Return Permeability

Test Your Knowledge

Quiz on Return Permeability

Instructions: Choose the best answer for each question.

1. What does "return permeability" refer to? a) The initial permeability of a reservoir before any EOR treatment. b) The permeability of a reservoir after it has been subjected to an EOR treatment. c) The permeability of a rock formation that is highly porous. d) The rate at which oil flows through a reservoir.

2. Why is return permeability an important factor in EOR? a) It helps predict the cost of implementing EOR techniques. b) It helps determine the amount of oil that can be extracted using EOR. c) It helps assess the effectiveness of different EOR techniques. d) All of the above.

3. Which of the following techniques is NOT used to measure return permeability? a) Coreflood experiments. b) Seismic surveys. c) Well testing. d) Production data analysis.

4. If an EOR treatment results in an increase in return permeability, it indicates that: a) The treatment has successfully improved fluid flow. b) The treatment has caused pore plugging. c) The treatment has had no impact on the reservoir. d) The treatment has increased the cost of oil production.

5. What is the main takeaway from understanding return permeability? a) EOR is always effective in increasing oil recovery. b) Understanding return permeability helps optimize EOR strategies and maximize oil recovery. c) Return permeability is irrelevant to the success of EOR techniques. d) Return permeability is only important for laboratory experiments.

Exercise on Return Permeability

Task: A reservoir has an initial permeability of 100 millidarcies. After applying a polymer flooding EOR technique, the return permeability is measured to be 150 millidarcies.

1. Calculate the percentage change in permeability.

2. Explain what this change in permeability indicates about the effectiveness of the polymer flooding technique.

3. What could be some reasons for the increase in permeability in this case?

Techniques

Return Permeability: A Comprehensive Guide

Introduction (as provided in the original text)

In the oil and gas industry, maximizing oil recovery from reservoirs is a constant pursuit. One crucial factor influencing oil production is permeability, the ability of a rock formation to allow fluids to flow through its pores. When using enhanced oil recovery (EOR) techniques, such as polymer flooding, surfactants, or nanoparticles, the permeability of the reservoir can be significantly affected. Understanding the "return permeability" becomes crucial in assessing the overall effectiveness of these methods.

Chapter 1: Techniques for Measuring Return Permeability

Measuring return permeability accurately is critical for assessing the success of EOR projects. Several techniques, both laboratory-based and field-based, are employed:

1.1 Coreflood Experiments: This is a common laboratory technique where a small, representative core sample of the reservoir rock is subjected to simulated EOR conditions. Fluids (water, oil, and the EOR agent) are injected through the core, and the pressure drop and effluent fluid composition are monitored. Initial permeability is determined before introducing the EOR agent. After the EOR process is simulated, the core's permeability is measured again to determine the return permeability. Different injection schemes (e.g., continuous injection, slug injection) can be tested. The advantages include precise control over experimental conditions and the ability to analyze various EOR chemicals. The disadvantages are the limitations of scaling up from a small core sample to a full reservoir and potential for core damage during the experiment.

1.2 Well Testing: Field-based well testing methods provide in-situ measurements of reservoir permeability. Techniques such as pressure buildup tests and pulse tests analyze pressure and flow rate data from a producing well before and after EOR treatment. Interpreting these data requires sophisticated reservoir models and can be affected by factors such as wellbore storage and skin effects. However, well testing provides valuable information on the overall reservoir response to EOR, accounting for larger scale heterogeneity that laboratory core samples may not capture.

1.3 Production Data Analysis: Analyzing historical production data, including oil and water production rates and reservoir pressure, can indirectly estimate changes in permeability. This approach relies on reservoir simulation models that incorporate various parameters and assumptions. While not as direct as coreflooding or well testing, production data analysis provides long-term insights into the effects of EOR on the reservoir's performance. It is crucial to account for other factors that might influence production data, such as changes in production strategies.

1.4 Imaging Techniques: Advanced imaging techniques like X-ray microcomputed tomography (micro-CT) can provide detailed visualizations of the pore structure before and after EOR treatment. This allows for direct observation of changes in pore geometry and connectivity that impact permeability. These methods are powerful in understanding the underlying mechanisms of permeability alteration but can be expensive and time-consuming.

Chapter 2: Models for Predicting Return Permeability

Predicting return permeability is crucial for optimizing EOR strategies and forecasting future production. Several models are employed:

2.1 Empirical Correlations: Simple empirical correlations relate the changes in permeability to parameters like the concentration of the EOR agent, rock properties (porosity, grain size distribution), and fluid properties (viscosity). These correlations are often reservoir-specific and require sufficient historical data.

2.2 Pore-Scale Models: These models simulate fluid flow at the pore level, providing a detailed understanding of the mechanisms influencing permeability alteration. However, these models are computationally intensive and require high-resolution images of the pore network.

2.3 Continuum Models: Continuum models describe fluid flow at a larger scale, simplifying the complexities of the pore network. These models are generally less computationally intensive than pore-scale models but may not accurately capture all the fine-scale details. Commonly used continuum models include Darcy’s law and its extensions. These require appropriate constitutive relationships for permeability alteration based on the EOR mechanisms.

2.4 Reservoir Simulation: Sophisticated reservoir simulators integrate various models to simulate the entire EOR process, including fluid flow, chemical reactions, and changes in rock properties. These simulators predict the long-term impact of EOR on production and can be used to optimize injection strategies.

Chapter 3: Software for Return Permeability Analysis

Various commercial and open-source software packages are available for analyzing return permeability data and simulating reservoir behavior:

• Commercial Reservoir Simulators: CMG, Eclipse, and Schlumberger's INTERSECT are examples of industry-standard reservoir simulators that incorporate modules for modeling EOR processes and analyzing return permeability. These packages offer advanced features for simulating complex reservoir geometries and fluid behavior.

• Pore-Scale Simulation Software: Open-source packages like OpenFOAM and commercial software like PoreFlow can be used for pore-scale modeling of fluid flow and EOR processes.

• Data Analysis Software: MATLAB, Python with libraries like SciPy and pandas, are commonly used for analyzing experimental data from corefloods and well tests.

Chapter 4: Best Practices for Return Permeability Studies

Effective return permeability studies require careful planning and execution. Best practices include:

• Representative Core Selection: Selecting core samples that accurately represent the reservoir heterogeneity is crucial for laboratory experiments.

• Careful Experimental Design: Coreflood experiments should be designed to minimize core damage and accurately simulate reservoir conditions.

• Accurate Data Acquisition and Processing: Data from well tests and production monitoring should be carefully collected and processed to minimize errors.

• Appropriate Model Selection: Choosing the most appropriate model for predicting return permeability based on available data and reservoir characteristics.

• Uncertainty Analysis: Quantifying the uncertainty associated with the return permeability predictions is essential for reliable decision-making.

Chapter 5: Case Studies of Return Permeability in EOR

Several case studies illustrate the importance of return permeability in EOR projects. These studies demonstrate the impact of various EOR techniques on reservoir permeability and their implications for production optimization. (Specific examples would be included here, detailing the EOR method used, the measured change in permeability, and the impact on oil recovery. For example, a case study might focus on polymer flooding in a carbonate reservoir, showing a decrease in permeability due to polymer retention, or a case study on surfactant flooding in a sandstone reservoir showing an improvement in permeability due to wettability alteration). These examples would showcase both successes and failures, highlighting the critical role of return permeability in assessing EOR performance and informing future projects.