Well rate surveillance is essential for reservoir characterization and selecting potential activities to enhance and optimize production. However, such variables usually lack consistent or direct field measurements, which is related to technology availability, equipment reliability, and cost control. As a result, many technologies have been developed to estimate well rates from indirect measurements (e.g., virtual metering or soft sensors).

The well rate estimation requires consistent pressure-volume-temperature (PVT) data, fit-for-purpose production well tests, and reliable sensors. In most cases, field data are used to ‘tune’ data-driven models. Missing, biased, or failing sensors may break the rate estimation, and a new calibration would be required. In addition, sensor input uncertainty and rate estimation confidence were commonly overlooked in previous approaches.

This paper discusses the implementation of a data validation and reconciliation pilot study in the Ceiba Field to estimate well rates. In this case, data, uncertainty, and models are combined to minimize a global error function. Rigorous statistics are used to calculate new sensor estimates. Unlike previous well rate estimation approaches, field-collected data are validated and corrected using physical models.

The pilot technical scope included calculating oil, water, and gas rates for each well; calculating the tolerance of rate measurements and gauge readings; and identifying sources of unreliable measurements. Although the approach is not new in the petrochemical industry, the application is ‘young’ in the upstream.

Project benefits included less downtime due to well testing and early problem detection, 65% less time expended on validating well tests and allocating individual well rates, and improved cost control due to calculating well-produced volumes hourly. These findings provide a better understanding of reservoir and well performance, which facilitates production optimization management. This paper presents a summary of current project status, the lessons learned during pilot implementation, and the procedure for further progression.

Project success criteria, application key performance indicators (KPIs), and expected benefits are reviewed and analyzed. As far as they can be evaluated at this stage, they were all achieved successfully.

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