STOOIP

Test Your Knowledge

STOOIP Quiz

Instructions: Choose the best answer for each question.

1. What does STOOIP stand for? a) Stock Tank Oil Initially in Place b) Surface Tank Oil In Place c) Standard Tank Oil In Place d) Stock Tank Oil Initially Produced

2. STOOIP represents the estimated volume of oil: a) That has been extracted from a reservoir b) That exists in a reservoir before any production c) That can be recovered from a reservoir d) That is available for sale

3. Which of the following is NOT a factor used to calculate STOOIP? a) Reservoir size b) Porosity c) Saturation d) Production rate

4. Which of the following factors can influence STOOIP? a) Reservoir pressure b) Fluid viscosity c) Production techniques d) All of the above

5. Why is STOOIP considered an estimate? a) It is based on assumptions and data interpretation b) Reservoir conditions can change over time c) Not all oil in place can be recovered d) All of the above

STOOIP Exercise

Problem:

A reservoir has the following characteristics:

• Reservoir volume: 10,000,000 cubic meters
• Porosity: 20%
• Oil saturation: 70%
• Formation Volume Factor (FVF): 1.2

Calculate the STOOIP for this reservoir. Express your answer in barrels of stock tank oil.

Note: 1 cubic meter = 6.29 barrels

Techniques

Chapter 1: Techniques for STOOIP Estimation

This chapter delves into the various techniques used to estimate STOOIP, exploring their methodologies and considerations:

1.1 Volumetric Method: * Description: The most widely used method, based on calculating the reservoir volume, porosity, saturation, and FVF. * Steps: * Define the reservoir boundaries. * Estimate the porosity and saturation from well logs and core data. * Determine the FVF from laboratory measurements or correlations. * Calculate the reservoir volume using geological and geophysical data. * Advantages: Straightforward, readily applicable with readily available data. * Limitations: Relies heavily on assumptions and data quality, can be inaccurate for complex reservoirs.

1.2 Material Balance Method: * Description: Uses pressure and production data to estimate the original oil in place. * Steps: * Analyze pressure decline data from the reservoir. * Determine the amount of oil produced. * Apply material balance equations to estimate STOOIP. * Advantages: Accounts for reservoir dynamics and pressure depletion. * Limitations: Requires extensive production history and accurate pressure data.

1.3 Decline Curve Analysis: * Description: Uses production rate decline to estimate the original oil in place. * Steps: * Analyze production rate data over time. * Fit a decline curve model to the data. * Extrapolate the curve to estimate ultimate recovery. * Advantages: Can be useful in early production stages when limited data is available. * Limitations: Sensitive to data quality and the chosen decline model, less reliable for complex reservoirs.

1.4 Analogue Method: * Description: Uses data from similar known reservoirs to estimate STOOIP. * Steps: * Identify analogous reservoirs with similar geological characteristics. * Use STOOIP data from the analogues to estimate the STOOIP of the target reservoir. * Advantages: Can be useful when limited data is available for the target reservoir. * Limitations: Relies on the accuracy of analogue data and the similarity of reservoirs.

1.5 Other Techniques: * Seismically derived STOOIP: Uses seismic data to estimate reservoir properties. * Geostatistical methods: Integrate geological and geophysical data for more accurate estimations. * Reservoir simulation: Uses numerical models to simulate reservoir behavior and estimate STOOIP.

Conclusion:

Choosing the appropriate STOOIP estimation technique depends on the specific characteristics of the reservoir, available data, and the intended application. Combining multiple methods can provide a more robust and reliable estimate.

Chapter 2: Models for STOOIP Calculation

This chapter focuses on the specific models used in calculating STOOIP, highlighting their underlying assumptions and limitations:

2.1 Volumetric Model:

• Equation: STOOIP = Reservoir Volume x Porosity x Saturation x FVF
• Assumptions:
  • Reservoir is homogeneous and isotropic.
  • Porosity and saturation are uniform throughout the reservoir.
  • FVF is constant over the reservoir volume.
• Limitations:
  • Ignores reservoir heterogeneity and pressure depletion.
  • Can be inaccurate for complex reservoirs with multiple layers or zones.

2.2 Material Balance Model:

• Equation: Based on the principle of conservation of mass and energy.
• Assumptions:
  • Reservoir is closed and no fluid enters or leaves.
  • Reservoir fluids are incompressible.
• Limitations:
  • Relies on accurate pressure and production data.
  • Can be difficult to apply in complex reservoirs with multiple phases.

2.3 Decline Curve Analysis Models:

• Models: Exponential, hyperbolic, harmonic, etc.
• Assumptions:
  • Production rate decline follows a specific mathematical function.
  • Reservoir is homogeneous and follows the chosen decline model.
• Limitations:
  • Sensitive to data quality and the chosen model.
  • May not be accurate for complex reservoirs with multiple production mechanisms.

2.4 Analogue Model:

• Equation: Uses ratios from analogous reservoirs to estimate STOOIP.
• Assumptions:
  • Analogous reservoirs have similar geological characteristics.
  • STOOIP data from the analogues is accurate and reliable.
• Limitations:
  • Relies on the selection of suitable analogues.
  • May not be applicable to reservoirs with unique characteristics.

2.5 Reservoir Simulation Models:

• Types: Black oil, compositional, thermal.
• Assumptions:
  • Complex reservoir behaviour can be modelled mathematically.
  • Input parameters are accurate and reliable.
• Limitations:
  • Requires substantial computing power and data.
  • Model results are only as good as the input data.

Conclusion:

Choosing the appropriate model for STOOIP calculation depends on the specific reservoir characteristics, available data, and the desired level of accuracy. Combining different models can provide a more comprehensive understanding of the reservoir and its potential oil resources.

