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Subsea production facilities may be “over-designed” if the worst-case “Design” conditions are included in the Flow Assurance analysis as the most onerous cases usually have low frequency of occurrence. With new frontier developments and challenging project economics, over design is no longer acceptable as the “default solution”. Risk-based flow assurance design employs a probabilistic analysis approach which allows for avoidance of over-design by quantifying key uncertainties and identifying major flow assurance risks through Monte Carlo simulations. Possible applications of risk-based decision making in flow assurance analyses include the sizing of flowlines, thermal insulation and surge capacity (slug catcher sizing), the determination of hydrate blockage formation likelihood, the definition of hydrate management strategy, the quantifying of hydrate inhibitor dosage requirement, and the optimisation of network systems.
In this paper, the application of probabilistic approach in the optimisation of flowline insulation requirement using a Risk Management and Optimisation (RMO) tool is discussed.
For a new phase of the B-field development (called “Phase 2” in this paper), new wells will be tied back to a new B-Manifold, and the fluids transported via a new insulated rigid CRA subsea flowline to an existing crossover manifold (A-XOM) where it is commingled with production fluids from the A-field. Combined production is transported via two existing Phase 1 flowlines to an offshore gas processing platform (GPP). Gas from the platform is subsequently sent to an onshore plant. A simplified field layout is provided in Figure 1.
Charlton, Thomas B. (The University of Western Australia) | Kegg, Stuart (Woodside Energy Ltd) | Morgan, Julie E. P. (Woodside Energy Ltd) | Zerpa, Luis E. (Colorado School of Mines) | Koh, Carolyn A. (Colorado School of Mines) | May, Eric F. (The University of Western Australia) | Aman, Zachary M. (The University of Western Australia)
This study provides valuable insights into hydrate management strategies as the industry transitions away from complete hydrate avoidance, particularly for the development of deep-water reservoirs with stricter economic margins. Transient simulation tools, such as the deployed hydrate deposition model, extend our ability to estimate blockage likelihood from heuristics to quantitative predictions. The model is applied to an insulated subsea tieback to identify the optimal no-touch-time (NTT) and depressurization pressure (DPP) following an unplanned shutdown. Two water-production scenarios are considered, from the lowest expected to the highest manageable rates. A complete hydrate blockage is predicted when the NTT was extended several hours beyond the nominal value for the highest water-to-gas ratio (WGR). Complete blockages are predicted for both low and high WGRs when the flowline is only partially depressurized, however, longer cooldown times for the high WGR case (due to greater volumes of residual liquids) meant a blockage took more than twice as long to occur than for the low WGR case. Fully depressurized restarts are both difficult and time consuming, leading to hydrate volume fractions (with respect to the pipe volume) exceeding 30 vol.%. An alternative hydrate management strategy is identified for cases with high volumes of water production, in which the flowline is only partially depressurized once the nominal NTT has elapsed, utilising the increased heat capacity of residual liquids. This reduces the quantity of gas sent to flare and simplifies the restart procedure.
Hardy, Madeline (Woodside Energy Ltd) | Baker, Mark (Woodside Energy Ltd) | Robson, Alex (Woodside Energy Ltd) | Williams, Jackson (Woodside Energy Ltd) | Murphy, Chris (Woodside Energy Ltd) | O'Sullivan, Liam (Woodside Energy Ltd)
Insights from appraisal well tests can take months to incorporate into subsurface modelling, causing delays to development planning and resulting in key decisions being made using incomplete data and sub-optimal methods. This is due to the time-consuming process of updating or rebuilding reservoir models, simulating them and subsequently analysing the results. In this project, a combination of automated geomodelling, rapid dynamic simulation and statistical analysis were applied to reduce the time to insights from months to days. Well test pressure data was used to condition a suite of reservoir models and evaluate the impact on the optimal development scenario. The application of this process increased confidence in the decision and reduced the modelled probability of low-side outcomes. In addition, we trialled a process to deliver an improvement to the geological understanding of the field through a reduction in the model uncertainties. We also discuss an extension of this concept to perform a robust value-of-information assessment of appraisal or development planning decisions.
Uncertainty is present at every stage of the subsurface modelling workflow and understanding it is an ongoing challenge for the petroleum industry. Quantifying this uncertainty is a rapidly growing field of study as increasingly available high-performance computing enables the application of traditional statistical methods to this problem. However, the extension of these methods to spatial data remains a challenge for which there is no immediate solution. This paper describes the use of data analytics techniques to incorporate spatial uncertainty in reservoir surfaces into subsurface modelling. A metric usually applied in image analytics, the Modified Hausdorff Distance, is adapted for this purpose. The workflow involves sampling the domain of possible surface realisations, characterising them using this metric and determining the most efficient subset to represent the entire data set. The value of this process is that the selected subset captures spatial uncertainty in the surface rather than only gross rock volume. The proposed technique proved to be a simple process that was able to easily select these surfaces from a stochastically generated set and has been successfully applied to the top reservoir surfaces in two fields.
