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Collaborating Authors
Results
Gas Coning Control for Smart Wells Using a Dynamic Coupled Well-Reservoir Simulator
Leemhuis, Anton Peter (TNO Science & Industry) | Nennie, Erik (TNO Science & Industry) | Belfroid, Stefan (TNO Science & Industry) | Alberts, Garrelt (TNO Science & Industry) | Peters, Lies (TNO) | Joosten, Gerard J.P. (Shell Intl. E&P BV)
Abstract A strong increase in gas inflow due to gas coning and the resulting bean-back because of Gas to Oil Ratio (GOR) constraints can severely limit oil production and reservoir drive energy. In this paper we will use a coupled reservoir-well model to demonstrate that oil production can be increased by using controlled inflow from a gas cone as a natural lift. This model was developed in the knowledge centre Integrated System Approach Petroleum Production (ISAPP) of TNO, TU Delft and Shell, and is based on a commercially available dynamic multiphase well simulation tool (OLGA) and a dynamic multi-phase reservoir simulator (MoReS). In order to give a proof of principle we have implemented a PID feedback controller, which controls the gas fraction in a well by changing its wellhead choke or inflow control valve (ICV) settings, on a realistic test case. We introduce a strategy to find an optimal production set point for this controller and the benefits of using downhole ICVs in comparison to the wellhead choke are investigated. Simulation experiments show that a PID controller is an effective means to prevent a full gas breakthrough and, moreover, can be used to increase the produced oil rate by tuning ICV settings to achieve an optimal well gas fraction. Results show that the coupled simulations could be significantly more accurate in comparison to stand-alone well or reservoir simulations. In current operations ICVs are mostly used to completely shut down well segments that experience gas coning. We show that by keeping these ICVs open in a controlled way the - otherwise undesirable - phenomenon of gas coning can be used to increase oil production. Introduction: Gas Coning Control Gas coning is a phenomenon where the gas-oil-contact (GOC) of a reservoir slowly moves towards a well as a result of oil drawdown. In case of horizontal or deviated wells this is often a zonal phenomenon, which occurs at a limited amount of perforations, and is referred to as 'cresting' (Figure 1). At a certain moment in the production life of a gas coning well the gas-oil-contact will reach the well and a gas breakthrough will occur. Upon breakthrough the well will experience a high gas inflow. Largely for three reasons this is an undesired phenomenon. Firstly because the gas phase may start to dominate production, which will deem the well to be uneconomical. Secondly, the inflow of gas may damage topside equipment that is not designed to process large quantities of this phase. Thirdly, after breakthrough the gas cap of the oil reservoir will be depleted fast, taking away its drive energy. The difficulty of containing these three negative consequences lies in the relative speed of a gas breakthrough - typically expressed in hours. Unfortunately the industry is increasingly faced with these hard to contain consequences because many mature fields experience gas coning. Also, oil is increasingly produced from reservoirs like thin oil rims that tend to cone easily.
- North America > United States (1.00)
- Europe > Netherlands > South Holland > Delft (0.24)
- Well Completion > Completion Monitoring Systems/Intelligent Wells > Flow control equipment (1.00)
- Well Completion > Completion Installation and Operations (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
Abstract Optimal well placement is crucial step in oil filed development but it is a very sophisticated process on account of different engineering and geological variables affect reservoir performance and they are often nonlinearly correlated. This study presents an approach where a hybrid optimization technique based on genetic algorithm (GA) and a Neuro-Fuzzy system as proxy was created and used to determine the optimal well locations regarding net present value (NPV) maximization as the objective. Neuro-Fuzzy system was used as proxy to decrease the numbers of costly and time consuming-simulations. Such a system has supplanted a conventional technology in some scientific applications and engineering systems, especially in modeling nonlinear systems. Neuro-Fuzzy modeling is a flexible framework, in which different paradigms can be combined, providing, on the one hand, a transparent interface with the designer and, on the other hand, a tool for accurate nonlinear modeling. The rule-based character of Neuro-Fuzzy models allows for the analysis and interpretation of the result. Within Hybrid Genetic Algorithm (HGA), a database of the completed simulations is made. This database is used to construct of Neuro-Fuzzy network. Then this network is used to estimate the fitness function at points that no simulations have not been done. This proxy is also able to get better during the optimization each time a new point is verified and visited points database is updated. A synthetic reservoir was tested and comparisons made among HGA, simple GA and non-proxy using approaches. Results showed that Neuro-Fuzzy system is very reliable proxy to estimate fitness function so the HGA will have a good chance to obtain the optimal place for the well in minimum possible duration. Introduction The main task of reservoir engineering team is to develop a plan to recover as much hydrocarbon as possible within ecumenical and physical limits. This task involves optimal well placement and production scheduling. Most of the time, a slightly better decisions