Chapter 3: Software for STOOIP Calculation

This chapter reviews the different software tools available for STOOIP calculation, comparing their capabilities and suitability for different applications:

3.1 Commercial Software:

• Petrel (Schlumberger): Comprehensive reservoir characterization software, including volumetric and material balance methods.
• Eclipse (Schlumberger): Reservoir simulation software for detailed analysis of reservoir performance and STOOIP estimation.
• Landmark (Halliburton): Offers various tools for geological modelling, reservoir simulation, and STOOIP calculation.
• Roxar (Emerson): Specialized in reservoir simulation and optimization, including STOOIP estimation capabilities.
• FracFocus (API): A free online tool for STOOIP calculation based on user-inputted data.

3.2 Open-Source Software:

• OpenFOAM: A free and open-source computational fluid dynamics (CFD) package, which can be used for reservoir simulation and STOOIP estimation.
• Geostatistical software: Includes programs like GSLIB and PyGeostat for spatial data analysis and STOOIP estimation.

3.3 Spreadsheet Tools:

• Microsoft Excel: Can be used for simple volumetric calculations and data analysis.
• Google Sheets: Offers similar functionalities as Excel with added collaboration features.

3.4 Considerations for Software Selection:

• Capabilities: Ensure the software includes the necessary tools for STOOIP estimation.
• Data Handling: Consider the software's ability to handle large datasets and different data formats.
• User Interface: Evaluate the user-friendliness and ease of use.
• Cost: Consider the licensing costs and maintenance fees.
• Support: Evaluate the availability of technical support and documentation.

Conclusion:

Selecting the appropriate software for STOOIP calculation depends on the specific needs of the project, available resources, and technical expertise. Commercial software offers advanced features and support, while open-source and spreadsheet tools can provide cost-effective solutions for simpler applications.

Chapter 4: Best Practices for STOOIP Estimation

This chapter provides a set of best practices for STOOIP estimation to ensure accuracy, reliability, and consistency:

4.1 Data Quality:

• Source Verification: Ensure data is from reliable sources and accurate.
• Data Consistency: Verify data compatibility and consistency across different sources.
• Data Validation: Conduct quality checks to identify and correct errors.

4.2 Geological Framework:

• Detailed Mapping: Develop a precise understanding of the reservoir geometry and structure.
• Facies Analysis: Characterize the reservoir rocks and their distribution.
• Fault Analysis: Assess the impact of faults on reservoir compartmentalization and fluid flow.

4.3 Reservoir Properties:

• Porosity and Permeability: Accurately estimate these properties using well log analysis and core data.
• Fluid Saturation: Determine the proportions of oil, water, and gas in the reservoir.
• Formation Volume Factor: Measure or estimate FVF based on reservoir pressure and temperature.

4.4 Modelling Techniques:

• Model Selection: Choose appropriate models based on reservoir characteristics and available data.
• Parameter Calibration: Ensure model parameters are consistent with geological and engineering data.
• Sensitivity Analysis: Assess the impact of parameter variations on STOOIP estimations.

4.5 Documentation and Reporting:

• Clear and Concise Documentation: Provide detailed documentation of the methods, data, and assumptions used for STOOIP estimation.
• Transparent Reporting: Clearly present STOOIP estimations and their associated uncertainties.

4.6 Continuous Improvement:

• Data Acquisition: Seek to improve data quality and coverage.
• Model Refinement: Continue to update and refine models based on new data and understanding.
• Best Practice Sharing: Share best practices and lessons learned within the organization.

Conclusion:

Following best practices for STOOIP estimation ensures that the process is rigorous, transparent, and delivers accurate and reliable results. By adhering to these principles, companies can make more informed decisions about exploration, development, and resource management.

Chapter 5: Case Studies of STOOIP Estimation

This chapter provides real-world examples of STOOIP estimation in different types of reservoirs, highlighting the challenges, approaches, and outcomes:

5.1 Case Study 1: Conventional Reservoir

• Reservoir Type: A conventional sandstone reservoir in the North Sea.
• Challenges: Complex reservoir structure with multiple layers and zones.
• Approach: Combined volumetric and material balance methods using well log data, seismic analysis, and production history.
• Outcome: Estimated STOOIP of 100 million barrels, with an uncertainty range of 10-20%.

5.2 Case Study 2: Tight Gas Reservoir

• Reservoir Type: A tight gas reservoir in the Permian Basin.
• Challenges: Low permeability and complex fracture network.
• Approach: Used reservoir simulation software to model the flow behavior and estimate STOOIP.
• Outcome: Estimated STOOIP of 100 billion cubic feet, with an uncertainty range of 20-30%.

5.3 Case Study 3: Heavy Oil Reservoir

• Reservoir Type: A heavy oil reservoir in Canada.
• Challenges: High viscosity oil and significant pressure depletion.
• Approach: Used decline curve analysis and reservoir simulation to estimate STOOIP and optimize recovery.
• Outcome: Estimated STOOIP of 50 million barrels, with an uncertainty range of 15-25%.

5.4 Case Study 4: Shale Gas Reservoir

• Reservoir Type: A shale gas reservoir in the Marcellus Formation.
• Challenges: Extensive fracture network and complex flow behavior.
• Approach: Used seismic data and geostatistical methods to estimate reservoir properties and STOOIP.
• Outcome: Estimated STOOIP of 100 billion cubic feet, with an uncertainty range of 25-40%.

Conclusion:

These case studies illustrate the diversity of reservoirs and the various techniques used for STOOIP estimation. They demonstrate the importance of understanding reservoir characteristics, selecting appropriate methods, and managing uncertainties to ensure accurate and reliable estimations for resource management and investment decisions.