Welcome to RokBites, your bi-weekly guide to brushing up on RokDoc techniques. 👩💻💡
The first post of the new year is based on a resolution - to know more about Rock Physics models in RokDoc! Follow me in this 2-part adventure, which starts today with the a refresher on the Rock Physics models types and new frontiers to explore for geoscientists.
Hit play on the video below or jump to the summary below to start!
Rock Physics provides a vital framework for relating geological and petrophysical properties to seismic attributes. By understanding factors like porosity, mineralogy, and fluid content, we can make accurate predictions about subsurface conditions, lithology, and fluid differentiation. This knowledge is essential for scenario modelling, model-based inversion, assessing drilling risks and improving interpretation accuracy.
A Rock Physics model connects variables such as porosity, mineralogical content, and saturating fluids to reconstruct the elastic properties of rocks. Different models cater to various rock types and depositional environments - the main idea is that we can describe with different models various lithologies and uncover how sensitive an elastic response can be based on specific physical parameters and quantities.
RokDoc categorizes rock physics models into four main types:
Empirical Models: These are based on observed data and are easy to use, though they may lack general applicability beyond their derived conditions.
Theoretical Models: Grounded in fundamental physics principles, these models offer deep insights but can be complex and assumption-dependent.
Heuristic Models: Relying on empirical observations and data fitting, these models are practical for quick predictions but may lack a strong physical basis.
Hybrid Models: Combining empirical data with theoretical frameworks, these models offer accurate predictions across a wider range of conditions.
Choosing the right model is crucial, and RokDoc makes calibration easy with interactive sliders and mesh templates. The mineral sets and fluid sets are also a way of improving accuray based on the field data, as they collect the elastic properties of the constituent minerals and fluids. By adjusting these parameters, users can achieve a better match with real data, reducing uncertainty in subsurface interpretations. After calibrating the parameters, the validation stage is fundamental to make sure that the model is accurate enough to predict the measured data correctly. Once the Rock Physics model is calibrated and validated, the model can be used in many ways: completing a dataset with forward or reverse modelling, create 2D or 3D models for further inversion stages, AVO modelling ...
RokDoc is at the forefront of innovation by integrating machine learning into its workflows. The Rock Physics Machine Learning (RPML) toolkit uses the expectation-maximisation algorithm to classify facies and optimize Rock Physics model parameters, starting from pressure trends and petrophysical log data. This toolkit provides a head start in creating Rock Physics model templates which can then be shared easily among teams and represent the base start for further refined calibration.
In summary, Rock Physics modeling is a fundamental aspect of geoscience, and RokDoc offers a comprehensive suite of tools to enhance your understanding and application of these models. Whether you're calibrating parameters or exploring new machine learning techniques, RokDoc empowers you to make informed decisions and improve your modeling accuracy.
Every step of the Quantitative Interpretation workflow will benefit from a Rock Physics study. We'll see more on AVO modelling, 2D and 3D modelling and reservoir characterisation on our next post, so stay up to date by subscribing to the blog!
We hope you found this post insightful. Feel free share your feedback and propose any topics you would like us to explore in future posts. Your input helps us create content that truly resonates with our community.
Thank you for being part of our journey - see you next post! ☕🍪