in this stage may lead to significant increase in project value. However optimal decision making is not an easy task, because different variables affecting reservoir performance are uncertain and they are often are often nonlinearly correlated. Numerical simulations used in oil industry provide precise approach to predict reservoir manner and assess the value of opportunities in filed. Those models are derived from complex studies involving a nonlinearly equations with high degree of uncertainty and a large amount of parameters that could be either independent or dependent on each other. The relation between the computed simulations result and the input data is generally highly nonlinear. Hence reservoir simulations normally require large computational effort and considerable time consumption; in a parallel manner the activities connected with reservoir simulators suffer severe limitations that make it difficult with the vigorous development. Also most of the time, the large number of possibilities, constraints on computational resources and the size of the simulation models limit the number of possible scenarios that may be considered. Analysis tools encoded in computer, programs can spend hours or date for processing a single run, depending on their sophistication and features. Moreover, it can be costly to prepare the input data if many hypotheses are going to be considered. Nowadays numerical simulation is widely used to place new wells. Even with these models, current practices are still the ad-hoc, single-well-configuration-at-a-time approach when infill prospects are sought. In each trial, well configuration is selected based on the intuition of reservoir engineer. For a single-well case, this one-well-at-a-time approach may lead to suboptimal decisions. The problem definitely compounds when multiple producers and injectors are involved in a field development scenario.
- Europe (1.00)
- North America > United States > Texas (0.28)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Fuzzy Logic (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Evolutionary Systems (1.00)
Abstract Most common training methods are not sufficient to prepare employees for complex or potentially dangerous tasks. We propose therefore Virtual Reality Training as a method for interactive and experienced based learning where employees can perform practical tasks in a virtual world without putting their health or the production at risk. The refinery of Schwechat has made a Virtual Reality training simulator part of their regular training curriculum. Experience has shown that the simulator helps in speeding up training time and effectively preparing employees for tasks which cannot be practically trained for in the real world. Introduction Virtual Reality (VR) is an established technology in the oil and gas industry where it is used for visualizing geophysical and engineering data in exploration and production [1]. Using VR for training is still a novelty in most industrial environments, however it is successfully applied in military training. Definition of Virtual Reality Virtual Reality is defined as the use of computer technology to create the effect of an interactive three-dimensional world in which the objects have a sense of spatial presence. Therefore, 3D games, e-learning, process simulators and flight simulators (the 3D world of a flight simulator is not interactive) are not considered Virtual Reality. Origins of Virtual Reality The generally accepted birth of Virtual Reality as a technology dates back to Ivan Sutherland's seminal paper "The Ultimate Display" from 1965 [2] which resulted in the world's first VR system: "The Sword of Damocles" and the development of the first head mounted display (HMD) in the year 1970. Today's Virtual Reality technology is an amalgamation of a variety of fields and influences, such as flight simulation, computer graphics, gaming technology and user interface design. The hype generated around Virtual Reality in the late 1980s and early 1990s resulted in more harm than good in the long run, as it created expectations which cannot even be met in the near future. However, Virtual Reality is now in daily use and has proven to be a very valuable technology especially for visualization and training. Motivation for a Virtual Refinery Simulator Training operators in control room simulators is a common sight today. Like airplane pilots, they are also prepared to handle delicate cases and emergencies. However, the same cannot easily be achieved for workers in the field due to the dangers associated with conventional training methods which could put the trainees' health and safety at risk. A VR simulator allows for training in extreme situations without endangering participants. Furthermore, in a VR world it is possible for trainees to make mistakes without the need for intervention by the trainer, so they can learn from their mistakes and live through the consequences of their actions. Having a virtual industrial plant as a kind of playground also offers a way of demonstrating the interrelationships of production, e.g. although inherently dangerous in the real world, closing a random valve in a VR refinery has no negative consequences for the trainee, the consequences for the virtual refinery, however, can be dramatic. Trainers at the refinery of Schwechat near Vienna (Austria) had the idea to develop a VR training simulator for occupational safety in 1997. They started a joint research project with the Johannes Kepler University of Linz, Austria. This project resulted in a full VR simulator in 2003 (see Figure 1) [3]. Since then the simulator has become part of regular training at the refinery and is a mandatory part of the curriculum for new employees.
