Geosteering Inversion With Local Anisotropy for Bed Boundary Estimation
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Solution Overview
Problem
Conventional geosteering inversion techniques fail to provide reliable estimates of formation properties due to insufficient sensitivity of downhole tools and difficulty in distinguishing varying formation layers, particularly when local anisotropy properties are not considered.
Innovation Solution
Implementing a knowledge-based geosteering inversion method using a downhole look-ahead look-around (LALA) tool to measure local anisotropy and apply gradient boundary models, incorporating shallow and ultra-deep resistivity profiles to improve formation modeling accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional inversion techniques are used, then the inversion process can be performed, but the estimates of formation properties are unreliable when measurements are not sufficiently sensitive or formation layers are difficult to distinguish
Solution Approach 1:
The formation model is divided into multiple layers with distinct boundary conditions. Each layer can have different resistivity characteristics, allowing the inversion to separately estimate properties of adjacent formation layers. This segmentation enables the model to handle cases where formation layers are difficult to distinguish by treating them as discrete entities with unique parameters.
Solution Approach 2:
The invention applies different resistivity model types (gradational vs. non-gradational) to different depth intervals or formation layers based on local geological conditions. By allowing local anisotropy properties to vary throughout the formation, the model can accurately represent regions with varying sensitivity characteristics and formation properties, improving reliability where measurements are not sufficiently sensitive.
2Productivity
If conventional inversion techniques are used, then the inversion can be performed quickly, but the model may not minimize discrepancy between modeled and observed formation properties
Solution Approach 1:
The inversion process dynamically selects between gradational and non-gradational resistivity models based on the specific formation conditions and measurement characteristics. This dynamic adaptation allows the system to maintain computational efficiency by using simpler models when appropriate while switching to more complex gradational models with local anisotropy when measurement accuracy and model fidelity are critical, thus balancing speed and precision.
Solution Approach 2:
The invention changes key model parameters such as resistivity distribution type and anisotropy characteristics to minimize the discrepancy between modeled and observed formation properties. By adjusting these parameters based on the inversion results and measurement quality, the system achieves better accuracy without requiring excessive computational resources.
3Device complexity
If gradient boundary models without local anisotropy are used, then the model complexity is reduced, but the model does not accurately represent realistic formation properties
Solution Approach 1:
The invention introduces local anisotropy properties that can vary throughout the formation, allowing different regions to have different electrical characteristics. This local differentiation enables the model to accurately represent realistic formation properties where anisotropy varies spatially, while still maintaining a computationally manageable structure by applying these variations only where needed rather than throughout the entire formation uniformly.
Solution Approach 2:
The formation model combines multiple material characteristics (resistivity, anisotropy, gradational vs. non-gradational properties) to create a composite representation of the subsurface. This composite approach allows the model to capture the complexity of real formations with varying properties across different layers and directions, improving reliability while keeping the overall model structure organized and computationally tractable.
Data Source
AI summary
In general, in one aspect, embodiments relate to a method and/or system for obtaining one or more measurements collected by a downhole tool at a wellbore depth and defining a piecewise-polynomial inversion model describing one or more formation parameters. Systems and methods herein may further be for performing an inversion on the piecewise-polynomial inversion model to determine one or more formation parameters of the inversion model, and adjusting a path of the downhole tool based at least in part on the or more formation parameters of the piecewise-polynomial inversion model.