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Collaborating Authors
Reservoir Description and Dynamics
Abstract Pressure-Transient-Analysis (PTA) is a technique that is routinely used by both Production and Reservoir Engineers for a variety of applications during the lifetime of a producing well. It is initially used for reservoir characterisation and, later on, for well performance monitoring and (wider) reservoir surveillance. The application of high precision, downhole, temperature sensors has resulted in PTA being complemented or replaced by Temperature-Transient-Analysis (TTA). Recent TTA research has shown that comprehensive information on the state of the near-wellbore zone and fluid flow rates and composition can potentially be derived from such measurements. However, the derivation and use of TTA solutions is challenging, due to both the small value of the measured temperature change and the more complex nature of the governing physics and equations. In particular, analysis workflows for wells producing gas or gas-liquid mixtures are still lacking since most published liquid TTA solutions cannot be applied in the presence of gas. This paper addresses the missing workflow for a (dry) gas producing well. It presents the derivation of novel analytical transient sandface temperature solutions together with the development and application of workflows for interpreting transient sandface temperature data in vertical, dry gas wells. Estimation of either the flow rate or the permeability. thickness (kh) product is demonstrated using a linearized analytical solution. Further, the radius and permeability of a near-wellbore zone of reduced permeability (due to formation damage) can be determined by tracking changes in the temperature transient using the thermal radius of investigation concept. The complete interpretation methodology has been validated by a bespoke numerical model and its application illustrated by case studies. The developed solution and workflows provides a simple and fast method for interpreting sandface temperature data. They will be invaluable for well testing and monitoring applications. It is a major step forward in the longer-term project of developing a full-spectrum of TTA methods for multiphase (gas-liquid) producing wells.
- Reservoir Description and Dynamics > Formation Evaluation & Management > Drillstem/well testing (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
Summary The popularity of intelligent wells (I-wells), which provide layer-by-layer monitoring and control capability of production and injection, is growing. However, the number of available techniques for optimal control of I-wells is limited (Sarma et al. 2006; Alghareeb et al. 2009; Almeida et al. 2010; Grebenkin and Davies 2012). Currently, most of the I-wells that are equipped with interval control valves (ICVs) are operated to enhance the current production and to resolve problems associated with breakthrough of the unfavorable phase. This reactive strategy is unlikely to deliver the long-term optimum production. On the other side, the proactive-control strategy of I-wells, with its ambition to provide the optimum control for the entire well's production life, has the potential to maximize the cumulative oil production. This strategy, however, results in a high-dimensional, nonlinear, and constrained optimization problem. This study provides guidelines on selecting a suitable proactive optimization approach, by use of state-of-the-art stochastic gradient-approximation algorithms. A suitable optimization approach increases the practicality of proactive optimization for real field models under uncertain operational and subsurface conditions. We evaluate the simultaneous-perturbation stochastic approximation (SPSA) method (Spall 1992) and the ensemble-based optimization (EnOpt) method (Chen et al. 2009). In addition, we present a new derivation of the EnOpt by use of the concept of directional derivatives. The numerical results show that both SPSA and EnOpt methods can provide a fast solution to a large-scale and multiple I-well proactive optimization problem. A criterion for tuning the algorithms is proposed and the performance of both methods is compared for several test cases. The used methodology for estimating the gradient is shown to affect the application area of each algorithm. SPSA provides a rough estimate of the gradient and performs better in search environments, characterized by several local optima, especially with a large ensemble size. EnOpt was found to provide a smoother estimation of the gradient, resulting in a more-robust algorithm to the choice of the tuning parameters, and a better performance with a small ensemble size. Moreover, the final optimum operation obtained by EnOpt is smoother. Finally, the obtained criteria are used to perform proactive optimization of ICVs in a real field.
- Europe (1.00)
- Asia > Middle East (0.67)
- North America > United States > California (0.28)
- Research Report > New Finding (0.48)
- Research Report > Experimental Study (0.34)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- (8 more...)
