No abstract

In assets like Shushufindi, water handling has become a challenge to achieve a proper field management. Apart from increasing water cuts, longevity and ullage of the processing facilities have turned into a challenge to sustain production and reduce lifting costs. A digital solution was implemented to allow predictive analysis for horizontal pump failures and line plugging as well as forecasting of injection rates on real-time to improve the efficiency of operations to maximize productivity. Numerous failures occurred in the water handling system due to the lack of real-time monitoring or fast detection. This caused around sixty ESP's to be shut-in every year, triggering production losses. Hardware for data collection in selected points and customized digital workflows using data analytics and machine learning processes were developed and implemented so that with the help of edge computing we were able to predict failures and estimate injection rates on real time. Using the connectivity provided by a satellite system, SCADA's optical fiber and an operations monitoring platform, the variables are now monitored on real time to make early identification of events, give a rapid response and to optimize the production of the field. The Northern Flow Station, located in the most prolific area of the field and where the water flooding scheme has the highest relevance was selected to implement the digital pilot. The implementation of this digital initiative has shown outstanding results. Monitoring for the first time the data from the water handling system on real time and applying the engineering workflows (data analytics and machine learning) led us to reduce up to 76% of the time used in manual processing, 75% of the time for commuting and to reduce 1-ton CO2eq emissions per year. The time saved is now used to improve other engineering workflows equally important to increase the productivity of the field. Due to the early identification of events, the prediction of potential failures and a timely response previous functional failure, the Operational team can reduce the deferred production associated to the Electrical Submersible Pumps shut-ins, which for the previous years represented 100,000 barrels of oil (~$2.4MM revenue for Shushufindi Asset). In addition, such actions have contributed to extend the ESPs’ run-life, optimized maintenance costs and reduce lifting costs by 0.2%. This paper shows the selection criteria of the surface facilities and measurement of critical points for data gathering, the application of data analytics with edge computing and the development of an innovative digital solution in conjunction with the client and different disciplines. This case shows the benefits of digital mindset in any oilfield operations to optimize production and cost, potentiating the digital transformation path in the energy industry.

In an aging field like Shushufindi, where an intensive activity of new wells and interventions take place, commingle production is establishing and a water flooding project is reaching maturity, it is of paramount importance to have high frequency and high accuracy production data to be able to fine-tune models, to evaluate, to estimate optimization impacts and to reduce losses. A digital solution was implemented to allow early identification of events, estimate production losses, and thus classify and prioritize events for optimizations using data analytics. Production monitoring and optimization have been arduous to accomplish due to problems at surface facilities like cross flow at manifold valves, shared production lines, unstable production from commingled wells and low well test repeatability. The last one due to reduced number of operative equipment such as separators and multiphase flow meters. With the application of big data and data analytics, a Virtual Flow Meter has been created, calibrated and run-on real time for several pilot wells. Such VFM uses the ESP's data and the fluid characteristics, integrated with a dashboard from a business intelligence tool that was developed to rank critical wells to intervene for optimizations. The pilot application allows to quantify the volume lost during operative events in the wells and the volume gained with optimizations, integrating a powerful production monitoring and ESP parameters surveillance tool to guarantee the continuity of operations, maximize wells optimization and to increase operation efficiency and productivity. An average of 475 bopd are associated to deferred production. Using a data base platform and the connectivity provided by SCADA's optical fiber, all the variables can be monitored on real time to develop data analytics and make an early identification of events, give a rapid response and to reduce the production losses. This digital implementation has shown remarkable results allowing to reduce 80% of manual process time, optimizing field operator's mobilizations (less transport time, CO2eq emissions/year and lower mobilizations risk involved), response time from days to minutes and ensuring the operative continuity of the production, optimizing cost, maximizing people efficiency, and evolving the monitoring process. This paper shows the pilot wells selection, the Virtual Flow Meter creation using data analytics, calibration and connections to run the digital application on real time with a dashboard from a business intelligence tool. This solution is a clear example of what The Digital Transformation capability brings to any oilfield, showing to the industry that is not only an example to optimize production but also to settle down that EDGE computing, data analytics and data science can be applied at all maturity levels in the oil and gas industry becoming a mind changer for the next generations.