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INTEGRATED ASSET MODEL SOLUTION

FOR PRODUCTION AND RESERVOIR

MANAGEMENT

Benefits

The key objective of an Integrated Asset Model (IAM) solution is to minimise the time spent

processing data by Engineering and Technical Support releasing them to focus on the

optimising production. Previous

implementations have shown that these

solutions can save “one” man day per week per

engineer as a minimum. In addition to the costs

savings, increases in production will occur as

decisions made will increase in accuracy as all the

data sources are validated so that all

departments work on the same set of data (“one

version of the truth”).

Philosophy behind IAM

The fundamental principle of the IAM solution is the integration and automation of:

• Data Management

• Workflows

• Visualization

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The IAM solution, developed is based on this concept of

an “integrated production model” which characterizes

the reservoir, wells and the surface production facilities.

Integrates production and surface data, cross references

the data with algorithms to automate the processing of

data from multiple sources to enable the data to be

processed effectively to serve many different workflows,

such as “surveillance by exception”, “production

allocation” or “optimization of ESP” as examples.

Scope delivered

The project involved integrating and automating the collection and validation of data from OSI

PI and the well test database. Using well and surface models built in Prosper and Gap,

integrating these sources with customized algorithms to automate the validation of data and

automation of the workflows in OVS was part of the integration platform from the OVS group.

It can handle frequent well tests in the most optimal manner with a strong focus on

predictability (forecast) and high expectations about surveillance. The project was for a green

field development, on shore, with a high number of wells (>100).

Production overview

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A single web style portal (HTML 5) was developed for the users to interact with the system,

allowing an easy critical review of the system results.

This portal was made available to several users, who were able to interact with it at various

levels, according to their individual privileges. Each user interface was customized to the user

requirements to increase adoption, while at the same time using Corporate Standard

Operating Procedures to standardize the processing of information.

For example, this single interface allows the Production Optimization team to get directly the

overview of the production, access to detailed results from virtual metering, optimization, and

to validate the well tests. The validated data is then used to update the production forecast.

The necessary data is collected automatically, different scenarios can be launched including

event (such as well shut in for maintenance) to examine the effect on the forecast, visualizing

and exporting the results to any format including Excel. All these workflow processes are

based on the same data, therefore eliminating the discrepancies that can occur when several

departments perform their calculations separately.

A number of different standard operating procedures (SOPs) and engineering workflows

have been defined to automate the following:

Well testing

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These workflows allow the user to review values from the well tests coming from Energy

Components. The model prediction is compared with the well test values, and the production

engineering team has the possibility to accept or reject the tests, to update suspicious values,

and to automatically calibrate the well models based on the well test data.

In order to provide engineers with all the necessary data needed to evaluate a well test

accurately, the real-time data during the test is presented, including the history of model

parameters, test values, dynamic heat maps of reservoir pressures, comments from the

operators in the field, etc.

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Network Validation

In order to ensure that the integrated model is able to reproduce reality, calibrating the well

models is not enough. The network and facility model should also be accurate. A workflow

and dedicated visualization screen was developed in order to quickly determine if

discrepancies existed by comparing the network model prediction with the available real-time

measurements in the field.

The engineers can then drill down into the data and perform a root-cause analysis of the

deviations, which can highlight an undesired behavior in the field performance (such as

eroded valves, plugged flow lines, biased measurement, etc.)

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Production Forecasting

In the forecast portal the engineer is able to create scenarios and events to turn wells off for

example and then run forecast to examine the result. The System interacts automatically with

the integrated model to generate the production forecast. As production develops as the

asset is developed more real time and well test is collected which is used to update the model

and improve the accuracy of the forecast. This enables Production to collaborate with the well

integrity team to analyse the effect of maintenance programs on production output.

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Optimization

Well rates from the field were used to create “Virtual meters”, which are calibrated following

well tests and network validation. The “Virtual Meters” have been developed to enable real

time surveillance and schedule well tests and future work-overs. They provide a continuous

and instantaneous view on the current well production. This enables collaboration of the

production optimisation team with field operations. A direct benefit from “Virtual Meters” is

a reduction in production deferment (using traditional well test packages) and better well

allocation for the reservoir department.

The results from the virtual metering solution are compared in real-time with the target rate

from the forecast, thereby cross validating the results.

Continuous validation of this production data enables the client to continually optimize

production in near real time.

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Conclusion

A Digital Oil Field solution is a journey of continuous development for the applications, the

systems and the organisation to improve operational excellence and as such the next

development of this system will include the following examples important to that assets

operation:

• Virtual measurement of pressure at the bottom hole, intake pressure and discharge

pressure of the ESP

• Validation of sensor measurements

• Analysis of the injection wells performance

• Optimization of reservoir pressure maintenance

• Identification of candidates for well intervention

• Decline curves analysis workflow

• Calculation of bottom hole conditions when the well is shut-in

To quote Chris Hadfield on this matter, “An Astronaut’s Guide to Life on Earth” “Sweat the

small stuff and work the detail”.