Abstract Intelligent Wells are distinguished from conventional wells by being equipped with downhole sensors to monitor the Inflow Control Valves (ICVs) to control the (multiple) zonal flow rates. The data from the downhole sensors monitors the properties of the fluid flowing into the well from the reservoir at a zonal or a well level. The sensor data is analysed to provide the necessary information for the ICVs to be operated in the optimum manner i.e. to increase the hydrocarbon recovery and prevent unwanted fluid production. This objective is simply stated, but the optimisation calculations required to identify the optimum ICV settings necessitates the repetitive solution of a complex, non-linear problem. Several commercial software providers have made such optimisation algorithms available to the industry to perform this task. However, experience has shown that challenges still arise when they are applied to large, complex models even though these algorithms work well on many simple cases. This is especially true when the optimisation algorithm is combined with a large, multi-well simulation model of multiple reservoirs with a complex, surface production network that is typical of those used today by operators to study real-field cases prior to field development. Inclusion of the optimisation algorithm not only dramatically increases the calculation time (up to 50 times when compared with the equivalent run without such optimisation); but also stability and convergence problems give additional increases in the running time. More importantly, the combined software will sometimes simply stop, due to erroneous control parameters being provided by the optimisation algorithm. The optimisation algorithm may also return unrealistic results at random time intervals, a problem that can lead to unnecessary complications as it may not be immediately recognised. Such problems are particularly acute if the software is performing multiple realisations, for example when it is being applied to analyse the impact of a multiple field development scenarios or when studying how uncertainty in the reservoirโs dynamic and static properties affect the fieldโs production performance. This paper will present a novel method based on the direct search algorithm for implementing an ICV control strategy. This method was chosen since it is not affected by the convergence problems which have caused many of the difficulties associated with previous efforts to solve our non-linear optimisation problem. Our control strategy will use the current, zonal inflow rate and water cut data to identify the optimal ICV choke positions. The availability of this data reduces the number of possible choke positions that have to be evaluated at each time step by the simulator. Run times similar to the base case are potentially possible while, equally importantly, the optimal value identified is similar to the value returned by the other published optimisation methods referred to above. This paper outlines the assumptions made and, after exploring the methodโs use in two single well models for reactive control of oil production from intelligent wells completed with discrete ICVs, its application to a large, reservoir simulation model will be illustrated. The latter application could be implemented rapidly, unlike some other optimisation software, because "tuning" of the model and/or the method was not required; the control algorithm being always convergent, fast and stable. The proposed approach is particularly valuable for the analysis of the impact of uncertainty of the reservoirโs dynamic a static parameters. This arises because the modified direct search method employed here, being convergent and independent of the initial point, ensures that the result from the multiple realisations are directly comparable because "tuning" of the algorithmโs parameters are not required in the middle of the calculation procedure.
- Europe (0.93)
- North America > United States > Texas (0.46)
- North America > Canada (0.28)
- Well Completion > Completion Monitoring Systems/Intelligent Wells > Flow control equipment (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Production and Well Operations (1.00)
- Data Science & Engineering Analytics (1.00)
Abstract This paper presents an advanced control method for online regulation of downhole Interval Control Valves (ICVs) to achieve optimal production via choke performance management. A Generalized Predictive Controller (GPC) has been shown to be capable of automatically controlling the area open to flow of multiple ICVs to achieve a specified production rate. A black box model was established using real-time, downhole, instrument data as a predictive model for the controller. The model parameters were updated in real-time using the Decay Recursive Least Squares (DRLS) method. A case study in which a multi-zone horizontal intelligent well was located in complex reservoir showed that the GPC operation is highly effective. The robustness of the technique was illustrated by its ability to operate effectively in the complex reservoir environment when the signal is perturbed by outliers or by random noise levels up to the control error limits. The value of these control error limits must be increased as the step size of each valve operation becomes larger. 1 Introduction Increasing use of advanced completions and real time downhole measurements in the oil field has made Real-Time Optimisation control (RTO) of well production a popular research topic in the upstream petroleum industry. Ref [1] defined RTO as "a process of measure-calculate-control cycles at a frequency which maintains the system's optimal operating conditions within the time-constant constraints of the system". This implies that RTO is a closed loop process. In addition, we need to take into account the different time constants of the various sub-systems that make-up the total system such as the reservoir, the well, the surface facilities, etc. Many papers ([2] [3] [4] [5]) have introduced the concept of multi-level, self-learning reservoir management during recent years. This approach has already become widely adopted in the chemical process and refining industries. Similarly, it is sufficiently general that it can to be used for real-time optimization of reservoir performance. Each of these multi-level processes requires systems operating at appropriate (but different) time-scales over which the different types of decisions have to be made. Model Predictive Control (MPC) is our chosen tool for the regulatory control level strategy. MPC is a proven, effective strategy for regulation of instrumentation at a chosen set point. MPC is a class of algorithms that can compute a sequence of adjustments to the control system in order to optimize the future behavior of a complex system. MPC was originally developed during the 1980s to meet the control needs of power plants and petroleum refineries. Since then the range of MPC applications has continually grown so that it can now be found in a wide variety of application areas; including chemicals, food processing, automotive, aerospace, paper, and metallurgical industries.
