Type Curve Region Clustering Using Spatial Autocorrelation

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Solution Overview

Problem

Existing type curve analysis methods for tight rock asset classes are labor-intensive, subjective, and fail to account for natural clustering of well performance due to changing reservoir and production parameters, leading to inaccurate characterization of subsurface volumes.

Innovation Solution

Utilize spatial autocorrelation and production data to generate probability values for type curve regions, clustering them objectively to improve characterization and reduce the number of regions tracked, enhancing forecasting and operational decisions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional type curve analysis methods are used, then analysis can be performed, but the process is labor-intensive and subjective leading to inaccurate characterization

Engineering Contradiction:

Improvecharacterization accuracyVSAvoidanalysis process complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces manual, subjective type curve analysis with an automated computer-implemented system that uses spatial autocorrelation algorithms and probability value calculations to objectively identify and cluster type curve regions, eliminating labor-intensive manual processes while improving characterization accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service analysis by automatically computing spatial autocorrelation correction factors, generating probability values, and clustering type curve regions without requiring manual intervention, allowing the analysis to perform itself through automated computational processes

Inventive Principle:

Principle #25Self-service

2Productivity

If traditional type curve analysis methods are used, then all type curve regions can be tracked, but the number of regions to track is excessive reducing efficiency

Engineering Contradiction:

Improveoperational efficiencyVSAvoidnumber of type curve regions

Core Design Contradiction:

ProductivityVSQuantity of substance

Solution Approach 1:

The patent merges geographically adjacent type curve regions into clusters based on spatial autocorrelation and probability values, combining multiple individual regions into fewer meaningful clusters that represent homogeneous subsurface volumes, thereby reducing the number of regions that need to be tracked while maintaining analytical accuracy

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system segments the subsurface volume into distinct type curve regions and then clusters them based on spatial relationships and production data characteristics, creating a hierarchical structure that reduces complexity by grouping similar regions together while maintaining the ability to analyze individual regions when needed

Inventive Principle:

Principle #1Segmentation

3Reliability

If traditional type curve analysis methods are used, then analysis can be completed, but it fails to account for natural clustering leading to inaccurate forecasts

Engineering Contradiction:

Improveforecasting accuracyVSAvoidautomation level

Core Design Contradiction:

ReliabilityVSExtent of automation

Solution Approach 1:

The patent replaces traditional manual type curve analysis with an automated computational system that calculates spatial autocorrelation correction factors and probability values to objectively identify natural clustering patterns in production data, improving forecasting reliability by accounting for spatial relationships that manual methods miss

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system incorporates feedback through autocorrelation analysis that continuously refines the clustering by comparing spatial relationships and production data patterns, using the calculated probability values to validate and adjust cluster formations, ensuring that natural clustering is properly accounted for in forecasts

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12494033B2Systems and methods for analyzing clusters of type curve regions as a function of position in a subsurface volume of interest

Publication Date: 2025.12.09 CHEVRON USA INC

AI summary

Methods, systems, and non-transitory computer readable media for analyzing type curve regions in a subsurface volume of interest are disclosed. Exemplary implementations may include: obtaining initial clusters of type curve regions in the subsurface volume of interest; obtaining production values as a function of position; generating an autocorrelation correction factor; attributing the autocorrelation correction factor to the production values as a function of position; generating type curve mean values; generating range distribution values; generating a type curve cluster probability value for each of the type curve regions; generating a first representation of the type curve regions as a function of position; clustering the type curve regions in updated clusters; generating a second representation of the type curve regions as a function of position; and displaying one or more of the first representation and the second representation.