ReO - Production Optimization Software

Quick Links to Information Below

• ReO Overview
• Typical Applications for ReO
• Why Reo?
• Key Technical Features of ReO

• ReO Technology Overview
• Economic Capability of ReO
• How ReO Works
• New Levels of Performance Achieved by ReO

PDF Documents

• EPS Software Brochure
• EPS Products At-A-Glance

ReO Overview

ReO is a simulation and optimization solution for surface networks incorporating everything from individual wellhead performance to the macro-details of the processing plant. ReO simulates the total production system behavior and provides essential information on current operating conditions and asset performance. Concurrently, ReO optimizes the user defined economic model and generates alternative production system management strategies, which typically result in substantial production increases and simultaneous reductions in operating cost.

Typical Applications for ReO

Optimization of Processing Capacity

Using available hydrocarbon processing capacities more effectively increases hydrocarbon production. Processing capacity optimization with ReO typically achieves production gains of three to five percent without any requirement for capital expenditure. For this and all other applications, ReO automatically returns the field and choke settings that provide increased hydrocarbon production.

Optimization of Production System

Increase hydrocarbon production by re-routing fluids making better use of the existing production system. ReO optimizations of this type also typically achieve production gains of three to five percent without any requirement for capital expenditure.

Asset-Based Gas Lift Optimization

Injection and production system pressure requirements as well as all production system and fluid processing constraints are used to optimize the allocation of lift gas to gas lifted wells. Gas lift optimization with ReO typically achieves a three to twelve percent gain in production revenue and a reduction in lift gas requirement of three to ten percent with associated cost reductions.

Gas and Oil Blending

Automatically blend gas and/or oil production from different reservoirs to achieve contractual quality standards.

Production Allocation

Simulate accurately how measured field production translates into individual well and reservoir off-takes. This application of ReO provides data which forms an essential input for asset and reservoir development planning.

Artificial Lift Design

Evaluate the effect of artificial lift design based on individual well parameters on full asset performance. Following the installation of artificial lift equipment in individual wells, ReO allows quick and accurate assessment whether production gains predicted lead to a corresponding increase in asset production, especially taking into account production and processing system constraints.

De-bottlenecking

Cost effective means of identifying which production or processing system constraints impede production and evaluating different ways of addressing such issues. ReO allows the user to create a detailed and highly accurate representation of the whole production system. Such detail allows bottlenecks to be identified correctly. It also ensures the full system’s response to measures to overcome such issues can be predicted quickly and accurately.

Process Facilities in ReO

Why Reo?

Versatile

ReO’s advanced solver technology makes it applicable to a large variety of advanced production and asset management problems.

Robust and Reliable

ReO models have been stress tested in online systems running six different automatically updated scenarios four times a day for months without a significant failure rate.

Fast and Efficient

The ReO solver has been designed for speed and efficiency. In contrast to many other available software packages, ReO solution times stay acceptable as problem complexity increases.

Scalable

ReO models have been built for a single platform asset in the North Sea to a set of intricately linked gas lifted well assets in the Middle East comprised of hundreds of wells and literally thousands of pipes. ReO has an unrivaled track record for robustness both in terms reliability and solution time for even the most complicated assets.

Easy to Use

ReO’s modern “drag and drop” user interface is highly intuitive. Bulk data entry and edit facilities allow fast and efficient model building. Results plotting and visualization aid effective results interpretation.

Key Technical Features of ReO

• Choice of fully compositional or black oil PVT modeling.
• Can handle all well types in arbitrary number and combination (naturally flowing, gas lifted, ESP, and other forms of artificial lift, and injection wells). Fully integrated with EPS’ WellFlo application.
• Full set of processing and network equipment including pipes, links, chokes, block and check valves, junctions, sinks, flares, compression and pumping equipment, heat exchangers, separation equipment, and gas processing equipment.
• Equipment can be modeled with varying degrees of sophistication according to engineering needs.
• Intuitive and flexible facility to define system constraints at any point in the network and to assign monetary rewards or costs to network fluids and other optimization variables.
• Robust solver based on advanced SLP technology to allow truly arbitrary network size, configuration, and complexity. Automatic calculation of flow directions.
• Graphic user interface (GUI) allows bulk data entry/editing and easy definition of view hierarchies to structure complex networks visually.
• Fully detailed multiphase fluid and equipment results at all points in the network.
• User configurable solution visualization and plotting. Comprehensive results reporting with export using various industry standard formats including Microsoft® Excel.

