Abstract

Shale reservoirs have become a focal point in the global energy landscape, with unconventional drilling and hydraulic fracturing techniques revolutionizing hydrocarbon production. The development cycle of shale plays often involves drilling a primary (parent) well, followed by the subsequent drilling of infill (child) wells in close proximity. Understanding the performance dynamics between parent and child wells is crucial for optimizing production and resource recovery.

In this study, we focus on predicting child well performance relative to its co-developed baseline. We quantify the detriment of the child well from a scenario where the child and parent wells would have been developed at the same time. Several analysis approaches were undertaken to aid in the quantification of child well detriment including data analytics, numerical simulation, and empirical case studies.

Over 200 attributes were created and tested to describe the child well detriment in the Montney through a Multi-Variate Regression (MVR) model. This enabled the quantification for effects such as distance, parent well density, and cumulative parent well production on the child well. These relationships were further validated and tested with a numerical simulation model where more direct analysis on reservoir properties (i.e., reservoir fluid type, resource in place, etc.) could be conducted. Finally, the absolute child detriment predictions were verified with direct empirical case studies. This workflow leveraged the strengths of each individual modeling technique and resulted in several key learnings in advancing our understanding of the impact on developing child wells. Specifically, we are able to demonstrate that the depletion effect of the parent well impacts child condensate production more severely than gas production, IP's are impacted more than EUR's, and that incremental impact on the child becomes smaller through time.

The utilization of multiple analysis techniques allows for different perspectives on how best to quantify the child detriment effect. We were able to leverage data analytics to assess a large number of potential variables and how they correlate to child detriment, dig deeper into specific questions with numerical simulation, and help to validate these findings with real world case studies. This multi-faceted approach helps to increase the confidence in predicting and adjusting future well programs to mitigate and find alternative approaches to decrease the child detriment effect.

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