Gas-condensate reservoirs suffer losses in well productivity due to near wellbore condensate dropout when the flowing bottomhole pressure declines below the dew point pressure. To alleviate the problem, pressure maintenance and gas cycling are common practices for developing gas condensate reservoirs. A study has been conducted to investigate the applicability of one-time produced gas injection in removing the condensate bank around the wellbore and thereby restoring well productivity. The study focused on two major issues: the optimum time of commencing gas injection and the optimum volume that will remove the condensate bank permanently and restore well productivity. The practice will accelerate the production rate per well and maximize the ultimate hydrocarbon recovery. Three gas-condensate fluid samples with maximum liquid dropout in the range of 6 %, 11 %, and 21 % were used. The benefit of the method was investigated using a full-field compositional reservoir simulation model of a gas condensate field. Reservoir simulation results indicated that, for the lean gases, the best time of starting the gas injection was when the average reservoir pressure around the producing well fell below the maximum liquid dropout pressures. For the rich gas, however, gas injection starting at average reservoir pressure above the maximum liquid dropout pressure resulted in better recovery. The study showed that one-time gas injection not only restored the well productivity and increased reserves but also accelerated the recovery process. These findings bring a different perspective to the development and management of gas condensate reservoirs. Introduction The productivity of wells in gas condensate reservoirs often decreases rapidly as the reservoir is depleted. The decrease is ascribed to a ring of condensate around the wellbore that grows with production time. The ring develops because the flowing bottom hole pressures drops below the dew point pressure of the reservoir gas1. Simulation studies and laboratory studies have indcated condensate saturation near the wellbore as high as 70%. A number of independent investigations have consistently shown that liquid dropout around the wellbore has been the primary reason for losses in well productivity. The presence of liquid around the wellbore reduces the effective permeability to gas2–5. The condensate occupies the gas flow channels and thus impedes gas flow6. The aim of this study has been to assess the impact of a once-off injection of gas in removing the condensate bank and reclaiming well productivity. Whilst condensate dropout translates into losses of revenue due to valuable hydrocarbon components being left in the reservoir, the main focus of the well intervention method is to restore productivity and increase recovery of gas. Single well models have been useful in understanding the phenomenon of condensate dropout or, indeed, in assessing the impact of remedial actions. Nevertheless, they still come short of accurately predicting behaviour on a full field scale. Most single well models are homogeneous or have simplistic reservoir properties and dimensions, in exchange for short simulation time and less complexity in setting them up. In this study, the proposed method was tested on a full field model of a North Sea field. Key questions pertain to timing of the initiation of the gas injection in the life of the reservoir and to determining the gas volumes to achieve maximum benefit. The study was also aimed at establishing the range of applicability of the method by investigating gas condensate fluids with a range of maximum condensate dropouts.
This multi-disciplinary study involved the use of parallel simulation using the same grid size as the geological model. Various production technology options for the remaining wells in a field development were investigated and the benefits of gas cycling were examined. A geological model covering an area of 40 km2 was simulated using two approaches; one using 3 levels of nested LGR's and the other using an areal upscaling, both had nearly the same number of active grid cells. The 12-component Equation-of-State ("EoS") was substituted by 5-component to optimise the run speed of the simulation, 12-component was used for investigating gas cycling. The final engineering model had 320,000 active cells and completed 1.5-year history-matching runs in 1 hr 27 min using 16 parallel 400MHz processors with 1GB RAM each. Both methods worked well and the history was matched with minor changes to the geological model. The primary match parameters were the relative permeability curves since relative permeabilities from special core analysis at reservoir conditions were not available. Correlations from the literature were used for the original relative permeability curves. These were modified to capture the effects of near-well velocity stripping by ‘straightening’ the curves and moving the end points. Through a combination of local grid refinement (LGR) and relative permeability modifications, the effects of the condensate dropout near wellbore and the transient effects were captured. These effects reduce the wells' productivity by 40% in the first weeks of production. In the prediction phase the full field model was used to examine, holistically, the effects and interaction of various options chosen by analytical screening. The main solutions compared were high angle (near horizontal) reservoir penetrations and hydraulically fractured wells. Hydraulic fractures were modelled using LGR to capture the behaviour of the condensate in the fractures. The high angle wells were surrounded by LGR. A large number of prediction cases were run, optimising the development scenario using stochastically derived drilling schedules. Considerations of capital allocation led to the final recommendation from the group of optimal solutions. Introduction The Field consists of two main areas; the subsea gathering centre producing from two layers, and the platform area producing from a further three main layers. The wells in the platform area suffer from a considerable loss of productivity, up to 40%, during the first few months of their producing lives. This loss is attributable to the transient effects and the formation of a condensate bank around the wellbore. At the beginning of the study 14 platform wells were on production; two had been hydraulically fractured and one high angle well (HAW) had been drilled. The fractured wells had not been produced due to facilities constraints and the HAW expressed lower productivity than expected. The subsea area was not included in the study as it is of generally better reservoir quality and consequently does not suffer from the loss of productivity in the early production life. The objective of the study was to develop productivity improvement solutions for four type wells representing the Platform Area. The well types are as follows:the main area: good permeability, three layers; no gas water contactwestern well: low permeability, one layer, far away from the platformnorthern well: two main layers, low permeability, overlaying a high pressure shaleeastern well: one better layer; one lower quality layer with GWC. Previous work had concentrated on well level solutions; in this study the overall impact of the solutions on the field was investigated. Before embarking on massive reservoir simulation, an analytical screening study was performed to identify the future well types. The idea was to perform minimum simulation runs to optimise the field performance.