- Energy > Oil & Gas > Upstream (1.00)
- Education > Educational Technology > Educational Software > Computer Based Training (0.69)
Abstract In this paper, we present an algorithm for optimizing reservoir production using smart well technology. The term smart well is used to indicate an unconventional well equipped with down hole inflow control valves (ICVs) and instrumentation. This additional instrumentation extends the degree of freedom in the field production planning, since production can be efficiently distributed on the different well segments available. By proper utilization of the ICVs through optimal production planning, an increased oil recovery for the reservoir can be expected. We propose a method for optimal closed-loop production known from control theory as model predictive control (MPC). A commercial reservoir simulator, ECLIPSE, is used for modeling and predictions. MPC is chosen for its ability to provide an optimal solution for the constrained multivariable control problem. To compute the optimal ICV settings, we propose using a nonlinear MPC (NMPC) application, which can handle the severe nonlinearities found in reservoir models. The NMPC uses a single shooting multi-step quasi-Newton (SSMQN) method to solve the optimization problem. As the term multistep suggests, this is an iterative method which solves a sequence of quadratic problems (QPs) in each time step. We apply our method to a benchmark reservoir model with multiple geostatistical realizations. This model has already proven potential for increased oil recovery by using optimization techniques. We show an even additional increase over the former approach in production totals, using the SSMQN method, with as much as 68% increase in one case, and 30% on average compared to a reference case. Introduction Reservoir management has traditionally been performed on the basis of long and short term plans made by production engineers in a manual, ad hoc fashion. The overall goal is obviously to maximize the total hydrocarbon production and recovery factor while minimizing total cost and staying within operational constraints. But reservoir models have generally been viewed as too large and computer resources too scarce to apply full scale production optimization. Meanwhile, on the downstream end of the production line and in process industry in general, advanced control techniques have been gradually developing and implemented with prosperous results. Recent technological advances have opened for new possibilities within reservoir production. New reservoir mapping techniques offer more accurate reservoir models and the computational cost of simulating the models has decreased significantly. Well completions are more sophisticated than ever and supply new dimensions of flexibility to the day to day field operation. This new well generation is better known as smart wells. A smart well is a unconventional well equipped down hole with ICVs. Smart wells offer control of the total flow through individual segments and branches, as well as temperature and pressure measurements. The potential benefits from proper use of ICVs in a real-time control application are substantial. This is because continuous redistribution of the production from the available branches can delay or avoid break through of gas and/or water for as long as possible.
- Europe > Netherlands (0.68)
- Europe > Norway (0.46)
- Asia > Middle East > Iran > Ilam (0.24)
Abstract Many experts admit a big gap between reservoir simulation and daily management of oil and gas fields. Because only a limited number of specialists can use the results of modeling and models get out of date quickly. The necessity to reduce access and update time has driven to development of a Live Model concept. It will reduce the gap between modeling and practical oil and gas field management. The targets of Live Model concept are:use models as interactive means of teaching; make models available to everyone, everywhere, anytime; build updating and monitoring tools for models. In accordance with Live Model concept we developed web-system which enables to perform model monitoring and analysis of reservoir simulation results. Using this tool any specialist by means of a web-browser can do the following:make analysis of dynamics well data; make maps, streamlines visualization; estimate the quality of the models history matching; create and calculate a simple model. This web-system can act as an integral part of corporate information-engineering portal. Then we have opportunity to use dynamic production data, which is stored in corporate database, and automatically update Live Models. The interaction of web-system for models monitoring and analysis with the corporate database and models accessibility helps the oil and gas companies to use modeling results in the process of decision-making more efficiently. Another crucial factor is the opportunity to employ Live Models as training aids, which significantly reduces learning curve and increases the quality of training. Introduction Oil and gas field reservoir simulation plays a key role in:the understanding of physical processes observed in the formation and in wells; the working out of a strategy at initial stages of field development; exercising control over the oil and gas fields at later stages of field development. However in daily base fields performance monitoring and management we face with the several factors which prevent an efficient using of reservoir simulation. First of all, only a limited number of specialists can use the results of modeling and have an access to these models. In most cases only model creater has an access to models he created. So, reservoir engineer became a bottle-neck to the simulation results (see Fig. 1).
