Velocity Model Prediction Using Generative AI for Operational Excellence

Generative AI has revolutionized the way we approach velocity model prediction, enabling us to create highly accurate models that drive operational excellence.

By leveraging generative AI, we can reduce the time and cost associated with traditional velocity model prediction methods, which can be cumbersome and prone to human error.

A key advantage of generative AI is its ability to learn from large datasets and identify complex patterns, allowing it to generate highly accurate velocity models that are tailored to specific operational needs.

This has significant implications for industries that rely on accurate velocity models, such as oil and gas, where even small errors can have major consequences.

The II Framework is built upon a robust foundation of AI technologies. It utilizes a VQ-VAE to generate discrete representations of velocity samples.

At the heart of this framework is the GPT model, which employs an autoregressive approach to generate new velocity samples. This model is trained on discrete representations obtained from the VQ-VAE.

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The GPT model's architecture is key to its success, featuring an embedding block that enhances the model's ability to learn complex patterns. This is achieved by projecting one-hot encoded discrete latent codes into a hidden dimension.

The GPT model's decoder blocks are a stack of Transformer decoders, which utilize masked multi-headed self-attention to predict subsequent velocity values based on previous inputs.

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With Vertex AI, you can transform content creation and discovery, research, customer service, and developer efficiency with the power of generative AI.

The platform is unified, meaning it brings together machine learning models and generative AI under one roof. This makes it easier to manage and deploy your AI projects.

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The II Framework is built on a two-stage process that integrates Vector-Quantized Variational Autoencoders (VQ-VAE) and Generative Pre-trained Transformers (GPT). This innovative approach allows for the effective encoding and decoding of velocity models.

The first stage involves using VQ-VAE to encode and decode velocity models. The process begins by obtaining discrete representations from the VQ-VAE, which are then fed into the GPT model.

The GPT model's architecture is designed to model the distribution of velocity models, enabling the generation of realistic and high-fidelity samples. This is achieved through an autoregressive approach that predicts subsequent velocity values based on previous inputs.

The GPT model's architecture includes an embedding block that projects one-hot encoded discrete latent codes into a hidden dimension. This enhances the model's ability to learn complex patterns.

The GPT model also includes decoder blocks that process the embedded data using masked multi-headed self-attention. This allows the model to effectively predict subsequent velocity values.

Here are the key components of the GPT model's architecture:

Data preparation is a crucial step in velocity model prediction using generative AI. We start by generating 128,000 synthetic data using two types of theoretical rock physics models.

The sampling ranges of these model parameters cover all possible values, as shown in Tables 1 and 2, which are determined from real field areas and experimental measurements. This ensures the generality and richness of the synthetic data.

Random values in the parameter's sampling space and random combinations of different parameters are used to obtain corresponding S-wave velocity datasets. To mimic real data, we add 10% Gaussian noise to the synthetic data, which helps enhance the robustness of the neural network.

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To create a robust dataset, 128,000 synthetic data points were generated using two types of theoretical rock physics models.

These models were chosen to ensure the generality and richness of the synthetic data, with sampling ranges covering all possible values, as shown in Tables 1 and 2.

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The sampling ranges of the model parameters were determined from real field areas and experimental measurements.

Random values within the parameter's sampling space and random combinations of different parameters were used to obtain corresponding S-wave velocity datasets.

To mimic real data, 10% Gaussian noise was added to the synthetic data, creating a full-sample labeled dataset that enhances the robustness of the neural network.

Real field data often contains noise from data acquisition, processing, and interpretation procedures, so this added noise helps to replicate real-world conditions.

In data preparation, selecting the right feature parameters is crucial for accurate predictions. The reservoir parameters, such as porosity, density, clay content, and P-wave velocity, are easily accessible in real field areas.

These parameters have a good correlation with the S-wave velocity, as shown in Figure 1. The P-wave velocity and density are positively correlated with the S-wave velocity, while porosity and clay content are negatively correlated.

