Pipeline Accident Risk, Cost, and Safety Analysis (2010–2017)

By abhishek.verma75000 · April 2, 2026

This data analysis project explores pipeline accident trends, causes, financial costs, environmental impact, and safety risks across multiple dimensions…

The analysis identified the 10 operators with the most pipeline accidents, along with their total associated costs. A bar chart has been generated showing each operator's accident count, with color intensity indicating their total costs — making it easy to spot which operators have both high frequency and high financial impact.

MATERIAL/WELD/EQUIP FAILURE is the dominant cause category, accounting for the most accidents (1,435) and the highest total net barrel loss (165,656 barrels). This single category outpaces all others in both frequency and impact.

The analysis produced a bar chart and data table showing the average liquid recovery rate as a percentage of unintentional releases for each year. The visualization breaks down how effectively liquid was recovered relative to the amount unintentionally released, year by year.

The analysis reveals two different leaders depending on the metric. 'HVL or Other Flammable or Toxic Fluid, Gas' has the highest average net loss at 602.72 barrels per incident, while 'Crude Oil' leads in total financial costs with $1,765,523,231. A grouped bar chart and data table were generated to visually compare all liquid types across both metrics.

The analysis reveals the top 15 states by accident frequency along with their total environmental remediation costs, shown in a dual-axis chart and data table. Texas (TX) dominates by a wide margin with 1,004 accidents and $70.1M in remediation costs, followed distantly by Oklahoma (236 accidents, $19.9M) and Louisiana (169 accidents, $10.7M).

The analysis successfully identified the top 10 most expensive pipeline accidents by total cost. A bar chart has been generated showing each accident by operator and year, color-coded by cause category, with costs displayed in millions of dollars. A data table is also available with full details including location, cause, and net liquid loss in barrels.

The analysis produced a side-by-side bar chart and data table comparing three key metrics — accident frequency, average net loss (in barrels), and average total costs (in USD) — across all pipeline types in the dataset. The visualization makes it easy to spot which pipeline types are most prone to accidents and which incur the greatest financial and environmental impact.

From 2010 to 2017, pipeline accidents generally increased before declining. Starting at 350 accidents in 2010, numbers climbed steadily each year, reaching a peak of 462 in 2015. After that peak, accidents dropped to 415 in 2016. A line chart and data table have been generated to visualize this trend across all eight years.

The analysis produced two visualizations showing the breakdown of total costs across all four categories: Property Damage, Environmental Remediation, Emergency Response, and Lost Commodity. A pie chart displays each category's share of total costs with dollar amounts and percentages, while a bar chart provides a side-by-side comparison of the absolute cost values for each category.

The average pipeline shutdown duration is 188.7 hours, which equals approximately 7.9 days. A bar chart has been generated showing the average shutdown duration broken down by cause category, ranked from longest to shortest. This helps identify which types of incidents tend to keep pipelines offline the longest.

Across all recorded incidents, there were 20 total injuries and 10 fatalities. The bar chart breaks down these figures by cause category, showing which types of accidents are most harmful. The three deadliest cause categories by combined injuries and fatalities are: ALL OTHER CAUSES (11 combined), INCORRECT OPERATION (8 combined), and OTHER OUTSIDE FORCE DAMAGE (7 combined).

The analysis identifies the counties with the highest cumulative environmental remediation costs from pipeline accidents. A bar chart has been generated showing the top 15 counties ranked by their total remediation spending, making it easy to compare which areas have faced the greatest environmental cleanup burdens.

The analysis identified 65 pipeline spills exceeding 1,000 barrels of net loss. Two charts were generated showing the most common cause subcategories and the top states where these large spills occurred.

The analysis examined 53 pipeline incidents that resulted in public evacuations, breaking them down by both cause category and liquid type. Two bar charts were generated showing the total evacuations and number of incidents for each group, making it easy to compare which categories pose the greatest evacuation risk.

The analysis produced data tables breaking down pipeline accident frequency by month, revealing seasonal risk patterns across the dataset. The tables show how accidents are distributed throughout the year, helping identify which months pose higher risks for pipeline incidents.

The analysis produced three data tables comparing onshore versus offshore pipeline accidents across frequency, severity, and costs. The tables break down key metrics including accident counts, average net loss in barrels, average and total costs, injuries, fatalities, ignitions, explosions, and public evacuations for each pipeline location type.

The analysis successfully identified the top 10 cause subcategories by frequency and their associated average costs. Two bar charts were generated: one showing the number of incidents per subcategory and another displaying the average total costs for each. A data table was also produced with the full breakdown.

The analysis identified 50 operators with recurring high-cost accidents, indicating potential systemic safety issues. A high-cost accident was defined as one exceeding $117,232 (the 75th percentile of all accident costs). Two visualizations were generated to highlight these operators: a bar chart showing the top 15 operators by total accident cost, and a scatter plot comparing accident frequency against average cost per incident — helping distinguish operators with many accidents from those with fewer but more expensive ones.

A dual-axis chart has been generated showing how both total and average All Costs per accident have changed year over year. The bar chart displays total costs for each year, while the line overlay tracks the average cost per accident, making it easy to spot trends in both volume and per-incident severity.