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Communications > Web (1.00)
Abstract Simulation technology from reservoir through process facility has advanced so much, that field development strategies can be developed within a new systematic workflow, using existing applications from many E&P departments. Detailed production data from many sources can be used within simulation models to give a good representation of future field wide behavior. In this paper a fictional case study of a reservoir that has been producing for some 12 years will be examined. The wells are all producing into a sub-sea manifold and then tied back via a 60km flow line and riser system. The reservoir is in severe decline with field production well below the original design capacity of the production system and surface facilities. Hence, further development options are being investigated for this asset. A new, nearby, reservoir has been discovered. A reservoir simulation model has been constructed for the new discovery. This second reservoir is a gas condensate system, much smaller than the existing reservoir and located 90 kms to the east. The current development plan shows six wells drilled and brought into production over an 18 month period. Reservoir 2 is a marginal development, the viability of producing this reservoir will depend on quantification of the reservoir uncertainty and finding a cost effective development strategy with existing processing facilities. The Business Development Team has suggested a number of possible options for developing this new reservoir; Option 1 involves tying in the new reservoir to the existing sub-sea infrastructure. Option 2 is to install a complete new flow line from the sub-sea template of the new reservoir and run this directly to the existing platform. But how do these options effect reservoir management and surface facilities performance? Evaluation is achieved by constructing an integrated asset model of the entire field, allowing the reservoir through facilities interaction to be evaluated in detail. Introduction Everybody wants one, but nobody has one. The Integrated Asset Model (IAM) has been the pursuit of many Oil & Gas companies in the last decade. Finally, the industry shows signs of achieving the prize of the IAM under the banner of the "Digital Oil Field". From reservoir to facility and from today to the end of field life, the IAM promises multi-discipline answers. This paper is intended to serve as a road map for the development and adoption of the IAM into the culture of Oil & Gas Operating Companies. Years from now, new graduates to the industry will have IAM training as part of their Oil & Gas company inductions and they will use the technology to solve many pains from production optimization, operations surveillance & asset planning to uncertainty analysis and fiscal determinations. However, existing work flows and applications will have to change. The questions are by how much, by when, at what cost, and with what benefit? Multiple vendors must collaborate to create cross-discipline compatibility and Oil & Gas companies will need to pilot, evaluate and recommend changes to the resulting IAM technology, which will evolve through a number of rounds of deployment. Collaboration that has never been seen before in the Oil & Gas industry will need to be established if suggested improvements such as $30mn per year per asset for optimization and over $90mn per year in improved Net Present Value (NPV) from planning solutions can be routinely exploited within the average asset. What is needed is a road map for the adoption and development of these IAMs, along with a statement and agreement of the principles that govern the IAM.
