Probabilistic and deterministic methods for reserves and resources evaluation are commonly used in isolation and often considered mutually exclusive. Subsurface uncertainties are critical factors impacting projects and reserves/resources especially in projects/areas where large sums of capital investment are required. Probabilistic methods allow a rigorous use of information on the ranges of uncertainty on key reservoir parameters like porosity, water saturation, permeability aquifer size for reserves estimation. A key output of probabilistic methods is the confidence levels associated with the reserves. Deterministic methods can't provide confidence levels associated with reserves and resources assessments reason why with its successful application is often relaying on the expert knowledge of the evaluator and the strict use of reserves or resources definitions. Technological advances in computing in the last decades have played a key role in advancing computationally intensive probabilistic methodologies including artificial intelligence. These advances have allowed integrated teams to perform studies using sophisticated workflows in feasible timeframes.
Design of offshore structures for arctic and subarctic regions requires consideration of wave, wind and ice actions. If individual actions are not mutually exclusive, then combined actions also need consideration. ISO 19906 recommends that, when possible, extreme level combined actions should be determined based on the joint probability distribution of the actions. As an alternative, ISO 19906 provides a framework where a user can determine principal and companion extreme actions independently, and sum these with calibrated combination factors applied. While the combination factors in ISO 19906 were calibrated over a range of conditions and platforms, site-specific information is not taken into account when applying the method. In this paper, a procedure is presented for determining extreme level combined actions for sea ice and waves based on site-specific sea ice and wave information, accounting for the joint probability distribution of the actions. The procedure is demonstrated for an example fixed structure on the Grand Banks off Canada's east coast. The results are compared with extreme actions determined using the ISO 19906 combination factors.
With increasing competition for land usage in Singapore, there is a need for more extensive use of underground space in Singapore, which can help to free up more surface land for other better usage. An Underground Master Plan Task Force was formed by the Ministry of National Development with active participation from key government agencies together with the planners to formulate a Master plan and also to develop guidelines on the use of underground spaces. In view of the need to better study the potential of deep underground developments, the Singapore Geological Office (currently renamed as the Geological and Underground Projects Department) was set up within Building Construction Authority (BCA) to investigate the country’s geology and identify sites which are suitable for such developments. The first part of the paper covers how the Geological Office plans and conducts the geological investigation works. The second part of the paper discusses the geosurvey results, especially the variability of the rock mass properties in one particular area revealed from the investigation works. Finally, the paper demonstrates how the variability and uncertainty of the rock mass can be accounted for using a probabilistic approach for assessment, and the results would provide a better insight of the areas suitable for cavern.
As Singapore has a land area of only 710km2, there is greater need to look into utilisation of underground space and this has become an integral part of Singapore’s strategy for space creation in the future with the ever-increasing competition for land use in this small island state. The Urban Redevelopment Authority of Singapore (URA) has hence put forth the creation of underground rock cavern as one of the land optimising approaches in their underground masterplan. Ever since Brown raised the potentiality of having underground spaces to be built in Singapore in 1989 (Zhou &; Cai, 2011), many deep underground feasibility studies have been conducted. To date, Singapore has successfully constructed the Underground Ammunition Facility (UAF) in 2008, which freed up 300 hectares of land above ground for other use (Wan, 2015), as well as South-East Asia’s first commercial underground liquid hydrocarbon storage facility at Jurong Island (i.e. Jurong Rock Caverns) built in 2014 and it free up 60 hectares of usable land above ground (Chia, 2014).
Adding to the list of activities and development on deep underground space in Singapore since 1990 (Lui, Zhao and Zhou, 2013), the Building and Construction Authority of Singapore (BCA) set up the Singapore Geological Office (currently renamed as the Geological and Underground Projects Department (GUPD)) to investigate and identify the country’s deep geology characteristics and identify sites suitable for such developments to support URA’s development of the underground masterplan (Lim, 2018).
Recoverable hydrocarbon resource assessments underpin decision making and business planning in the oil and gas industry. Understanding the uncertainty associated with the resource assessments are key to sound decisions that are robust against low or high outcomes. This paper outlines a probabilistic approach to resource assessment in order to characterise resource uncertainty in a portfolio containing primarily Coal Seam Gas resources.
The Probabilistic Resource Assessment (PRA) process outlined in this paper allows calculation of risked and unrisked probabilistically derived commercially recoverable resources at a field or permit level as well as at a portfolio level. This process incorporates Undiscovered ("Prospective") resources and Contingent Resources as well as resources that are producing or are under development. The key steps in this process include: definition of input distributions, probabilistic calculation of technically recoverable resources at a field level, estimation of economic chance of success, probabilistic estimate of commercially recoverable resource and aggregation of resources to a portfolio level.