- Well Completion > Completion Monitoring Systems/Intelligent Wells (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring (1.00)
- (2 more...)
Abstract Advanced completions using technologies such as Inflow Control Devices or Interval Control Valves are successfully optimising oil and gas production. They are often accompanied by the installation of Distributed Temperature Sensors and Permanent Downhole Gauges, providing continuous, real-time monitoring of the downhole pressure and temperature. Interpretation of such data to locate influxes and to track zonal inflow performance is an important step towards a comprehensive well and field control strategy. Interpretation of these downhole measurements requires accurate modelling of the well's pressure and temperature. Modelling of the pressures associated with the production of wells with advanced completion has been available for sometime. However, the workflow required for temperature modelling is not yet complete. This paper discusses using available theoretical models and software tools to model the temperature distribution in wells with advanced completion. The strengths and weaknesses of the various approaches for data interpretation will be discussed. Several methods for the quantitative interpretation of downhole temperature measurements are proposed. The application of both currently available and novel theoretical models will be discussed. The workflow will be shown to be capable of providing both zonal flow rates and phase compositions. The interpretation and analysis techniques presented here form the basis of a well and/or field monitoring and production control workflow. 1.0 Introduction The installation of advanced well completions is increasing in popularity 1,2 worldwide. One of the main priorities of these technologies is the creation and improvement of hardware for monitoring, managing and controlling the well inflow at the level of an individual zone. The control capabilities of these devices can be subdivided into active {Interval Control Valves (ICVs)} and passive {Inflow Control Devices (ICDs)} while autonomous ICDs combine features from both devices. They are able to solve a wide range of fluid production problems 3,4. ICDs are installed to ensure a uniform production or injection profile along the complete completion length of the well, thus delaying breakthrough, improving clean-up, optimizing injection or steam-assisted gravity drainage, etc. ICVs are being used for recovery optimization in complex reservoirs and/or wells where the completion is required to dynamically respond to changing and uncertain production behaviour 5. Efficient inflow (or outflow) control rests on the availability of an advanced monitoring system which provides real-time values of the phase flow distribution in the well. Modern downhole monitoring devices, installed as part of the advanced completion, provide sufficient information that they, along with the flow control hardware, form the basis of the intelligent field. Advanced monitoring technologies, such as optical fibre based Distributed Temperature or Pressure Sensors (DTS or DPS), Permanently installed Downhole Gauges (PDG), multiphase flow meters, etc., produce large amounts of downhole data in real-time 6,7. Traditional analytical approaches have proved to be insufficient to retrieve useful information from the vast quantities of downhole data provided by the increasing employment of temperature and pressure measurement sensors. The realisation that temperature behaviour is a complex process that has had less research attention than its pressure counterpart resulted in the initiation of research in this area of petroleum data processing and analysis. A significant part of this effort has been directed towards the analysis of temperature data to evaluate flow rates and phase distributions at specific points along the completion's length In this paper we will discuss the success of several available analytical and simulation tools to accurately model steady-state, temperature and pressure profiles in wells. The ability to estimate phase rates in wells completed with ICDs and/or ICVs will be discussed. We will also present several novel analytical solutions on reservoir temperature distribution calculation. Finally a group of calculation methods to estimate inflow and permeability distributions and flow rate profiles will be presented.
