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.

This paper describes several engineering workflows as part of the Digital Oil Field Solution implemented in a Gas Asset where there are different technical challenges. The main objective of this technology application is to revert the production decrease and optimize the operations in the asset, enhancing the capabilities of monitoring and surveillance and also to improve the analysis capability, taking the advantage of the benefits of the real time instrumentation, data integration, data quality, in addition to the definition of a complete automation process for all the information in different frequencies covering all the stages such as: data acquisition, transmission, integration, until monitoring and surveillance. With the implementation of operational and engineering workflows better efficiencies can be achieved, improving in time of response, and the decision making process, having tools for early anomaly detection to take proactively corrective actions that minimize the deferred production and losses.