Objective/Scope Achieve operational excellence in field of Electrical Submersible Pump (ESP) surveillance and optimization by benefitting Petroleum Development Oman (PDO) asset teams in identifying optimization opportunities, use of real time data for well test validation and improving overall asset KPIs (Key Performance Indicator) through a fully automated Well Management System (WMS). Methods, Procedures and Process The ESP well models are built and sustained in WMS by automated processes to update recent well model information from PDO corporate databases. WMS provides real time well and pump information, along with well model based calculated outputs, pump operational status, workbenches to validate well tests, design inputs, modeling sensitivities for users to perform daily tasks using well-defined standard operating procedures. Operating envelopes based Exception Based Surveillance (EBS) were improved by replacing them with pattern recognition based EBS to improve accuracy, avoid wastage of time and resources. Continuous hands on coaching were conducted to users to strengthen their skills. Benefits, Results, Observations, Conclusions Since implementation of WMS, it has maintained more than 1600 ESP well by building well models automatically for new or workover ESP wells successfully saving huge effort and time from engineers in gathering data and building model. Production Engineers have created more than 100 ESP design input datasheet using the WMS resulting in improved ESP design by avoiding human errors. WMS have already supported assets in identifying around 138 ESP wells with optimization opportunity resulting in increasing around 3 % net oil gain only in year 2017. Real time pump operational status calculation from WMS and sensitivity (Tubing Head Pressure and Hz) based operating point analysis reduced upthrust / downthrust ESP wells by ~50% across PDO in few months. Early detection through Pattern Recognition based EBS has resulted in minimizing ESP downtime by around 30% through quick realization of sub-optimum wells. Validating well tests utilizing downhole gauge data has helped in identifying dubious well tests because of faulty well test equipments thus saving time and money for sequencing the well for re-test and improving allocation. A proper tool and SOPs utilization tracking mechanism is already in place highlighting areas for continuous improvement. Novel Ideas The utilization of real time data and well model together has proven to be a unique and novel way in improving ESP surveillance and optimization for managing around 1600 wells. The replication of same methodology is already undertaken for Progressive Cavity Pump (PCP) and Beam Pump (BP) surveillance and optimization utilizing WMS for PDO assets.

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 optimizationSelf-sustainable check on data quality and data availability in the WMS which has facilitated the automated well modelNew 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.