A focused ecosystem of reservoir-engineering tools for production data analysis, formation evaluation, and field-data web delivery.

The goal of my project space is to provide practical, transparent, and reusable software for reservoir and production engineering workflows:

• Fast diagnostics from production history.
• Robust decline-curve and transient-analysis workflows.
• Reservoir property and PVT-related engineering utilities.
• Formation-level interpretation support.
• Web-based delivery of engineering results to broader teams.

Together, these repositories are intended to reduce manual spreadsheet work, improve reproducibility, and accelerate decision cycles.

Core Python toolkit for production and reservoir-engineering calculations, including:

• Decline-curve analysis (Arps variants and related utilities).
• Production allocation and schedule-centric workflows.
• Reservoir property modules (fluid, rock, relative permeability, capillary pressure).
• Wellbore flow utilities (single-phase and two-phase contexts).
• Material balance, transient analysis (PTA & RTA), and porous-media simulation components.

Overall readiness: ~6/10

A companion repository for petrophysical and formation interpretation workflows (project-level positioning):

• Log-based formation quality screening.
• Pay identification and interval ranking.
• Integration-ready outputs for reservoir modeling and completion planning.

Overall readiness: ~6/10

A web application layer for operationalizing engineering insights:

• Visual dashboards for production and reservoir diagnostics.
• Collaboration-friendly interfaces for engineers and asset teams.
• Potential APIs/services to connect analytics outputs with end-user tools.

Overall readiness: ~6/10

A typical workflow across the ecosystem:

1. Ingest field data & run engineering calculations (DCA | PTA | RTA) in production-data-analysis.
2. Cross-check subsurface intervals and petrophysical context in formation-evaluation.
3. Publish dashboards and decision views via wellx-webapp.

This separation keeps each codebase focused while allowing clear integration points.

• Engineering-first: methods should reflect real reservoir workflows.
• Transparent math: equations and assumptions should be inspectable.
• Reproducible outputs: code and notebooks over one-off manual analysis.
• Composable modules: small building blocks that can be chained into larger studies.
• Practical adoption: interfaces that support both technical experts and downstream stakeholders.

python -m pip install -U pip
python -m pip install -e .

Run tests:

pytest -q

Explore examples in:

• docs/

Contributions and collaboration ideas are welcome. If you are working on reservoir-engineering workflows and would like to align methods, validation datasets, or tooling patterns, feel free to open an issue or pull request.

Feel free to reach out to me via:

• 📧 Email: jshiriyev.longhorn@gmail.com
• 🔗 LinkedIn: click

This project is licensed under the MIT License (see LICENSE).