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The correlation coefficients of these parameters are greater than 0.4, indicating a strong relationship. Among them, the P-wave velocity has the strongest correlation with the S-wave velocity.

The reservoir parameters reflect the characteristics of the reservoir, and their connection to the S-wave velocity is essential for accurate predictions. The four selected parameters will be used as the input features of the S-wave velocity prediction network.

A Deep S-Wave Velocity Prediction Network is a powerful tool for predicting S-wave velocities. It's a combination of DNNs (Deep Neural Networks) and rock physics models.

This network is specifically designed to improve the accuracy of velocity modeling in data science. Incorporating geological features and well velocities is crucial for its success.

The network is applied to both laboratory data and real field data, providing a comprehensive understanding of S-wave velocities. The results are presented in a clear and concise manner.

P- and S-wave velocities of samples from published literatures are visualized in a graph, showing the variation in velocities across different samples.

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The VelocityGPT framework is a game-changer in the field of velocity modeling, allowing for the generation of larger, more realistic seismic velocity models without additional training.

This capability is particularly impressive because it's based on smaller velocity models, demonstrating the model's efficiency and adaptability in various geological contexts.

The framework has been tested on the OpenFWI benchmarking dataset, showcasing its robustness as a seismic velocity generator.

Lab data is a crucial component of many applications, and understanding how to apply it effectively is essential for achieving accurate results.

Machine learning models can be trained on lab data to make predictions and classify new data points, as seen in the "Predictive Modeling" section.

Lab data can be used to identify patterns and correlations that may not be immediately apparent, such as the relationship between gene expression and disease progression.

In the "Data Analysis" section, we discussed how lab data can be used to identify outliers and anomalies that may indicate a problem with the experiment or equipment.

Lab data can be used to develop new treatments and therapies by identifying specific molecular targets, as mentioned in the "Therapeutic Targeting" section.

By applying lab data to real-world problems, researchers can develop more effective solutions and improve patient outcomes.

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Field log data can be a goldmine of information, and applying it to real-world problems is a crucial step in unlocking its potential.

By analyzing field log data, companies can gain valuable insights into equipment performance, helping them identify areas for improvement and optimize maintenance schedules.

This can lead to significant cost savings and increased efficiency.

In industries like manufacturing and construction, field log data can be used to track equipment hours, identify trends, and predict when maintenance is needed.

For example, a construction company might use field log data to track the number of hours a crane has been in use, allowing them to schedule maintenance before a critical failure occurs.

This proactive approach can help prevent costly downtime and ensure projects stay on schedule.

Improving knowledge workflows is a game-changer for businesses. Generative AI models, like Large Language Models (LLMs), can analyze unstructured data, streamlining workflows and reducing manual workloads for knowledge workers.

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By automating repetitive tasks, businesses can free up their employees to focus on more strategic and creative work. Generative AI models excel in data-intensive functions, making them a valuable tool for knowledge workers.

One of the key benefits of using generative AI is the ability to analyze vast amounts of unstructured data. This can be especially helpful for businesses that deal with a lot of customer feedback or social media data.

Here are some ways that generative AI can improve knowledge workflows:

* TaskTime SavedManual coding and debuggingUp to 90%Data analysisUp to 80%

By automating these tasks, businesses can save time and increase productivity, allowing their employees to focus on more strategic and creative work.

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Industry Use Cases are where generative AI really shines. It's transforming the way we work in various sectors, from legal firms to financial services and sales and marketing teams.

In the legal industry, AI technology is being used to support research, document analysis, and draft preparation, allowing human reviewers to focus on high-value tasks. This is evident in London-based law firm Macfarlanes, which leverages AI tool Harvey to boost efficiency.

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Generative AI is also improving operational resilience in financial institutions. Morgan Stanley's AI-powered assistant enables financial advisors to instantly retrieve insights on market trends and internal recommendations, improving client service efficiency.

For sales and marketing teams, AI enhances outreach and personalisation. Gong's AI-driven call summaries guide sales teams on effective follow-up actions, optimising resource use in customer engagement.

Here are some key industry use cases for generative AI: