Technological advances in horizontal drilling, subsea processing and complex tie-backs have had a significant impact on pushing the boundaries of operability. As the continuous drive to stretch the envelope of operability has resulted in more and more sophisticated operations, it has become increasingly problematical to operate these facilities in an optimal way. Dynamic flow instabilities account for a large proportion of the additional operational burden that needs to be overcome. Experiences within BP have shown that a good understanding of the flow dynamics experienced during start-up can have a significant impact on well up-time and overall production rates. Similarly, ensuring stable flow during normal operations can have a significant impact on the overall operational capacity of the process, raising the overall rate of production. Multiphase flow instabilities are often complex in nature and difficult to interpret. Significant efforts in the field of multiphase modelling have led to an improved understanding of multiphase flow, yet the level of expertise needed to run dynamic models has restricted their widespread use for operational support. The key challenge is to blend the operational insight that multiphase modelling can provide with the people, process and technology elements that have become grouped under the umbrella of intelligent energy. Within this paper, the value of deciphering flow instabilities will be established in light of the typical operational difficulties experienced at BP today, along with projections for future flow instability. A hybrid modelling and data analytical approach will be presented for the analysis of flow dynamics during start-up and for normal operation. Finally, the importance and value of applying effective control solutions to manage potentially unstable flow conditions will be discussed, with reference to the successful implementation of a ‘slug controller’ at one of BP's offshore assets.
Steady state flow modelling has become common practice across the industry. Over the past decade, continued developments in the underlying modelling software have led to a greater up-take in steady state modelling as clear solutions emerge for a wide range of operational problems.
Since the inception of Intelligent Energy over a decade ago, there has been a gradual transition from the collection and visualization of large data sets through to the intelligent analysis and interpretation of data. A recent drive towards automation has evolved from the desire to streamline ongoing activities, reduce exposure to human error and continuously position operations at the optimum operating point. Attempts to automate the optimization process, whether through closed loop optimization or multi-variable control, have largely focused on the facilities. This approach has merit when production is on plateau, as the rates will typically be constrained by the processing capacity of the facilities. As production declines, the handling capacity of the facilities becomes less of a burden and the deliverability of the well stock gains prominence. Productivity is now constrained by well uptime along with the health of the completion. Adequate control of the drawdown can have a positive impact on both the health of the completion and on the uptime of the well. For weak wells, the well must be beaned up at a certain rate to prevent liquid loading. Control of the drawdown is therefore paramount in ensuring a successful restart. For wells that cut water or free gas and transect multiple reservoir layers, the drawdown will drive coning and promote cross flow. As both are detrimental to production, overall productivity is driven by the ability to control and optimise the drawdown. Finally, if the rock is poorly consolidated, rapid fluctuations in the drawdown can have a detrimental impact on the rate of sand production, screen erosion and skin growth. Within this paper, the value of controlling the drawdown will be presented along with a control solution that has proved effective at controlling the drawdown during start-up, continuous production and ramp down to within 5 psi.
Recent technological advances in the oil and gas industry such as extended reach drilling, subsea processing and tiebacks from satellite fields have added to the complexity of daily operations. With a myriad of flow conditions, the dynamic interactions between different components of the gathering system can be significant. Deciphering these flow interactions can be difficult. Optimising them is a real challenge. Whilst steady-state and dynamic modelling can provide a valuable insight into the occurring flow dynamics, the limitations of multiphase modelling and the level of reservoir uncertainty renders it difficult to determine an "optimal" operating point. Within BP, an alternative paradigm for system optimisation has evolved from the desire to generate an operability map of the process. By combining data analytics with first principle-modelling and visualisation techniques, it is possible to generate operating maps for our oil and gas installations that are analogous to the "weather map". Process mapping provides a valuable insight into the operability of the process that can be accessed in a quick and easy manner. Operators can immediately relate to the encapsulated information by comparing their own observations with those plotted in front of them. Over time their knowledge of the process is enhanced and the process is manoeuvred towards the "optimal point". Process mapping has led to significant increases in production within BP. Expressing the virtual insight of a model in a format that both the onshore and offshore teams can relate to has naturally led to an impressive level of asset engagement. In this paper, the people, process and technical implications of process mapping will be discussed alongside the value experienced to date.
The Azeri-Chirag-Guneshli (ACG) field is a giant oil development located in the Azerbaijan Sector of the Caspian Sea. The presence of multiple layers and a secondary gas cap has seeded a range of transient phenomena that includes well slugging, cross flow and gas coning. Dynamic transients seen across the field have contributed to a detrimental impact on production. Well slugging has led to the failure of downhole screens and decreased production. The cross-flow of water between the different reservoir layers is another problem that can increase the water cut by 30% during start up, making it extremely difficult to return a well to production. The gas to oil ratio (GOR) remains very dynamic. When shut in, gas will migrate away from the well bore leaving little free gas to lift the oil. Similarly, the GOR will rise during periods of production as gas is coned into the well. As a result production can be backed out. Underpinned by extensive dynamic modelling work, BP, together with its Partners and the State Oil Company of the Azerbaijan Republic, has been successful in deploying technology to assist in stabilising and enhancing production across the ACG field. Well uptime has been improved by optimising the dynamic transients during start-up. Conditioning the well before it is shut in has proved effective at reducing the cross-flow of water that occurs immediately afterwards. In this paper, the challenge of optimising the dynamic transients across the ACG field will be discussed. Particular emphasis will be given to the solutions developed to address the underlying transient phenomena and how these solutions were deployed at scale and pace across the field.
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