- North America > United States > Texas (0.28)
- Europe > Norway (0.28)
- Europe > Netherlands (0.28)
Abstract Risk management has become an integral part of the decision-making workflow in the oil and gas upstream business. As many oil fields reach a mature state, the need for rejuvenation and decline mitigation of assets set ground for Improved-Oil Recovery (IOR) opportunities. However, the associated decision-making process requires incorporating screening, reservoir simulation and financial evaluation, demanding complex multidisciplinary team efforts. It is important that any stage of the analysis, technical, strategic and economically sound decisions should be made. On one hand, IOR screening, whether based on technical grounds or ‘gut feeling’ experience, or better yet on both criteria, leaves a number of possible IOR processes available for evaluation through simulation. Analytical simulation and applicability screening tools are often favored on early stages. However, their crude application could mislead the decision process if results are not carefully interpreted and combined with reservoir engineering expertise and additional evaluation criteria. We propose to combine IOR screening strategies with spatial reservoir information to help to create appropriate sector models as starting point for more detailed evaluations. For this purpose, we couple an analytical simulator/IOR screening tool with a software tool that aids framing the IOR decision-making problem effectively, in the form of influence diagrams. From these diagrams, it is possible to create Tornado Diagrams, Decision Trees and Monte Carlo profiles that assist Reservoir Engineers with the task of properly and rationally framing the decision process, for example with regard to economic risk assessment and NPV analysis associated with IOR. The coupling between both software solutions is proposed in a way that avoids the inflexible monolithic constructions. We illustrate advantages of the proposed approach through a speedy analysis of a publicly available case. Introduction We define here Improved Oil Recovery (IOR) operation to comprise the injection of energy and fluids typical of tertiary recovery as well as technologies that enable extension of field life via access to reserves, such as special well architectures. Some of the IOR methods become viable in the current scenario of high oil price. However, IOR projects involve higher complexity than traditional E&P operations, not only because of the typically high CAPEX and sometimes-high OPEX values, but also because of the number of options available, with the concomitant more complex decision-making process. On the other hand, improper choice of IOR processes for a given asset or a portfolio could lead to elevated risks. IOR projects generally follow a workflow that includes screening, preliminary evaluation, detailed appraisal simulation and economic evaluation to launch the project, as described by Goodyear and Gregory.[1] Figure 1 summarizes this workflow. In their paper, Goodyear and Gregory discuss important elements of risk assessment and management of IOR projects. Thompson and Goodyear[2] elaborate further on identification of IOR potential, within the framework of Risk Management. Their approach to using financial risk indicators such as the risk and reward chart that allows one to compare IOR projects with other Exploration & Production projects.
Abstract Proper field management for optimal performance of hydrocarbon reservoirs must capture the interdependence of the subsurface reservoir behavior and surface facility constraints. In this work we describe how full coupling improved development of a Saudi field by reducing water production by 30% while maintaining the target plateau for the required period of time. This was achieved by an iterative procedure that was able to devise an optimal producing strategy. The strategy involved time-dependent well production/injection rate allocations in response to field behavior. The strategy devised take into account production network constraints, network bottlenecks/under-utilization, and reservoir engineering complexities in producing three different reservoirs that make up the field. This work was realized by linking Saudi Aramco's in-house developed simulator (POWERS) with a commercially available surface network simulator (PE-GAP). The paper will highlight some of the major challenges in creating the link from engineering as well as from software/hardware perspectives. Due to this successful endeavor, the workflow will be applied to more fields. Furthermore connection to SCADA system for real-time monitoring is under developed. Introduction Motivation Field A, a giant Saudi oil field, consists of eight oil bearing reservoirs, of which the three largest were selected for initial development. The development team assigned to Field A was tasked with developing the field to maintain a 30+ years plateau period while maintaining the blended crude quality within a very narrow range. Additionally, maintain potential drilling and water production was to be kept at a minimum. The three developed reservoirs have distinctly different crude grades, H2S concentrations, and reservoir properties. The two lighter crudes were to be produced into a common manifold system, while the heavier, lower pressure reservoir would produce into a separate system. Two gas oil separation plants (GOSPs) are utilized to produce the blended crude streams and separate out water and gas from the crude. The development team needed a means of accounting for the interaction between wells producing into a common manifold system while being able to optimize production rates to maintain the aggressive plateau target. Additionally, because of the varying reservoir properties the development team felt the only true representation of the reservoir performance would come from the detailed numerical reservoir simulation model. Finally, H2S concentrations also needed to be kept below a certain threshold value due to facilities constrains. It therefore became necessary to have a seamless connection between the reservoir simulation models, the well models, and the models for the surface gathering system.
- Asia > Middle East > Saudi Arabia (1.00)
- North America > United States (0.69)
- Europe (0.68)
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > Asia Government > Middle East Government > Saudi Arabia Government (0.71)
- Production and Well Operations > Production Chemistry, Metallurgy and Biology > Corrosion inhibition and management (including H2S and CO2) (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Fluid modeling, equations of state (0.94)
- Health, Safety, Environment & Sustainability > Health > Noise, chemicals, and other workplace hazards (0.93)
- (2 more...)
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Communications > Networks > Sensor Networks (0.54)