Summary This paper presents general guidelines to determine the feasibility of offshore petroleum projects in terms of field appraisal, subsurface development planning, and facilities options. It also illustrates the multidisciplinary nature of various tasks and includes examples for illustration. The emphasis is on oil fields, particularly marginal fields. Introduction The life of every oil and gas field begins with its discovery. Almost immediately, we want to know what its potential is (in terms of reserves and monetary value) and what the development options are in terms of subsurface plan and facilities. To answer these questions, a systematic approach is required to evaluate the discovery, to forecast the reservoir behavior under expected producing conditions, and to design the optimum facilities to meet forecasted production. This paper outlines the required process for studying the feasibility of developing offshore petroleum fields. A petroleum development project typically is divided into a number of major phases: exploration (including permit acquisition), field appraisal (primary and possibly secondary), feasibility study, project implementation (construction), and field production (operation and maintenance, management, and facilities upgrades, including secondary development phases). Different technical departments, each with specific aims, usually manage these phases (Fig. 1). While the development sequence is similar for all fields, there are notable differences between onshore and offshore projects. Most significantly, the engineering requirements and capital expenditure tend to be one or two orders of magnitude greater for offshore projects than onshore developments. Furthermore, offshore developments tend to have a much longer development schedule before they come on stream. Reserves and well productivity need to be substantially greater for offshore projects to cover the greater capital expenditure and operating cost, respectively.
The Carman-Kozeny formulation has been used as a basis to provide a new perspective of flow zone units (FZUs), by mapping similar rocks in terms of "Characteristic Envelopes", for different geological depositional environments. A large amount of data, covering several fields and different Australian basins, has been analysed. The methodology has been used for well-to-well correlation, reverse modelling for better identification of depositional trends, diagenetic affects and grain characteristics. It is also shown how photomicrographs, scanning electron micrographs (SEM), and log shapes can be incorporated in a detailed analysis. The method is ideal for validating plug samples used in special core analysis. It is shown how various data types, geological attributes and engineering parameters can be integrated. Results from such analysis can then be used in consistent model preparation and better quantification of petroleum recovery efficiency. Introduction Prediction of recovery efficiency and petroleum reservoir productivity is an important task for petroleum engineers, requiring detailed analysis of various reservoir properties and their interrelationship. To be successful, such reservoir description and analysis requires the integration of geological and engineering parameters. Core description and analysis gives information about pore structure and their characteristics, where the geometry is the end result of a long geological process involving deposition and diagenesis. Geoscientists have traditionally classified rocks according to porosity, grain size and distribution, grain sorting, mineralogy and petrophysical parameters, whereas reservoir engineers tend to emphasize the flow behaviour of rocks. The Carman-Kozeny (C-K) equation may be used to bridge this gap, considering variation in flow behaviour as a function of geological facies, the correlation parameter being the hydraulic radius. For a particular reservoir, various layers or facies may be grouped together to form Hydraulic Flow Zone Units (FZUs) or Hydraulic Units (HUs). This paper provides a new perspective of FZUs, by mapping regions or envelopes in the C-K space, for different geological depositional environments, analysing a large amount of data from a number of fields and covering several Australian basins. "Characteristic Envelopes" may be seen as a key feature in FZU modelling and they can be defined for very specific situations, with envelope boundaries covering a limited or entire depth range, including diagenetic variation. HU composition inside each envelope may thus reflect sorting, compaction, variation in grain characteristics, pore structure, and energy of deposition for the particular depositional environment under consideration. More generally, major depositional environments, for example channel environments, have been grouped for comparison, covering different parts of the C-K domain and demonstrating individual quality. The definition of each envelope has been derived by analysing similar depositional sequences for several fields, showing good agreement. To further verify and validate this approach, particularly for non-uniform intervals, use has also been made of log shapes and petrographic (also SEM) images and several of these are given to demonstrate the concepts. Finally, the outlined methodology may be used as a prediction tool for the case of a new geological province, where a particular geological environment has a certain chance of occurrence. In this case, a specific envelope may be considered, to predict possible formation characteristics, and related porosity, permeability and other property values. Study results may be used in prediction of recovery efficiency and reservoir/well productivity. The described methodology is also compared to other methodologies.
