Saline Aquifer CO2 Rate Estimation from Water Injectivity Tests

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Solution Overview

Problem

Existing methods for estimating carbon dioxide injection rates in saline aquifers are inefficient and resource-intensive, often requiring costly carbon dioxide injectivity tests, which can be avoided by using water injectivity test data and predictive models.

Innovation Solution

A method utilizing water injectivity test data, petrophysical parameters, and machine learning models to predict carbon dioxide injection rates, eliminating the need for carbon dioxide injectivity tests and optimizing well design.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If carbon dioxide injectivity tests are performed to estimate carbon dioxide injection rates, then measurement precision is improved, but loss of time and resource consumption increase

Engineering Contradiction:

Improvecarbon dioxide injection rate estimation accuracyVSAvoidwell design process time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent uses water injectivity test data as a substitute (copy) for direct carbon dioxide injectivity testing. By establishing a relationship between water and carbon dioxide injectivity through petrophysical parameters, the method obtains carbon dioxide injection rate estimates without performing actual carbon dioxide tests, thus saving time while maintaining reasonable accuracy

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent introduces petrophysical parameters (permeability, porosity, saturation) as intermediary variables that connect water injectivity test results to carbon dioxide injection rate predictions. These intermediaries enable indirect estimation by translating water flow characteristics into carbon dioxide injection capabilities through established geological relationships

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If carbon dioxide injectivity tests are conducted to ensure reliable injection rate data, then reliability is improved, but resource consumption and cost increase

Engineering Contradiction:

Improveinjection rate data reliabilityVSAvoidresource consumption

Core Design Contradiction:

ReliabilityVSLoss of substance

Solution Approach 1:

The patent makes water injectivity test data serve multiple functions: it provides both water flow characteristics and serves as the basis for predicting carbon dioxide injection rates. This multi-functionality eliminates the need for separate carbon dioxide testing, reducing resource consumption while maintaining data reliability through the use of fundamental petrophysical relationships that govern both fluid types

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Productivity

If water injectivity test data is used to predict carbon dioxide injection rates, then productivity is improved, but measurement precision may be compromised

Engineering Contradiction:

Improvewell design efficiencyVSAvoidinjection rate prediction accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent transforms water injectivity test parameters into carbon dioxide injection rate predictions by applying petrophysical relationships and adjusting for fluid property differences. The method changes the parameter context from water flow to carbon dioxide injection potential while maintaining precision through established geological and physical relationships between the two fluid systems

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250320814A1Carbon dioxide injection rates in saline aquifer estimation

Publication Date: 2025.10.16 SAUDI ARABIAN OIL CO

AI summary

Systems, computer-readable storage media, and methods include receiving, from probes, petrophysical data indicative of reservoir conditions within a subterranean region. A water injectivity test executes within the subterranean region using test constants based on the petrophysical data. A water-related variable is generated by using an output of the water injectivity test. A well production potential is determined by using a nodal analysis and the output of the water injectivity test. Carbon dioxide injection rates are predicting by using a carbon dioxide estimation model. The carbon dioxide estimation model processes the water-related variables, the test constants, and a ratio of carbon dioxide density at reservoir condition to carbon dioxide density at standard conditions.