As gas fields mature and water production increases, understanding and managing the dynamic flow behaviour of the well and production system are critical for maintaining, and even optimising, production. This knowledge could be the difference between a successful and an unsuccessful attempt at re-starting a wet gas well after it is shut-in. When a well is in production, choking the well to optimise stable facilities operation and maintain water production within the water handling constraints of the facilities can be a fine line between achieving continuous stable production and the well ceasing production due to high liquid loading.
This paper describes the successful kick-off and unloading of two high-water producing gas wells within the operational constraints of the offshore facility. Transient multiphase flow models were developed for a platform well and a subsea well to simulate the wellbore flow dynamics during start-up. The models were tested over a range of values for parameters such as reservoir pressure, inflow performance and water gas ratio for different kick-off strategies but always honouring the facility's water surge management constraints.
The outcome of these simulations facilitated the development of tailored bean-up strategies for each high-water producing gas well, which provided a mechanism to engage with key stakeholders and demonstrate confidence in the execution of these strategies. Dedicated procedures were developed and subsequently executed successfully to re-start the two wells with the wells continuing to produce after kick-off and unloading, operating within the water surge management limits of the facility. Similar strategies are being developed for other high-water producing gas wells including those with material sand production.
This paper demonstrates strategic capability to realise additional value using dynamic modelling to kick-off mature high-water producing gas wells through proactive development of mitigation strategies which avoid production disruption.
In an era of automated workflow-assisted dynamic modelling, Special Core Analysis (SCAL) parameters require updating for each static realisation and evaluation at a quantifiable, probabilistic level-of-certainty. Additionally, SCAL data gaps combined with limited reliable SCAL data drive the need to establish trends and correlations from analogues.
SCAL parameters from analogue fields were selected and filtered by depositional environment and laboratory experiment type (centrifuge versus displacement). These analogue SCAL parameters were allocated to statistical bins defined by absolute permeability ranges. Statistical analysis of each SCAL parameter allocated to each permeability bin produced a probability distribution discretised by percentile. Multi-variable linear regression (MVLR) was then implemented to correlate each SCAL parameter, as the response variable, to input variables absolute permeability and percentile. SCAL correlations of reasonable to excellent quality were obtained.
The depositional environment was of second order influence in establishing these SCAL correlations. This was due to the selection of core plugs for laboratory analysis from layers of similar quality irrespective of the depositional environment, highlighting the need to select samples characterising a range of lithology and reservoir quality. Centrifuge experiments of water displacing gas were discarded as unreliable due to the compression of the gas phase by the experimental technique.
The multi-variable linear regression methodology enabled SCAL parameters to be determined as a function of both absolute permeability and probability. This approach should enable an automated implementation of SCAL parameters within each dynamic model realisation.
This paper presents case studies on reservoir and well management of two laterally and vertically compartmentalized Western Australian Triassic gas condensate reservoirs, developed by five multi-zone "smart" wells with sand control, tied back to an offshore platform via a subsea network. In managing assets with such complexity, it is imperative to understand reservoir performance on a zone-by-zone basis. Quantifying performance allows management of flux through downhole sand control systems and optimisation of offtake strategy. The majority of the material published to date on "smart" wells has been focused on completion design optimisation and minimisation of unwanted oil/water production. There are few existing articles about production and reservoir optimisation of high rate gas wells requiring flux management.
This paper showcases how remotely-operated selective completions ("smart" wells with permanent downhole gauges for each completion coupled with subsea flow meters for each well) have been instrumental in facilitating prompt analysis of zonal reservoir performance and thus in yielding insights into reservoir connectivity and allowing optimisation of zonal contributions. Various case studies will be presented showing how reservoir surveillance data is acquired and interpreted to optimize well zone-by-zone production and to manage flux limits on each producing zone. These case studies will include manipulation of downhole valves to provide information for established techniques such as interference testing and P/Z analysis.
Data acquisition and interpretation challenges are highlighted along with fit-for-purpose solutions developed to overcome those challenges.
The insights presented could facilitate better planning of similar systems in the future.
