AI Physical Properties Determination for Rocky Formations
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Solution Overview
Problem
Current methods for determining the physical properties of rocky formations during drilling operations are either expensive and time-consuming, such as laboratory core analysis, or costly and limited in applicability, like borehole well logs, especially for complex well geometries.
Innovation Solution
A method and system utilizing artificial intelligence trained on datasets including XRF, XRD measurements, and drilling parameters to compute physical properties of rocky formations in real-time or quasi-real-time using surface logging data, enabling accurate and economical assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If laboratory core analysis is used to determine physical properties of rocky formations, then measurement precision is improved, but loss of time and cost increase significantly
Solution Approach 1:
The patent replaces the mechanical laboratory core analysis system with an artificial intelligence-based computational system. The AI model processes drilling parameters and surface logging data to predict physical properties, eliminating the need for time-consuming physical core analysis while maintaining acceptable accuracy for industrial applications
Solution Approach 2:
The patent creates a virtual model of the rocky formation by training an AI system on drilling data and surface logging data. This virtual model replicates the physical properties of the formation without requiring physical core samples, enabling rapid prediction of geomechanical parameters
2Measurement precision
If borehole well logs are used to determine physical properties, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts the essential information needed for physical properties determination from the complex borehole well log system. By using only surface logging data and basic drilling parameters, it eliminates the need for complex downhole measurement equipment while still achieving reliable predictions of formation properties
Solution Approach 2:
The patent replaces expensive, complex borehole well log equipment with simpler, more economical surface-based measurement systems. The approach uses readily available drilling data and surface logging information that can be obtained without deploying costly specialized measurement tools into the wellbore
3Measurement precision
If borehole well logs are used for measurements, then measurement precision is improved, but adaptability decreases for complex well geometries
Solution Approach 1:
The patent creates a universal AI-based prediction system that can handle various well geometries including highly deviated wells and wells with rough walls. The system processes surface logging data and drilling parameters that are applicable to any well type, making the solution broadly adaptable without requiring specialized downhole equipment for each configuration
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the determination of key geomechanical properties like Young's modulus and uniaxial compressive strength in a simple, reliable, and cost-effective manner, compatible with industrial requirements, even in challenging drilling contexts.
Implementation Method 1
said independent variables comprise X-ray fluorescence (XRF) measurements
Implementation Method 2
said independent variables comprise X-ray diffraction (XRD) measurements
Data Source
AI summary
Method for determining physical properties of rocky formations, comprising: training a first artificial intelligence system (AI1) on a first training dataset (TR1). Said first training dataset (TR1) comprises independent variables (V1), associated with one or more rocky formations, comprising at least one of X-ray fluorescence (XRF) measurements, X-ray diffraction (XRD) measurements, and gamma-ray measurements. The independent variables (V1) further comprise one or more drilling parameters. Said first training dataset (TR1) comprises one or more dependent variables (V2), comprising one or more physical properties of said one or more rocky formations. Said first training dataset (TR1) is obtained from one or more training wells. Said method further comprises: determining operating data (OP), associated with a drilling of an operating well and comprising values of XRF and/or XRD measurements and values of said one or more drilling parameters (DP); executing a processing operation, wherein values of one or more of said one or more physical properties (PP) of a rocky formation crossed by said operating well are computed on the basis of said operating data (OP) by means of at least said first artificial intelligence system (AI1).