Dynamic digital twins

Kapil Mukati and Brian Sidle, Kongsberg Digital, discuss the development of digital twin technology for remote monitoring, virtual sensing and operation support.

The power of digitalisation initiatives is becoming undeniable. It is impossible to watch television, surf LinkedIn, or attend a tradeshow/ conference without banging headfirst into tangible examples. Computing power improvements, cheaper sensors (real or virtual), and machine learning have all become readily available and companies are folding them into applications designed to improve day-to-day efficiencies. Even industries that are traditionally

resistant to change have begrudgingly accepted that there might be better ways to work.

One tool driving this transformation is digital twins, a virtual representation of an asset and its dynamic performance. These are being incorporated across industries, including pipeline transmission, to increase visibility and planning while decreasing costs and risks. With the ability to incorporate multiphase flow and dynamic simulations, digital twins can provide real-time monitoring capability for what were previously considered unmeasured or unmeasurable information. Twins are subsequently becoming essential tools for operation optimisation, predictive analysis and maintenance, and training.

In this article, we focus on how dynamic, Kognitwin® digital twins can be used for remote monitoring, virtual sensing, and operational support for pipelines using multiple examples.

Permian-produced water pipeline network Produced water disposal is becoming more and more crucial due to both the quantity and completion type of oil wells in the Permian Basin. Produced water can be blended with brackish and/or fresh water during recycling operations for hydraulic fracturing purposes or disposed of via water injection wells. As oil production ramps up, insufficient disposal and recycling capacity become a bottleneck to operations. A large oil producer in the Permian Basin had planned to build recycling and injection facilities, including laying pipelines in a staged manner to accommodate increasing production over a five year period.

In reviewing the project, considerations were given to look beyond conventional ways of design. For example, steady state tools, which are sufficient for pump capacity, pipeline size, and boosting station requirements for a given maximum expected flow are unable to provide insight in other areas such as pressure surges, operational control, and ensuring desired flow to correct locations. These are crucial design elements for engineers focused on preventing unintentional shut-ins or diversions.

Independent models were developed for various parts of this large pipe network (>200 miles) to provide timely assistance to engineers for critical design needs. These models were then integrated together to create larger models providing easy scalability. A dynamic process simulation software was used to model pump stations and process control in high fidelity, and to integrate various pipe flow models together. Breaking down a large pipe network into smaller, modular components provides scalability and faster model speeds without sacrificing accuracy. These dynamic models were used to perform pressure surge or water hammer analysis, which is a common cause of pipeline failure that happens when highvelocity liquid flow is stopped suddenly, either because of valve closure or pump stoppage. In addition, these dynamic models provide a perfect tool to decide the best control strategy for remote pump stations and create operational procedures, which is difficult, if not impossible, for a large area without considering process transients. The pipelines are designed for maximum expected flow capacity, but this flow may

Figure 1. Dynamic model of produced water pipeline network with all sources, sinks and pump stations. Colours show various regions of pipe network installed over time, highlighting modular and scalable nature of the digital twin. Pressure, temperature, flowrates, velocities, fluid properties anywhere in this network can be extracted as a virtual sensor.

Figure 2. Santos gas gathering system scope; digital platform HMI example; and simulation vs measurement comparison highlighting model accuracy.

not be reached for years. This means that pipelines may be operating in two-phase flow conditions depending upon pipe size, elevation, and flow conditions. All the expected and even unexpected flow scenarios can be handled by the developed multiphase dynamic models.

Incorporating these models, first in design and then operations, into the digital twin can provide numerous benefits. On the design side, benefits can be derived from sharing and collaborating across the same data sets, visualising their layout with elements of elevation or temperature changes (night/day) and the respective impacts.

Moving into construction, virtual commissioning of the assets can be accomplished to ensure everything works seamlessly from day one. And in operations, the same models can utilise real-time monitoring and virtual sensing to build a decision support system for the network. This Operational Centre, powered by these models, provides the end-users with a remote, centralised decision center to optimise production.

Offshore gas gathering pipeline network The Santos Basin gas pipeline network is a complex network of pipelines collecting gas and condensate from four FPSOs, plus an oil platform and an onshore gas processing plant. This network is expanding to collect gas from an additional thirty FPSO facilities, with the potential for multiple entry points and the addition of two onshore gas plants. The network handles wet gas with varying composition and has pipeline lengths ranging from 10 - 380 km. It is also possible for FPSOs to import gas from this network.

Currently, high-fidelity, compositional, multiphase models have been developed for the Santos gas pipeline network as well as dynamic models for the FPSO export, oil platform, and onshore gas treatment facilities. All the models are integrated to accurately simulate the pressure, flow, and composition interactions throughout the system. The resulting integrated dynamic simulator – called Santos Operational Simulation System (Santos OSS) – is an important tool to assist flow assurance and production engineers in better understanding the behavior of these complex systems, conduct engineering studies, and create operating procedures for safer and more efficient operations.

All the Santos OSS data can be visualised remotely through either a desktop application or web browser using an integrated data management system built using a digital twin platform. These web-based data management systems and their ability to create and incorporate custom dashboards provides a convenient way for all authorised users to access relevant data. The Santos OSS provides real-time monitoring, what-if studies, and predictive analysis, helping operators and engineers operate the Santos facilities and gas transport network safely and optimally. Real-time monitoring includes hydrate monitoring, pig tracking, chemical tracking, corrosion monitoring, and slug monitoring, all which are enabled with a dynamic digital twin.

