Digital Solution for Water Handling System Surveillance to Enhance Operations and Avoid Production Losses in a Brown Field
Authors: Yánez Jorge , Quevedo Hugo , Bonilla Christian , Calvache Galo , Marchán Luis , Sepulveda Willem , Azancot Annalyn , Vargas Angelica , Gallardo Juan , Carrera Julia , Ramos Geovanny , Chicango Edgar , Dávalos Santiago , Tacuri Jaime , Bedón Ernesto , Pineda Alexander , Estevez Diego 
Published: October 2022

In a brown field in Ecuador, water handling has become a challenge to achieve proper field management. Apart from increasing water cuts, longevity and ullage of the processing facilities have turned into a key condition to sustain production and reduce lifting costs. A digital solution was implemented to enable predictive analysis for horizontal pump failures and line plugging, as well as forecasting of injection rates in real-time to improve the efficiency of operations to maximize productivity.

Numerous failures occurred in the water handling system because of the lack of real-time monitoring or fast detection. This caused approximately 60 Electrical Submersible Pumps (ESPs) to be shut-in every year in this brown field, 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, the system was able to predict failures and estimate injection rates in real time. Using the connectivity provided by a satellite system, supervisory control and data acquisition (SCADA) optical fiber and operations monitoring platform, the variables are now monitored in real time to enable early identification of events, give a rapid response, and optimize production of the field.

The northern flow station of this brown field, located in the most prolific area 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. For the first time, monitoring the data from the water handling system in real time and applying the engineering workflows (data analytics and machine learning) led to a 76% reduction in the time spent in manual processing, 75% reduction in the time spent in of the time for commuting, and 1-ton carbon dioxide equivalent (CO2eq) reduction in emissions per year. The time saved is now used to improve other engineering workflows that are equally important in increasing the productivity of the field. Because of the early identification of events, the prediction of potential failures, and a timely response to previous functional failures, the operational team can reduce the deferred production associated with ESPs shut-ins, which for the previous years represented 100,000 barrels of oil (approximately USD 2.4 million revenue for the asset). In addition, such actions have contributed to extend the ESPs’ run-life, optimized maintenance costs, and reduce lifting costs by 0.2%.

In this paper we show the selection criteria of 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 a digital mindset in any oilfield operation to optimize production and cost, potentiating the digital transformation path in the energy industry.