Production Data Management Collaboration Effort in an Integrated Journey for More than 1,000 Wells in the Northern Kuwait Heavy Oil Fields
Authors: Gonzalez Santiago , Al Muhanna Tamadhor , Abdalla Waeil , Pandey Dharmesh , Al Naqi Ahmad , Al Mezal Loloh , Al Saqer Aisha , Rajab Sayed , Safar Ahmed , Gonzalez Gregorio , Malik Satinder , Fadul Ibrahim , Hamlaoui Aissa 
Published: December 2018

Abstract
Based on the North Kuwait Heavy Oil fields’ development plan, by the end of 2019 more than 1,000 wells will be connected to the producing facilities. An extensive amount of surface and subsurface data will be collected and transmitted to the central databases.
This paper describes the Data Management processes and workflows currently in place not only to use the captured and analyzed data for production and facilities optimized and safe operation but also the strategic plan for future integrated wells and facilities management. Different approaches for both at wells and surface facilities data sets are being implemented not only to monitor and optimize the wells and field performance but also to provide other disciplines with the right data in the right format at the right time.
The vision is to move away from the current approach to a new one to handle automated real-time data capture, data analysis, data visualization and Exception Based surveillance within the domain of CWE (Collaborative Work Environment). A single data repository has been used to ensure seamless communication from the field facilities and wells directly to the end-user workstations. Data algorithms are run in daily basis to detect anomalies in millions of data point of parameters allowing either proactive interventions or understanding the reasons for deviation from normal expected operating parameters.
By implementing a daily surveillance routine and simple exception-based monitoring rules along with advanced data algorithms on wells (including artificial lift system) and facilities parameters, it was possible to detect wells without production, production recirculation due to holes in the tubing, flowlines plugging and downhole sand issues. The importance of Data Management falls into predictive analysis techniques focused on increasing the uptime of wells and facilities, supported by typical data science algorithms such as clustering, advance filtering, detection of data anomalies, regression and classification.
This paper will also discuss how a holistic approach has evolved in managing the current operations, capture the lessons learned for not only optimizing the current field operation but also use the knowledge gained for future development strategy; the result: an approach to a collaborative environment to help the team to analyze performances, make decisions and create strategies to increase production, reduce lead time and reduce costs.
The Production Data Management strategies implemented in the early stages of the project have already generated significant value in picking up and prioritizing wells with issues, detection of flowline plugging, and Artificial lift system issues resulting not only in maintaining the production plateau but also reducing operating expenses while improving the restoration time.