Steam distribution pipe network Steam is widely used for district heating systems across the globe. Typically, centralised, high-capacity boilers provide saturated steam to residential and business customers via large, insulated pipe networks, either above or below ground. Steam distribution systems; however, suffer from ever-changing supply and demand with significant lag times between boiler ramp-up and steam arrival to the customers. This results in a highly dynamic, complex system.

Furthermore, condensate-induced water hammer (CIWH) is a serious problem in steam distribution pipe networks, which can cause fatalities and significant property damage (>12 Con Edison steam pipe explosions in New York City since 1987). CIWH happens when steam bubbles

Figure 3. Steam distribution pipe network dynamic digital twin.

Figure 4. Oil shipping system and pipe network.

become entrapped in cold condensed water, leading to sudden and violent collapse of bubbles, causing a pipe explosion.

A dynamic digital twin solution was recently developed for one of the world’s largest district heat providers with 100+ miles of steam distribution pipe network. It provides a real-time, simulation-based, decision support system for the owner-operator and accounts for not only the two-phase steam-water flow in pipe networks, but also the transient effects of steam traps, customer service flows, man-hole water levels, steam plant supply variations, and weather conditions. As such, it can also help predict CIWH.

This real-time CIWH predictor model is the first of its kind applied on a district steam distribution network anywhere in the world and should be invaluable for the prevention of further accidents. This dynamic steam distribution model is also used to develop what-if scenarios and predictive analysis, incident investigation, estimate varying customer demand and condensate loss, and prepare for inclement weather conditions that can impact steam operations. The model is tightly integrated within the digital platform for wider visibility and use across the broader organisation remotely, providing end-users the tools they need wherever they are.

Oil shipping system (OSS) Down in Southcentral Texas, Kongsberg had the opportunity to develop a new model for an OSS. Typical volumes from the original handling facility were 60 - 80 000 bpd distributed out to four delivery points (customers). The plan was to ultimately ship total volumes from 105 000 bpd (design) to ~120 000 bpd (maximum, as limited by pipeline MAOP) with the nominated volumes shipped to each delivery point variable monthly.

Some of the challenges of the existing system included unwanted interactions between the control loops at the terminals. This was primarily due to pressure transients created by the start/stop of pumps associated with the daily batch shipment of oil to one of the delivery points. Another concern was frequent, over-pressurisation shutdowns resulting from the planned upstaging of the OHF shipping pumps from 8 to 15 stages to handle increasing production rates.

A scalable, dynamic, two-phase simulation was required to design a new control system. The new system needed to provide the best, overall control to meet the requirements of the OSS, considering several different operating scenarios through the various stages of commissioning – current operation, addition of new shipping terminal to the network, and planned increase in shipping rates via pump upstaging. An integrated, dynamic model was created using modeling tools and transient flow software that modeled the terminals and pipe network accurately. Incorporating simulators enabled the operator to try out various control schemes on both the shipping and delivery terminals.

The dynamic simulation model was used to make recommendations for which control strategies would be used at the OHFs and pipeline delivery terminals to achieve robust, stable operation of the overall OSS across the range of expected shipping nominations at each delivery point. Both batch and continuous shipments were considered in the developed models for thoroughness.

This new dynamic model provided a means by which the operator could improve their overall system while increasing capacity via: ) PID-controller tuning methods, tuning constants, and controller setpoint ramp rates.

) Optimum control valve actuator stroking speeds for opening and closing directions.

) Optimum shutdown valve opening and closing speeds to minimise the impact on the OSS while meeting facility constraints.

) Setpoints for protective devices, such as high-pressure shutdowns and PSVs, which have a sufficient operating margin to avoid false trips.

) A means to minimise impact of one or all pump trip or start-up at OHF.

) Ultimate shipping capacities when injecting drag reducing agents (DRA) at the OHFs and its impact on the

OSS equipment and controls.

This new model, which could be accessed remotely via a digital twin interface, also provided engineers with a perfect test-bench to perform what-if studies and operation planning, while simultaneously providing a set-up for operator training.

Conclusion Technology in this space is rapidly changing. New solutions to drive efficiency and safety improvements are becoming available and widely integrated. In the above examples, dynamic digital twins provide a platform by which highfidelity simulators can become accessible remotely for a broader network of end-users.

By combining physics-based models, data science approaches and cloud scalability, the Kognitwin digital twin helps operators streamline and test hypothetical scenarios to maximise performance. This allows for improved prediction of impact options and decision-making, leading to overall enhanced productivity, improved safety levels and more sustainable operations.

And as the digital transformation journey continues, these tools will be crucial for understanding and better managing remote operations.

Bibilography

TSANG, J., MUKATI, K., ‘Steam Simulator Model’, Thermal Distribution Workshop, CampusEnergy2020, Denver, Colorado. ZAMPIERI, T., ROTAVA, E., KHATIB, H., SERPEJANTE, C., RUSSO, E., TEIXEIRA, A., A. INACIO, A., BASTOS, M., GOMES, A., MUKATI, K., ‘Santos Operational Simulation System: Integrated Dynamic Compositional Simulation of Multiphase Pipeline Network and Treatment Facilities to Assist in Planning, Operation and Production Management’, Rio Pipeline 2015, Rio de Janeiro, Brazil.