Pick (seismic)

Test Your Knowledge

Quiz: Picking the Right Path

Instructions: Choose the best answer for each question.

1. What does "picking" in seismic exploration refer to?

a) Selecting the best seismic data to analyze. b) Identifying and marking specific points or features on a seismic record. c) Interpreting the meaning of seismic data. d) Creating 3D models of the subsurface.

2. Why is picking important in seismic exploration?

a) It helps identify potential drilling locations. b) It allows for mapping the subsurface. c) It enables quantitative analysis of seismic data. d) All of the above.

3. What is a specific example of a "pick" in seismic exploration?

a) Marking the location of a fault. b) Identifying the top of a sandstone reservoir. c) Tracing the path of a seismic wave. d) Both a) and b).

4. What factors can affect the accuracy of picking seismic data?

a) The quality of the seismic data. b) The complexity of the subsurface. c) The experience of the interpreter. d) All of the above.

5. How are advancements in technology improving picking in seismic exploration?

a) Making the process more efficient and reliable. b) Allowing for more detailed analysis of seismic data. c) Increasing the accuracy of picks. d) All of the above.

Exercise: Picking a Feature

Scenario: Imagine you are a geophysicist analyzing a seismic record. The record shows a series of reflections representing different rock layers. You are tasked with identifying the top of a limestone layer, which is a potential reservoir for hydrocarbons.

Task:

1. Draw a simple sketch: Create a basic representation of a seismic record. You can use lines to represent different reflections.
2. Mark the pick: On your sketch, clearly mark where you would identify the top of the limestone layer.
3. Explain your reasoning: Briefly describe the characteristics of the reflection that helped you identify the limestone layer.

Techniques

Chapter 1: Techniques for Seismic Picking

Seismic picking, the process of identifying and marking significant features on seismic data, employs various techniques to achieve accurate and efficient results. These techniques can be broadly categorized as manual, semi-automatic, and automatic.

Manual Picking: This traditional method involves a human interpreter visually inspecting seismic sections and manually marking points of interest using interactive software. While requiring expertise and time, manual picking allows for detailed interpretation and consideration of subtle geological features often missed by automated methods. Techniques within manual picking include:

• Horizon Tracking: Tracing a continuous reflection event across multiple seismic sections. This is crucial for mapping geological layers.
• Event Picking: Identifying specific points on a seismic trace, such as the arrival time of a reflection or refraction.
• Fault Interpretation: Manually tracing fault lines by identifying discontinuities in reflections.

Semi-automatic Picking: These techniques combine human expertise with automated algorithms to improve efficiency and accuracy. Examples include:

• Assisted Picking: Software provides suggestions for picks based on algorithms, but the interpreter retains final approval and can override suggestions.
• Interactive Editing: Software allows for automated picking followed by manual refinement and correction by the interpreter. This helps address the limitations of automated methods in complex geological settings.

Automatic Picking: These methods utilize advanced algorithms to automatically identify and pick features on seismic data. They can be significantly faster than manual picking but require high-quality data and may not be reliable in complex areas. Techniques employed include:

• Pattern Recognition: Algorithms identify repeating patterns in seismic data to automatically pick horizons.
• Machine Learning: Sophisticated algorithms learn from previously interpreted data to predict picks on new datasets. This approach continually improves with more training data.
• Seismic Attributes Analysis: Using derived attributes (e.g., instantaneous frequency, amplitude) to enhance the identification of geological features before picking.

The choice of technique depends on the complexity of the data, the required accuracy, available resources, and the interpreter's expertise. Often a combination of techniques is employed to achieve optimal results.

Chapter 2: Models in Seismic Picking

Accurate seismic picking relies on understanding the underlying geological models and their representation in seismic data. Several key models are fundamental to the process:

1. Geological Models: These represent the subsurface geology, including layer boundaries, faults, and other structural features. Prior geological knowledge, including well logs and geological maps, is crucial in creating these models. These models guide the picking process, providing context and expectations for the seismic data.

2. Seismic Velocity Models: Seismic waves travel at different speeds through various rock types. An accurate velocity model is crucial for correctly positioning reflections in depth and converting time-based seismic data to depth-based images. These models are often built using well-log data and seismic tomography techniques. Inaccurate velocity models can lead to significant errors in picking.

3. Stratigraphic Models: These models represent the layering and depositional history of the rocks. Understanding stratigraphic relationships helps in interpreting seismic reflections and identifying key horizons. This knowledge helps to distinguish between different geological layers and anticipate the expected reflection patterns.

4. Structural Models: These models represent the structural deformation of the rocks, including faults, folds, and other tectonic features. Accurate structural models are critical for understanding the geometry of hydrocarbon traps and for interpreting the complex patterns observed in seismic data. These models can be built from seismic interpretation and geological field data.

