Case Study Data-Driven Well Evaluation for Faster Drilling Investment Decisions
How we built a unified well analytics and financial modeling platform on cloud containers to accelerate drilling evaluations and forecasting.
The Client
Driving digital innovation in energy exploration and engineering.
The client is a global technology provider serving traditional and emerging energy industries with integrated engineering, digital, and production optimization solutions. Operating across subsea and surface technologies, the organization designs and deploys equipment, platforms, and analytics software that help energy companies evaluate reserves, manage production fields, and optimize drilling operations.
In the upstream oil and gas sector, drilling investment decisions involve capital expenditures running into millions of dollars per well. Before committing capital, asset teams must analyze historical production profiles of neighboring wells, evaluate geological configurations, and run financial models to project NPV (Net Present Value), IRR (Internal Rate of Return), and cash flows.
However, the client's asset evaluation processes were hindered by data fragmentation. Production data, geological surveys, and financial models were maintained in separate systems and offline spreadsheets, slowing down evaluations.
The Challenge
Fragmented data sources and manual investment modeling.
The energy provider's main challenges stemmed from disconnected data silos. Production logs, water cuts, and geological metrics were stored across separate databases. Asset engineers spent days collecting historical data from neighboring wells before they could begin forecasting.
Furthermore, financial modeling was performed manually using complex offline spreadsheets. Analysts had to copy production curves into separate files to calculate revenue projections and run NPV/IRR simulations. This manual, multi-step process slowed down drilling evaluations and made it difficult to compare different locations.
Production and geological records were stored in disconnected databases.
Manual data aggregation delayed the analysis of neighboring well trends.
Forecasting future oil, gas, and water output was complex and slow.
Financial modeling (NPV, IRR) was managed in separate spreadsheets.
Comparing multiple drilling locations and risk scenarios was time-intensive.
What our audit found
Exposing the inefficiencies in asset evaluation and forecasting.
We conducted a workflow analysis of the client's asset evaluation teams. The audit showed that engineers and analysts spent up to 60% of their evaluation time on data extraction and preparation. Gathering history, verifying data quality, and lining up geological metrics took days before modeling could begin.
Because forecasting and financial analysis were performed in separate spreadsheets, running a single "what-if" scenario took hours. This lack of integration delayed decision-making, occasionally causing the client to miss optimal drilling leases.
Manual data collection delayed drilling evaluation timelines.
Disconnected tools led to data transcription errors between models.
Siloed workflows prevented collaborative scenario modeling.
Lack of real-time geospatial mapping made spatial comparisons difficult.
The Solution
How we turned it around.
Engineering a Production Forecasting Engine
We developed a Python-based production forecasting engine that pulls raw well data from the client's central database, filters outliers, and structures it for analysis. The engine applies decline curve analysis to project oil, gas, and water output for each proposed well, and generates production profiles automatically from type curves. It also computes water cut and gas-to-oil ratio projections, replacing days of manual spreadsheet preparation with a repeatable, automated forecast.
What we shipped
- Engineered data processing pipelines to clean and structure well data.
- Built decline curve forecasting models using Python.
- Enabled automatic generation of production profiles based on type curves.
- Automated water cut and gas-to-oil ratio projections.
Building a Unified Web-Based Well Analytics Platform
We designed and built a web-based analytics platform with a React frontend and a Node.js backend. The application aggregates production logs, well coordinates, and completions data into a single interactive dashboard, so engineers no longer stitch results together across separate spreadsheets. A REST API gateway retrieves well history on demand, structured metadata is stored in a relational database, and role-based access controls keep sensitive exploration data secure across the global team.
What we shipped
- Designed interactive dashboard displays using React.
- Built a Node.js REST API gateway to retrieve well history.
- Integrated relational databases to store structured well metadata.
- Implemented user access control levels for security.
Integrated Financial Modeling and Geospatial Comparison
We integrated financial modeling directly into the platform so analysts no longer move numbers between tools. The system combines production forecasts with pricing assumptions to automate net present value, internal rate of return, and payout-timeline calculations. Geospatial mapping lets users compare drilling sites side by side, run multi-scenario what-ifs against variables like pricing or lateral length, and export a one-click report for investment review committees in seconds rather than hours.
What we shipped
- Programmed automated calculations for NPV, IRR, and payout timelines.
- Integrated map-based visualizations using Geospatial APIs.
- Enabled multi-scenario comparison (e.g., pricing fluctuations, lateral lengths).
- Configured one-click reporting exports for investment review committees.
The Numbers
Outcomes we can talk about.
The deployment of the unified well analytics platform modernized the client's evaluation processes. By automating data aggregation and production forecasting, the energy provider accelerated the drilling evaluation lifecycle.
With financial modeling integrated directly into the web application, analysts run scenario comparisons and compute NPV/IRR metrics without manual calculations.
The container-based cloud deployment ensures that the platform scales as new datasets are integrated, providing the global engineering team with real-time access to geological data and location rankings.
Note on Metrics: Due to the proprietary nature of the client's investment models and exploration locations, quantitative financial and drilling metrics were restricted from public release. Project success was validated by the deployment of the web application, the integration of the forecasting engines, and the adoption of the dashboard by the global asset team.
What We Built
What's Next
Integrating machine learning models for reservoir prediction.
The next phase of the digital roadmap involves integrating machine learning algorithms into the forecasting pipeline. By analyzing multi-variant geological parameters, completions configurations, and production records, the models will provide automated recommendations for well designs and fracture placement.
Frequently Asked Questions
About This Project
The questions teams usually ask when they want to run a similar engagement.
The engine supports standard Arps equations (exponential, hyperbolic, harmonic) and customized models for shale reservoirs.
The Real Numbers
Need real numbers? Let's talk.
We kept the names off the page. The story is real, the outcomes are real, and we're always happy to walk a serious team through the rest of it.
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