Abstract

The objective of this project was to achieve standardization across PDO for ESP surveillance and optimization by fully utilizing Well Management System (WMS). The main aim was to develop and sustain automated ESP well modeling, implement Pattern recognition "EBS" (Exception Based Surveillance) by adopting a LEAN approach where it define standard operating processes, minimize waste of resource and time and ensure process sustainability.

To observe existing practices, the project team performed a Gemba walk across PDO ESP wells on surveillance and optimization practices. This was followed by a KAIZEN workshop where, amongst others, the Value Stream Maps (VSM) for Current State and Future State were established which resulted from identifying and eliminating the "wastage" of resource and time on ESP life cycle (Refer  Appendix: Figure 1) framework. The Standard Operating Procedures (SOPs) were established for all the key activities based on the VSM process to ensure standardization and sustainability.

To ensure the WMS Sustainability, ESP well models is generated by automating data feeds required for building the well models from different corporate databases based on most recent well information and real time data. In addition, the sustainability is warranted through automated notifications on specific data quality and/or data un-availability issues to respective sources.

The PDO's customized WMS, which generates automatically online well models across the fields, has helped, amongst others, in identifying some 4% potential oil gains from activities such as optimizing running frequency, system bottlenecks (pressure/rates), operational issues and altering equipment design capacities. These projected optimization opportunities are being realized through various activities to achieve relevant oil gains.

The implemented Pattern Recognition EBS has indicated the time saving of around 20% for ESP problem detection, operational decision-making and remedial planning. This has resulted in minimizing ESP well downtime by around 30% through quick realization of sub-optimal wells and accelerated response actions to recover production of these wells. The Lean project, which has implemented a new established future state VSM process, has overall managed to achieve 40% faster compared to current state VSM or previous practices. The most significant areas of improvement areas are:

  • Standardised usage of customized WMS by Production Engineers and Operation Engineers for surveillance and optimization

  • Self-sustainable check on data quality and data availability in the WMS which has facilitated the automated well model

  • New engineered way to validate well test by cross checking with real time automated well model.

  • More accurate detection of abnormal/sub-optimum/deviation of ESP well performance using well model based EBS from Pattern Recognition EBS.

  • Facilitate ESP design requests using customised WMS by generating ESP well models integrated with pump curves and cross checking of ESP vendor's design using up to date on-line models and well data for an improved design.

This paper highlight the way forward, as further improvement on overall ESP surveillance and optimization, an integration of the customized automated well model based WMS and surface facility network models for a total field optimum production.

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