Initial, irreducuble water saturation, Swir is an important parameter that needs to be determined accurately when attempting to characterize hydrocarbon reservoirs. Swir is also one of the key parameters in relative permeability relationships. Furthermore, an unrepresentative value of Swir may lead to invalid residual oil saturation estimates when the latter is correlated with the former. Swi may have a dependence on several other parameters, including: absolute rock permeability, porosity, pore size distribution and capillary pressure. The above parameters are directly influenced by geological deposition and subsequent changes, such as diagenesis effects (for example clay-filled pores). It is a common practice to measure Swir utilizing representative core plugs by measuring capillary pressure with a centrifuge, at speeds equivalent to the maximum representative (reservoir) capillary pressure. However, a semi-empirical model that could estimate Swir to a good degree of accuracy would be of significant value. Over the last few years, artificial neural networks have found their application in petroleum engineering. In some cases such models have outperformed models employing conventional statistical and regression analysis. In this study, an Artificial Neural Network (ANN) model has been developed for the prediction of Swi (specifically irreducible saturation, Swir) using data from a number of onshore and offshore Australian hydrocarbon basins. The paper outlines a methodology for developing ANN models and the results obtained indicate that the ANN model developed is successful in predicating values of Swir over the range of data used for calibration. This neural network based model is believed to be unique for Australian reservoirs Introduction The accurate estimation of initial water saturation for a hydrocarbon reservoir is an essential practice in the oil and gas industry. The exact determination of Swi leads to a precise evaluation of initial hydrocarbon in place, which in turn provides valuable insight into further field development plans. Moreover, and as an important aspect in multiphase flow problems, the calculation of valid Swir values is an important requirement for obtaining accurate relative permeability relationships and for determining residual oil saturation. Swir is measured as part of primary drainage capillary pressure experiments on core plugs obtained from the reservoir under consideration. In a centrifuge test, a plug is saturated with formation or synthetic brine and positioned in the centrifuge cup filled with oil. The centrifuge is then spun at different speeds, pushing the water phase outward. The volume of the expelled water is measured and the centrifuge speed is increased until it reaches a pre-determined limit, which represents the maximum capillary pressure. Having that in mind, a careful calculation of the maximum reservoir capillary pressure and consequently the maximum capillary pressure under laboratory conditions are of importance (1). As an alternative to the centrifuge test described above, a core-flood test may be conducted. In such tests, the plug is initially saturated with brine. The oil phase is then introduced, resulting in a reduction in water saturation. The test continues until the plug reaches a stabilized condition where no more water is expelled. Care must be taken when interpreting the results from both tests described above. This is attributed to the fact that both tests employ different mechanisms and consider different influencing forces, including gravitational forces for centrifuge tests and viscous forces in core flooding tests(1).
SPE Members Abstract Production practices for oil fields with gas caps usually centre around conservation of the gas cap (energy) to maximise oil recovery. A less conventional but effective reservoir management approach involves an early gas cap blowdown phase in situations where the gas cap is small and a strong aquifer is present. This paper describes the critical parameters and the benefits from a less orthodox depletion plan. After discussing this subject from a general point of view, the reservoir management plan for the Skua Field, located in the Timor Sea, is cited as a successful application. Introduction In developing oil fields the aim is usually to maximise ultimate (oil)recovery and at the same time minimise capital expenditure (Capex) and operating expenditure (Opex), the optimum plan resulting in a maximum Net Present Value (NPV).To achieve this goal, oil fields may be produced in a variety of ways, constraint by the physical situation, commercial considerations and government regulation. In terms of reservoir considerations, the most important factors tend to be initial (reservoir) conditions, that is pressure, temperature and depth; and fluid and formation (and rock) properties. Overall, of these the dominant reservoir drive mechanism(s) and its effect on pressure maintenance and sweep efficiency (effectiveness of pushing oil towards the producing wells) are often the chief criteria for implementing the chosen subsurface development plan the number, type and location of wells and production policy. Drive mechanisms may be natural -(gas in) solution drive, (primary)gas cap drive, aquifer drive and compaction drive; or the reservoir energy and sweep efficiency may be supplemented by injecting fluids commonly water or gas, or more exotic substances, leading to enhanced oil recovery (EOR).In this paper the above mentioned natural drive mechanisms are reviewed for the purpose of comparing the benefits from a primary gas cap and a natural aquifer. In other words, when is the size of a primary gas cap more important than a certain aquifer? More specifically it is shown by means of a field example, the Skua Field located in the Timor Sea, how the ultimate recovery is being maximised by "blowing down' the primary gascap! Figure 1 compares the latter production strategy utilised for the Skua Field Development with a more conventional approach. RESERVOIR ENERGY One measure of the relative importance of the various drive mechanisms is the intrinsic energy of the different substances, more specifically the compressibility-volume product, which compensates for reservoir voidage(production) in maintaining reservoir pressure. In order to appreciate the wide range of compressibilities, Figure 2shows these for an "average" situation. P. 307^
Capillary pressure and associated relative permeability are special core analysis parameters which are vital in accurately describing fluid distributions and movement in porous media when two (or more) immiscible fluids are present. Conventionally, the determination of capillary pressure requires laboratory experiments, which are expensive and time consuming. Assuming that core material is available, typically a limited number of core plugs are considered for testing, often resulting in incomplete reservoir description. This situation then leads to motivation for developing mathematical capillary pressure models, as an alternative to adequately describing fluid behaviour in reservoirs.The well known Carman-Kozeny (C-K) equation (Carman 1937;Kozeny 1927) is commonly used to model permeability as a function of other pore structure parameters. More recently parametric groups were established for the C-K equation, which are in regular use in reservoir characterisation. Specifically, the determination of flow zone units for the description of distinct (litho)facies is now widely used in linking fluid flow to geological descriptions. In this paper, a new and innovative deployment of the C-K equation is investigated to derive capillary pressure relationships. The new mechanistic, drainage capillary pressure model uses the principle of "effective saturation", and is developed and validated with data sets from onshore and offshore Australian hydrocarbon basins, giving a good comparison with laboratory measurements and other established models. Most significantly, the new mechanistic formulation does not just fit capillary relationships but is able to predict drainage curves by knowing or assuming up to six properties: absolute permeability; effective porosity; irreducible water saturation; maximum capillary pressure, which may be related to a particular reservoir situation or laboratory standard; capillary entry pressure; and associated entry water saturation. The last two parameters are typically required for a higher permeability (or better quality) rock. Absolute permeability is incorporated indirectly. Furthermore, there is no requirement for correlation parameters or constants but the analysis determines two parameters which give a measure of heterogeneity of the sample being investigated.
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