Kerogen Hydrocarbon Diffusion Modeling Grid
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Solution Overview
Problem
Existing petroleum systems modeling techniques fail to accurately model hydrocarbon diffusive transport processes within kerogen, specifically the expulsion of hydrocarbons from kerogen to pore space, which hinders precise predictions of hydrocarbon generation and migration.
Innovation Solution
A method and system that simulate hydrocarbon movement within kerogen using a grid-based model, identifying kerogen-containing cells and generating a model of hydrocarbon expulsion to pore space, incorporating diffusive transport processes to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing petroleum systems modeling techniques are used, then the modeling process is simple, but the prediction accuracy of hydrocarbon generation and migration is poor
Solution Approach 1:
The modeling domain is segmented into multiple grid cells, with each cell capable of independently representing different rock types, hydrocarbon contents, and transport properties. This segmentation allows the complex continuous subsurface to be discretized into manageable units that can be modeled individually and then assembled to create the overall hydrocarbon generation and migration model.
Solution Approach 2:
Different grid cells are assigned different properties based on their local characteristics, such as rock type, porosity, permeability, and hydrocarbon content. This allows the model to capture spatial variations in diffusive transport properties, with each cell reflecting its specific local conditions rather than using a uniform model throughout.
2Reliability
If hydrocarbon diffusive transport processes are incorporated, then the prediction of hydrocarbon volumes and quality improves, but the computational complexity increases
Solution Approach 1:
The complex physical process of hydrocarbon diffusive transport through kerogen is replaced with a computational model that uses mathematical equations to simulate the same process. This substitution allows the diffusive transport to be modeled without requiring physical experimentation or complex laboratory measurements, reducing overall computational complexity while maintaining prediction reliability.
Solution Approach 2:
The model uses adjustable parameters such as diffusion coefficients, temperature, and pressure to control and simulate hydrocarbon transport. By changing these parameters, the model can adapt to different geological conditions and predict hydrocarbon generation and migration under various scenarios without requiring complete redesign of the computational framework.
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
Enhances the prediction of hydrocarbon volumes and quality, improving the reliability of petroleum system modeling by quantifying kerogen diffusion effects, benefiting both conventional and unconventional petroleum systems.
Implementation Method 1
Kerogen is a solid organic matter that, when heated, converts in part to liquid and gaseous hydrocarbons. As such, hydrocarbons may diffuse from kerogen to the pore space.
Data Source
AI summary
A method, comprising receiving input data representing a subterranean formation, defining a grid representing the input data, with the grid including cells. The method also includes identifying at least one of the cells in which kerogen is present based on the input data, simulating hydrocarbon movement within the kerogen using the grid, and generating a model of hydrocarbon expulsion to pore space based on the simulating.