Why Your Production Data is Your Most Valuable Asset (And How to Unlock It)

You've been collecting production data for years—maybe decades. Daily production volumes, pressure readings, equipment run times, workover history, completion details. It's all sitting somewhere: spreadsheets, databases, SCADA systems, paper files.

Here's what most operators don't realize: That data is worth more than most of your equipment.

The difference between operators who thrive and operators who struggle often comes down to one thing: how well they use their data to make decisions.

The Hidden Value in Historical Production Data

What's Actually in Your Data

Production History:

• Daily oil, gas, water volumes (going back years)
• Well performance over time
• Response to workovers and interventions
• Seasonal patterns and trends
• Equipment performance and failures

Operational Events:

• When wells went down and why
• Workover activities and results
• Equipment changes and upgrades
• Artificial lift optimization attempts
• Operating condition changes

Economic Data:

• Operating costs by well and time period
• Revenue by well (production × price)
• Capital investments and returns
• Equipment costs and depreciation
• Labor and service costs

Completion and Well Data:

• Formation characteristics
• Completion designs
• Perforation data
• Stimulation details
• Well configuration

What This Data Can Tell You

Question 1: Which wells are underperforming (and why)?

• Compare actual production to decline curve predictions
• Identify wells producing below peer expectations
• Pinpoint when performance degraded
• Determine if problems are fixable
• Value: Target workovers on wells with best ROI

Question 2: When will equipment fail (before it fails)?

• Analyze patterns before past failures
• Identify early warning indicators
• Predict failures 2-4 weeks in advance
• Schedule preventive maintenance
• Value: Avoid $5K-$10K emergency repairs

Question 3: What's the optimal well spacing?

• Analyze production from offset wells
• Understand interference effects
• Optimize future development
• Avoid over-drilling costly infill wells
• Value: Better capital allocation decisions

Question 4: Which completion designs work best?

• Compare EUR (estimated ultimate recovery) by completion type
• Analyze cost vs. production benefit
• Inform future completion decisions
• Value: 10-20% better well performance

Question 5: Where should we allocate capital?

• Calculate well-level economics
• Rank opportunities by ROI
• Compare workover vs. new drill decisions
• Value: Better returns on capital

The Dollar Value of Better Decisions

Conservative Example (100-well operator):

Optimization Opportunity 1: Better workover targeting

• Current: $500K workover budget spread across 10 wells
• Improved: Data-driven targeting of best 10 candidates
• Result: 20% higher production response
• Value: $300K additional annual revenue

Optimization Opportunity 2: Predictive maintenance

• Current: 12 unplanned equipment failures/year at $7K average
• Improved: Predict 75% of failures, preventive maintenance
• Result: $5K preventive vs. $7K reactive, plus less downtime
• Value: $50K annual savings

Optimization Opportunity 3: Operating expense optimization

• Current: $400/barrel LOE (lease operating expense)
• Improved: Identify high-cost wells, optimize or shut in
• Result: 10% LOE reduction
• Value: $240K annual savings (assuming 6K barrels/day production)

Total Value: $590K/year from better use of data you already have

Investment Required: $80K-$120K for data platform and analytics

ROI: 400-600% in Year 1

Common Data Problems (And How to Fix Them)

Problem 1: Data Trapped in Silos

The Issue:

• Production data in Excel
• SCADA data in proprietary system
• Financial data in QuickBooks
• Equipment data in maintenance spreadsheet
• Well data in PDFs and paper files

Why It's Expensive:

• Can't see the full picture
• Manual effort to combine data
• Decisions based on incomplete information
• Missed optimization opportunities

The Solution: Cloud Data Warehouse

What It Is: Single database that consolidates all your data sources.

