3D Grid Modeling for Coalbed Methane Gas Production
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Solution Overview
Problem
Current methods for evaluating and predicting gas production in coalbed methane reservoirs are inadequate due to the non-uniform distribution of coal, ash, and water, which complicates the application of traditional gas resource evaluation and production modeling techniques.
Innovation Solution
A three-dimensional grid modeling method that includes drilling, sampling, and applying a Langmuir function to calculate gas adsorption based on ash, water, and coal content, allowing for precise prediction of gas production by accounting for pressure changes over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional gas resource evaluation and production modeling techniques are applied to CBM reservoirs, then the evaluation process is simplified, but the precision of gas content estimation deteriorates due to non-uniform distribution of coal, ash, and water
Solution Approach 1:
The CBM reservoir is divided into multiple three-dimensional grids, with each grid representing a discrete volumetric element. This segmentation allows the model to capture spatial variations in coal, ash, and water content across different regions of the reservoir, thereby maintaining measurement precision while managing evaluation complexity through systematic discretization
Solution Approach 2:
The modeling approach assigns distinct properties to each three-dimensional grid based on local characteristics of coal, ash, and water content. By determining these contents individually for each grid rather than applying uniform averages, the model preserves local quality variations that are critical for accurate gas content estimation in non-uniform CBM reservoirs
2Device complexity
If traditional production modeling methods are used for CBM, then the modeling framework remains simple, but the reliability of production prediction deteriorates due to inadequate consideration of pressure changes and geological variability
Solution Approach 1:
The production modeling incorporates dynamic pressure change calculations that evolve over time for each three-dimensional grid. By modeling pressure depletion and gas desorption as time-dependent processes rather than static conditions, the framework captures the dynamic behavior of CBM reservoirs, significantly improving production prediction reliability
Solution Approach 2:
The model transitions from traditional two-dimensional cross-sectional representations to a full three-dimensional grid system. This dimensional expansion allows simultaneous consideration of spatial variations in all three dimensions (length, width, depth) along with temporal pressure changes, creating a comprehensive four-dimensional modeling approach that greatly enhances prediction reliability
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
This method enhances the accuracy and reliability of gas content estimation and production modeling, enabling more economical and efficient gas production by reflecting the actual geological and depositional environments, thus improving the precision and efficiency of gas production.
Implementation Method 1
methane gas is adsorbed on coal acting as a matrix in a coal layer, that is, a reservoir
Implementation Method 2
applying a Langmuir function to each of the cells in the grids to calculate an amount of gas detected due to pressure change in each cell over time
Data Source
AI summary
A method for modeling gas production in a coalbed methane gas (CBM) reservoir including (a) dividing a reservoir in a CBM development area into three-dimensional grids to form the three-dimensional grids having a plurality of cells; (b) forming at least one drilling hole in the development area and obtaining a sample for each depth in the reservoir; (c) modeling the amounts of ash, water, and gas for the plurality of cells in the three-dimensional grids; (d) modeling a pressure change over time in each of the cells in the grid under a premise in which a production well is formed in the grids; and (e) performing a Langmuir experiment for a core sample obtained from step (b).