McLean, D. L. (The University of Western Australia) | Macreadie, P. (Deakin University) | White, D. J. (The University of Southampton and The University of Western Australia) | Thomson, P. G. (The University of Western Australia) | Fowler, A. (New South Wales Department of Primary Industries) | Gates, A. R. (National Oceanography Centre) | Benfield, M. (Louisiana State University) | Horton, T. (National Oceanography Centre) | Skropeta, D. (University of Wollongong, NSW) | Bond, T. (The University of Western Australia) | Booth, D. J. (University of Technology) | Techera, E. (The University of Western Australia) | Pattiaratchi, C. (The University of Western Australia) | Collin, S. P. (The University of Western Australia) | Jones, D. O. B. (National Oceanography Centre) | Smith, L. (Woodside Energy Ltd) | Partridge, J. C. (The University of Western Australia)
This paper describes the potential global scientific value of video and other data collected by Remotely Operated Vehicles (ROVs). ROVs are used worldwide, primarily by the offshore oil and gas industry, to monitor the integrity of subsea infrastructure and, in doing so, collect terabytes of video and
This new collaboration prompted a team of international engineers and marine scientists to gather together with West Australian based members of the oil and gas sector and ROV operators, to examine the global scientific value of ROV-collected data. If made available for research, these data have immense value for science to quantify the marine ecology and assist good stewardship of this environment by industry. It was found that most ROV operations are conducted by industry in a way that fulfils immediate industry requirements but which can confound scientific interpretation of the data. For example, there is variation in video resolution, ROV speed, distance above substrate and time (e.g. both seasonal and time of day), and these variations can limit the quantitative conclusions that can be drawn about marine ecology. We examined potential cost-effective, simple enhancements to standard ROV hardware and operational procedures that will increase the value of future industrial ROV operational data, without disrupting the primary focus of these operations.
The ecological value of existing ROV data represents an immense and under-utilized resource with worldwide coverage. We describe how ROVs can unravel the mysteries of our oceans, yield scientific discoveries, and provide examples of how these data can allow quantification of the ecological value of subsea infrastructure. By using these data, we can greatly improve our knowledge of marine biodiversity on and around offshore infrastructure and their environmental impact on marine ecosystems, both of which are particularly important in the consideration and selection of decommissioning strategies. Predicting the environmental consequences of removing or retaining subsea structures after decommissioning relies on an understanding of the ecological communities that have developed in association with these structures during their operational lives. Making industrial ROV data available for scientific research, and collating it in the future using modified protocols, would provide a very positive contribution to both science and industry, allowing the environmental impacts of subsea infrastructure to be quantified. It will also allow industry to contribute to a broader scientific understanding of our oceans, given the location of ROVs in areas that can rarely be accessed by independent researchers. This would provide novel and valuable information about under-researched and little known regions of the world's oceans.
Adaptive waveform inversion (AWI) is one of a new breed of full-waveform inversion (FWI) algorithms that seek to mitigate the effects of cycle skipping (Warner & Guasch, 2016). The phenomenon of cycle skipping is inherent to the classical formulation of FWI, owing to the manner in which it tries to minimize the difference between oscillatory signals. AWI avoids this by instead seeking to drive the ratio of the Fourier transform of the same signals to unity. One of the strategies most widely employed by FWI practitioners when trying to overcome cycle skipping, is to introduce progressively the more nonlinear components of the data, referred to as multiscale inversion. Since AWI is insensitive to cycle skipping, we assess here whether this multiscale approach still provides an appropriate strategy for AWI.
Presentation Date: Tuesday, September 26, 2017
Start Time: 3:05 PM
Presentation Type: ORAL
The robustness of diving wave Full-Waveform Inversion (FWI) has been proven in industry, but the effectiveness is limited by its penetration depth. To target deeper reservoirs, the application of FWI using reflection energy is necessary. This paper presents a real data 25Hz VTI FWI case study from North-West Shelf (NWS) Australia utilizing the full wave-field. Starting from a high-quality reflection tomography VTI model, a top-down approach has been adopted. Diving wave FWI updates the shallow, then reflection FWI is introduced to further update the deeper section. The updated FWI model demonstrates significant uplifts in increasing resolution and conformance with underlying geology. Two promising aspects can be observed: (1) the fairly solid uplifts in mitigating the imaging challenges: FWI reduces wave-field distortions, leads to overall improved focusing, gather flatness, continuity, and better positioning in depth; and (2) uncovers geological features beyond imaging: high-resolution FWI delineates small shallow anomalies and velocity boundaries across faults, and reveals the strong acoustic impedance contrasts at reservoir level. It demonstrates FWI can aid both in reducing the velocity uncertainty as well as understanding underlying geological formation.
Presentation Date: Tuesday, September 26, 2017
Start Time: 2:15 PM
Presentation Type: ORAL