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SIH26120SoftwareSmart Automation
₹1,50,000 Prize (₹75k Joint)
•
Deadline: 20 September 2026
•
Submitted Ideas: 0/500

Digital Twin for Well-to-Surface Optimization of Cyclic Steam Stimulation (CSS) and Sucker Rod Pump (SRP) Operations for Heavy Oil Wells of Baghewala Field.

Ministry / Organization: Oil India Limited
SIH Official Portal
Technical Complexity
8/10
Dataset Accessibility
7/10
36h Hackathon Viability
9/10
Step 0 • Zero-Jargon Layman Summary

“Oil India needs a smart software system to manage heavy oil wells that are difficult to pump. By combining data from steam heating cycles and mechanical pumps, the system will predict when to inject steam and how to adjust pump speeds to prevent equipment failure and maximize oil output.”

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Deconstructed Problem Blueprint

Step 1

WHAT TO BUILD?

An AI-powered Digital Twin platform that integrates reservoir simulation, wellbore physics, and surface pump control systems into a unified dashboard.

Step 2

WHO IS IT FOR?

Primary beneficiaries are Oil India Limited field engineers, production managers, and onsite maintenance crews at the Baghewala Field.

Step 3

WHY DOES IT EXIST?

Current operations rely on manual, reactive adjustments based on historical guesses. This leads to energy waste, frequent mechanical rod failures due to thick oil, and inefficient steam usage.

Step 4

INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE

01 • Inputs & Telemetry

Historical production logs, CSS cycle records (steam pressure/volume), VFD/SRP sensor telemetry (stroke length, SPM, torque), fluid property data, and rod failure history.

02 • Processing & AI Core

Physics-informed Machine Learning (PIML) to model reservoir cooling, time-series forecasting for pump efficiency, and optimization algorithms (Genetic Algorithms or Reinforcement Learning) for VFD control.

03 • Actionable Deliverables

Real-time optimization dashboard, predictive alerts for rod failure, automated recommendations for steam injection timing, and performance reports.

Step 5

CONSTRAINTS

  • Must handle high-viscosity fluid dynamics
  • Integration with legacy SCADA/IoT field data
  • Low-latency inference for real-time pump adjustment
  • Must operate in remote field environments with limited connectivity
Step 6

JURY METRICS

  • Reduction in Steam-Oil Ratio (SOR)
  • Mean Time Between Failures (MTBF) for sucker rods
  • Prediction accuracy for pump efficiency
  • System response time to sensor anomalies
Step 7

CORE BOTTLENECK

The 'Physics-Data Gap': Creating a model that accurately bridges the gap between subsurface reservoir temperature decay and surface mechanical pump performance without needing massive, unavailable real-time sensor arrays.

Why You Should Choose This PS (Pros)

  • High industrial relevance with clear ROI for Oil India
  • Strong potential for 'Digital Twin' visualization which is a major 'wow' factor
  • Well-defined problem scope with accessible historical data types

Challenges & Risks to Watch (Cons)

  • Requires domain knowledge of petroleum engineering
  • Risk of over-engineering the simulation aspect at the expense of the UI/UX
  • Data quality issues in historical logs may require heavy preprocessing
Hackathon Blueprint

Recommended Tech Stack & Grand Finale Features

Frontend & Mobile
React.jsThree.js or React-Three-Fiber for 3D visualizationTailwind CSS
Backend & APIs
FastAPIPython (Pandas/NumPy/Scikit-learn)
AI / ML & Models
XGBoost/LightGBM for failure predictionLSTM/GRU for time-series forecastingReinforcement Learning (Stable Baselines3) for VFD optimization
Database & Cloud
InfluxDB (for time-series data)PostgreSQLRedis

Killer Features to Impress the SIH Jury

A 'What-If' Scenario Simulator allowing engineers to test steam injection strategies before implementation
Automated 'Rod Health Score' that uses color-coded heatmaps to identify failing pumps
Physics-Informed Neural Network (PINN) that respects fluid dynamics constraints
Offline-first mobile sync for field technicians to view alerts without constant internet
View Official SIH 2026 Portal Raw Text
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•Background Baghewala Field in Rajasthan produces heavy crude oil (17–19° API) from the Jodhpur Sandstone reservoir. The reservoir is characterized by High crude viscosity, High asphaltene content, Low reservoir pressure, Low reservoir temperature (46–48°C) and Poor oil mobility under primary recovery. Consequently, artificial lift and thermal enhanced oil recovery are critical for sustained production. At present, CSS cycle design and SRP operation are optimized separately using historical experience. As reservoir temperature declines after steam injection, crude viscosity increases, leading to reduced pump efficiency, higher energy consumption, rod floating issues, rod failures and lower oil recovery. There is a need for an integrated, data-driven system that continuously optimizes both CSS and artificial lift operations.
•Problem Description Current operations face the following challenges:
•CSS parameters (steam volume, injection pressure, soak time and production cut-off)

are largely based on historical practices.

•SRP operating parameters (stroke length, SPM and VFD settings) are adjusted manually and reactively.
•Heavy crude causes rod floating, impact loading, frequent pump unsetting, rod failures and increased maintenance.
•Reservoir behaviour, wellbore conditions and SRP performance are not optimized together.
•Lack of predictive analytics results in higher Steam-Oil Ratio (SOR), increased energy consumption and reduced production efficiency.
•Expected Outcome / Solution Develop an AI-enabled Well-to-Surface Digital Twin that integrates reservoir, wellbore and surface production systems to provide real-time monitoring, prediction and optimization.

The solution should:

•Optimize CSS cycle parameters.
•Predict reservoir heating, cooling and production performance.
•Continuously optimize SRP operation by adjusting stroke speed and SPM based on well conditions.
•Detect rod floating and minimize impact loading.
•Improve pump efficiency and equipment reliability.
•Optimize steam and energy consumption while reducing operating cost.
•Expected Benefits
•Increased oil production and recovery.
•Reduced Steam-Oil Ratio (SOR).
•Lower energy consumption per barrel.
•Reduced rod failures and pump unsetting.
•Improved equipment life and operational reliability.
•Data-driven and predictive decision making.
•Relevant Data Availability The field has sufficient historical and operational data, including:
•Production history
•CSS cycle records
•Steam injection parameters
•VFD and SRP operating data
•Rod failure and pump unsetting history
•Well completion and reservoir data
•Fluid properties and pressure data