Recent developments in completion design have seen the evolution of the intelligent well. The ability of this well to both measure and control production, and/or injection, within the subsurface is considered a major step forward in field management. It is therefore crucial to be able to model and understand the impact on the field of the technology prior to installation. By modelling the entire system the ideal completion design and field location are evaluated, whilst the reservoir response to the variable choke is predicted for the life of the well. The economic value of the technology can now be understood. Previous attempts at modelling intelligent wells have crudely adjusted either perforation skin, or near wellbore permeability, to mimic the choking effect. However, the challenge is to firstly, develop a model which correctly predicts the fluid and pressure distribution throughout the completion as well as across the reservoir for each choke orifice, and then to apply the model as a reservoir management tool. This paper describes a method of executing the challenge by incorporating the surface network and variable choke model of a reservoir simulator within the subsurface. The model simulates the multiphase flow and pressure distribution within the annulus and tubing for any choke orifice. Production is allocated to each choked section and perforation accordingly. Simple adjustments, or the incorporation of new components, within the network, readily permits simulation of the field response to various completion designs. Performance sensitivity of the model is evaluated in simple homogeneous and heterogeneous environments. Intelligent well models are then developed for two conceptual field developments. In each, field performance is compared with traditional completion designs. Introduction Since the development and installation of the first intelligent well, several authors1,2,3 have presented and discussed the concept of real-time reservoir management at the sandface. The intelligent well advanced this cause by incorporating a) downhole sensors, able to measure temperature, pressure and flow rates, and b) downhole control devices, able to control production from specific reservoir intervals. This combination of real-time downhole measurement and control is a major attraction to anyone involved in reservoir management. As the intelligent well is able to have such a profound influence on the fluid distribution within the reservoir, it is essential to evaluate the impact of the technology on the entire field prior to any development. In doing this, both the subsurface and surface infrastructure requirements and performance must also be considered. In addition, as controlling production from specific intervals helps mitigate the effects of reservoir heterogeneity, it is likely that the technology is a candidate for more complex reservoirs. There is therefore a need to model an intelligent well with a full field reservoir simulator. This paper develops an intelligent well completion model utilising existing and recent software developments within a simulator. The study objectives are to test the model performance in simple homogeneous and heterogeneous environments and understand how both the choke orifice and completion design influence the pressure and flow distribution within the well and the reservoir. This is achieved by creating a node network able to output node pressures and flow rates at specific times for specific chokes. The model is adapted and introduced into two field development scenarios. The first develops a layered heterogeneous reservoir with a vertical intelligent well, whilst the second develops a faulted compartmentalised reservoir with a dual branch multilateral, incorporating a variable choke. In each case pressure support is maintained by water injection. A comparison of field performance with conventionally completed wells is undertaken in both cases.
In recent years, the deployment of intelligent completion technology has increased as has the range and functionality of the systems available. The focus has been on developing intelligent completions that will allow a greater degree of controlling reservoir inflow. Improvements in the methods that could be used to model the values of these complex completions have in general lagged behind. In this paper, a phased process for evaluating the productivity benefit offered by an intelligent completion system for a multi-layered, shallow gas reservoir system is described in detail. To start the process, an intelligent completion system was selected that was compatible with the well architecture and would provide maximum flow control capability (infinitely variable choke). Analytical methods where then used to conduct a screening exercise in order to evaluate the production benefit this high-end intelligent completion could offer. This screening exercise provided a fuller understanding of the hydraulic interactions occurring within the wellbore. As such, it was possible to tailor the level of intelligent completion functionality to the production benefits derived. A detailed study was then carried out using dynamic 3D-simulation modelling, which incorporated the selected intelligent completion components as a part of the reservoir simulation. This phased approach helped in determining the optimum intelligent completion configuration taking into account the time related reservoir performance changes. Introduction Intuitively, a completion that can be reconfigured from surface in response to changes in reservoir performance should be the preferred choice over a conventional well completion. However, the complexity of the technology involved in intelligent completions results in higher costs compared to conventionally completed wells. Thus, the decision to install an intelligent completion system in place of a conventional completion is dependent on demonstrating that sufficient economic benefit can be created. Economic benefit can be derived through reduced intervention costs and reduced well count as well as through production related gains. Potential intervention and well-cost savings are, in general, the most readily quantifiable. However, determination of production gains is less easily quantified. For such determinations, it is necessary to have the ability to predict the production performance improvement related to the intelligent completion system. In comparison with performance predictions for conventionally completed wells, the production forecast must include the reaction of the intelligent completion to the changing conditions within the reservoir e.g. gas/water movement and pressure changes. The precursor to any intelligent completion evaluation is the ability of the engineer to perform a realistic production forecast from a reservoir model, which incorporates all the heterogeneities and uncertainties associated with the reservoir. Such reservoir models should also include the intelligent completion system with all its relevant components. There should be the flexibility to model the changes in intelligent completion system configuration (choke position) that can be implemented in response to changing reservoir behaviour. In this paper, the application of this evaluation process is described in detail for the potential deployment of an intelligent completion system in a shallow clastics gas field operated by Shell Sarawak. The aim of this study was to identify an optimum intelligent well configuration for this field development and make realistic predictions for the resulting improvement in production performance.
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