MPD operation from a floater. The Norwegian Continental Shelf (NCS) has a thousand well candidates which would benefit from being drilled with MPD from floaters. As highlighted by OG21 TTA3, broad implementation of MPD from floaters could translate into major cost reductions. If the challenges are met, as much as 45% of NCS production could in 2020 come from MPD drilled wells.

We believe that significant improvements in drilling are only met if several innovations are integrated around a common goal. This is a guideline for the demonstration project. Integration is however not without its own challenges. In addressing this, we lean on the ongoing project “Improved Model Support in Drilling Automation”, supported by the Norwegian Research Council. Several results from this project will support the demonstration. This includes self-correcting automation systems with a higher degree of autonomy with respect to handling errors, as well as increased robustness of system integrations.

Real-time in-situ mud monitoring

The recent years, a novel method of automatically and continuous evaluation of the drilling fluid has been examined in several papers and reports.

The method is based on employing dual differential pressure sensors between the drilling fluid pumps and the top drive, as shown in Figure 1. The measurement setup is often referred to as the “instrumented standpipe” system (ISPS).

Based on the horizontal differential pressure measurement and the vertical differential pressure measurement, both the density and the apparent viscosity may be calculated.

High-fidelity modeling

In general, a robust and reliable high-fidelity real-time model adds value to an automated system by filling in where sensors do not measure, by providing redundancy when sensors fall out or do not measure for other reasons, and by enabling automatic detection of anomalous events coming up. These factors get increasingly important as automation of drilling progresses, as the right action to a given response often depends on whether there is an anomalous event going on, and on what kind of event that is.

On the other hand, high-fidelity models are comprehensive, demanding to use, and it is hard to eliminate fully the risk of running into numerical instabilities. Therefore, work is ongoing to develop a simplified model that is significantly simpler, faster, and more robust than normal high-fidelity model, and yet sufficiently accurate for the modelling the systems response to operational changes.

Also in the simplified model mass transport calculations include dynamic effect in that e.g. an increase in pressure causes a delay in flow out due to compression of the fluid in the well. However, accuracy during the transient phases is relaxed somewhat in the simplified model to allow calculations to be done with a minimum of numerical iterations. With this, the algorithm becomes much quicker and more robust, provided the changes are done in the right way to avoid numerical instabilities.

Another simplification is to keep temperature profile fixed, rather than doing the dynamic temperature calculation that many high-fidelity models can do. This simplification is acceptable because changes in temperature profile normally goes much slower than rapid changes in the pressure profile due to operational changes, and therefore a good tuning algorithm can be used to take care of the slowly drifting consequences of temperature changes.

Some additional simplifications are made in sub-models to reduce need for numerical iterations, but still calculations are relatively sophisticated, and include for example pressure and temperature dependent fluid properties, with density either from published correlations or from input tables of laboratory data. An automatic tuning algorithm is used to reduce the effect of sub-model simplifications.

Similar to advanced high-fidelity models the simplified model is capable of handling a train of multiple fluids with independent properties, in order to enable automatic pressure control during fluid displacements and cementing operations.

Automatic Mud treatment with real time rheology

Real time measurements and automatic injection solutions for additives open up for a fully automatic mud mixing process in the future. An automatic system can result in reduced costs, improved mud quality and improved drilling performance. In addition, an automatic mud-mixing system can enhance the pressure controllability in MPD operations.

Huisman introduces an automated mud-mixing subsystem that functions continuously in real-time mode instead of mixing in batches. The required volumes are derived from the pressure measurement, quantity of removed cuttings and ROP. The quantity and types of required pre-mixed additives will be fed in a realtime mode based on continuous mud and pressure measurements in combination with the predictions from different models discussed earlier.

The mud mixing will function by means of a pre-prepared recipe of different mud types with different characteristics. The basic idea is to adjust and control mud density and rheology (viscosity) by mixing of two fluids A and/or B with the return mud from the well (active mud). Mud A will be mainly used to adjust the density; while mud B will be mainly used to adapt the rheology (viscosity).

Integrating an automated mud treatment system with real-time mud monitoring will result in a sophisticated MPD and mud control system where not only pressure is controlled, but also mud is managed simultaneously. This will ensure an optimal control of both the pressure and mud properties at the well bottom.

A closed loop mud control system – integrated system with link to downhole

The control system consists mainly of algorithms to control the auto-mixer. In the first implementation of the control system, the Huisman mixing controller will get a density and viscosity setpoint for mixing. This setpoint is determined from the SINTEF flow model and is determined by requirements such as: pore pressure, fracture pressure, desired BHP, efficient cuttings transport, and a minimum pressure under the MPD-choke, which is necessary to maintain controllability of the BHP by the choke.

In order to keep the fluid data in the model up to date, density and rheology measurements of fluid entering and leaving the well are fed into the model in real time. A parameter search algorithm, which runs several instances of the flow model with different parameter combinations in parallel, is employed to find the setpoint for the mixer that best satisfies the requirements.

The controller will send a mixing sequence to the auto-mixer to add the corresponding mud volume.

The update frequency of the setpoint is subject to a trade-off between fast response to changes in mud requirements and the time needed for the mixer to adjust the mixture in the active tank.

The model estimates the required mud parameters based on the current active mud volume and different sensor measurements at surface and downhole.

Figure 2 shows the control scheme that will be used in the test pilot.

Pilot test at the Huisman Innovative Tower

To prove to the Oil and Gas industry that we practice what we teach, Huisman has built a full scale (90m high) Multi-Purpose Tower called Huisman Innovation Tower (HIT). It was built to show how drill floor robotics could contribute to safer and more efficient offshore drilling operations. The HIT was also built basing on the Dual Multi-Purpose Tower (DMPT) concept developed by Huisman. The main purpose of the HIT is to demonstrate new Huisman drilling equipment, the robotic tubular handling, longer stands and higher hook loads.

Engineered with 3.0-3.6 million lbs hook load capacity, the HIT was designed and built to prove the robotic advancements, including the automated tripping technology at 5,000ft/hr, using actual 180ft stands and running of 150ft risers. The HIT will be also used to test future equipment and systems as well as for training purposes. In addition, the HIT has a feature to simulate heave effects. Dynamic testing at angles of 2 degrees in each direction is realized by rocking the tower over a period of 8 seconds. This dynamic movement simulates real offshore vessel conditions will be used to test Huisman drilling equipment at off-shore conditions.

In addition to the tower, a 400 m deep well has been recently drilled and cased. The well is provided with sensors along its length and two will be installed to simulate situations such as kicks and loss of circulations.

The overall setup at the HIT consists of three main components:

• The sensors.
• The mud mixer.
• The models.

When all of the components and integrations have been demonstrated separately, the full system is ready to be tested. The pilot test will fulfil the goal of the project to demonstrate that sensors, models and automixing can work together in concert to realize a drilling control system with mud treatment integrated.

Conclusion

The technology demonstrated includes real-time pressure and density sensors, an automated continuous mud mixing system to replace manual batch mixing, and a real-time mathematical model to link topside fluid properties to downhole constraints. The new technology components enable a fully automated continuous optimization of drilling fluid properties, and thus opens the way to further and more accurate automation of drilling.

The system will improve accuracy and consistency of any drilling operation, and by that reduce risk and help early detection of unwanted events. Combining the new technology with automatic pressure control in managed pressure drilling (MPD) is used as an example because it is particularly important when applying MPD as a mean to drill through depleted reservoirs with very tight pressure margins