While the importance of pre-job design has always been appreciated, detailed post-job evaluation of gravel pack execution data (both surface and downhole) is equally valuable as it provides a means to learn from past treatments, calibrate models, and improve future designs. Traditionally, post-job analysis has been limited to high-level pack evaluation and failure investigation but has a much wider range of applications in the confirmation of success, better understanding of downhole mechanisms and validation of simulation models. Downhole data analysis techniques can be used to isolate sections of the flow path and develop a more detailed understanding of each stage of the treatment from running in the hole (RIH) to pulling out of the hole (POOH).
Detailed post-job evaluation is often skipped due to the significant effort involved in data handling as well as the lack of a defined workflow and an integrated software tool. This paper provides an overview of the evaluation and calibration of surface and downhole data along with the steps, workflow and tools required to process the data in the easiest and most efficient manner, enabling faster, more detailed and more accurate analysis of operations.
Various case studies are used to demonstrate how post-job evaluation using downhole gauges can be used to efficiently analyse the various stages of the operation including wellbore displacements, reverse and circulating step rate tests and gravel packing operations. A variety of important phenomena are identified and quantified, such as friction pressures, packing mechanisms, fluid displacements, screen plugging and roping, which may otherwise be missed. The paper further illustrates how the defined workflow can maximize the likelihood of success by using post-job evaluation results to better identify and minimize risks during pre-job design stages while reducing the need for excessive safety factors within the operational window.
The analysis workflows introduced here will maximize the value of downhole gauge data and serve as a reference to practicing completion engineers in the efficient processing, analysis and interpretation of post-job data. It can be used to revisit and better understand historical sand control treatments, and continuously improve future treatments.