Reservoir Health Indicators Driving Performance Through Data Analytics

Reservoir management leverages on surveillance practices to diagnose reservoir conditions which aid in the identification of treatments that maximize the business value of reservoir deliverability while protecting the long-term sustainability. However, operators struggle to exploit value from data because of big data avalanches, data dispersion and ambiguity in the data definitions across department and companies. Typically, operators are satisfied by meeting average targets within certain tolerance. This is obtained by calculating the ratio of plan vs actual performance. In this work, reservoir management excellence is pursued by an integrated review of leading and lagging indicators, which are represented by continuous and proactive KPI computation and monitoring. The objective of this work is to simplify reservoir performance data analysis on such a way that performance management is decomposed in 5 key areas (business, operation, quality, recovery and predictability) driving continuous improvement, and yet establishing a culture of variance reduction and sustainable consistency in results delivery. The scope of this work entails the definition and case studies of implementing performance indicators that facilitate the analysis of reservoir performance and field development strategic decisions. Such indicators are leading pointers of quality, recovery status and predictability, which ultimately affect business and operations performance at multiple time scales. A solution to continuously compute reservoir health indicators and assure reservoir performance is implemented across various assets, leveraging big data management with automated scheduled extraction, transformation and loading (ETL) capabilities. Raw and calculated data are further provided to end user via commercially available business intelligence (BI) analytics. Each indicator measures the compliance between actual and planned values, and the roll-up is done by computing the volume-weighted average of each underlying element. For this purpose, key performance indicators (KPI) are calculated and creatively concatenated from well to reservoir level, from reservoir to field level and from the field to the operating company level. KPI rollout showed a new way to report and monitor performance on a proactive, sustainable and cost-efficient manner. Some of the realized benefits included reducing more than 90% the time require to identify variances between actual performance and expectation during the execution of projects and improving compliance to the reservoir management guidelines from ~61% to ~84%. This ensures long-term production sustainability while mitigating shortfalls proactively.