ReO is based on sequential linear programming (SLP), which can model complex, non-hierarchical networks with no limits on network complexity. A typical nodal analysis approach solves parts of the network individually. The ReO solution process considers the network as a whole.

ReO Technology Overview

ReO is proven software for simulating and optimizing hydrocarbon production systems.

In upstream oil and gas production, various technologies are used to address different aspects of hydrocarbon production modeling and optimization. These different approaches and the software technology developed to serve them address various business objectives, for example: - reservoir simulation provides a useful 'life of field' view of a reservoir while on-line facilities optimization addresses the problem of maximizing the value of feedstock throughput, in real time.

ReO addresses the need to optimize production operations i.e. between reservoir and facilities, in three main areas:

• To aid in the design of new production capacity, both conceptual and in detail.
• To optimize production systems either off-line or in real time.
• To forecast performance and create production profiles for alternative development scenarios.

ReO integrates complex engineering calculations, practical constraints and economic parameters to determine the optimal configuration. The applications map below illustrates the role ReO plays in upstream processes.

Application Map of Oil and Gas Upstream

ReO is an integrated product with the capability of modeling the entire production system from the reservoir through to the processing plant. It is also modular, enabling customers to configure the individual modules to meet their particular business requirements. ReO is a new approach to production modeling replacing conventional Nodal packages extensively used in the industry extending the capability of production modeling and optimization to more complex fields while including real world constraints.

Objectives

The purpose of ReO is to provide a software tool that can be used in all phases of field life, from planning through development and operations, and to enable petroleum, production, facility and other engineers to share the same integrated model of the field and perform critical analysis and design activities:

• Conceptual design in new developments
• Equipment sizing, evaluation and selection
• Daily production optimization, on- or off-line
• Problem and bottleneck detection/diagnosis
• Production forecasting
• Reservoir management
• Data management

After an initial research and development phase in the mid-nineties, ReO was launched in 1999. Version 5 was released in September 2002 and version 6 is due early in 2003. The current options list for ReO is as follows:

• ReO Base Module - this module is the basic network solver and optimizer required as a building block for the entire system.
• ReO Gas Lift - this module extends the capability of the base module system to include gas lift modeling and optimization capability.
• ReO Advanced Compressor - this module allow the user to build detailed multi-stage compressor models including accurate modeling of turbines, gearboxes, gas recycling and fuel gas usage.
• ReO Compositional - ReO can operate in Black Oil or in Compositional mode, this module extends the fluid modeling to include multiple component equation of state modeling of fluids.
• ReO Forecast - this module links the ReO application with eP's Matbal reservoir modeling software, and uses eP's WellFlo generated well performance surfaces to produce accurate production profiles against different production scenarios.

ReO Key Features

ReO is based on leading technology in the areas of fluid modeling, simulation, optimization and software design to provide the most accurate engineering and economic solutions. It's flexibility enables engineers to model the behavior of a single well or pipeline, through to the most complex of fields with 1000's of wells. This flexibility is combined with simplicity and ease of use.

Solver Technology

ReO’s optimizer technology is based on sequential linear programming techniques.

This is a fundamentally different approach from other commercially available production optimization software solutions, as most other commercial techniques are merely simulation, not optimization.

Because the network is solved simultaneously rather than sequentially, as is the case for nodal analysis techniques, the system can optimize and simulate accounting for targets, objectives and constraints anywhere in the network.

A key feature of ReO is that it is both a production simulation and optimization tool. Simulation determines the pressures, temperatures and fluid flow rates within the production system whilst optimization determines the most economic production strategy subject to engineering or economic constraints. The economic modeling capability inherent within ReO takes account of the revenues from hydrocarbon sales in conjunction with the production costs, to optimize the net revenue from the field.

Other benefits include:

• Scalability – ReO can solve networks of thousands of wells in a robust and efficient manner.
• Topological complexity – any degree of complexity can be handled by the software including loops and branches as well as distribution and injection networks.
• Total system modeling– injection and production networks can be solved and optimized simultaneously.

The basic logic of the solver is outlined in the following flowchart.

Flow Chart of ReO Solver Approach

Real production networks have operational constraints and targets to be met throughout the network, often conflicting with each other. ReO allows these constraints and targets to be defined at any point in the system ensuring that the optimized solution calculated will honor these constraints as far as is physically possible. A typical bounded constraint is illustrated below.

Schematic Illustrating a Bounded Constraint

This type of constraint might be set to ensure that the operating pressure of a separator is kept within a required tolerance. In this case the 'bounds' are considered 'hard' limits.

