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Quality checking your reservoir models - EnginSoft

Friday, February 5, 2016

Having a centralised platform for data management can be very helpful for reservoir engineers to do quality checks of reservoir simulation models and perform analytical analysis, says Oludare Elebiju of EnginSoft.

'If you have a single platform where you analyse post simulation [reservoir] models, and integrate all your data (reservoir, wells, pressures and rates), you could probably save a lot of time,' said Oludare Elebiju, consultant reservoir engineer with EnginSoft.

'You would be in a better position to make timely decision,' he said, speaking at the Digital Energy Journal Sept 29 Aberdeen conference, 'Using Analytics to Improve Production.'

With a central data repository, it is easy to create workflows that allow for various tasks to be performed without compromising the quality of the actual data, he said.

This approach operates within a single environment integrating numerical and analytical analysis and comparing the results, for example numerical vs. analytical production forecasting.

A centralised system also helps to reduce the amount of time spent on quality control of the same data.

This will enable several tasks to be performed by different engineers if the data quality has not been compromised, he said.

This will also reduce the amount of time reservoir engineers spend on data management pre and post simulation.

Typically, if a reservoir engineer has two weeks to do a job, which requires obtaining data from different sources and applications, it might take a great deal of time in evaluating the quality of the data (QA/QC).

If about 50-60 per cent of the total time is spent on data quality check and control, then the engineer might not have sufficient time to carry out the 'real' job, he said.

The reservoir engineer is expected to carry out designated tasks and make recommendations based on results achieved. The quality of the result can be directly matched to the quality of data the engineer has to work with.

If the quality of data is poor and sufficient time is not invested in QA/QC before embarking on specific tasks (reservoir modelling/simulation, history matching and forecasting), then results and standards are easily flawed.

So a tool that provides an efficient platform to perform effective reservoir data and model QC/QA will contribute a significant value in optimising processes, reducing data QA/QC time and maximising simulation, interpretation and reporting time, he said.

EnginSoft provides Computer-Aided Engineering (CAE) and intelligent Digital Prototyping (iDP) tools, as well as process simulation and design chain optimisation.

EnginSoft is distributors of 'Kraken', a reservoir data manipulation and integration platform, which can be used as a central repository for all reservoir data, and used to support structured workflows.

The software is developed by ESSS, a South American engineering simulation solutions company, and was originally developed for a large well-known Brazilian oil and gas company.

Data accuracy

Managing reservoir data centrally will also make it easier to ensure accuracy. It is common for oil companies to manage production data using spreadsheets only, and then pass the spreadsheets onto the reservoir engineers, he said

'For example, you might be working on an asset where you receive production data from the field (weekly/monthly) after allocations are done. It is possible to spot inconsistent trends in the data (well rates, pressures, well potential, etc.) which are mostly due to allocation errors. Such errors can be minor or significant and may require time to correct.

Decision making

Kraken can make it easier for reservoir engineers to understand reservoir performance. 'You want to know, how I am doing in terms of production, injection, over time.'

For example, 'you want to see your production by well, block, field. You want to see the water oil ratios by well, by block, by field.'

Any decisions, for example about maintenance, water flooding and EOR, can be made using the full range of available reservoir data.

Kraken can also support structured workflows and better multidisciplinary collaboration.

Optimising

Kraken can help engineers create customised process templates that can easily be documented and reused from time to time for implementing different process workflows. This helps to optimise the processes to ensure that the same project is done much more quickly and efficiently, enabling better and timely decision making.

Companies have different approaches to reservoir data analytics, architecture and integration to specific reservoir/petroleum engineering workflows. It is important to critically evaluate the integration process to ensure that resource time is optimally utilised during project execution so that cost is reduced and value is maximised, Mr Elebiju said.



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