This process has been applied within an integrated joint venture supplying Liquefied Natural Gas (LNG) and domestic gas markets. The process has been used primarily to understand the uncertainty range of the total resource as well as the production profile within the upstream portfolio. Sensitivities to product prices or development costs can be investigated to enable a deep understanding of the key drivers and variables of the resource assessment.
Various methods for determining recoverable hydrocarbon resources have been well documented. Broadly speaking, these methods can be categorised as probabilistic methods and deterministic methods. Typically, unconventional resources are assessed using deterministic methods. The process presented here is a robust probabilistic approach to determine a risked view of recoverable resources within an entire portfolio including both unconventional and conventional resources.
Geng, Meixia (Institute of Geophysics and Geomatics, China University of Geosciences, and Department of Earth Sciences, Memorial University of Newfoundland) | Welford, J. Kim (Department of Earth Sciences, Memorial University of Newfoundland) | Farquharson, Colin G. (Department of Earth Sciences, Memorial University of Newfoundland) | Peace, Alexander L. (Department of Earth Sciences, Memorial University of Newfoundland)
We present 3-D inversion results of gravity gradiometry data over the Budgell Harbour Stock (BHS) intrusion, in northern-central Newfoundland, Canada, obtained using a probabilistic inversion method. We examine multiple density contrast models obtained by inverting the single component Tzz and by jointly inverting five independent components. The inversion results show that
Presentation Date: Tuesday, October 16, 2018
Start Time: 1:50:00 PM
Location: 213B (Anaheim Convention Center)
Presentation Type: Oral
The extent of support in tunneling projects can vary greatly depending on the geological conditions, and in turn may greatly affect project costs. For the purpose of project pre-feasibility assessment, methodologies are proposed according to the tunnel construction method, conventional tunneling (NATM), or mechanized tunneling (TBM). For both cases, statistical analysis tools in conjunction with stress and strain calculations are used in order to assess the volume of tunnel support. It is suggested that for TBM tunnels, the Probability of Failure is adopted as a failure criterion, and the volume of support computed accordingly. For NATM tunnels, a fixed Factor of Safety should be used in order to predict the support distribution and resultant support volume. A practical example is presented, for the comparison of support cost and construction schedule estimation of the two excavation methods, for a tunnel to be excavated through weak rock.
Perhaps the greatest challenge in underground engineering is dealing with the inherent uncertainty that stems from the heterogeneous nature of geological formations. In tunneling and underground caverns, the strength characteristics of the ground, whether rock or soil, dictate the support required for stabilization. Studies have shown that the extent of support has a significant impact on the overall project budget (Paraskevopoulou and Benardos, 2012).
Naturally, the degree of uncertainty is greatest at the prefeasibility stage. Decision makers must determine whether to carry out a project, or to choose between different construction alternatives, based on very limited data. Considering the high cost and risks of underground projects, it is essential that reliable analysis tools are made available. Currently, studies have shown that cost over-runs in tunnel projects are a global phenomenon. For example, Efron and Read (2012) examined 158 tunnel projects from 35 countries, and concluded that in every case the final cost was higher than the initial estimation.
In this work, we present four different methodologies for reducing the computational effort of fatigue assessment for offshore wind turbine support structures. To test these methods, we use them to predict the total fatigue damage of several modified support structure designs based on subsets that represent a reduction of about 6-17 times the original size of the load case set. Three of the methods are able to give quite accurate predictions, with expected errors of no more than 4-8%, though there are some limitations due to the variance inherent in some of the methods.
One of the main challenges for the design of offshore wind turbines support structures is the complexity of both the structure itself and the offshore environment. This complexity means that assessing the performance of the structure requires not only the use of detailed models, but also investigating a large number of different scenarios. Specifically, with reference to the standards that the design must conform to (e.g. International Electrotechnical Commision (2009)), there are literally thousands of different design load cases (DLCs) that must be assessed for any given structure, covering both all the various environmental states one expects to encounter at a given site and all the various scenarios that the structure is likely to experience. To summarize, we need to run detailed models and we need to run them many times. For one single assessment of a design, this can be accommodated by ever improving computer hardware and increased access to computer clusters for both institutions and individuals. However, for those wishing to run either probabilistic assessments or to optimize the design (or worse still, both of these at the same time), the large number of DLCs remains an important challenge. One that should be addressed not just by improved hardware, but by improved methodology. This is the main topic of the work to be presented below.
