The importance of interpretation through DFITs in characterizing reservoirs is widely recognized, leading to their incorporation into major commercial PTA software packages. However, certain limitations inherent in classical methodologies, especially for low-permeability reservoirs, have been overcome through the adoption of type-curve interpretation methodologies (Craig, 2014), and whose advantages have been exposed by Gonzalez and Arhancet (2022).
Given the complexity and lack of available tools for employing this methodology, a hybrid team with technical and programming expertise developed a Python application that facilitates the integration of this new methodology and makes it accessible to all technical staff within the company, increasing efficiency and saving costs.
The use of type-curve methodology offers a significant advantage in the interpretation of initial pressures, transmissibility, and permeability in low-permeability reservoirs, which could not be obtained using classical techniques. Until now, this new workflow has been carried out using spreadsheets in a handmade and rudimentary manner, requiring considerable time from the user. Although the data is often available, spreadsheets methodology makes interpretation difficult for end users, and it is very time compsuming.
To address this issue, an ad hoc Python application was developed, using popular libraries such as pandas and matplotlib. This application allows users to interact with multiple screens to load and preprocess data in an agile, intuitive, and standardized manner.
The development of an application with a standardized and well-organized workflow significantly improves the quality and efficiency of interpretation, especially for users with less experience. Having such a tool reduces the need to understand the functioning of spreadsheets and decreases the possibility of errors. The use of this application allows for maintaining an updated database with more than 200 records in a consistent manner.
In addition to the benefit related to data interpretation, in-house hybrid team development allowed for faster time to value and enabled the tool to be developed in an agile manner, adapting to business needs.
This means lower costs compared to other development methods, such as hiring a programming company or adopting commercial software.
Having a tool that is currently not available in commercial software allowed for the consolidation of this methodology, which was already being used in a more handmade way and enabled the valuation of a large number of DFITs that could not be interpreted with the classical methodology. Having updated databases improves the quality of subsequent analyses (correlations, mappings, etc.).
The tool has both the classical and type curve methodologies in a single environment, allowing the user to perform a complete analysis without the need for other software. In future steps, an upgrade will be made to include interpretation of post-frac fall offs. And although the application was born for a specific need for unconventional formations, its use can be extrapolated to any formation type.
