Upscaling Petrophysical Characteristics to Whole Core Scale
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Upscaling formation petrophysical characteristics from small subsamples to a whole core scale is challenging, especially in heterogeneous systems like carbonate rocks, due to the difficulty in mapping subsample results to low-resolution tomography core images, which hinders the creation of accurate large-scale reservoir simulation models for hydrocarbon recovery.
Innovation Solution
The method involves using computed tomography (CT) imaging to create high-resolution images of both whole cores and subsamples, segmenting these images to identify different textures and pores, and then using numerical simulations to compute formation property characteristics such as permeability and capillary pressure. These characteristics are then upscaled to a whole-core scale, incorporating the effects of pore structure across various spatial scales, and used to populate dynamic reservoir models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If small subsamples are used for measurement, then measurement precision is improved, but the representativeness of whole core properties deteriorates
Solution Approach 1:
The core is segmented into multiple subsamples that are imaged and analyzed individually at high resolution, then the results are aggregated to represent the whole core. This allows precise measurement of each subsample while maintaining representativeness through systematic sampling and upscaling procedures.
Solution Approach 2:
A multi-scale imaging and upscaling framework acts as an intermediary between subsample-scale measurements and core-scale properties. The framework uses sequential imaging at different resolutions and numerical upscaling to bridge the scale gap, ensuring that subsample measurements accurately represent whole core characteristics.
2Measurement precision
If high-resolution imaging is used for whole core, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The core imaging process is segmented into multiple passes at different resolutions. A low-resolution whole-core image is acquired first to identify regions of interest, then only those specific regions are imaged at high resolution. This selective approach maintains measurement precision where needed while minimizing total imaging time.
Solution Approach 2:
Instead of imaging the entire core at maximum resolution, the method applies high-resolution imaging only to selected subsamples or regions that require detailed analysis. This partial application of high-resolution imaging achieves sufficient measurement precision for the critical areas without the time cost of full-core high-resolution scanning.
3Manufacturing precision
If iterative subsample extraction and imaging is performed, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
Low-resolution imaging and preliminary analysis are performed on the whole core before extracting subsamples for detailed study. This preliminary action identifies which subsamples require detailed analysis, allowing the iterative upscaling process to focus resources on critical regions and reduce the total number of iterations needed.
Solution Approach 2:
Different levels of analysis depth are applied to different regions of the core based on their importance and heterogeneity. Regions with significant heterogeneity or geological importance receive more iterative subsample extraction and high-resolution imaging, while homogeneous or less critical regions require fewer iterations, optimizing the balance between accuracy and time.
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 approach bridges the gap between pore-scale and reservoir-scale processes, providing improved modeling of multi-phase fluid flow and optimizing hydrocarbon recovery by capturing rock heterogeneity, thus enhancing production forecasts and asset value.
Implementation Method 1
imaging, at an imaging resolution, a core of a subsurface formation to create a core image at at least one energy level
Implementation Method 2
using computed tomography (CT) imaging to create high-resolution images of both whole cores and subsamples
Data Source
AI summary
A method includes imaging, at an imaging resolution, a core of a subsurface formation to create a core image and iteratively performing the following operations until a defined feature of a rock of the subsurface formation exceeds a viewable image feature threshold: extracting a number of subsamples from the core for a first iteration and from each of the number of subsamples previously extracted for a subsequent iteration; increasing the imaging resolution; and imaging each subsample. The method includes performing the following operations for the subsamples last extracted: determining at least one formation property characteristic; determining a guiding rock property for each voxel of the core image and the number of subsample images; and determining a subsample that is a shortest distance to the voxel based on the number of guiding rock properties; and mapping, for each voxel, the at least one formation property characteristic that is the shortest distance.