PlayInsight Suite: Oil and Gas Well Spacing, Forecasting, and Economic Analysis

Introduction

PlayInsight Suite is a Python-based toolkit designed to accelerate oil and gas property evaluations by automating key technical workflows. It provides fast, scalable calculations for:

• Well spacing and 2D distance analysis
• Parent-child relationship identification
• Arps production forecasting (hyperbolic, harmonic, exponential)
• Discounted cash flow (DCF) economic evaluation

PlayInsight integrates seamlessly with analytics databases, enabling engineers, analysts, and asset teams to handle large datasets and drive decisions in unconventional resource plays.

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Repository Overview

This repository includes:

AnalysisAndDBScripts/

Core modules implementing the PlayInsight workflows:

• dcf_functions.py: Future cash flow and DCF calculations
• fluid_properties.py: Oil and gas fluid property calculations
• geo_functions.py: Sampling from geologic grids
• prod_fcst_functions.py: Arps-based production forecasting
• sql_connect.py: Helpers for MS SQL Server interaction
• sql_schemas.py: Sample SQL schema for use with the suite
• well_spacing.py: 2D spacing and parent-child logic

config/

Configuration system for customizing workflows:

• analytics_config.yaml: Stores all user-defined settings
• config_loader.py: Helper to load config in Python

data/grids/

Example geologic grids to test grid sampling functions.

play_assessment_tools/

End-to-end pipeline scripts for applying spacing, forecasting, and analysis:

• arps_autofit.py: Fit Arps models to production data
• parent_child_assignments.py: Determine parent-child well relationships
• well_spacing_calcs.py: Measure inter-well spacing
• update_geology.py: Associate wells with geologic data
• Jupyter notebooks used during prototyping and development

spotfire_utils/

Scripts and helpers (CSS, IronPython, data functions) to build a GUI in TIBCO Spotfire.

sql/

Example SQL scripts for building and populating a data warehouse compatible with PlayInsight.

• Table creation scripts
• Stored procedures
• SQL views to power analytics dashboards

devcontainer/ (Optional)

VS Code Remote - Containers configuration:

• Dockerfile: Python + dependencies environment
• devcontainer.json: VS Code configuration

Using this is optional, but highly recommended for contributors and power users.

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Other Files

• requirements.txt: Python libraries required
• setup.py: Installation and module packaging
• .gitignore: Files and folders excluded from version control
• Dockerfile: Standalone image build for manual Docker usage
• PlayInsight.pdf: Overview presentation of core workflows
• README.md: This file
• USAGE.md: Step-by-step instructions for setup and usage

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Getting Started

See USAGE.md for setup instructions, including:

• Cloning the repo
• Using VS Code with Dev Containers
• Running analysis pipelines
• Connecting to SQL and Spotfire

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License

This project is licensed under the MIT License.
© 2025 Jay Engineering, LLC.
By using this software, you agree to the terms of the license.

📧 Questions? Contact [email protected].