As it stands, it is not possible to completely replace the standard assessment with something new. Rather, one seeks to approximate the results of such full assessments by a less computationally demanding procedure. If the approximation is good enough, it may then serve well as a replacement for the conventional procedure when small deviations from the true estimates (e.g., fatigue damage) are allowable. Especially in a context like optimization, simplifications leading to such small deviations are often expected and, if the size of the deviations can be estimated, one may even incorporate these as modeling errors in a probabilistic analysis. Previous work attempting to find approximate simplified assessments have encountered some success, but have tended to be very simplified (for example in terms of the types of DLCs studied), have struggled to get a sufficiently accurate approximation while also getting a sufficient decrease in analysis time or have faced a combination of these issues. One approach is to completely abandon the time domain and instead attempt to analyze the structure in the frequency domain (see e.g. van der Tempel (2006)), but this approach has its own set of issues and we will here focus on methods in the time domain.
This paper addresses challenges in fatigue management of marine structural assets with a holistically approach, by jointly considering fatigue design, inspection and maintenance decisions, whilst taking into account sources of uncertainties affecting life cycle performance. A risk-informed and holistic approach is proposed for jointly optimizing fatigue design, inspection and maintenance based on the same fatigue deterioration model. The optimization parameters are fatigue design factor (FDF) and inspection intervals, while the objective is to minimize expected life cycle costs (LCC). The framework is to guide design process as well as to formulate optimal maintenance strategies. The proposed approach is exemplified for the marine industry through a fatigue-prone detail in a ship structure to obtain the life cycle optimal management solution that achieves a best compromise between structural safety and life cycle costs.
Marine structures are designed, constructed and managed to provide a variety of functions in support of transportation, production, leisure, etc. With the development of technology, functional requirements, budgeting control, safety and reliability are increasingly paramount. Local failures and structural collapse are normally avoided by design analysis and in-service inspections and maintenance, to achieve an acceptable failure probability. Deterioration factors, excessive deformations and vibrations are controlled so that structures are durable and serviceable within the required service lives. Other safety-related structural requirements concern redundancy, robustness and resilience (Faber, 2017). Besides, it is required that engineering design, inspection and maintenance activities are viable and sustainable, both economically and the environmentally. In order to ensure that structures meet the performance requirements, it is becoming normal practice to develop and identify safety check lists and formats to avert potential threats and failure modes in the design stage (HSE, 2001).
Fatigue crack growth is one of major threats for marine structures exposed to the sea environments, in which cyclic wave loading lead to deterioration in terms of crack initiation and growth in structures and if undetected lead to failure. Compared with other threats, fatigue cracks are safety-critical, as they can cause sudden rupture of structural cross-sections, leading to losses to human lives, commercial assets and the environment (Fricke, 2003). Crack initiation can be caused by several mechanisms, e.g. cyclic loading, local stress concentrations, corrosion, imperfections in materials, etc. Fatigue cracks are very common in marine assets operating at seas, and detecting and repairing fatigue cracks represent a substantial and expensive task. According to the characteristics of crack development, fatigue cracks are typically very small during a significant part of the service life, and therefore the time is usually very short for cracks to develop from a detectable size a0 to the critical size ac (Fig. 1). This poses a real challenge for detecting cracks reliably before they may cause catastrophic failures.
Although relatively uncommon, rocky submarine outcrops with high to nearly vertical gradients do occur in various offshore settings, including high latitude fiords and tectonic margins with narrow or absent shelves, or high-gradient aprons constituted by ancient lithified carbonate reefs. Here pipelines and flowlines are subject to the hazard of landslides developed in the instable rock-mass and consequent impact of individual boulders. This paper proposes a methodology for the evaluation of landslide and rock fall hazard for pipelines in submarine rocky slopes. The analysis includes results from several common marine site investigations such as high resolution geophysical surveys, Side Scan Sonar imaging, ROV video transects and conventional geotechnical boreholes, and specific datasets including in-hole structural drilling and data from analogue outcrops into a probabilistic framework, aimed at establishing the risk of pipeline structural damage. The assessment of rock fall hazard for onshore infrastructures such as railways and roads is routinely evaluated based on well-established methodologies. Such analyses, however, either requires a full geomechanical understanding of the rock mass, hardly achievable offshore due to data limitations or a probabilistic evaluation of historical events, not applicable offshore. The methodology we propose overcomes these limitations and allows, after defining the maximum allowable impact energy for the design pipeline, obtaining the overall frequency of offshore pipeline damage and thus quantifying the risk associated to rock fall based on relevant standards and guidelines. The rock slope stability is evaluated using a limit equilibrium approach which includes pseudo-static accelerations. Given the uncertainties associated to key input parameters such as the mechanical properties of the rock mass, as well as to the size and trajectories of the failed rock wedges, a Monte Carlo probabilistic approach is preferred. The main outcome of the proposed methodology is the probability of pipeline failure in environments where rock mass instability and consequent rockfall phenomena represent a potential hazard.