In the case below a target has been set which has a penalty associated with a solution which deviates from a target.

Schematic of Target and Penalty Functions Used in ReO Within a Valid Region

This type of 'target' is required to find the best compromise among conflicting objectives in a system. An example might be ensuring maximum production by driving down well head pressure in a gas field while maintaining optimum intake pressures to a compressor train.

While the previous examples allow for different constraint and target handling the objective is often to maximize or minimize a particular variable and this can be achieved with a maximize/minimize objective which the user can specify in ReO as shown:

Schematic of Objective Function Control Available in ReO

This combination of objective function maximization and minimization combined with the ability to solve any complexity of network is the key to ReO's unique technology.

ReO Use for Complex On-line Optimization

The screen shot below illustrates the level of complexity ReO's solver can handle.

Example of Complex Networks ReO can Handle

In this example production gathering networks for oil and gas have been combined with the high pressure gas distribution network for gas lift. The system also includes separators, pumps and compressors creating an integrated model of the entire producing system. The operator of this field integrated the ReO optimization software with the real time control system to optimize the system performance on a daily basis accounting for the value of exported gas as well as the cost of gas used as fuel. Each of the squares in the above diagram represents a sub sheet which contains groups of wells or multiple stage compressors. The arrows indicating flow direction are determined by ReO.

The complexity of such a system can only be addressed with solver technology as sophisticated as that used in ReO.

Compositional Fluid Modeling

One of the most important aspects of modeling production systems is the correct calculation of fluid PVT properties. Variable detail and quality often characterizes the PVT data available to the engineer and ReO is designed to accommodate this. If complete compositional analysis has been performed, this can be used directly. If only Black Oil data is available, ReO will use a splitting technique to define a set of components to use in the compositional description. This approach means that different fluids, with different levels of detailed description can be combined into the same base set of components.

Where wells are producing fluids of different composition, the mixing of these fluids is accurately modeled in the system. The composition is reported at all the nodes in the network. This is highly valuable in field with differing wells compositions.

The inclusion of fully compositional fluid modeling in ReO provides a sound basis on which further production chemistry studies can be addressed (future ReO applications are planned).

The compositional library in ReO holds the characteristics of 45 hydrocarbon and inorganic compounds. This library can be used “as is” or edited to match measured data where available. A user selected subset of this library defines the components that will make up the base description of the fluids in the system. The number of components chosen depends on the quality of data and the detail available. Computation time is also dependant on the number of components selected.

These descriptions are used in the Peng Robinson, Soave Redlich Kwong, Patel Teja or Valderrama–Patel–Teja equations of state to calculate accurate phase splits and fluid properties throughout the network (tuned equations of state derived from other sources can be used where available).

Object Oriented Design

ReO has been developed using object-oriented technologies. The integral object oriented database (the market-leading ObjectStore TM) stores all production system and network configuration data along with computed results. It enables different scenarios to be created, evaluated and easily compared. The interface and engineering models are also object-oriented. This approach provides robustness and ensures engineering longevity, as extending the system to handle more engineering models is straightforward.

Detailed Well Modeling

ReO uses the eP’s WellFlo program to provide detailed and accurate well models. WellFlo is widely used and the investments made in using it can be leveraged by ReO without further effort.

This well modeling tool can handle all types of production and injection wells enabling the detailed well modeling and tuning which forms the basis of complete production system models.

Graphical User Interface

A key feature of ReO is that all modeling and analysis is carried out through a Graphical User Interface with familiar mouse driven “drag and drop” capability. Production system models may be quickly constructed and spread-sheeting facilities enable rapid data entry and visualization of information. A nested sub-sheet facility enables engineers to “drill down” to greater levels of detail to avoid excessive on-screen complexity for large production systems.

Screen Shot Illustrating ReO Multi-Window Interface

Input data and results may be displayed either on the system layout, as a table view, or in detail object-by-object. Units can be selected and changed as required.

Maps or other diagrams may be imported to allow production models to be correctly and swiftly laid out.

Engineering Models

ReO imposes no restrictions on the complexity of the production system other than physical and logical consistency. Thus complex networks including loops, branches, crossovers, parallel flow lines, recycle loops may be modeled without constraints or excessive compute time overheads.

A range of equipment or facility models is available, from industry standard correlations requiring minimal data entry to the most comprehensive models based on specifications provided by manufacturers. This flexibility enables engineers to select the right level of detail for the task in hand. Equipment models can be extracted from the production network, analyzed and tuned in a stand-alone mode and then replaced back into the network model.