How It Works:

1. Automated pipelines pull data from source systems
2. Data is cleaned and standardized
3. Everything stored in one place
4. Users access through dashboards and reports

Technology:

• Azure SQL Database or Synapse Analytics
• Azure Data Factory (for ETL)
• Power BI (for visualization)

Cost: $40K-$70K to build, $500-$2K/month to run

Benefit: Access to all data in one place, real-time or daily updates

Problem 2: Poor Data Quality

The Issue:

• Duplicate records
• Missing data
• Incorrect values (typos, impossible numbers)
• Inconsistent naming (Well 1A vs. Well 001A)

Why It's Expensive:

• Analysis produces wrong answers
• Decisions based on bad data
• Time wasted tracking down errors
• Loss of confidence in systems

The Solution: Automated Data Quality

Validation Rules:

• Range checks (production can't be negative)
• Cross-reference checks (well names must match master list)
• Reconciliation (production should match sales)
• Duplicate detection

Implementation:

• Built into data pipelines
• Automated flagging of issues
• Dashboard showing data quality metrics
• Alerts for critical problems

Cost: $10K-$20K to implement

Benefit: 95%+ data accuracy (vs. 80-90% typical)

Problem 3: Can't Access Historical Data

The Issue:

• Old data in legacy systems
• Backup tapes nobody can read
• Excel files on old computers
• Paper records in storage

Why It's Expensive:

• Can't do long-term trend analysis
• Can't build predictive models
• Limited decline curve analysis
• Can't learn from past

The Solution: Historical Data Migration

Process:

1. Identify all historical data sources
2. Extract data (from systems, files, paper)
3. Clean and standardize
4. Load into cloud data warehouse
5. Validate completeness

Cost: $20K-$40K (depending on how messy it is)

Benefit: Access to 10-20 years of history for analysis

Problem 4: No Tools for Analysis

The Issue:

• Data exists but no way to analyze it
• Excel skills vary by person
• No standardized reports
• Each analysis is custom, time-consuming

Why It's Expensive:

• Staff time wasted on manual analysis
• Inconsistent methods produce different answers
• Analysis takes days or weeks
• Only simple questions can be answered

The Solution: Analytics Platform

Components:

Dashboards (for daily operations):

• Production overview
• Well performance
• Equipment status
• Alerts and exceptions

Reports (for regular needs):

• Monthly production summary
• Well economics
• Operating cost analysis
• Regulatory reports

Self-Service Analytics (for ad-hoc questions):

• Power BI or Tableau
• Pre-built data models
• Training for key users
• Library of example analyses

Cost: $30K-$50K to build

Benefit: Questions answered in minutes instead of days

What Good Data Architecture Looks Like

The Modern Data Stack for Oil & Gas

Layer 1: Data Collection

• SCADA integration (real-time or hourly)
• Mobile apps (pumpers, field staff)
• Automated imports (purchaser statements, invoices)
• Manual entry (for data not in systems)

Layer 2: Data Storage

• Cloud data warehouse (Azure, AWS)
• Raw data preserved (never lose original)
• Processed data for analysis
• Historical archive

Layer 3: Data Processing

• ETL pipelines (extract, transform, load)
• Data quality checks
• Business logic (calculations, aggregations)
• Scheduled automation (nightly, hourly)

Layer 4: Analytics & Visualization

• Dashboards (Power BI, Tableau)
• Reports (automated generation)
• APIs (for custom applications)
• Data science tools (Python, R)

Layer 5: Access & Security

• Role-based access (users see what they need)
• Audit logging (track who accessed what)
• Data encryption
• Backup and disaster recovery

Example Architecture

For a 100-well operator:

Data Sources:

• SCADA system (hourly production data)
• Mobile app (daily pumper rounds)
• QuickBooks (operating expenses)
• Purchaser statements (revenue data)
• Equipment tracking spreadsheet

Cloud Platform:

• Azure SQL Database (data warehouse)
• Azure Data Factory (ETL pipelines)
• Azure Functions (automated processing)
• Power BI (dashboards and reports)

Data Flow:

1. Hourly: SCADA data syncs to cloud
2. Daily: Pumper entries from mobile app
3. Weekly: Operating expense import from QuickBooks
4. Monthly: Purchaser statements processed
5. Continuous: Dashboards update automatically

Outputs:

• Executive dashboard (key metrics)
• Operations dashboard (daily production)
• Financial dashboard (well economics)
• Regulatory reports (RRC, EPA)
• Alerts (equipment failures, unusual production)

Cost: $60K-$90K to build, $2K-3K/month to operate

Practical Use Cases: From Data to Decisions

Use Case 1: Decline Curve Analysis

Traditional Approach:

• Export production data to Excel
• Manually create decline curves per well
• Fit curves by hand or with Excel solver
• Takes 1-2 hours per well
• For 100 wells: 100-200 hours

Data-Driven Approach:

• Automated decline curve fitting
• All wells analyzed automatically
• Results in dashboard
• Update monthly or on-demand
• For 100 wells: 0 hours (automated)

Value:

• Faster forecasting for planning
• Identify wells declining faster than expected
• Better workover targeting
• Improved reserves estimates

Investment: $20K-$30K to build

ROI: 95% time savings, better decision quality

Use Case 2: Well Economics (Netback Analysis)

The Question: Which wells are profitable? Which are marginal? Which should we shut in?

