Problem Statement

Due to the availability of large quantities of real-time data, many operators are faced with the challenge of extracting meaningful information from it. In many cases, analysis of the data, the updating of models for Wells, Reservoirs and Facilities requires human intervention. When WRFM teams do not have analysed information, there are delays in the decision making to process required for optimisation.


To demonstrate how auto analysis can reduce time consuming and repetitive tasks of comparing real time data with operating envelopes. Exception based applications and the updating of multiple operating envelopes and models without human intervention, enables the analyst to concentrate on anomalies and opportunities to optimise. The constant visualisation of analysed information should assist the decision making process.

  • Highlight the problems of managing large quantities of data

  • Show how alerts can be sorted, analysed and prioritised automatically

  • Explain how operating envelopes and models can be auto-updated

  • Visualise analysed information using digital signage

  • The initial deployment of the EBS system produced more than expected anomalies

  • Human activity is concentrated on higher value-add actions

  • Daily reporting was streamlined

  • Visualisation was increased


The analysing of data by automated methods means that all wells and facilities are under surveillance and updated models are available for production system optimisation. Analysts have more information and time to focus on critical issues rather than mundane tasks.


This approach can be used for wells, reservoirs and facilities surveillance and model updates.

  1. Monitor by exception

  2. Auto updating models

  3. Better visualisation

  4. Increased productivity

Significance of Subject Matter

This demonstrates how very large amounts of data can be analysed and translated into meaningful information for managers to take key decisions.

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