Pressures, flow rates and temperatures are calculated and reported at each point in the production system using thermodynamic coupled pressure-temperature modeling

The interface shown above illustrates a three stage separator connected to a simple network. The Optimizer Statistics graph identifies the statistics of the convergence process. The multi-window graphical interface makes it possible to present the model and results at different levels of detail allowing results to be reviewed in summary or at component level.

ReO network modeling

ReO can solve any complexity of production network and has been applied to oil, gas and condensate systems.

For gas networks, ReO includes an option to perform detailed modeling of compression facilities. A typical ReO screen shot of a simple gas field is shown below. The details of the platform compression units are shown in a sub sheet within a separate window. Floating toolbars, used to create the network, are also shown.

Screen Shot Illustrating Gas Network with Compressor Sub sheet

The facility models available in ReO for gas networks include pipeline, chokes (both variable and fixed diameter), block valves, standard compressors (polytropic model), heat exchangers (inter-coolers), gas and gas condensate wells, sinks (separators, gas export and delivery points, flares or vents), manifolds, links (no pressure loss pipelines), flanges (no flow constraint).

Production constraints may be defined at any point within the production system in terms of pressure and/or flow rate along with objective functions for maximizing and minimizing flow rate or pressure in terms of sales revenues and costs.

ReO is seamlessly integrated with the eP WellFlo application. WellFlo may be run from within ReO and new well models may be defined or existing well models used to simulate inflow and tubing performance.

The most complex application of ReO to date has been in Latin America where a network system including several hundred wells is optimized on a daily basis through a SCADA system (the screen shot illustrates the network). This system includes a low pressure gas gathering network integrated with a number of compressor trains and a high pressure gas injection and distribution network.

This initial installation has formed the basis of further development leading to the eP intelligent Daily Operations solution (i-DO).

This system achieved gains of 4% in oil production through the optimization of the gas used in the gas lifted wells in the field. Large savings were also made in the amounts of gas utilized in the gas lift system providing additional gas for export from the field. The operator is now deploying this technology throughout their other assets.

Detailed Compressor Model

The standard compression models mentioned the above sections are typically used for the phasing and sizing of compression. The detailed compression model described in this section provides a significantly more comprehensive representation of the power generation and compression process.

The schematic below shows the features included in the detailed compression option. A series of components are available to enable engineers to construct a model of the compression facility, including the gas turbine, compressor stages (one or many), gearboxes and inter or after-coolers. Fuel gas may be removed from the gas flow at any point in the network to generate power. The model parameters are those usually supplied by the major turbine and compressor manufacturers. Gearboxes, which are optional, provide the power and speed coupling between the power turbine and/or individual compressor stages. Surge and stonewall conditions can be defined and recycle loops controlled by variable chokes will automatically ensure that gas is recycled if throughput falls below the surge limit. Turbine and compressor stage models may be tuned to match actual in-service operational performance where measurements are available.

Schematic of Multistage Compressor Option Modeled in ReO

These detailed compression models may be run in stand-alone mode or be connected into the full gas production and/or injection system network.

ReO modeling of multiphase networks

ReO Multiphase Network extends the functionality of ReO Gas Network to include modeling of multiphase fluids. It was released in January 2000. ReO Multiphase network includes options for modeling artificially lifted wells, including gas-lift optimization, pumps and multi-stage separation facilities.

Well performance is modeled using the eP WellFlo application, which includes both gas-lift and electrical subsurface pump options.

Several correlations for modeling the fluid flow in the pipelines and other facilities are available including common industry standards.

Multi-stage separation includes a general separator model, which accurately calculates 3-phase oil-water-gas separation at the prevailing separator pressure and temperature conditions. Separator stages may be linked in series with intermediate pressure control valves enabling different configurations. The separation process will allow entrainment within fluid streams to be modeled

Gas from the separators may be linked to ReO Gas Network where compression, lift gas for well production or gas export pipelines can modeled Now that these products have been merged into a single application, ReO enables the entire coupled production system to be simulated and optimized.

Constraints may be placed on gas, oil and water flow rates as well as pressure and total liquid flow. The economic model will permit independent pricing of both gas and oil.

ReO Application to Production Systems Design

In one of the studies performed with ReO the operator needed to investigate the possibility of installing separation capacity and a separate gas pipeline upstream of a multiphase pipeline. This was proposed to reduce back pressure and, hence, to increase production from the wells.