Traditional Approach:

• Pull production data
• Pull revenue data
• Pull cost data
• Manually calculate per-well economics
• Update quarterly if lucky

Data-Driven Approach:

• Automated netback calculation daily
• Dashboard showing well profitability
• Rank wells by economics
• Alerts when wells go unprofitable

Formula:

Netback = (Oil × Oil Price) + (Gas × Gas Price) - Operating Costs - Transportation

Value:

• Identify unprofitable wells to shut in
• Prioritize high-value wells for optimization
• Support workover justification
• Better capital allocation

Investment: $15K-$25K (if you have data platform)

ROI: Shutting in 2-3 unprofitable wells saves $50K-$100K/year

Use Case 3: Predictive Equipment Maintenance

The Question: Can we predict equipment failures before they happen?

Traditional Approach:

• Reactive: Fix it when it breaks
• Time-based: Replace every X months
• Neither is optimal

Data-Driven Approach:

• Analyze historical failure patterns
• Identify leading indicators (vibration, temperature, pressure trends)
• Machine learning model predicts failures
• 2-4 week advance warning

Requirements:

• 1-2 years historical data
• SCADA or sensor data
• Failure event records

Value:

• Preventive maintenance ($5K) vs. emergency repair ($10K)
• Reduce unplanned downtime by 30-50%
• Schedule maintenance during planned downtime

Investment: $40K-$60K (includes ML model development)

ROI: Typical payback in 6-12 months

Use Case 4: Production Optimization

The Question: Are wells producing at their potential?

Approaches:

1. Peer Well Comparison:

• Group wells by similar characteristics
• Compare production performance
• Identify underperformers
• Investigate and address issues

2. Artificial Lift Optimization:

• Analyze pump card data (if available)
• Optimize pump speed and stroke length
• Reduce gas interference
• Improve pump efficiency

3. Decline Curve Comparison:

• Compare actual vs. expected production
• Identify when wells fell below forecast
• Correlate with operational events
• Determine root cause

Value:

• 5-10% production increase typical
• For 100-well operator at 5K barrels/day: 250-500 barrels/day
• At $75/barrel: $18K-$37K additional monthly revenue

Investment: $30K-$50K (analytics + implementation)

ROI: 200-400% in Year 1

Quick Wins: Where to Start

Start Here: Data Consolidation

Goal: Get all production data in one place

Steps:

1. Identify data sources (SCADA, spreadsheets, etc.)
2. Build cloud database
3. Create automated imports
4. Build basic dashboard

Timeline: 6-8 weeks

Cost: $30K-$50K

Benefit: Foundation for all other analytics

Next: Automated Reporting

Goal: Eliminate manual report generation

Examples:

• Daily production summary
• Monthly operations review
• Quarterly board reports
• RRC compliance reports

Timeline: 4-6 weeks

Cost: $15K-$25K

Benefit: 20-40 hours/month saved

Then: Advanced Analytics

Goal: Answer complex questions

Examples:

• Decline curve analysis
• Well economics
• Peer comparisons
• Predictive maintenance

Timeline: 8-12 weeks per use case

Cost: $30K-$60K per use case

Benefit: $100K-$500K value per use case

Take Action

Free Data Value Assessment:

We'll review your data and estimate:

• What's the hidden value in your existing data?
• What quick wins could you achieve?
• What's the recommended roadmap?
• What's the investment and ROI?

No obligation, no sales pitch—just honest assessment.

Schedule Free Data Assessment →

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About Strataga

We help Permian Basin operators unlock the value in their production data. Our custom analytics platforms turn data you already have into better decisions and higher profits.

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