- Europe (0.68)
- Asia > Middle East > Israel > Mediterranean Sea (0.24)
- Geophysics > Borehole Geophysics (0.88)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (0.74)
- Well Completion > Completion Monitoring Systems/Intelligent Wells > Downhole sensors & control equipment (1.00)
- Reservoir Description and Dynamics (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
Abstract Formation damage created during drilling or workover operations significantly reduces the performance of many wells. Long, horizontal and multilateral wells crossing heterogeneous, possibly multiple, reservoirs often show greater formation damage than conventional wells. This is partly due to the longer exposure of the formation to the drilling and completion fluid due to the well geometry as well as to the greater overbalance pressure often applied during drilling such wells and poorer cleanup. The typical well clean up process involves flowing the well naturally or aided by artificial lift to remove the external and internal mudcake and flow-back the mud filtrate. This process can be effective in conventional wells but is not adequate in long horizontal and multilateral wells suffering from increased frictional pressure drop along the wellbore and heterogeneity. The cleanup efficiency is improved by employing Advanced Well completions. Inflow Control Valves (ICVs) control the contribution from individual laterals or a specific zone along the extended horizontal wellbore. Inflow Control Devices (ICDs) equalise the contribution along the (long) completion length. In addition, Autonomous ICDs can manage the influx of unwanted fluids. This paper studies the cleanup performance of such wells completed with these advanced, downhole flow control technologies. It provides valuable insights into how these completions improve the well cleanup process and compares the ability of (A)ICD and ICV technologies to provide the optimum:Drawdown to lift off the filter cake formed by different mud systems (without causing sand production). Recovery rate of the invaded mud filtrate. Guidelines for Advanced Well Completion cleanup along with simulated results of synthetic and real field cases are included. 1 Introduction Formation damage is a deterioration of the near wellbore, reservoir formation characteristics. It has been described as: "The impairment of the invisible, by the inevitable and uncontrollable, resulting in an indeterminate reduction of the unquantifiable" [1]. Its causes include: "physico-chemical, chemical, biological, hydrodynamic, and thermal interactions of porous formation, particles and fluids and mechanical deformation of formation under stress and fluid shear" [2]. These processes can be triggered at all stages of the well or field's life: drilling, workover, completion, gravel packing, production, injection, stimulation, etc. Formation damage reduces the absolute formation permeability and/or causes an unfavourable relative permeability change; both of these will adversely impact the well and reservoir performance. Increasing the well-reservoir contact has become an increasingly popular well construction option. It brings a number of potential advantages - increases in the well productivity, drainage area and sweep efficiency plus delayed water or gas breakthrough. Drilling, workover and (re)completion are all major interventions that result in severe formation damage in Extended Reservoir Contact (ERC) wells. External and internal mudcakes are often formed at the sandface in addition to mud filtrate invasion into the near wellbore area during these interventions. Increased levels of formation damage is to be expected in ERC wells compared to conventional wells due to the increased exposure to the reservoir, use of a higher overbalance pressure and the increased time required to drill and complete these wells. Both water and oil based mud are used to drill ERC wells. Polymers are added to these mud systems to enhance their ability to suspend drill cuttings within the long and tortuous wellbores so that they can be circulated to surface. These polymers will absorb on water wet, formations; altering the irreducible water saturation around the wellbore and complicating the water based filtrate's flow back during the cleanup process.
- North America > United States > Texas (1.00)
- Europe (1.00)
- Asia > Middle East > Israel > Mediterranean Sea (0.24)
- North America > United States > California > Sacramento Basin > 3 Formation (0.99)
- Europe > Norway > North Sea > Northern North Sea > North Viking Graben > PL 054 > Block 31/6 > Troll Field > Sognefjord Formation (0.99)
- Europe > Norway > North Sea > Northern North Sea > North Viking Graben > PL 054 > Block 31/6 > Troll Field > Heather Formation (0.99)
- (13 more...)