Case Study from ReO to an Oilfield

The diagram above shows the proposed new gas pipeline in dark blue, which will carry the gas is now separated at the red platform before the pipeline to the main platform.

In this case the model built allowed for two scenarios – the first matched the existing production performance and the second included the additional separation facilities and pipeline.

ReO enabled the gains from the revised configuration to be quantified making it a straightforward process to calculate the economic return on the investment.

ReO Forecast

At each time step well and facility status and configuration can be changed and the constraints and economic parameters varied. Economic or production driven logic can be defined to re-complete or shut in wells.

ReO Forecast Enables Production Profiles to be Generated

Conclusion

ReO is now a well established technology with a growing number of users around the world. The unique capabilities of the technology have been demonstrated in applications from field design to real time optimization.

ReO is a powerful new tool to maximize the return companies can achieve from their assets through reduced operating and lifting costs, and increased production and ROI - precisely the requirement of today's industry.

Economic Capability of ReO

A major feature of ReO is the integrated simulation, optimization and economic analysis capability. This extends far beyond the traditional gas-lift optimization functions of most other products.

This critical feature enables full field economic optimization, driven by gas and oil prices and costs of equipment operation such as compressor fuel gas, water disposal or electrical power generation. Integrating simulation and optimization ensures that the economic benefits predicted by the model are actually achievable, by accounting for the production constraints and pressure losses throughout the system.

Simple Economic Example

ReO can provide you with a very powerful tool for simulating and optimizing a network, taking into account the financial production costs.

To illustrate this we shall look at a simple two well network, with identical wells and pipelines, but differing gas prices.

Simple Two Well Network

The diagram above shows a network in ReO with the additional blue text defining our constraints:

• That the delivery pressure will not be lower than 1000 psia.
• That the network can deliver up to 50 MMSCF/D in total from the two wells.

And the expected outcomes:

• That of the infinite number of possible solutions, the optimum solution will have as much gas as possible coming from Well B (because the gas is more valuable).
• That the remaining gas to achieve a total of 50 MMSCF/D will come from Well A, which will need to be choked back.

With the constraints defined the optimization can be run.

Network Results in ReO

In the above diagram you can see the black text which shows a summary of the optimizer calculations, with the blue text illustrating the simulation criteria that ReO has internally taken into account to deliver its conclusions. The red text shows the optimization ReO has made to the network.

How ReO Works

ReO is not a Nodal Analysis program. Nodal Analysis field modeling programs, such as FieldFlo, calculate network pressures, flow rates and temperatures by dividing the network into a number of nodes.

Each node has an inflow (all of the components upstream of the node) and an outflow (all downstream components) and the network properties are solved in a sequential node-by-node manner as the solution is propagated throughout the network. This method works satisfactorily with hierarchical networks, but cannot be reliably extended for non-hierarchical networks where there are flow loops, flow splits, liquid or gas off-takes etc.

In ReO, the fluid flow within a network is described by a complex set of non-linear equations which describe how the fluids flow through the network but these non-linear equations are not solved directly. ReO simultaneously solves and optimizes the network problem using linear programming methods (Linearizing). Simulation involves finding the solution to a fully determined problem; the user constructs a problem in such a way that there is only one possible solution. Optimization involves finding a solution to an underdetermined problem; the user sets a goal with objectives and constraints (i.e. maximize revenue with a minimum delivery pressure of 1000 psi) and the software determines which, of the many possible solutions lying within the feasible solution space, is the optimum.

Linearizing

ReO does this by successively linearizing the model equations and applying linear programming to find optimal values of the variables, a process called Sequential Linear Programming.

The linear programming (LP) problem is multi-dimensional and is solved using a Simplex Algorithm, which is a clever way of examining the values of the Objective function to find the optimal solution. It is clever because, typically, it only needs to examine relatively few solutions out of (potentially) millions to find the optimum.

This approach allows constraints to be defined at any point in the network. Equipment variables, such as  compressor power or choke diameter, can be placed under optimizer control to satisfy the constraint requirements. In addition, the Linear Programming method requires an objective function to be defined, which may be either maximized or minimized. Typically, an objective function may be defined to maximize gas production or minimize fuel gas.

The power of this approach, compared with Nodal Analysis, lies in the fact that there is no limit to the complexity of the system being modeled, constraints and objectives can be easily applied at any point in the network and it allows integration of economic and physical models.

Database

A further key feature is the integral database which stores all production system and network configuration data along with computed results. It enables different scenarios to be created, evaluated and easily compared.

