Official entry for the 2024 Plotnine Contest
You can install jbcPlotnine from GitHub with:
# install.packages("devtools")
devtools::install_github("JBC-Inc/jbcPlotnine")
|
|
This data set encapsulates a detailed analysis of historical and forecasted data for a natural gas well, organized for strategic decision making in the energy sector.
Each entry in the pandas.DataFrame, with 123 observations, demonstrates predictive modeling used in hydrocarbon exploration and production. The original forecasts for decline and recovery were generated about 2 years after production had began. The results shown are the actual production 8 years later, and how well the original forecasts held up over time, observable within the quality plot.
Region: The designation of data points within the plot, categorized as
either FIT or PREDICT, offering clarity regarding the origin of
each observation and its respective utilization in historical
analysis or future predictions.
Date: A chronological record, timestamped in datetime64 format, which
provide insight into historical production trends over time.
Rate: instantaneous rate of natural gas production, measured in standard
units CCF (hundred cubic feet) indicative of the well's productivity
at one month time intervals.
Rate_Hat: A prognostic indicator denoting the projected production rate
derived from forecasting methodologies.
Cum: The cumulative volume of natural gas extracted from the well since
inception, a pivotal metric in assessing reservoir depletion and
long-term yield potential.
Cum Fit: Forecasts encompassing both fitted and predictive models
for cumulative production, enabling stakeholders to
anticipate estimated ultimate recovery reservoir dynamics and
optimize extraction strategies accordingly.
Error: The deviation between actual and predicted production metrics,
offering insights into the efficacy of forecasting methodologies
and the inherent uncertainty associated with hydrocarbon
extraction.
Mean Squared Error: A quantitative measure of predictive accuracy,
facilitating rigorous performance evaluation and
model refinement.
Quality Metric: An aggregate assesment of data quality and modeling
efficacy, paramount in ensuring the reliability and
robustness of analytical insights.
Drift Analysis: Examination of production drift, elucidating underlying
reservoir behavior and guiding reservoir management
strategies for sustained productivity.
Historical: Archival records capturing past production trends and
operational benchmarks, serving as a foundational reference
for trend analysis and predictive modeling.
Forecasted: Projections and prognostications extrapolated from historical
trends, empowering stakeholders with anticipatory insights
into future production dynamics and resource depletion
trajectories.
This DataFrame combines past data with future predictions. It gives useful information to help make smart decisions, empowering stakeholders with actionable insights for strategic decision-making and operations in natural gas exploration and production.
The plot was originally intended to be a stand alone python file but many technical hurdles (mostly because plotnine is missing functionality or supporting libraries present in ggplot2) prevented this so I decided to integrate the plot(s) into an interactive Shiny for Python web app hosted on shinyapps.io. My first Python for Shiny app!
To view the completed plot visit this link: 2024 PLOTNINE CONTEST
Enjoy!