5. Reservoir Models: These models represent the properties of hydrocarbon reservoirs, such as porosity, permeability, and fluid saturation. Seismic data, in conjunction with other data sources like well logs, can be used to build reservoir models and estimate the size and potential production of a reservoir. Picking helps define the reservoir boundaries for subsequent modeling.

The interplay between these models and the seismic picking process is iterative. Initial geological models guide the picking process, and the picked data are then used to refine and update the models, leading to a better understanding of the subsurface.

Chapter 3: Software for Seismic Picking

Several software packages are available for seismic picking, ranging from basic to highly sophisticated systems. The choice of software depends on the complexity of the data, the required accuracy, and the budget.

Commercial Software: Major players in the oil and gas industry offer comprehensive seismic interpretation software suites, such as:

• Petrel (Schlumberger): A widely used platform offering a full range of seismic interpretation tools, including automated and manual picking capabilities.
• Kingdom (IHS Markit): Another comprehensive software suite with advanced seismic interpretation and visualization tools.
• OpenWorks (Halliburton): Known for its integrated workflow and capabilities for various geoscience tasks, including seismic interpretation.

These packages often include advanced features such as:

• Automated picking algorithms: Employing techniques described in Chapter 1.
• Interactive editing tools: Allowing for refinement of automated picks.
• 3D visualization: Providing a comprehensive view of the subsurface.
• Attribute analysis: Facilitating the identification of subtle geological features.
• Integration with other geoscience data: Allowing for a holistic interpretation.

Open-Source Software: While less common for full-scale seismic interpretation, some open-source options exist for basic seismic data processing and visualization, offering opportunities for specialized development and customization. Examples include Seismic Unix (SU).

Regardless of the software chosen, effective use requires training and expertise in seismic interpretation. Understanding the software's capabilities and limitations is critical to ensure the accuracy and reliability of the picks.

Chapter 4: Best Practices in Seismic Picking

Achieving accurate and reliable seismic picks requires adherence to best practices throughout the entire workflow. These practices encompass data quality control, picking strategies, and quality assurance procedures.

Data Quality Control:

• Pre-processing: Ensuring the seismic data is properly processed to minimize noise and enhance signal quality. This includes steps like deconvolution, multiple attenuation, and noise reduction.
• Data Validation: Verifying the accuracy and consistency of the seismic data before commencing picking.

Picking Strategies:

• Consistency: Maintaining a consistent picking approach across the entire dataset.
• Collaboration: Encouraging collaboration among interpreters to ensure consistency and identify potential errors.
• Multiple Interpreters: Employing multiple interpreters to independently pick the same data for comparison and quality control.
• Use of Well Logs: Integrating well log data to improve the accuracy and confidence of picks.
• Geological Context: Considering the geological context and prior geological knowledge when making picks.

Quality Assurance:

• Regular Checks: Performing regular checks and quality control throughout the picking process.
• Cross-checking: Cross-checking picks against other data sources, such as well logs and geological maps.
• Error Analysis: Identifying and correcting errors promptly. Tracking errors helps refine procedures and improve future work.
• Documentation: Thorough documentation of the picking process, including the methods used, the assumptions made, and any uncertainties.

Adherence to these best practices minimizes errors, increases confidence in the results, and enhances the overall value of the seismic interpretation.

Chapter 5: Case Studies in Seismic Picking

Several case studies illustrate the application and challenges of seismic picking in diverse geological settings.

Case Study 1: Subsalt Imaging: Picking seismic reflections beneath salt bodies presents a significant challenge due to the complex wave propagation effects of the salt. Advanced techniques, including pre-stack depth migration and sophisticated velocity modeling, are necessary to achieve accurate picks in such settings. The success of this relies on careful velocity model building and detailed understanding of wave propagation through the salt.

Case Study 2: Fractured Reservoirs: Identifying fractures in seismic data requires careful interpretation of subtle seismic attributes. Advanced techniques, such as curvature analysis and coherence analysis, can be employed to highlight fracture networks, improving the accuracy of picking related to the reservoir's properties.

Case Study 3: Thin-Bed Reservoirs: Picking thin layers in seismic data can be difficult due to resolution limitations. Techniques like spectral decomposition and high-resolution seismic processing can improve the ability to resolve and pick these thin layers, improving reserve estimation.

Case Study 4: Automated Picking vs. Manual Picking: A comparison of automated and manual picking methods applied to a similar dataset can highlight the strengths and limitations of each approach. In areas of high complexity, manual picking may still be superior, while in simpler areas, automation offers significant time savings.

These case studies demonstrate the versatility and importance of seismic picking in various geological scenarios. The choice of techniques and methodologies depends heavily on the specific geological setting and the objectives of the seismic interpretation. The iterative nature of model building and picking ensures increasingly accurate subsurface characterizations.