Laminated Formation Model Inversion for Saturation Accuracy
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Solution Overview
Problem
Existing well logging methods struggle to accurately estimate formation properties in thinly laminated formations due to underestimation of hydrocarbon saturation and errors in pore shape estimation caused by thick isotropic layer assumptions, particularly in formations with transverse isotropic shale and isotropic sand layers.
Innovation Solution
A formation modeling and inversion workflow that represents different rock types within a laminated reservoir, using joint inversion of sonic, resistivity, and density data to derive radial profiles of properties such as porosity, fluid saturation, and pore aspect ratios, accounting for the effects of mud-filtrate invasion and mechanical damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a formation model with thick isotropic layers is used, then the inversion process is simplified and computation is faster, but hydrocarbon saturation is underestimated and pore shape estimation contains errors
Solution Approach 1:
The formation is segmented into multiple thin laminated layers instead of treating it as a single thick isotropic layer. Each layer can have different rock types (shale, sand) and anisotropic properties, allowing the model to capture the complex internal structure of thinly laminated formations while maintaining computational feasibility through systematic layer-by-layer inversion
Solution Approach 2:
The patent applies different rock type classifications and anisotropic parameters to different local regions (layers) within the formation. Each thin layer is assigned specific properties such as transverse isotropic shale or isotropic sand with locally varying pore shapes and fluid saturations, enabling accurate representation of spatially heterogeneous formation characteristics
2Device complexity
If a formation model with thick isotropic layers is used, then the inversion workflow is simpler, but pore shape estimation from sonic logs contains errors
Solution Approach 1:
The formation is divided into multiple thin laminated layers, each capable of having distinct anisotropic pore structures. This segmentation allows the inversion to separately estimate pore shapes (aspect ratios) for each layer based on sonic log data, capturing the true complexity of pore architecture without requiring overly simplified isotropic assumptions
Solution Approach 2:
The patent transitions from isotropic parameters to anisotropic parameters (including pore aspect ratios and orientation angles) in the formation model. By introducing these additional parameters that describe pore shape and orientation, the model can accurately represent the effects of mechanical damage and stress on pore structure while maintaining a systematic inversion approach
3Measurement precision
If thin laminated layers are modeled with different rock types, then hydrocarbon saturation and pore shape accuracy improve, but the formation model and inversion process become more complex
Solution Approach 1:
The formation is segmented into thin laminated layers with distinct rock type classifications (shale, sand, or mixtures). This segmentation enables the model to capture the alternating patterns of high and low permeability layers, which is critical for accurate hydrocarbon saturation estimation while managing complexity through standardized layer processing
Solution Approach 2:
Different rock types are assigned to different local layers based on their specific properties. Shale layers are modeled with transverse isotropic properties and aligned micropores, while sand layers use isotropic properties with randomly oriented macropores. This local differentiation allows accurate saturation and pore shape estimation without requiring the entire formation to be modeled with maximum complexity
4Measurement precision
If thin laminated layers with anisotropic properties are modeled, then pore shape and fluid saturation are accurately determined, but computational requirements increase
Solution Approach 1:
The formation is divided into a finite number of thin layers, each with its own anisotropic parameters. This segmentation transforms the complex continuous problem into a discrete set of manageable layers, allowing computational resources to be efficiently allocated to each layer's parameter estimation while maintaining overall accuracy through the cumulative effect of all layers
Solution Approach 2:
The patent employs anisotropic parameters (pore aspect ratios, orientation angles) that can be systematically varied and optimized during inversion. By using parameterized models for anisotropic properties, the computational complexity is managed through efficient parameter estimation algorithms rather than requiring full numerical simulation of complex pore structures
Data Source
AI summary
A method for determining properties of a laminated formation traversed by a well or wellbore employs measured sonic data, resistivity data, and density data for an interval-of-interest within the well or wellbore. A formation model that describe properties of the laminated formation at the interval-of-interest is derived from the measured sonic data, resistivity data, and density data for the interval-of-interest. The formation model represents the laminated formation at the interval-of-interest as first and second zones of different first and second rock types. The formation model is used to derive simulated sonic data, resistivity data, and density data for the interval-of-interest. The measured sonic data, resistivity data, and density data for the interval-of-interest and the simulated sonic data, resistivity data, and density data for the interval-of-interest are used to refine the formation model and determine properties of the formation at the interval-of-interest. The properties of the formation may be a radial profile for porosity, a radial profile for water saturation, a radial profile for gas saturation, radial profile of oil saturation, and radial profiles for pore shapes for the first and second zones (or rock types).