Reservoir Quality Prediction via Spatial Distribution Function
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Solution Overview
Problem
Current methods for determining drilling sites based on Poisson's ratio are inadequate as they fail to accurately predict reservoir quality, leading to the risk of drilling low or non-producing wells, especially in unconventional oil and gas reservoirs where hydrocarbons remain trapped in source rocks.
Innovation Solution
A method that classifies reservoir portions using production data and correlates elastic and petrophysical properties to generate a spatial distribution function, predicting reservoir quality and optimizing drilling and production operations by identifying high, medium, or low producing zones within the reservoir.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If drilling sites are determined based on Poisson's ratio alone, then the method is simple and easy to apply, but the accuracy of reservoir quality prediction is insufficient
Solution Approach 1:
The patent combines multiple parameters (Poisson's ratio, elastic properties, petrophysical properties, and production data) into an integrated classification system. This merging of multiple data sources and parameter types enables more accurate reservoir quality prediction while maintaining practical applicability through systematic integration.
Solution Approach 2:
The invention creates a composite classification framework that integrates different types of data (seismic, well log, production) and properties (elastic, petrophysical) into a unified reservoir quality assessment model. This composite approach allows the system to leverage the strengths of each individual parameter type while compensating for their individual limitations.
2Measurement precision
If multiple parameters are integrated for reservoir evaluation, then prediction accuracy improves, but the complexity of the evaluation system increases
Solution Approach 1:
The patent segments the reservoir evaluation process into distinct classification stages based on production data, elastic properties, and petrophysical properties. This segmentation allows each parameter type to be processed and integrated systematically, managing complexity through structured decomposition of the evaluation workflow.
Solution Approach 2:
The invention develops a universal classification framework that can handle multiple parameter types (seismic data, well log data, production data) and apply them collectively to reservoir quality assessment. This multi-functional system uses a common classification approach that works across different data types, reducing overall system complexity despite the diversity of inputs.
3Measurement precision
If production data is used to classify reservoir portions, then the correlation to actual reservoir quality improves, but the requirement for historical production data increases
Solution Approach 1:
The patent uses historical production data to establish classification relationships between reservoir portions and their actual performance before applying the classification model to predict reservoir quality. This preliminary action of learning from existing production data enables the system to make accurate predictions about untapped reservoir areas without requiring extensive additional data collection.
Data Source
AI summary
A method for evaluating portions of a reservoir includes classifying producing reservoir portions in the reservoir into multiple classifications based on production data associated with the producing reservoir portions. Each classification corresponds to a range of the production data. The method further includes generating a correlation between the classifications of the producing reservoir portions to a petrophysical property and elastic property of the subterranean formation, generating, based on the correlation, a spatial distribution function of reservoir quality to represent predicted classifications as a function of physical locations in the reservoir, and evaluating, using the spatial distribution function, a physical location in the reservoir for reservoir quality.