Saturation Estimation via Transverse Resistance and Stochastic Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current mCSEM data evaluation methods face inaccuracies due to weak optimization algorithms, low-dimensional inversions, and coarse-scale measurements, leading to uncertainties in resistivity and saturation estimates, particularly in subsea geological formations.
Innovation Solution
The method involves defining transverse resistance (TR) from mCSEM data over large areas, incorporating stochastic petrophysical modeling to account for parameter uncertainties and spatial variability in porosity and saturation, enabling pre-well saturation estimates without the need for wells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prior art modeling methods apply resistance directly from mCSEM inversion results into saturation-resistivity relations, then the processing workflow is simple, but the saturation estimates are inaccurate due to weak optimization algorithms and coarse-scale measurements
Solution Approach 1:
The patent segments the reservoir into multiple discrete blocks with varying porosity and saturation properties. Instead of treating the reservoir as a homogeneous unit, it divides it into N blocks where each block can have different petrophysical parameters. This segmentation allows the model to capture spatial variability and improve saturation estimation accuracy while maintaining computational feasibility through systematic parameter sampling.
Solution Approach 2:
The patent transitions from traditional 1D vertical resistivity profiles to 3D resistivity modeling with horizontal and vertical dimensionality. By incorporating horizontal spatial variation alongside vertical layering, the inversion model captures the true 3D nature of subsurface resistivity distributions. This dimensional expansion improves measurement precision by accounting for lateral heterogeneity that 1D models miss.
2Measurement precision
If mCSEM data is acquired at coarse scale, then the survey coverage area is large, but the measurements cannot resolve variations within the reservoir column
Solution Approach 1:
The patent segments the large survey area into multiple discrete reservoir blocks that can be independently characterized. By dividing the coverage area into N blocks with distinct petrophysical properties, the model resolves fine-scale variations within each block while maintaining overall large-area coverage. This segmentation enables precise resistivity measurement at the block level even when the total survey area is extensive.
Solution Approach 2:
The patent adds horizontal spatial dimensionality to the traditional vertical profiling approach. By modeling resistivity variations in both horizontal and vertical dimensions across N blocks, the system achieves fine-resolution measurements within each block while covering large lateral areas. This multi-dimensional approach reconciles the need for both broad coverage and detailed resolution.
3Reliability
If constant porosity and saturation are assumed within the CSEM discretization, then the inversion processing is simplified, but the model does not reflect true reservoir heterogeneity
Solution Approach 1:
The patent segments the reservoir into N discrete blocks, each capable of having different porosity and saturation values. This segmentation replaces the unrealistic constant-parameter assumption with a block-based heterogeneous model. The systematic sampling of parameter combinations across blocks improves model reliability by capturing true reservoir variability while keeping processing manageable through algorithmic efficiency.
Solution Approach 2:
The patent introduces dynamic parameter variation across spatial blocks rather than static uniform parameters. By allowing porosity and saturation to vary dynamically from block to block based on sampled distributions, the model reflects the dynamic heterogeneity of real reservoirs. This dynamic approach improves reliability without excessive complexity through efficient parameter sampling methods.
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 provides more robust saturation estimates by leveraging TR sensitivity and incorporating uncertainties, resulting in improved accuracy and reliability of water and hydrocarbon saturation assessments in subsea formations.
Implementation Method 1
a mCSEM system comprises an electromagnetic sender, or antenna, that is either towed from a vessel, stationary in the body of water or on the seabed, and likewise a plurality of electromagnetic receivers
Implementation Method 2
The receivers can detect variations in electrical resistance as a function of variations in source signal, offset between the source and receiver and the properties of the geological layers, including their inherent electrical conductive properties
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method for estimating saturation using mCSEM data and stochastic petrophysical models by quantifying the average water saturation in a reservoir given the transverse resistance (TR) obtained from mCSEM data, comprising the following steps: a)obtaining mCSEM survey data from a subsurface region of interest, b)performing an inversion of said obtained mCSEM data, c)subtracting a background resistivity trend from said mCSEM inversion data from the resistivity trend of said mCSEM inversion data from inside a hydrocarbon reservoir, d)estimating the location of an anomaly in the mCSEM inversion data, e)estimating the magnitude of the transverse resistance associated with an anomaly from the mCSEM inversion data, f)estimating an initial average reservoir saturation corresponding to transverse resistance using a stochastic petrophysical model and Monte Carlo simulation connecting reservoir parameters to transverse resistance, and g)integrating the obtained saturation distribution as a function of transverse resistances over the assumed distribution of transverse resistances to obtain a final estimation of the fluid saturation probability.