Matthew Nelson

Created a multi-level Netback & Operating Cost Dashboard in PowerBI. Planning and forecasting functionality was embedded in a beautiful and intuitive interface

Designed a Corporate Capital Dashboard in PowerBI, with a similar multi-level structure to the Operating Cost Dashboard and consistent design styling.

Redesigned a Weekly Well Production report which was generated manually in Spotfire weekly. The new report implemented best practices in data visualization to greatly improve readability, and the full update/pdf/email process was automated using Spotfire's Automation Services and Microsoft's Power Automate.

Built a simple Lift Gas Optimization Dashboard in PowerBI to help the Production Engineers understand the lift gas usage across the company, and to identify opportunities for optimization.

Replaced a completely manual task of choosing the sequence for a zipper fracturing design on new well pads with a Python web app. The app was built with Streamlit, and integrated a purpose built algorithm to intelligently choose the next stage to stimulate while reducing operations time at the surface and staying 'ahead' of offsetting wells on frac azimuth.

Designed a Streamlit app to provide an interface for estimating the level of development required to fill a gas processing plant to capacity, taking in a base forecast, type curve forecast(s), and simulation parameters specific to the situation.

Fully implemented a local, modern, end-to-end data stack with multiple existing data processes replicated using python (ingestion), dbt core, cube.js semantic layer, DuckDB and PowerBI. Replaced antiquated processes using R and serving data as csv with a single executable which processes data from multiple databases, orchestrates an ELT DAG, and serves clean data in a semantic layer to PowerBI. Benefits from all of the features of dbt and cube.js. Can be spun up on any machine as a multi-service architecture with docker.

As a senior resource with significant cloud and analytic stack experience, advocated for the use of a true cloud based data analytics stack. Educated on the importance of thoughtful design for data ingestion, data engineering, semantic layers, and data visualization / data consumption.

Envisioned, constructed, and maintained an all-encompassing dbt Cloud / Snowflake solution for end-to-end data workflows throughout the company. Snowflake now serves as the foundation for all inter-platform Operational Data, Business Intelligence, and Advanced Analytics at Canlin.

Interconnected all data sources and sinks, including SCADA, Production, Reserves, Financial, and Accounting, to provide a single source of truth for all data at Canlin.

Utilized software engineering best practices within dbt Cloud to ensure a robust, scalable, and maintainable data solution.

Utilized Snowflake Streams, Tasks, and Functions for efficient data processing and automation.

Implemented Tableau Prep Flows for collaborative workflows and raw data loads into Snowflake.

Employed Python Scripts and UDFs (Snowpark) for data cleaning, aggregation, and simple ML models.

Implemented general business intelligence using Tableau Server for data visualization and reporting.

Trained and deployed a modified XGBoost model to predict the next 30 days of production for 8000+ wells, used for near term corporate reporting and marketing. New predictions are inferred daily.

Trained and deployed a 2 year Gas and Oil rate forecasts for all wells, used for long term corporate reporting and marketing.

Developed a Well Risk Ranking Labeling app in Streamlit to crowdsource subject matter expert risks for a subset of wells. Trained a classification model to predict the corporate risk for all remaining wells, and describe predictions / feature importance to the business.

Trained a Gas Well Event Detection model to identify anomalous gas well production events, such as hydrates, liquid loading, or downhole mechanical failures.

Evaluated and justified the DataRobot platform for ease of ML model development and deployment