Saudi arabian oil company (20240280017). SYSTEM AND METHOD FOR PREDICTING WELL CHARACTERISTICS simplified abstract

SYSTEM AND METHOD FOR PREDICTING WELL CHARACTERISTICS

Organization Name

saudi arabian oil company

Inventor(s)

Fatai A. Anifowose of AI-Khobar (SA)

Maimona Washie of Dhahran (SA)

SYSTEM AND METHOD FOR PREDICTING WELL CHARACTERISTICS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240280017 titled 'SYSTEM AND METHOD FOR PREDICTING WELL CHARACTERISTICS

• • Simplified Explanation:**

This patent application describes a method for predicting total organic carbon (TOC) and sensitive elements in unsampled intervals of a well by using machine learning models based on log data.

• • Key Features and Innovation:**
• Obtaining log data from sampled intervals of a well.
• Generating a model representing the relationship between log data and TOC and sensitive elements data.
• Predicting TOC and sensitive elements in unsampled intervals using the model and additional log data.

• • Potential Applications:**

This technology can be applied in the oil and gas industry for better understanding of subsurface formations and reservoir properties.

• • Problems Solved:**

The method addresses the challenge of predicting TOC and sensitive elements in unsampled intervals of a well without the need for physical sampling.

• • Benefits:**
• Improved accuracy in predicting TOC and sensitive elements.
• Cost-effective solution compared to traditional sampling methods.
• Enhanced understanding of subsurface geology for better decision-making in drilling operations.

• • Commercial Applications:**

Predictive modeling for oil and gas exploration and production, reservoir characterization services for energy companies.

• • Prior Art:**

Researchers can explore existing literature on machine learning applications in geosciences and reservoir characterization to find relevant prior art.

• • Frequently Updated Research:**

Stay updated on advancements in machine learning algorithms for subsurface data analysis and reservoir characterization techniques.

• • Questions about Predictive Modeling for TOC and Sensitive Elements:**

1. How does machine learning improve the accuracy of predicting TOC and sensitive elements in well intervals? 2. What are the potential limitations of using predictive modeling for subsurface data analysis in the oil and gas industry?

Original Abstract Submitted

a method for predicting total organic carbon (toc) and sensitive elements related to unsampled intervals of a well, is provided. the method includes obtaining first log data related to sampled intervals of a well, the first log data comprising a plurality of parameters corresponding to one or more of toc data and sensitive elements data associated with the sampled intervals, generating a model representing a nonlinear relationship between the first log data and the toc data and sensitive elements data using a machine learning engine, obtaining second log data related to unsampled intervals of the well, and determining predicted toc and predicted sensitive elements associated with the unsampled intervals of the well using the model and the second log data.