Abstract Effective well cleanup during well start-up ensures efficient formation damage removal and maximises the resulting well production potential. Horizontal wells are more susceptible than vertical wells to formation damage due to the longer completion length, the longer drilling time, the potentially increased overbalance and the reduced cleanup efficiency caused by the heal-toe effect. Extensive modelling and simulation work has been previously performed analysing the impact of formation damage and well cleanup in horizontal wells. This paper extends that work to advanced completions employing Interval Control Valves (ICVs) and Inflow Control Devices (ICDs). It reports a comparative study that illustrates the greater cleanup efficiency of advanced, long horizontal well completions over that achieved by the equivalent, conventional, openhole completion. The highest cleanup efficiency is predicted to be achieved by an intelligent completion employing both sensors and ICVs. The well's full production potential will only be realised if a proper, real-time, cleanup monitoring and control procedure is implemented to optimise the choking strategy. Only then will the near wellbore cleanup efficiency be maximised. A dynamic well simulator has been used to illustrate the advantages of employing such a proper, real-time, cleanup monitoring and choke control strategy. This only becomes possible if an intelligent completion is employed. Sensitivity analysis is used to illustrate how an ICV completion gave the highest cleanup efficiency for almost all the parameters studied. The single zone cleanup strategy employed by an intelligent completion requires that extra time be spent on the initial stages of the cleanup process. Guidelines are required to ensure economic as well as technical optimisation of the cleanup process. This can be achieved by use of the presented, practical downhole monitoring procedures for efficient well cleanup together with a novel procedure for identifying the time when the near wellbore region is sufficiently clean. 1.0 Introduction Formation damage is one of the major factors controlling actual well productivity 1. This is especially true for long, horizontal wells that have been drilled and completed overbalance with water-based fluids 2, 3. Perforating may bypass the contaminated zone, but is itself susceptible to damage. It has been long recognised that well cleanup complications increase with increasing well length and number of completion zones. Cleanup management has been recognised as essential for successfully bringing the well on production with the highest possible production potential. Recent publications 4, 5 provided a qualitative discussion on cleanup as part of a comparative framework for the evaluation of the strengths and weaknesses of advanced and conventional completions. This paper sets out to quantify the advantages of advanced completions to improve cleanup by use of their permanently installed, downhole flow control equipment and measurement sensors. Intelligent wells add additional value by providing more effective cleanup than conventional ones. Subdividing the total producing length into a number of zones which are opened successively during the well start-up period is a field proven practice that maximises the drawdown to a particular zone and minimises the chance of flow conduit blockage by deposition of produced sand. The increased drawdown created by unloading the separate well zones sequentially leads to more effective formation cleaning. This temporary zonation of the wellbore can be achieved with specially pre-installed devices (e.g. clean-out or sandface valves). Real-time, downhole pressure data can be used to ensure that the flowing bottomhole pressure is kept above the sand production limit 6. Intelligent wells break the completion into a number of zones with downhole valves while their multiple gauges can be used to control and monitor the zonal production. They also have the additional capability of optimizing the cleanup operation. This paper will first discuss the processes that cause formation damage in the near wellbore area due to drilling and completion fluids. We will then compare the conventional well's success in cleaning up this damage with that of an advanced well completed with either Interval Control Valves (ICVs) or Inflow Control Devices (ICDs). Finally we will develop recommendations for improved cleaning techniques.
- Europe (0.28)
- North America > United States (0.28)
- Asia > Middle East (0.28)
- Well Drilling > Drilling Operations > Directional drilling (1.00)
- Well Completion > Completion Monitoring Systems/Intelligent Wells > Flow control equipment (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Drillstem/well testing (1.00)
Abstract Advances (from conventional wells to horizontal and then multi-lateral) in well architecture for maximising reservoir contact have been paralleled by advances in completion equipment development of both "Passive" Inflow Control Devices (ICDs) and "Active" Interval Control Valves (ICVs). These devices provide a range of fluid-flow control-options that can enhance the reservoir sweep efficiency and increase reserves. ICVs were initially employed for controlled, commingled production from multiple reservoirs; while ICDs were developed to counteract the "Heel-Toe" Effect. The variety of their reservoir applications has since proliferated, so that their application areas now overlap. It has become both complex and time consuming to select between ICVs or ICDs for a well's completion. This publication along with a companion paper summarises the results of a comprehensive, comparison study of the functionality and applicability of the two technologies. It maps out a workflow of the selection process based on the thorough analysis of the ICD and ICV advantages in major reservoir, production, operation and economic areas. Detailed analysis of the modelling, gas and oil field applications, equipment costs and installation risks, long term reliability and technical performance are covered. The systematic approach and tabulated results of this comparison forms the basis of a screening tool of the potential applicable control technology for a wide range of situations. The selection framework can be applied by both production technologists and reservoir engineers when choosing between "Passive" or "Active" flow control in advanced wells. The value of these guidelines is illustrated by their application to synthetic and real field case studies. Introduction Increasing well-reservoir contact has a number of potential advantages in terms of well productivity, drainage area, sweep efficiency and delayed water or gas breakthrough. However, such long, possibly multilateral, Extreme Reservoir Contact (ERC) wells bring not only advantages by replacing several conventional wells; but also present new challenges in terms of drilling and completion due to the increasing length and complexity of the well's exposure to the reservoir [1]. The situation with respect to reservoir management is less black and white. An ERC well improves the sweep efficiency and delays water or gas breakthrough by reducing the localized drawdown and distributing fluid flux over a greater wellbore length; but it will also present difficulties when reservoir drainage control is required. Production from a conventional well is normally controlled at the surface by the wellhead choke; increasing the total oil production by reducing the production rate of a high water cut, conventional well afflicted by water coning. Such simple measures do not work with an ERC well, since maximization of well-reservoir contact does not by itself guarantee uniform reservoir drainage. Premature breakthrough of water or gas occurs due to:Reservoir permeability heterogeneity. Variations in the distance between the wellbore and fluid contacts e.g. due to multiple fluid contacts, an inclined wellbore, a tilted oil-water contact, etc. Variations in reservoir pressure in different regions of the reservoir penetrated by the wellbore. The "heel-toe" effect that leads to a difference in the specific influx rate between the heel and the toe of the well, especially when the reservoir is homogeneous.
- North America > United States > Texas (1.00)
- Europe (1.00)
- Asia > Middle East > Saudi Arabia > Eastern Province (0.67)
- North America > United States > Gulf of Mexico > Central GOM (0.46)
- South America > Brazil > Sergipe > Sergipe-Alagoas Basin > Carmopolis Field (0.99)
- North America > United States > Gulf of Mexico > Central GOM > East Gulf Coast Tertiary Basin > Mississippi Canyon > Block 657 > Na Kika Project (0.99)
- North America > United States > Gulf of Mexico > Central GOM > East Gulf Coast Tertiary Basin > Mississippi Canyon > Block 608 > Na Kika Project (0.99)
- (47 more...)
- Well Completion > Completion Monitoring Systems/Intelligent Wells > Flow control equipment (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Conformance improvement (1.00)
Abstract Intelligent well system technology enables downhole monitoring and zonal, fluid production control in real time. This allows well control decisions to be implemented that optimize recovery and avoid future problems. Currently, both pressure and temperature are usually monitored downhole; while often only the pressure data is used to provide the key information about the downhole performance. Temperature measurements have the potential to be as informative as pressure measurements in reflecting the reservoir's and the well's production performance. However, currently available models are unable to simulate the temperature profile of an intelligent well correctly. This paper provides a theoretical underpinning for the temperature data interpretation workflow. A novel temperature model for multiphase flow with (possible) phase changes in an intelligent completion will be presented. Reservoir and sandface temperature performance is also analyzed and coupled with the intelligent well temperature model. Three example cases illustrate the temperature model's utility. They demonstrate that a small, but significant temperature response can be observed in the complex system of an intelligent well equilibrated with a reservoir zone. The magnitude of the signal used for influx detection is quantified for wells with different inclinations and influx locations. The importance of measurement resolution is also discussed. This is a building block for a method to detect gas and/or water influxes and obtain phase flow rates at various locations within an intelligent well, is an important step towards a comprehensive well management scheme. 1.0 Introduction Intelligent wells, equipped with downhole interval flow control devices, allow production optimization for reservoirs of different complexity and flow conditions. Downhole monitoring systems in such wells give the possibility to trace their inflow performance in real time and to take appropriate, early control decisions to avoid future, technical problems. Downhole data can also be used to update the reservoir model, leading to a better description of the reservoir, an improved performance prediction and to a more effective, reservoir depletion strategy. An intelligent well is thus a powerful instrument in the reservoir management tool box. Development of measurement devices such as permanently installed, downhole, point measurement gauges or distributed temperature sensors has lead to the ability to make pressure and temperature measurements with a high temporal and spatial resolution. Location of such gauges or sensors along an inflow interval potentially gives the opportunity to track the inflow performance distribution in real time 1. However, this requires the availability of an interpretation method for the measured data that can calculate the pressure and temperature profiles along an intelligent completion. Further, analysis and understanding of how these profiles depend on the inflow can be used to interpret them both qualitatively (e.g. for breakthrough detection) as well as quantitatively (e.g. for rate allocation). Pressure calculation and analysis is, in some circumstances, a relatively straightforward problem since currently available pressure equations and correlations can be reliably applied to the area of interest 2, 3. However, temperature calculations, the subject of this paper, need further attention. This paper presents a calculation method for the temperature distribution along the wells with multizone intelligent (or simpler) completion under conditions of multiphase flow in which the reservoir-well thermal interaction is fully accounted for. It incorporates the necessary equations to calculate the temperature profile of the full reservoir-well system, i.e. in the reservoir, along the completion and across interval control valves (Figure 1). The derivation of these equations is given. The method will be used to describe the behavior of three intelligent well cases. It is a building block in the construction of an accurate, downhole data interpretation and soft-sensing tool for influx detection, tracking and quantification.
- Well Completion > Completion Monitoring Systems/Intelligent Wells (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
Abstract Well performance prediction is a key Petroleum Engineering task. However, large discrepancies between Petroleum Engineering models and reality still frequently occur; despite the continuous increase in the complexity and predictive quality of reservoir models. To-day's field development decisions are still made with a high level of uncertainty in the underlying data and its economic impact. The degree of data uncertainty is greatest during the exploration stage, but decreases as the reservoir development plan is executed and production data is obtained. Standard, probabilistic workflows have been developed to quantify this uncertainty. These workflows are usually framed by the reservoir scale development plan and end prior to the well's detailed completion design. This is despite the fact that expensive, advanced completions have become common during recent years and the additional investment in such completions can only be justified if it is shown to be paid-back by improved overall project economics which is subject to a significant level of uncertainty. This paper illustrates the quantification of the long-term benefits of advanced completions using the probabilistic approach. It will be shown how choice of the optimum advanced completion design will reduce the impact of geostatistical uncertainty on the production forecast. Geostatistical realisations of a benchmark reservoir model were generated with a suitable level of data uncertainty. The reservoir was developed by a single horizontal well in a fixed location. The well could be equipped with a variety of completions - an Open Hole with a sand control screen or a perforated pipe, Inflow Control Devices (ICDs) and Interval Control Valves (ICVs). The probabilistic (P10, P50, P90) oil-recovery distribution was then used to identify the optimum completion design. This completion not only achieved the largest recovery, but also showed the least uncertainty in this value. 1. Introduction Well performance prediction is one of the major tasks when preparing an oil or gas field development plan. The complexity and predictive quality of models used to support this activity have increased significantly during the last two decades, partly driven by the ever decreasing cost coupled with the increasing power of computers However, large discrepancies between the model and reality still frequently occur. They stem from:The lack of data (e.g. the unknown distribution of petrophysical properties in reservoir). Deliberate simplifications to make the problem more tractable (e.g. upscaling, black oil PVT models, neglect of thermal effects, etc.). Computational (sub-grid) errors and An incomplete understanding of the physics and chemistry of the subsurface. Petroleum researchers still work on the more precise description of the laws governing hydrocarbon production (e.g. multiphase flow, relative permeability effects associated with gas condensate flow in porous media, effect of water salinity on oil recovery, etc.). Many E&P development decisions are made under a high level of uncertainty. The degree of uncertainty and its impact on decision making is naturally greatest at the exploration stage of the field development process. This is one reason why a probabilistic analysis is part of reserves estimation and other standard workflows used in making early development decisions. The predictive accuracy of reservoir models should increase as the field development proceeds since the quality and the quantity of reservoir data will continually increase. Reservoir models should be continually updated by field production data, history matching, and the ever increasing number of (logged) reservoir penetrations. However, uncertainty quantification always remains an important task; even during the later, more mature phase of reservoir development.
- Europe (1.00)
- North America > United States > Texas (0.69)
- Asia > Middle East > Israel > Mediterranean Sea (0.24)
- North America > United States > Arkansas > Smart Field (0.99)
- Europe > Norway > North Sea > Northern North Sea > North Viking Graben > PL 054 > Block 31/6 > Troll Field > Sognefjord Formation (0.99)
- Europe > Norway > North Sea > Northern North Sea > North Viking Graben > PL 054 > Block 31/6 > Troll Field > Heather Formation (0.99)
- (11 more...)