It is the intention that in the future the database will allow measured production data to be stored for reporting and the comparison between actual and model results.

Fully compositional fluid modeling is inherent within the structure of ReO and fluid properties are derived using Equation of State methods. Both traditional Black Oil and detailed compositional fluid descriptions may be defined, modeled and simultaneously tuned through the ReO's fluid pre-processor.

A range of equipment or facility models is available, from industry standard correlations requiring minimal data entry to the most comprehensive models based on specifications provided by manufactures. This flexibility enables engineers to select the right level of detail for the task in hand.

Pressures, flow rates, temperatures and fluid composition are calculated and reported at each point in the production system using fully thermodynamic coupled pressure-temperature.

New Levels of Performance Achieved by ReO on SGI Workstation

eP's production optimization software ReO is a sophisticated and compute-intensive application optimizing the production of oil and gas in networks of any level of complexity.

The company and its customers use the software on a wide range of projects and are always interested in achieving the highest execution speeds on complex models in the interests of efficiency.

The new SGI model 320 workstation was provided to the company recently for evaluation and has resulted in the benchmark performance listed below with two other PC configurations provided for comparison.

These models are relatively small consisting of 20 or so wells and 30 pipelines but also include compressor models and after coolers. When the software is applied to more complex models with a more detailed compositional description or a much higher number of wells these performance difference can bring a significant benefit in efficiency.

ReO Version 5

ReO 5 is the latest version of eP's leading technology for upstream optimization.

ReO 5 New Features

Black Oil ReO
ReO 5 offers the choice of using Compositional or Black Oil PVT models. In a Black Oil PVT model, the fluid is defined in terms of stock tank oil and gas gravities and phase ratios. The fluid properties (e.g. viscosity) as a function of pressure and temperature may be described using a number of industry standard correlations. In Black Oil PVT mode the total volumetric flow rate of each phase in the system is the sum of all the contributions from sources in the system.

In a Compositional fluid description, the fluid is described in terms of components (e.g. C1, C2, H2S), their properties (e.g. molecular weight) and the composition of the fluid. The stock tank oil and gas volumes depend on the separator configuration and therefore, the stock tank volumetric flow rate of each phase in the system will not necessarily be the sum of the individual contributions from all sources.

Phase Optimization
If a Black Oil fluid model has been selected then a new "Phase Optimization" option is available. Black Oil models allow optimization based on the individual fluid Phases (Gas, Oil, Water), or on Total Flow. Compositional models are always optimized on a Total Flow basis.

The two wells shown have identical performance curves in terms of liquid rate, but have different water-cuts. The water-cut of their co-mingled output could range anywhere from 0% (Well A only) to 50% (Well B only) for the same total flow rate, but the Total Flow Optimizer sees only one value at each step of the solution process, which it assumes stays constant for the rest of the step.

If a price has been defined for the oil at the wellheads, then Well A flow will have a higher value than Well B and will be preferentially produced. However if no prices have been defined and the Total Flow Optimizer is asked to maximize the oil rate, then it will not know which well gives it the best oil return and may return a sub-optimal solution, or no solution at all.

When Phase optimization is chosen, the Phase Optimizer receives information about each individual phase rate at all points in the network. The Phase Optimizer will know that Well A produces more oil and will try to meet any Maximize objective with this in mind. It also allows the User to place additional objectives/constraints on other phases, for example a limit on the total amount of water produced, which the Total flow Optimizer might not solve even with Source prices and costs on the wells.

Source Objects
Source objects are a new feature in ReO 5 and are designed to produce a specified fluid within a pre-defined range of rates and pressures. Typical examples of the use of source objects in networks would include:

• Modeling Rod-pumped wells
• Introduction of 3rd party fluid to a process facility

No performance data is required to be entered - the user simply specifies the fluid type, fluid control objectives, production pressure and temperature ranges.

Global Text Views
Sheet Text Views, currently available in ReO 4, only allow data to be entered and viewed for certain equipment items that are visible and directly editable at the current sheet level, but not for equipment at a higher or lower sheet level.

Global Text Views allow the data for certain equipment items at any level in the model to be viewed and edited. The user can view the data for all equipment of a certain type (wells for example) within the model regardless of the location of the equipment within the model.

Text Views for Gas lifted and Non-Gas lifted Wells
Included in both the Sheet Text Views and the Global Text Views, users can now view data for all gas lifted and non-gas lifted wells using one dialog box which provides the same functionality as the individual well edit dialog boxes.